Data Cleaning for the Tombstone Project

Using StringR to clean a human created excel sheet full of typos and formatting inconsistencies. Then matching excel data to photo names.

Project Overview

I’m working on a project for my father that will culminate in a website for his genealogy research. There are a couple of different parts that I’m working on independently. This part involves linking photos of family gravestones to an Excel sheet that records the GPS location of the tombstones. This combined dataset is used to generate a leaflet map. This portion focused on data cleaning and the photo matching. I do generate a leaflet map at the end, but it is not the final map. I’ll do the styling of the map in a separate post.

This post is intended both to document what I did for my father so he understands any changes to the data and what results were obtained, but also as a tutorial on how to approach a messy problem. I’ve been solving problems using code for a long time. There are a ton of tutorials that focus on how to solve a specific problem, but fewer that show how to approach an undefined problem. And even fewer tutorials show mistakes and false starts. But these things happen when you are solving real world problems. Constantly checking your results against what you expect to get is critical and then figuring out how you messed up and fixed it is also important. The hard errors to find and fix are the logic errors. Everything runs fine. You get an output that may look right. But you still might not be getting the correct result. You have to approach every output critically and check your work carefully.

I generally write my posts in a “code-along” style. I include almost everything I do, including dead ends. I could present more polished posts, where I write everything after I achieved the end result. This style of post would only include the steps that directly lead to the end result. I don’t do that because I don’t think the mechanics of getting to an end result is necessarily the hard part. Thinking your way through and self-checking the work is the hard part. If you know what you are trying to do, you can always find some code snippets to achieve that result. If you don’t know what you are trying to do, then all the code snippets in the world won’t help.

Some sections I do omit mistakes and go to the final product, just so this tutorial doesn’t end up being 5 million pages long. Generally, the first time I do something, I will go into more detail than following times. For the data cleaning portion, the Cleaning Up Cemetery Names section shows the entire process, including mistakes. For the matching part, everything before Round 2 is in detail, including mistakes, and then the other rounds are much less detailed.

I did also code a simplified version of this project all the way through using only one round of matching and 30 photos, just to make sure the basic elements were working. That isn’t shown here.

If for some reason you want to run this yourself, you can get a zipped copy of all the photos from here. I don’t upload the photos in this repo because the files size is too large.

Setting Up

Loading Libraries

I’ll include more info and reference information about the packages at the code blocks where I use them.

library(tidyverse) # who doesn't want to be tidy?
library(gt) # For nice tables
library(openxlsx) # importing excel files from a URL
library(fuzzyjoin) # for joining on inexact matches
library(sf) # for handling geo data
library(leaflet) # mapping
library(here) # reproducible file paths
library(magick) # makes panel pictures

File Folder Names and Loading Data

Here set-up some variables that I use for the file/ folder structure and I read in the spreadsheet.

# folder names
blog_folder <- "posts/2023-08-04-data-cleaning-tombstone"
photo_folder <- "Photos"
archive_folder <- "Archived Photos"
unmatched_folder <- "Unmatched Photos"
match1 <- "Matched_Round1"
match2 <- "Matched_Round2"
match3 <- "Matched_Round3"
match4 <- "Matched_Round4"


#data_file <- "Tombstone_Data_small.xlsx"
data_file <- "Tombstone Data.xlsx"
# read in excel sheet
tombstones_raw <-
  read.xlsx(here(blog_folder, data_file),
    sheet = 1
  )

The here Package for Reproducible File Structures

I have folder structure that reflected the sequential nature of the matching, so photos get moved into different folders depending on what round they were matched in. I am use here to generate the paths. Quarto documents start the file path in the folder where the document resides, while r files start in the project folder. here always starts in the project folder, so it allows for easy recycling of code between r files and Quarto files and generally prevents you from getting lost in your file structure. It also allows me to easily move between an independent project and the project that is my website without having to recode all the folder names in the code. All I need to do is setup the sub-folder structure and names (as I did above) and then use them to generate file paths relative to here. You can see that usage in the loading of the excel sheet.

Reformat and Clean the Data

Cleaning the data is an iterative process. A quick scan of the data reveals a bunch of really obvious issues, but as the analysis proceeds, other errors pop up that can be traced back to improperly cleaned data. Continually checking the results against expected results is critical to find the mistakes. This is part of the reason I have temporary variables (tombstone_1, tombstone_2, etc.). If I’m not sure about something, I’ll store the results in the temporary variable, so I don’t have to rerun everything from the start to get a clean copy to work with. I can just go back one or two code blocks and regenerate from a working partially cleaned version.

Deciding on ground rules for what you will and will not correct is important. For this project, I decided I would not change any photo file names. I’m working with a copy of his photo archive; he has his own filing and naming scheme, and he also corresponds with other genealogists and shares information. Changing photo names on my copy would lead to a set of photos that no longer matched those out there in various places. This decision will lead to missed matches since some photos do appear to have typos in the names such as Octava instead of Octavia. Other photos seem to not follow his normal naming convention of last name first name middle name. Some use first name last name. This again is something that could be corrected programatically, but I won’t because of my ground rules. For another project, a different decision might make more sense. (I’d definitely correct file names if it were my own data!)

I also decided that any inferred data in the spreadsheet (usually denoted in [] here) would not be used. Everything going into the map is data directly from the photos.

The tidyverse packages stringr and tidyr both have very powerful tools for data cleaning and tidying. For most tasks, there are multiple ways to accomplish the goal. I’ll illustrate several different ways to perform tasks; there is likely one that is best suited for your application so it is good to know the various methods.

Fixing the GPS data

The GPS data is stored as a string representing degrees, minutes, and seconds of latitude and longitude. I’m going to want this as a decimal lat/long (numerical) as I know that is accepted by many mapping programs. Dealing with this data has two parts: cleaning up the typos/ formatting and then converting to the decimal number.

Viewing the GPS Data (strings)

When you view the GPS data you can see a couple of issues.

tombstones_raw %>% 
  select(Surname, N, W) %>% 
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Surname	N	W
Anderson	36o56.472	86 86.961
Anderson	36 56.472	86 86.961
Anderson	37 53.396	88 41.321
Anderson	37 52.856	88 39.163
Anderson	37 52.856	88 39.163
Anderson	37 52.855	88 39.163
Anderson	37 52.853	88 39.164
Anderson	37 52.853	88 39.167
Anderson	37 52.852	88 39.165
Appleton	36 29.552	86 46.793
Baldwin	38 33.025	87 06.328
Baldwin	38 33.025	87 06.328
Baggett	36 29.553	86 46.793
Beasley	36 35.891	86 43.204
Beasley	36 36.755	86 43.145
Beasley	36 36.755	86 43.145
Bell	36 15.064	86 11.669
Bell	36 15.064	86 11.669
Brazelton	35 09.411	86 03.624
Brazelton	35 09.410	86 03.624
Brown	40O 40.760’	75O 31.705'
Brown	40O 40.760’	75O 31.705'
Bundy	37 45.623
Bundy	37 53.380	88 44.474
Bundy	37 53.380	88 44.474
Bundy	37 53.380	88 44.474
Bundy	37 53.379	88 44.474
Bundy	37 52.875	88 39.118
Bundy	37 52.875	88 39.118
Bundy	37 52.873	88 39.188
Bundy	37 52.873	88 39.188
Burgess	37 49.224	88 54.527
Burgess	37 49.224	88 54.527
Clayton	37 50.788	88 50.968
Clayton	37 50.788	88 50.968
Clayton	37 50.795	88 50.977
Clayton	37 50.795	88 50.977
Chapman	37 29.894	88 54.045
Chapman	37 29.894	88 54.046
Chapman	37 25.692	88 53.951
Chapman	37 25.691	88 53.949
Chapman	37 25.691	88 53.949
Chapman	37 25.692	88 53.951
Chapman	37 25.692	88 53.951
Chapman	37 25.694	88 53.951
Chapman	38 33.026	87 06.327
Crockett	36 22.801	86 45.985
Crockett	36 22.804	86 45.984
Davis	37 44.682	88 55.994
Davis	37 44.683	88 55.993
Davis	36 14.260	86 43.129
Dolch	38 44.563	82 58.988
Dolch	38 44.584	82 58.987
Dolch	38 44.564	82 58.987
Doley	38 44.615	82 58.882
Doley	38 44.615	82 58.882
Doley	38 44.615	82 58.882
Doley	38 44.615	82 58.882
NA	38 44.618	82 58.884
NA	38 44.618	82 58.884
NA	38 44.618	82 58.885
NA	38 44.618	82 58.885
NA	38 44.618	82 58.886
Doley	38 44.615	82 58.882
Doley	38 44.610	82 58.923
Doley	38 44.611	82 58.922
Doley	38 44.611	82 59.012
Doley	38 44.611	82 59.013
Doley	37 49.907	88 35.306
Doley	37 49.907	88 35.306
Doley	37 49.907	88 35.306
Doley	37 58.810	88 55.084
Doley	37 58.810	88 55.084
Doley	37 58.751	88 55.161
Doley	37 58.751	88 55.161
Dorris	36o28.798’	86o46.011’
Dorris	36 28.811	86 46.008
Dorris	36 28.812	86 46.008
Dorris	36 28.812	86 46.008
Dorris	36 28.813	86 46.007
Dorris	NA	NA
Dorris	NA	NA
Dorris	36 26.485	86 48.329
Dorris	36 26.484	86 48.329
Dorris	38 07.067	88 51.870
Dorris	38 07.067	88 51.870
Dorris	38 07.067	88 51.870
Dorris	38 07.081	88 51.903
Dorris	38 07.081	88 51.903
Dorris	37 54.310	88 58.084
Dorris	37 54.309	88 58.083
Dorris	37 54.309	88 58.083
Dorris	37 54.310	88 58.084
Dorris	37 54.310	88 58.084
Dorris	37 58.746	88 55.204
Dorris	37 58.749	88 55.205
Dorris	37 47.990	88 53.488
Dorris	37 47.988	88 53.489
Dorris	37 51.571	88 54.939
Dorris	37 51.571	88 54.939
Dorris	37 50.787	88 50.972
Dorris	37 50.788	88 50.971
Dorris	37 50.794	88 50.975
Dorris	37 50.794	88 50.975
Dorris	37 50.786	88 50.974
Dorris	37 50.771	88 50.986
Dorris	37 50.783	88 50.983
Dorris	37 50.775	88 50.986
Dorris	37 50.775	88 50.986
Dorris	37 50.775	88 50.980
Dorris	37 50.775	88 50.980
Dorris	524	783
Dorris	528	783
Drake	36 35.870	86 43.184
Dreisbach	40o 44.177'	75 29.596'
Dreisbach	40o 44.177'	75 29.593'
Everett	38 33.026	87 06.327
Farris	37 24.687	88 50.538
Farris	37 24.678	88 50.538
Finch	44 34.662'	37 27.129'
Follis	37 51.764'	88 56.897
Follis	37 51.758'	88 56.894
Follis	37 51.761'	88 56.893
Follis	37 51.761'	88 56.896
Follis	37 51.759'	88 56.895
Follis	37 51.	88 56
Follis	37 51.758'	88 56.896
Follis	37 51.758'	88 56.901'
Follis	37 51.758'	88 56.901'
Follis	37 51.758'	88 56.904
Ford	37 52.851	88 39.161
Fox	37 48.023	88 53.449
Frost	37 17.909	87 28.852
NA	37 17.910	87 28.852
Frost	37 17.909	87 28.854
Fuqua	36 38.189	86 51.516
Gregory	38 44.609	82 58.922'
Gregory	38 44.611'	82 58.922'
Hart	37 51.757'	88 56.900
Hess	37 25.687	88 53.947
Hess	37 25.687	88 53.949
Hess	37 25.688	88 53.952
Hess	37 25.688	88 53.952
Hess	37 25.688	88 53.952
Hess	37 25.687	88 53.947
Hess	37 25.688	88 53.952
Hess	37 25.687	88 53.948
Hess	37 25.689	88 53.952
Hess	37 25.689	88 53.952
Hess	37 25.693	88 53.949
Hess	37 25.693	88 53.947
Hess	37 25.693	88 53.949
Hess	37 25.690	88 53.951
Holt	NA	NA
Holt	NA	NA
Horlacher	40o 30.928'	75o 25.072'
Horlacher	40o30.930’	75o 25.070’
Horrall	37 54.090	88 54.218
Horrall	38 33.026	87 06.326
Horrall	38 36.963	87 11.369
Hurt	36 28.804	86 46.007
Jacobs	38 21.315'	85 41.307'
Jacobs	38 21.317'	85 41.306'
Johnson	37 52.872	88 39.183
Johnson	37 52.872	88 39.183
Jones	37 47.994	88 53.504
Jones	37 47.994	88 53.504
Jones	37 47.997	88 53.483
Jones	37 47.995	88 53.483
Jones	37 47.995	88 53.483
Jones	37 48.024	88 53.451
Jones	37 48.024	88 53.451
Jones	37 48.020	88 53.465
Jones	37 48.020	88 53.465
Jones	37 51.747	88 52.933
Karnes	37 58.749	88 55.161
Karnes	37 58.749	88 55.161
Keith	NA	NA
Keth	35 09.410	86 03.624
Kleppinger	40o 44.178'	75 29.601'
Lipsey	38 33.917	89 07.571
Lockwood	NA	NA
Lockwood	NA	NA
Loomis	37 36.925	89 12.220
Mensch	40o 39.557'	75 25.586'
Merrell	35 43.945	80 18.669
Merrell	35 43.942	80 18.671
Meredith	39O 41.114’	76O 35.858'
Meredith	39O 41.115’	76O 35.855'
Meredith	39O 41.116’	76O 35.855'
Meredith	39O 41.116’	76O 35.854'
Meredith	39O 41.117’	76O 35.853'
Bell	39O 41.117’	76O 35.853'
John	39O 41.117’	76O 35.853'
Meredith	39O 41.118’	76O 35.852'
Meredith	39O 41.112’	76O 35.857'
Meredith	39O 41.112’	76O 35.857'
Meredith	39O 41.112’	76O 35.856'
Meredith	39O 41.112’	76O 35.856'
Meredith	39O 41.112’	76O 35.855'
Meredith	39O 41.112’	76O 35.854'
Meredith	39O 41.113’	76O 35.855'
Meredith	39O 41.113’	76O 35.855'
Tipton	39O 41.114’	76O 35.855'
Meredith	39O 41.114’	76O 35.854'
Meredith	39O 41.114’	76O 35.854'
Mildenberger	40o 44.194’	75O 29.608
Mildenberger	40o 44.179	75 29.574
Miller	37 48.023	88 53.449
Minnich	40o 40.757’	75O 31.679'
Minnich	40o 40.759’	75O 31.679'
Mory	40o 33.585'	75 23.776'
Mory	40o 33.586'	75 23.776'
Mory	40o 33.586'	75 23.774'
Mory	40o 33.585'	75 23.776'
Nagel	40o 33.585'	75 23.745'
Nagel	40o 44.191'	75 29.603'
Nagel	41 13.033'	75 57.329'
Nagel	40o39.575'	75o25.555'
Nagel	40o39.577'	75o25.549'
Nagel	40o 44.197'	75O 29.605’
Nagel	41 13.031'	75 57.333'
Nagel	40o 39.575'	75 25.555'
Nagle	38 44.582'	82 58.978'
Nagle	38 44.582'	82 58.978'
Nagel	38 44.582'	82 58.978'
Nagel	38 44.582'	82 58.978'
Nagel	38 44.582'	82 58.978'
Nagel	38 44.582'	82 58.978'
NA	NA	NA
Nutty	37 25.674	88 54.020
Nutty	37 25.682	88 54.020
Nutty	37 25.678	88 54.020
Ritter	37 52.861	88 39/178
Ritter	37 52.861	88 39/178
Odom	37 58.794	88 55.324
Odom	37 58.795	88 55.326
Odom	37 47.993	88 53.510
Odom	37 47.994	88 53.510
NA	37 47.992	88 53.506
Odum	NA	NA
Odum	37 47.187	88 50.175
Odum	37 47.187	88 50.175
Peters	37 47.244	88 55.354
Peters	37 47.244	88 55.354
Pickard	38 04.918	88 52.028
Pickard	38 04.919	88 52.028
Pickard	38 04.917	88 52.028
Pletz	37 44.684	88 55.998
Russell	37 44.683	88 55.998
Pickard	38 04.918	88 54.028
Pickard	38 04.919	88 54.028
Pickard	38 04.917	88 54.028
Pulliam	37 25.697	88 53.922
Pulliam	37 25.697	88 53.922
Rex	37 45.776	88 55.111
Rex	37 45.776	88 55.111
Rex	37 45.777	88 55.110
Rex	37 45.777	88 55.115
Rex	37 45.776	88 55.112
Rex	37 45.774	88 55.109
Rex	37 45.776	88 55.108
Rex	37 45.776	88 55.108
Rex	37 45.776	88 55.108
Rex	32 22 549	90 52.100
Rex	37 44.784	88 55.855
Rex	37 44.785	88 55.855
Richardson	37 44.766	88 55.776
Richardson	37 44.787	88 55.775
Riegel	37 49.828	88 35.346
Riegel	37 49.828	88 35.346
Ritter	37 52.853	88 39.174
Ritter	37 52.853	88 39.174
Rockel	40o 39.556'	75 25.585'
Rockel	40o 39.555'	75 25.585'
Rockel	40o 39.560'	75 25.560'
Rockel	40o 39.560'	75 25.559'
Ross	37 58.752	88 58.162
Ross	37 58.752	88 58.162
Ruckel	NA	NA
Ruckel	NA	NA
Russell	37 44.681	88 55.998
Russell	37 44.682	88 55.998
NA	37 44.682	88 55.994
NA	37 44.682	88 55.994
NA	37 44.682	88 55.994
Siliven	37 28.189	88 48.007
Sinks	36 14.451'	86 43.526'
Sinks	37 54.081'	88 54.293'
Sinks	37 54.081'	88 54.293'
Sinks	37 54.089'	88 54.207'
Sinks	37 52.619	88 55.430
Sinks	37 52.619	88 55.430
Sinks	37 52.619	88 55.430
Sinks	37 47.989	88 53.489
Sinks	37 47.989	88 53.489
Sinks	37 47.986	88 53.489
Sinks	37 47.985	88 53.491
Sinks	37 47.984	88 53.490
Sinks	37 47.984	88 53.490
Sinks	37 47.984	88 53.488
Sinks	37 47.982	88 53.491
Sinks	37 48.024	88 53.464
Sinks	37 48.024	88 53.463
Sinks	37 48.020	88 53.463
Sinks	37 44.702	88 55.998
Sweet	37 44.704	88 55.995
Sinks	37 44.702	88 55.997
Sinks	37 44.704	88 55.998
Sinks	37 44.704	88 55.998
Sinks	38 33.836	89 07.580
Sinks	38 33.837	89 07.579
Sinks	38 33.917	89 07.572
Sinks	38 02.272	88 50.161
Sinks	37 44.770	88 55.779
Sinks	37 44.770	88 55.779
Solt	40o 48.686'	75 37.120
Solt	40o 48.693'	75 37.119
Solt	40o 48.690'	75 37.113
Sfafford	37 52.608	88 55.434
Sfafford	37 52.608	88 55.434
Steen	38 33.025	87 06.328
VanCleve	37 25.694	88 53.921
VanCleve	37 25.694	88 53.921
VanCleve	37 33.397	88 46.363
VanCleve	37 33.397	88 46.363
VanCleve	37 33.397	88 46.363
VanCleave	38 04.924	88 52.030
VanCleave	38 04.924	88 52.030
Veach	37 29.916	86 54.044
Veach	37 29.916	86 54.044
Veach	37 29.895	86 54.023
Veach	37 29.895	86 54.022
Veach	37 29.895	86 54.020
Veach	37 25.692	88 53.942
Veach	37 26.692	88 53.942
Veatch	37 26.692	88 50.527
Veatch	37 26.692	88 50.527
Veach	37 26.693	88 50.540
Veach	37 26.692	88 50.531
Veach	37 26.692	88 50.531
Veach	37 29.895'	88 54.022
Veach	37 29.897'	88 54.022
Veatch	37 28.187'	88 49.000'
Veatch	37 28.187'	88 48.999'
Veatch	37 28.187'	88 49.001'
Veatch	37 28.186	88 48.005
Veach-Nutty	37 25.682	88 54.017
Veach	37 25.681	88 54.017
Veach	37 25.679	88 54.017
Veach	37 25.682	88 54.017
Ware	37 52.856	88 39.186
Ware	37 52.856	88 39.186
Ware	37 52.867	88 39.176
Ware	37 52.867	88 39.176
Webber	37 49.829	88 35.336
Webber	37 49.826	88 35.336
Weir	36 15.064	86 11.669
Weir	36 15.064	86 11.669
Wier	37 49.208	88 46.787
Whiteside	37 26.743	88 50.534
Whiteside	37 26.743	88 50.534
Willis	36 35.889	86 43.203
Wilson	36 26.350	86 47.072
Wilson	36 26.361	86 47.070
Wilson	36 29.553	86 46.791
Wilson	36 28.812	86 46.023
Wilson	36 28.803	86 46.007
Wilson	NA	NA
Wilson	37 48.034	88 53.443
Wilson	37 48.034	88 53.443
Wilson	36 26.351	86 47.070
Wilson	36 26.351	86 47.070
Wilson	36 26.351	86 47.070
Wilson	36 26.350	86 47.073
Wilson	36 26.350	86 47.073
Wilson	36 26.351	86 47.071
Wilson	NA	NA
Wilson	NA	NA
Wilson	NA	NA
Wilson	NA	NA
Wise	37 50.352	88 31.612
Wollard	37 54.076'	88 54.322'
Woolard	37 54.075'	88 54.322'
Woolard	37 58.721	88 55.211
Woolard	37 58.721	88 55.211
Woolard	37 58.721	88 55.211
Woolard	37 58.723	88 55.212
Woolard	37 58.721	88 55.213
Woolard	37 58.720	88 55.213
Woolard	37 58.720	88 55.213
Woolard	37 51.394	88 41.745
Woolard	37 51.395	88 41.746
Woolard	37 51.396	88 41.747
Woolard	37 51.391	88 41.742
Woolard	37 51.397	88 41.741
Woolard	37 52.853	88 39.160
Woolard	37 52.853	88 39.161
Woolard	37 52.853	88 39.160
Woolard	37 52.853	88 39.159
Woolard	37 52.854	88 39.160
Woolard	37 51.742	88 52.935
Woolard	37 51.742	88 52.935

Latitude and longitude data contains some stray degree and minute symbols. The degree symbol appears both as a straight and curved apostrophe and the degree symbols appear both as o and O. This cleaning needs to be done on both N and W columns. The str_replace_all() function from stringr looks at a string, finds a pattern, and replaces it with a replacement. Here, the pattern is each of those symbols and the replacement is a space.

Styling Tables with gt

I’m using the gt package to format my tables. Here I’m not doing much styling, but it is super easy to make really nice tables with just a few lines of code.

I write and code in RStudio using Quarto. This allows you to alternate text and code chunks. You can run all the code chunks normally in RStudio or you can “render” the quarto document, which runs all the code chunks and produces the html page that becomes the page I publish on my website. When just running the code chunks, I get a table with scroll bars, but when rendering the webpage, I get a multi-page table that displays everything. This is fixed by specifying the size of the container for the table. With the container, the table is truncated to a few rows and a scroll bar appears. The container.padding option just makes sure the data isn’t truncated in the middle of a row.

Cleaning up Typos in the GPS Data (strings)

I put all my cleaned data in a new dataframe. If something unexpected happens, I can check against the original data without having to reload it. I tend to use separate mutates for operation. I know it could be all in one mutate, but even when being careful about indents, I end up missing commas and parentheses as I add and remove steps. Individual mutates makes visually checking for syntax errors much easier for me.

tombstones <- tombstones_raw %>%
mutate(N = str_replace_all(N, pattern = "’", " ")) %>%
mutate(N = str_replace_all(N, pattern = "O", " ")) %>%
mutate(N = str_replace_all(N, pattern = "o", " ")) %>%
mutate(N = str_replace_all(N, pattern = "'", " ")) %>% 
mutate(W = str_replace_all(W, pattern = "’", " ")) %>%
mutate(W = str_replace_all(W, pattern = "O", " ")) %>% 
mutate(W = str_replace_all(W, pattern = "o", " ")) %>%
mutate(W = str_replace_all(W, pattern = "'", " ")) 

Look at the cleaned data.

tombstones %>%
  select(Surname, First.Name, N, W) %>%
  gt()  %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

Surname	First.Name	N	W
Anderson	Abraham	36 56.472	86 86.961
Anderson	Elizabeth	36 56.472	86 86.961
Anderson	Zady	37 53.396	88 41.321
Anderson	Albert	37 52.856	88 39.163
Anderson	Adesia	37 52.856	88 39.163
Anderson	May	37 52.855	88 39.163
Anderson	E	37 52.853	88 39.164
Anderson	William	37 52.853	88 39.167
Anderson	Nancy	37 52.852	88 39.165
Appleton	Richard	36 29.552	86 46.793
Baldwin	John	38 33.025	87 06.328
Baldwin	William	38 33.025	87 06.328
Baggett	Mahalia	36 29.553	86 46.793
Beasley	E	36 35.891	86 43.204
Beasley	Josephine	36 36.755	86 43.145
Beasley	Fanning	36 36.755	86 43.145
Bell	John	36 15.064	86 11.669
Bell	Mary	36 15.064	86 11.669
Brazelton	Wm	35 09.411	86 03.624
Brazelton	Esther	35 09.410	86 03.624
Brown	Elizabeth	40 40.760	75 31.705
Brown	Joel	40 40.760	75 31.705
Bundy	Hope	37 45.623
Bundy	Clem	37 53.380	88 44.474
Bundy	Nancy	37 53.380	88 44.474
Bundy	W	37 53.380	88 44.474
Bundy	Charles	37 53.379	88 44.474
Bundy	Thomas	37 52.875	88 39.118
Bundy	Octavia	37 52.875	88 39.118
Bundy	George	37 52.873	88 39.188
Bundy	Lora	37 52.873	88 39.188
Burgess	W	37 49.224	88 54.527
Burgess	Alzada	37 49.224	88 54.527
Clayton	G	37 50.788	88 50.968
Clayton	Ellen	37 50.788	88 50.968
Clayton	L	37 50.795	88 50.977
Clayton	Mary	37 50.795	88 50.977
Chapman	Daniel	37 29.894	88 54.045
Chapman	Elizabeth	37 29.894	88 54.046
Chapman	Caroline	37 25.692	88 53.951
Chapman	Daniel	37 25.691	88 53.949
Chapman	Lucretia	37 25.691	88 53.949
Chapman	Samuel	37 25.692	88 53.951
Chapman	Elizabeth	37 25.692	88 53.951
Chapman	Laura	37 25.694	88 53.951
Chapman	Polly	38 33.026	87 06.327
Crockett	Mandy	36 22.801	86 45.985
Crockett	John	36 22.804	86 45.984
Davis	Ezra	37 44.682	88 55.994
Davis	Lizzie	37 44.683	88 55.993
Davis	Fred	36 14.260	86 43.129
Dolch	Catherine	38 44.563	82 58.988
Dolch	Christian	38 44.584	82 58.987
Dolch	Peter	38 44.564	82 58.987
Doley	George	38 44.615	82 58.882
Doley	Katie	38 44.615	82 58.882
Doley	Mary E	38 44.615	82 58.882
Doley	Henriettie	38 44.615	82 58.882
NA	MED	38 44.618	82 58.884
NA	HD	38 44.618	82 58.884
NA	GD	38 44.618	82 58.885
NA	Mother	38 44.618	82 58.885
NA	Father	38 44.618	82 58.886
Doley	George	38 44.615	82 58.882
Doley	James	38 44.610	82 58.923
Doley	May	38 44.611	82 58.922
Doley	John	38 44.611	82 59.012
Doley	Maggie	38 44.611	82 59.013
Doley	William	37 49.907	88 35.306
Doley	Dora	37 49.907	88 35.306
Doley	L[eaman]	37 49.907	88 35.306
Doley	G[uilford]	37 58.810	88 55.084
Doley	D[ora]	37 58.810	88 55.084
Doley	Eugene	37 58.751	88 55.161
Doley	Lou	37 58.751	88 55.161
Dorris	J[oseph]	36 28.798	86 46.011
Dorris	Joseph	36 28.811	86 46.008
Dorris	Sarah	36 28.812	86 46.008
Dorris	W	36 28.812	86 46.008
Dorris	A	36 28.813	86 46.007
Dorris	J	NA	NA
Dorris	Elizabeth	NA	NA
Dorris	Robert	36 26.485	86 48.329
Dorris	Rebecca	36 26.484	86 48.329
Dorris	Monroe	38 07.067	88 51.870
Dorris	Della	38 07.067	88 51.870
Dorris	Mary M	38 07.067	88 51.870
Dorris	Harve	38 07.081	88 51.903
Dorris	Carrie	38 07.081	88 51.903
Dorris	Smith	37 54.310	88 58.084
Dorris	Ada	37 54.309	88 58.083
Dorris	William	37 54.309	88 58.083
Dorris	Harvey	37 54.310	88 58.084
Dorris	Cora	37 54.310	88 58.084
Dorris	John	37 58.746	88 55.204
Dorris	W	37 58.749	88 55.205
Dorris	Gustavus	37 47.990	88 53.488
Dorris	Sarah	37 47.988	88 53.489
Dorris	Joseph	37 51.571	88 54.939
Dorris	Della	37 51.571	88 54.939
Dorris	William	37 50.787	88 50.972
Dorris	Harriet	37 50.788	88 50.971
Dorris	William	37 50.794	88 50.975
Dorris	Mary	37 50.794	88 50.975
Dorris	James	37 50.786	88 50.974
Dorris	Sarah	37 50.771	88 50.986
Dorris	W[illiam]	37 50.783	88 50.983
Dorris	E[lisha]	37 50.775	88 50.986
Dorris	Sarah	37 50.775	88 50.986
Dorris	James	37 50.775	88 50.980
Dorris	Georgia	37 50.775	88 50.980
Dorris	William	524	783
Dorris	Malinda	528	783
Drake	Mary	36 35.870	86 43.184
Dreisbach	Catherina	40 44.177	75 29.596
Dreisbach	Johannes	40 44.177	75 29.593
Everett	Semantha	38 33.026	87 06.327
Farris	Elizabeth	37 24.687	88 50.538
Farris	Elizabeth	37 24.678	88 50.538
Finch	Isaac	44 34.662	37 27.129
Follis	Fawn	37 51.764	88 56.897
Follis	Ralph	37 51.758	88 56.894
Follis	A	37 51.761	88 56.893
Follis	Christian	37 51.761	88 56.896
Follis	G	37 51.759	88 56.895
Follis	Ralph	37 51.	88 56
Follis	E	37 51.758	88 56.896
Follis	William	37 51.758	88 56.901
Follis	Martha	37 51.758	88 56.901
Follis	Jeff	37 51.758	88 56.904
Ford	Florence	37 52.851	88 39.161
Fox	Frances	37 48.023	88 53.449
Frost	Ebenezer	37 17.909	87 28.852
NA	NA	37 17.910	87 28.852
Frost	NA	37 17.909	87 28.854
Fuqua	William	36 38.189	86 51.516
Gregory	Leonard	38 44.609	82 58.922
Gregory	Lucille	38 44.611	82 58.922
Hart	Parmelia	37 51.757	88 56.900
Hess	Amalphus	37 25.687	88 53.947
Hess	Adolphus	37 25.687	88 53.949
Hess	Samuel	37 25.688	88 53.952
Hess	Augusta	37 25.688	88 53.952
Hess	Ulysses	37 25.688	88 53.952
Hess	Ulysses	37 25.687	88 53.947
Hess	William	37 25.688	88 53.952
Hess	William	37 25.687	88 53.948
Hess	Jerome	37 25.689	88 53.952
Hess	Franklin	37 25.689	88 53.952
Hess	Samuel	37 25.693	88 53.949
Hess	Bernice	37 25.693	88 53.947
Hess	Catherine	37 25.693	88 53.949
Hess	George	37 25.690	88 53.951
Holt	Lucinda	NA	NA
Holt	William	NA	NA
Horlacher	Daniel	40 30.928	75 25.072
Horlacher	Margaretha	40 30.930	75 25.070
Horrall	Polly	37 54.090	88 54.218
Horrall	James	38 33.026	87 06.326
Horrall	William	38 36.963	87 11.369
Hurt	Elizabeth	36 28.804	86 46.007
Jacobs	Jeremiah	38 21.315	85 41.307
Jacobs	Rebecca	38 21.317	85 41.306
Johnson	James	37 52.872	88 39.183
Johnson	Mary	37 52.872	88 39.183
Jones	Levi	37 47.994	88 53.504
Jones	Hester	37 47.994	88 53.504
Jones	Ridley	37 47.997	88 53.483
Jones	James	37 47.995	88 53.483
Jones	Tina	37 47.995	88 53.483
Jones	Ezra	37 48.024	88 53.451
Jones	Nannie	37 48.024	88 53.451
Jones	Samuel	37 48.020	88 53.465
Jones	Melverda	37 48.020	88 53.465
Jones	John	37 51.747	88 52.933
Karnes	Willard	37 58.749	88 55.161
Karnes	Ruth	37 58.749	88 55.161
Keith	James	NA	NA
Keth	Nancy	35 09.410	86 03.624
Kleppinger	Anna	40 44.178	75 29.601
Lipsey	Joe	38 33.917	89 07.571
Lockwood	Eugenia	NA	NA
Lockwood	Leland	NA	NA
Loomis	Jon	37 36.925	89 12.220
Mensch	Abraham	40 39.557	75 25.586
Merrell	Azariah	35 43.945	80 18.669
Merrell	Abigail	35 43.942	80 18.671
Meredith	Eleandra	39 41.114	76 35.858
Meredith	Micajah	39 41.115	76 35.855
Meredith	Samuel	39 41.116	76 35.855
Meredith	Elizabeth	39 41.116	76 35.854
Meredith	Ruth	39 41.117	76 35.853
Bell	Sarah	39 41.117	76 35.853
John	Bell	39 41.117	76 35.853
Meredith	Mary	39 41.118	76 35.852
Meredith	Clarence	39 41.112	76 35.857
Meredith	Cora	39 41.112	76 35.857
Meredith	W	39 41.112	76 35.856
Meredith	Susan	39 41.112	76 35.856
Meredith	Hannah	39 41.112	76 35.855
Meredith	Mary	39 41.112	76 35.854
Meredith	Samuel	39 41.113	76 35.855
Meredith	Belinda	39 41.113	76 35.855
Tipton	Susannah	39 41.114	76 35.855
Meredith	Thomas	39 41.114	76 35.854
Meredith	Sarah	39 41.114	76 35.854
Mildenberger	Anna	40 44.194	75 29.608
Mildenberger	Nicolaus	40 44.179	75 29.574
Miller	Myrtie	37 48.023	88 53.449
Minnich	Elizabeth	40 40.757	75 31.679
Minnich	John	40 40.759	75 31.679
Mory	Catherina	40 33.585	75 23.776
Mory	Gotthard	40 33.586	75 23.776
Mory	Magdelena	40 33.586	75 23.774
Mory	Peter	40 33.585	75 23.776
Nagel	Anna	40 33.585	75 23.745
Nagel	Anna	40 44.191	75 29.603
Nagel	Caty	41 13.033	75 57.329
Nagel	Daniel	40 39.575	75 25.555
Nagel	Frederick	40 39.577	75 25.549
Nagel	Friedrich	40 44.197	75 29.605
Nagel	Johann	41 13.031	75 57.333
Nagel	Maria	40 39.575	75 25.555
Nagle	John	38 44.582	82 58.978
Nagle	Mary	38 44.582	82 58.978
Nagel	Henry	38 44.582	82 58.978
Nagel	Mary	38 44.582	82 58.978
Nagel	Will	38 44.582	82 58.978
Nagel	Adeline	38 44.582	82 58.978
NA	NA	NA	NA
Nutty	John	37 25.674	88 54.020
Nutty	Beatrice	37 25.682	88 54.020
Nutty	John	37 25.678	88 54.020
Ritter	NA	37 52.861	88 39/178
Ritter	NA	37 52.861	88 39/178
Odom	Archibald	37 58.794	88 55.324
Odom	Cynthia	37 58.795	88 55.326
Odom	G	37 47.993	88 53.510
Odom	Sarah	37 47.994	88 53.510
NA	Thomas	37 47.992	88 53.506
Odum	Britton	NA	NA
Odum	Wiley	37 47.187	88 50.175
Odum	Sallie A	37 47.187	88 50.175
Peters	Daniel	37 47.244	88 55.354
Peters	Charlotte	37 47.244	88 55.354
Pickard	William	38 04.918	88 52.028
Pickard	Harriet	38 04.919	88 52.028
Pickard	Louise	38 04.917	88 52.028
Pletz	Karl	37 44.684	88 55.998
Russell	Caroline	37 44.683	88 55.998
Pickard	William	38 04.918	88 54.028
Pickard	Harriet	38 04.919	88 54.028
Pickard	Louise	38 04.917	88 54.028
Pulliam	Frieda	37 25.697	88 53.922
Pulliam	Amos	37 25.697	88 53.922
Rex	William	37 45.776	88 55.111
Rex	Elmina	37 45.776	88 55.111
Rex	Mamie	37 45.777	88 55.110
Rex	George	37 45.777	88 55.115
Rex	Bertie	37 45.776	88 55.112
Rex	Lulie	37 45.774	88 55.109
Rex	Lily	37 45.776	88 55.108
Rex	Arthur	37 45.776	88 55.108
Rex	George	37 45.776	88 55.108
Rex	Jno	32 22 549	90 52.100
Rex	Guy	37 44.784	88 55.855
Rex	Harlie	37 44.785	88 55.855
Richardson	Annabelle	37 44.766	88 55.776
Richardson	Alfred	37 44.787	88 55.775
Riegel	Solomon	37 49.828	88 35.346
Riegel	Catherine	37 49.828	88 35.346
Ritter	J	37 52.853	88 39.174
Ritter	Mary	37 52.853	88 39.174
Rockel	Balzer	40 39.556	75 25.585
Rockel	Elisabetha	40 39.555	75 25.585
Rockel	Johannes	40 39.560	75 25.560
Rockel	Elizabeth	40 39.560	75 25.559
Ross	George	37 58.752	88 58.162
Ross	Euna	37 58.752	88 58.162
Ruckel	Mary	NA	NA
Ruckel	Melchir	NA	NA
Russell	James	37 44.681	88 55.998
Russell	Ana	37 44.682	88 55.998
NA	NA	37 44.682	88 55.994
NA	NA	37 44.682	88 55.994
NA	NA	37 44.682	88 55.994
Siliven	Jenniel	37 28.189	88 48.007
Sinks	A	36 14.451	86 43.526
Sinks	Francis	37 54.081	88 54.293
Sinks	Delphia	37 54.081	88 54.293
Sinks	Salem	37 54.089	88 54.207
Sinks	Daniel	37 52.619	88 55.430
Sinks	Martha	37 52.619	88 55.430
Sinks	Roy	37 52.619	88 55.430
Sinks	Elizabeth	37 47.989	88 53.489
Sinks	infant son	37 47.989	88 53.489
Sinks	John	37 47.986	88 53.489
Sinks	Mary	37 47.985	88 53.491
Sinks	William	37 47.984	88 53.490
Sinks	Charlotte	37 47.984	88 53.490
Sinks	Anna	37 47.984	88 53.488
Sinks	Leonard	37 47.982	88 53.491
Sinks	Etta Faye	37 48.024	88 53.464
Sinks	John	37 48.024	88 53.463
Sinks	Sena	37 48.020	88 53.463
Sinks	William	37 44.702	88 55.998
Sweet	Jewell	37 44.704	88 55.995
Sinks	Francis	37 44.702	88 55.997
Sinks	Arlie	37 44.704	88 55.998
Sinks	Viola	37 44.704	88 55.998
Sinks	Leonard	38 33.836	89 07.580
Sinks	Mae	38 33.837	89 07.579
Sinks	Bessie	38 33.917	89 07.572
Sinks	Caroline	38 02.272	88 50.161
Sinks	Arlie	37 44.770	88 55.779
Sinks	Eva	37 44.770	88 55.779
Solt	Conrad	40 48.686	75 37.120
Solt	Conrad	40 48.693	75 37.119
Solt	Maria	40 48.690	75 37.113
Sfafford	Trice	37 52.608	88 55.434
Sfafford	Phebe	37 52.608	88 55.434
Steen	Richard	38 33.025	87 06.328
VanCleve	Martin	37 25.694	88 53.921
VanCleve	Florence	37 25.694	88 53.921
VanCleve	W	37 33.397	88 46.363
VanCleve	Nancy	37 33.397	88 46.363
VanCleve	J	37 33.397	88 46.363
VanCleave	W	38 04.924	88 52.030
VanCleave	Elizabeth	38 04.924	88 52.030
Veach	Pleasant	37 29.916	86 54.044
Veach	Victoria	37 29.916	86 54.044
Veach	Ward	37 29.895	86 54.023
Veach	Cynthia	37 29.895	86 54.022
Veach	James	37 29.895	86 54.020
Veach	James	37 25.692	88 53.942
Veach	Nannie	37 26.692	88 53.942
Veatch	John	37 26.692	88 50.527
Veatch	Eleanor	37 26.692	88 50.527
Veach	William	37 26.693	88 50.540
Veach	James	37 26.692	88 50.531
Veach	Rachel	37 26.692	88 50.531
Veach	Pleasant	37 29.895	88 54.022
Veach	Mary	37 29.897	88 54.022
Veatch	Parmelia	37 28.187	88 49.000
Veatch	Mary	37 28.187	88 48.999
Veatch	Elnor	37 28.187	88 49.001
Veatch	Frelin	37 28.186	88 48.005
Veach-Nutty	NA	37 25.682	88 54.017
Veach	John	37 25.681	88 54.017
Veach	Rose	37 25.679	88 54.017
Veach	Ruth	37 25.682	88 54.017
Ware	Turner	37 52.856	88 39.186
Ware	Martha	37 52.856	88 39.186
Ware	Joseph	37 52.867	88 39.176
Ware	Caroline	37 52.867	88 39.176
Webber	Dick	37 49.829	88 35.336
Webber	Pearl	37 49.826	88 35.336
Weir	James	36 15.064	86 11.669
Weir	Mary	36 15.064	86 11.669
Wier	Leticia	37 49.208	88 46.787
Whiteside	Lucinda	37 26.743	88 50.534
Whiteside	John	37 26.743	88 50.534
Willis	Matha	36 35.889	86 43.203
Wilson	Jessie	36 26.350	86 47.072
Wilson	Mary	36 26.361	86 47.070
Wilson	Joseph	36 29.553	86 46.791
Wilson	Elisha	36 28.812	86 46.023
Wilson	Sallie	36 28.803	86 46.007
Wilson	Lutetita	NA	NA
Wilson	Thomas	37 48.034	88 53.443
Wilson	Sarah	37 48.034	88 53.443
Wilson	Elisha	36 26.351	86 47.070
Wilson	Martha	36 26.351	86 47.070
Wilson	Charles	36 26.351	86 47.070
Wilson	Zack	36 26.350	86 47.073
Wilson	Juritha	36 26.350	86 47.073
Wilson	Elisha	36 26.351	86 47.071
Wilson	Drury	NA	NA
Wilson	Mary	NA	NA
Wilson	Sandifer	NA	NA
Wilson	Nancy	NA	NA
Wise	Luvena	37 50.352	88 31.612
Wollard	John	37 54.076	88 54.322
Woolard	Nettie	37 54.075	88 54.322
Woolard	Millie	37 58.721	88 55.211
Woolard	Lawrence	37 58.721	88 55.211
Woolard	Etta	37 58.721	88 55.211
Woolard	John	37 58.723	88 55.212
Woolard	James	37 58.721	88 55.213
Woolard	C	37 58.720	88 55.213
Woolard	Blanche	37 58.720	88 55.213
Woolard	L	37 51.394	88 41.745
Woolard	Ama	37 51.395	88 41.746
Woolard	Robert	37 51.396	88 41.747
Woolard	James	37 51.391	88 41.742
Woolard	Romey	37 51.397	88 41.741
Woolard	Anna	37 52.853	88 39.160
Woolard	James	37 52.853	88 39.161
Woolard	Francis	37 52.853	88 39.160
Woolard	Turner	37 52.853	88 39.159
Woolard	William	37 52.854	88 39.160
Woolard	George	37 51.742	88 52.935
Woolard	Nancy	37 51.742	88 52.935

Much better. There is some missing data, encoded both as blanks and as NAs. There are also some coordinates that don’t make sense, like 524 (for the entry Dorris William). This will need to be dealt with.

Converting to Decimal Coordinates (Numeric)

Next, I’m converting the N and W data to decimal latitude and longitude. S/W should be “-” and N/E should be “+”. I split the degree/minute/second data into parts and then do the conversion. I delete the intermediate components when done. I used str_split_fixed() here, which stores the parts in a matrix in your dataframe, hence the indexing to access the parts. The related function str_split() returns a list. Both functions take the string, a pattern. str_split_fixed() also requires the number of parts (n) to split into. If it doesn’t find that many parts it will store a blank (““) rather than fail. More info about the str_split family can be found here. (A function like separate() would be more straightforward for this application. I originally included another example here where I use separate, so both methods were illustrated, but I have moved that to a module of this project that isn’t posted yet.)

I want to break a coordinate into 3 parts. So 37 25.687 becomes 37 25 and 687. First I break the coordinate into two parts, using the space as the separator. So 37 and 25.687. I then coerce the first part (which is the degree part of the coordinate) into a numeric. I then split the second part ( 25.687) using the . as the separator and again coerce the results into numbers. The coercion does lead to warning about the generation of NAs during the process, but that is fine. I know not all the data is numeric- there were blanks and NAs to start with. Lastly, I convert my degree, minute, second coordinates to decimal coordinates using the formula degree + minute/60 + second/3600.

Escaping Characters in stringr

It is important to note that stringr defaults to considering that patterns are written in regular expressions (regex). This means some characters are special and require escaping in the pattern. The period is one such character and the correct pattern is “\\.” Otherwise, using “.” will match to every character. The stringr cheat sheet has a high level overview of regular expressions on the second page.

Using Selectors from dplyr

I named all the original output from the string splits such that they contained the word “part” and I can easily remove them using a helper from dplyr, in this case, contains. I highly recommend using some sort of naming scheme for intermediate variables/ fields so they can be easily removed in one go without lots of typing. I retain the original and the numeric parts so I can double check the results.

tombstones <- tombstones %>%
  mutate(part1N = str_split_fixed(N, pattern = " ", n = 2) ) %>%
  mutate(N_degree = as.numeric(part1N[,1])) %>%
  mutate(part2N = str_split_fixed(part1N[,2], pattern = '\\.', n = 2)) %>%
  mutate(N_minute = as.numeric(part2N[,1])) %>%
  mutate(N_second = as.numeric(part2N[,2])) %>%
  mutate(lat = N_degree + N_minute/60 + N_second/3600)

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `N_minute = as.numeric(part2N[, 1])`.
Caused by warning:
! NAs introduced by coercion

#converting to decimal longitude  
tombstones <- tombstones %>%
  mutate(part1W = str_split_fixed(W, pattern = " ", n = 2) ) %>%
  mutate(W_degree = as.numeric(part1W[,1])) %>%
  mutate(part2W = str_split_fixed(part1W[,2], pattern = '\\.', n = 2)) %>%
  mutate(W_minute = as.numeric(part2W[,1])) %>%
  mutate(W_second = as.numeric(part2W[,2])) %>%
  mutate(long = -(W_degree + W_minute/60 + W_second/3600)) 

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `W_minute = as.numeric(part2W[, 1])`.
Caused by warning:
! NAs introduced by coercion

tombstones <- tombstones %>%
  select(-contains("part"))

Taking a quick look at the results

tombstones %>%
  select(Surname, First.Name, N, N_degree, N_minute, N_second, lat) %>%
  gt()  %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

Surname	First.Name	N	N_degree	N_minute	N_second	lat
Anderson	Abraham	36 56.472	36	56	472	37.06444
Anderson	Elizabeth	36 56.472	36	56	472	37.06444
Anderson	Zady	37 53.396	37	53	396	37.99333
Anderson	Albert	37 52.856	37	52	856	38.10444
Anderson	Adesia	37 52.856	37	52	856	38.10444
Anderson	May	37 52.855	37	52	855	38.10417
Anderson	E	37 52.853	37	52	853	38.10361
Anderson	William	37 52.853	37	52	853	38.10361
Anderson	Nancy	37 52.852	37	52	852	38.10333
Appleton	Richard	36 29.552	36	29	552	36.63667
Baldwin	John	38 33.025	38	33	25	38.55694
Baldwin	William	38 33.025	38	33	25	38.55694
Baggett	Mahalia	36 29.553	36	29	553	36.63694
Beasley	E	36 35.891	36	35	891	36.83083
Beasley	Josephine	36 36.755	36	36	755	36.80972
Beasley	Fanning	36 36.755	36	36	755	36.80972
Bell	John	36 15.064	36	15	64	36.26778
Bell	Mary	36 15.064	36	15	64	36.26778
Brazelton	Wm	35 09.411	35	9	411	35.26417
Brazelton	Esther	35 09.410	35	9	410	35.26389
Brown	Elizabeth	40 40.760	40	40	760	40.87778
Brown	Joel	40 40.760	40	40	760	40.87778
Bundy	Hope	37 45.623	37	45	623	37.92306
Bundy	Clem	37 53.380	37	53	380	37.98889
Bundy	Nancy	37 53.380	37	53	380	37.98889
Bundy	W	37 53.380	37	53	380	37.98889
Bundy	Charles	37 53.379	37	53	379	37.98861
Bundy	Thomas	37 52.875	37	52	875	38.10972
Bundy	Octavia	37 52.875	37	52	875	38.10972
Bundy	George	37 52.873	37	52	873	38.10917
Bundy	Lora	37 52.873	37	52	873	38.10917
Burgess	W	37 49.224	37	49	224	37.87889
Burgess	Alzada	37 49.224	37	49	224	37.87889
Clayton	G	37 50.788	37	50	788	38.05222
Clayton	Ellen	37 50.788	37	50	788	38.05222
Clayton	L	37 50.795	37	50	795	38.05417
Clayton	Mary	37 50.795	37	50	795	38.05417
Chapman	Daniel	37 29.894	37	29	894	37.73167
Chapman	Elizabeth	37 29.894	37	29	894	37.73167
Chapman	Caroline	37 25.692	37	25	692	37.60889
Chapman	Daniel	37 25.691	37	25	691	37.60861
Chapman	Lucretia	37 25.691	37	25	691	37.60861
Chapman	Samuel	37 25.692	37	25	692	37.60889
Chapman	Elizabeth	37 25.692	37	25	692	37.60889
Chapman	Laura	37 25.694	37	25	694	37.60944
Chapman	Polly	38 33.026	38	33	26	38.55722
Crockett	Mandy	36 22.801	36	22	801	36.58917
Crockett	John	36 22.804	36	22	804	36.59000
Davis	Ezra	37 44.682	37	44	682	37.92278
Davis	Lizzie	37 44.683	37	44	683	37.92306
Davis	Fred	36 14.260	36	14	260	36.30556
Dolch	Catherine	38 44.563	38	44	563	38.88972
Dolch	Christian	38 44.584	38	44	584	38.89556
Dolch	Peter	38 44.564	38	44	564	38.89000
Doley	George	38 44.615	38	44	615	38.90417
Doley	Katie	38 44.615	38	44	615	38.90417
Doley	Mary E	38 44.615	38	44	615	38.90417
Doley	Henriettie	38 44.615	38	44	615	38.90417
NA	MED	38 44.618	38	44	618	38.90500
NA	HD	38 44.618	38	44	618	38.90500
NA	GD	38 44.618	38	44	618	38.90500
NA	Mother	38 44.618	38	44	618	38.90500
NA	Father	38 44.618	38	44	618	38.90500
Doley	George	38 44.615	38	44	615	38.90417
Doley	James	38 44.610	38	44	610	38.90278
Doley	May	38 44.611	38	44	611	38.90306
Doley	John	38 44.611	38	44	611	38.90306
Doley	Maggie	38 44.611	38	44	611	38.90306
Doley	William	37 49.907	37	49	907	38.06861
Doley	Dora	37 49.907	37	49	907	38.06861
Doley	L[eaman]	37 49.907	37	49	907	38.06861
Doley	G[uilford]	37 58.810	37	58	810	38.19167
Doley	D[ora]	37 58.810	37	58	810	38.19167
Doley	Eugene	37 58.751	37	58	751	38.17528
Doley	Lou	37 58.751	37	58	751	38.17528
Dorris	J[oseph]	36 28.798	36	28	798	36.68833
Dorris	Joseph	36 28.811	36	28	811	36.69194
Dorris	Sarah	36 28.812	36	28	812	36.69222
Dorris	W	36 28.812	36	28	812	36.69222
Dorris	A	36 28.813	36	28	813	36.69250
Dorris	J	NA	NA	NA	NA	NA
Dorris	Elizabeth	NA	NA	NA	NA	NA
Dorris	Robert	36 26.485	36	26	485	36.56806
Dorris	Rebecca	36 26.484	36	26	484	36.56778
Dorris	Monroe	38 07.067	38	7	67	38.13528
Dorris	Della	38 07.067	38	7	67	38.13528
Dorris	Mary M	38 07.067	38	7	67	38.13528
Dorris	Harve	38 07.081	38	7	81	38.13917
Dorris	Carrie	38 07.081	38	7	81	38.13917
Dorris	Smith	37 54.310	37	54	310	37.98611
Dorris	Ada	37 54.309	37	54	309	37.98583
Dorris	William	37 54.309	37	54	309	37.98583
Dorris	Harvey	37 54.310	37	54	310	37.98611
Dorris	Cora	37 54.310	37	54	310	37.98611
Dorris	John	37 58.746	37	58	746	38.17389
Dorris	W	37 58.749	37	58	749	38.17472
Dorris	Gustavus	37 47.990	37	47	990	38.05833
Dorris	Sarah	37 47.988	37	47	988	38.05778
Dorris	Joseph	37 51.571	37	51	571	38.00861
Dorris	Della	37 51.571	37	51	571	38.00861
Dorris	William	37 50.787	37	50	787	38.05194
Dorris	Harriet	37 50.788	37	50	788	38.05222
Dorris	William	37 50.794	37	50	794	38.05389
Dorris	Mary	37 50.794	37	50	794	38.05389
Dorris	James	37 50.786	37	50	786	38.05167
Dorris	Sarah	37 50.771	37	50	771	38.04750
Dorris	W[illiam]	37 50.783	37	50	783	38.05083
Dorris	E[lisha]	37 50.775	37	50	775	38.04861
Dorris	Sarah	37 50.775	37	50	775	38.04861
Dorris	James	37 50.775	37	50	775	38.04861
Dorris	Georgia	37 50.775	37	50	775	38.04861
Dorris	William	524	524	NA	NA	NA
Dorris	Malinda	528	528	NA	NA	NA
Drake	Mary	36 35.870	36	35	870	36.82500
Dreisbach	Catherina	40 44.177	40	44	177	40.78250
Dreisbach	Johannes	40 44.177	40	44	177	40.78250
Everett	Semantha	38 33.026	38	33	26	38.55722
Farris	Elizabeth	37 24.687	37	24	687	37.59083
Farris	Elizabeth	37 24.678	37	24	678	37.58833
Finch	Isaac	44 34.662	44	34	662	44.75056
Follis	Fawn	37 51.764	37	51	764	38.06222
Follis	Ralph	37 51.758	37	51	758	38.06056
Follis	A	37 51.761	37	51	761	38.06139
Follis	Christian	37 51.761	37	51	761	38.06139
Follis	G	37 51.759	37	51	759	38.06083
Follis	Ralph	37 51.	37	51	NA	NA
Follis	E	37 51.758	37	51	758	38.06056
Follis	William	37 51.758	37	51	758	38.06056
Follis	Martha	37 51.758	37	51	758	38.06056
Follis	Jeff	37 51.758	37	51	758	38.06056
Ford	Florence	37 52.851	37	52	851	38.10306
Fox	Frances	37 48.023	37	48	23	37.80639
Frost	Ebenezer	37 17.909	37	17	909	37.53583
NA	NA	37 17.910	37	17	910	37.53611
Frost	NA	37 17.909	37	17	909	37.53583
Fuqua	William	36 38.189	36	38	189	36.68583
Gregory	Leonard	38 44.609	38	44	609	38.90250
Gregory	Lucille	38 44.611	38	44	611	38.90306
Hart	Parmelia	37 51.757	37	51	757	38.06028
Hess	Amalphus	37 25.687	37	25	687	37.60750
Hess	Adolphus	37 25.687	37	25	687	37.60750
Hess	Samuel	37 25.688	37	25	688	37.60778
Hess	Augusta	37 25.688	37	25	688	37.60778
Hess	Ulysses	37 25.688	37	25	688	37.60778
Hess	Ulysses	37 25.687	37	25	687	37.60750
Hess	William	37 25.688	37	25	688	37.60778
Hess	William	37 25.687	37	25	687	37.60750
Hess	Jerome	37 25.689	37	25	689	37.60806
Hess	Franklin	37 25.689	37	25	689	37.60806
Hess	Samuel	37 25.693	37	25	693	37.60917
Hess	Bernice	37 25.693	37	25	693	37.60917
Hess	Catherine	37 25.693	37	25	693	37.60917
Hess	George	37 25.690	37	25	690	37.60833
Holt	Lucinda	NA	NA	NA	NA	NA
Holt	William	NA	NA	NA	NA	NA
Horlacher	Daniel	40 30.928	40	30	928	40.75778
Horlacher	Margaretha	40 30.930	40	30	930	40.75833
Horrall	Polly	37 54.090	37	54	90	37.92500
Horrall	James	38 33.026	38	33	26	38.55722
Horrall	William	38 36.963	38	36	963	38.86750
Hurt	Elizabeth	36 28.804	36	28	804	36.69000
Jacobs	Jeremiah	38 21.315	38	21	315	38.43750
Jacobs	Rebecca	38 21.317	38	21	317	38.43806
Johnson	James	37 52.872	37	52	872	38.10889
Johnson	Mary	37 52.872	37	52	872	38.10889
Jones	Levi	37 47.994	37	47	994	38.05944
Jones	Hester	37 47.994	37	47	994	38.05944
Jones	Ridley	37 47.997	37	47	997	38.06028
Jones	James	37 47.995	37	47	995	38.05972
Jones	Tina	37 47.995	37	47	995	38.05972
Jones	Ezra	37 48.024	37	48	24	37.80667
Jones	Nannie	37 48.024	37	48	24	37.80667
Jones	Samuel	37 48.020	37	48	20	37.80556
Jones	Melverda	37 48.020	37	48	20	37.80556
Jones	John	37 51.747	37	51	747	38.05750
Karnes	Willard	37 58.749	37	58	749	38.17472
Karnes	Ruth	37 58.749	37	58	749	38.17472
Keith	James	NA	NA	NA	NA	NA
Keth	Nancy	35 09.410	35	9	410	35.26389
Kleppinger	Anna	40 44.178	40	44	178	40.78278
Lipsey	Joe	38 33.917	38	33	917	38.80472
Lockwood	Eugenia	NA	NA	NA	NA	NA
Lockwood	Leland	NA	NA	NA	NA	NA
Loomis	Jon	37 36.925	37	36	925	37.85694
Mensch	Abraham	40 39.557	40	39	557	40.80472
Merrell	Azariah	35 43.945	35	43	945	35.97917
Merrell	Abigail	35 43.942	35	43	942	35.97833
Meredith	Eleandra	39 41.114	39	41	114	39.71500
Meredith	Micajah	39 41.115	39	41	115	39.71528
Meredith	Samuel	39 41.116	39	41	116	39.71556
Meredith	Elizabeth	39 41.116	39	41	116	39.71556
Meredith	Ruth	39 41.117	39	41	117	39.71583
Bell	Sarah	39 41.117	39	41	117	39.71583
John	Bell	39 41.117	39	41	117	39.71583
Meredith	Mary	39 41.118	39	41	118	39.71611
Meredith	Clarence	39 41.112	39	41	112	39.71444
Meredith	Cora	39 41.112	39	41	112	39.71444
Meredith	W	39 41.112	39	41	112	39.71444
Meredith	Susan	39 41.112	39	41	112	39.71444
Meredith	Hannah	39 41.112	39	41	112	39.71444
Meredith	Mary	39 41.112	39	41	112	39.71444
Meredith	Samuel	39 41.113	39	41	113	39.71472
Meredith	Belinda	39 41.113	39	41	113	39.71472
Tipton	Susannah	39 41.114	39	41	114	39.71500
Meredith	Thomas	39 41.114	39	41	114	39.71500
Meredith	Sarah	39 41.114	39	41	114	39.71500
Mildenberger	Anna	40 44.194	40	44	194	40.78722
Mildenberger	Nicolaus	40 44.179	40	44	179	40.78306
Miller	Myrtie	37 48.023	37	48	23	37.80639
Minnich	Elizabeth	40 40.757	40	40	757	40.87694
Minnich	John	40 40.759	40	40	759	40.87750
Mory	Catherina	40 33.585	40	33	585	40.71250
Mory	Gotthard	40 33.586	40	33	586	40.71278
Mory	Magdelena	40 33.586	40	33	586	40.71278
Mory	Peter	40 33.585	40	33	585	40.71250
Nagel	Anna	40 33.585	40	33	585	40.71250
Nagel	Anna	40 44.191	40	44	191	40.78639
Nagel	Caty	41 13.033	41	13	33	41.22583
Nagel	Daniel	40 39.575	40	39	575	40.80972
Nagel	Frederick	40 39.577	40	39	577	40.81028
Nagel	Friedrich	40 44.197	40	44	197	40.78806
Nagel	Johann	41 13.031	41	13	31	41.22528
Nagel	Maria	40 39.575	40	39	575	40.80972
Nagle	John	38 44.582	38	44	582	38.89500
Nagle	Mary	38 44.582	38	44	582	38.89500
Nagel	Henry	38 44.582	38	44	582	38.89500
Nagel	Mary	38 44.582	38	44	582	38.89500
Nagel	Will	38 44.582	38	44	582	38.89500
Nagel	Adeline	38 44.582	38	44	582	38.89500
NA	NA	NA	NA	NA	NA	NA
Nutty	John	37 25.674	37	25	674	37.60389
Nutty	Beatrice	37 25.682	37	25	682	37.60611
Nutty	John	37 25.678	37	25	678	37.60500
Ritter	NA	37 52.861	37	52	861	38.10583
Ritter	NA	37 52.861	37	52	861	38.10583
Odom	Archibald	37 58.794	37	58	794	38.18722
Odom	Cynthia	37 58.795	37	58	795	38.18750
Odom	G	37 47.993	37	47	993	38.05917
Odom	Sarah	37 47.994	37	47	994	38.05944
NA	Thomas	37 47.992	37	47	992	38.05889
Odum	Britton	NA	NA	NA	NA	NA
Odum	Wiley	37 47.187	37	47	187	37.83528
Odum	Sallie A	37 47.187	37	47	187	37.83528
Peters	Daniel	37 47.244	37	47	244	37.85111
Peters	Charlotte	37 47.244	37	47	244	37.85111
Pickard	William	38 04.918	38	4	918	38.32167
Pickard	Harriet	38 04.919	38	4	919	38.32194
Pickard	Louise	38 04.917	38	4	917	38.32139
Pletz	Karl	37 44.684	37	44	684	37.92333
Russell	Caroline	37 44.683	37	44	683	37.92306
Pickard	William	38 04.918	38	4	918	38.32167
Pickard	Harriet	38 04.919	38	4	919	38.32194
Pickard	Louise	38 04.917	38	4	917	38.32139
Pulliam	Frieda	37 25.697	37	25	697	37.61028
Pulliam	Amos	37 25.697	37	25	697	37.61028
Rex	William	37 45.776	37	45	776	37.96556
Rex	Elmina	37 45.776	37	45	776	37.96556
Rex	Mamie	37 45.777	37	45	777	37.96583
Rex	George	37 45.777	37	45	777	37.96583
Rex	Bertie	37 45.776	37	45	776	37.96556
Rex	Lulie	37 45.774	37	45	774	37.96500
Rex	Lily	37 45.776	37	45	776	37.96556
Rex	Arthur	37 45.776	37	45	776	37.96556
Rex	George	37 45.776	37	45	776	37.96556
Rex	Jno	32 22 549	32	NA	NA	NA
Rex	Guy	37 44.784	37	44	784	37.95111
Rex	Harlie	37 44.785	37	44	785	37.95139
Richardson	Annabelle	37 44.766	37	44	766	37.94611
Richardson	Alfred	37 44.787	37	44	787	37.95194
Riegel	Solomon	37 49.828	37	49	828	38.04667
Riegel	Catherine	37 49.828	37	49	828	38.04667
Ritter	J	37 52.853	37	52	853	38.10361
Ritter	Mary	37 52.853	37	52	853	38.10361
Rockel	Balzer	40 39.556	40	39	556	40.80444
Rockel	Elisabetha	40 39.555	40	39	555	40.80417
Rockel	Johannes	40 39.560	40	39	560	40.80556
Rockel	Elizabeth	40 39.560	40	39	560	40.80556
Ross	George	37 58.752	37	58	752	38.17556
Ross	Euna	37 58.752	37	58	752	38.17556
Ruckel	Mary	NA	NA	NA	NA	NA
Ruckel	Melchir	NA	NA	NA	NA	NA
Russell	James	37 44.681	37	44	681	37.92250
Russell	Ana	37 44.682	37	44	682	37.92278
NA	NA	37 44.682	37	44	682	37.92278
NA	NA	37 44.682	37	44	682	37.92278
NA	NA	37 44.682	37	44	682	37.92278
Siliven	Jenniel	37 28.189	37	28	189	37.51917
Sinks	A	36 14.451	36	14	451	36.35861
Sinks	Francis	37 54.081	37	54	81	37.92250
Sinks	Delphia	37 54.081	37	54	81	37.92250
Sinks	Salem	37 54.089	37	54	89	37.92472
Sinks	Daniel	37 52.619	37	52	619	38.03861
Sinks	Martha	37 52.619	37	52	619	38.03861
Sinks	Roy	37 52.619	37	52	619	38.03861
Sinks	Elizabeth	37 47.989	37	47	989	38.05806
Sinks	infant son	37 47.989	37	47	989	38.05806
Sinks	John	37 47.986	37	47	986	38.05722
Sinks	Mary	37 47.985	37	47	985	38.05694
Sinks	William	37 47.984	37	47	984	38.05667
Sinks	Charlotte	37 47.984	37	47	984	38.05667
Sinks	Anna	37 47.984	37	47	984	38.05667
Sinks	Leonard	37 47.982	37	47	982	38.05611
Sinks	Etta Faye	37 48.024	37	48	24	37.80667
Sinks	John	37 48.024	37	48	24	37.80667
Sinks	Sena	37 48.020	37	48	20	37.80556
Sinks	William	37 44.702	37	44	702	37.92833
Sweet	Jewell	37 44.704	37	44	704	37.92889
Sinks	Francis	37 44.702	37	44	702	37.92833
Sinks	Arlie	37 44.704	37	44	704	37.92889
Sinks	Viola	37 44.704	37	44	704	37.92889
Sinks	Leonard	38 33.836	38	33	836	38.78222
Sinks	Mae	38 33.837	38	33	837	38.78250
Sinks	Bessie	38 33.917	38	33	917	38.80472
Sinks	Caroline	38 02.272	38	2	272	38.10889
Sinks	Arlie	37 44.770	37	44	770	37.94722
Sinks	Eva	37 44.770	37	44	770	37.94722
Solt	Conrad	40 48.686	40	48	686	40.99056
Solt	Conrad	40 48.693	40	48	693	40.99250
Solt	Maria	40 48.690	40	48	690	40.99167
Sfafford	Trice	37 52.608	37	52	608	38.03556
Sfafford	Phebe	37 52.608	37	52	608	38.03556
Steen	Richard	38 33.025	38	33	25	38.55694
VanCleve	Martin	37 25.694	37	25	694	37.60944
VanCleve	Florence	37 25.694	37	25	694	37.60944
VanCleve	W	37 33.397	37	33	397	37.66028
VanCleve	Nancy	37 33.397	37	33	397	37.66028
VanCleve	J	37 33.397	37	33	397	37.66028
VanCleave	W	38 04.924	38	4	924	38.32333
VanCleave	Elizabeth	38 04.924	38	4	924	38.32333
Veach	Pleasant	37 29.916	37	29	916	37.73778
Veach	Victoria	37 29.916	37	29	916	37.73778
Veach	Ward	37 29.895	37	29	895	37.73194
Veach	Cynthia	37 29.895	37	29	895	37.73194
Veach	James	37 29.895	37	29	895	37.73194
Veach	James	37 25.692	37	25	692	37.60889
Veach	Nannie	37 26.692	37	26	692	37.62556
Veatch	John	37 26.692	37	26	692	37.62556
Veatch	Eleanor	37 26.692	37	26	692	37.62556
Veach	William	37 26.693	37	26	693	37.62583
Veach	James	37 26.692	37	26	692	37.62556
Veach	Rachel	37 26.692	37	26	692	37.62556
Veach	Pleasant	37 29.895	37	29	895	37.73194
Veach	Mary	37 29.897	37	29	897	37.73250
Veatch	Parmelia	37 28.187	37	28	187	37.51861
Veatch	Mary	37 28.187	37	28	187	37.51861
Veatch	Elnor	37 28.187	37	28	187	37.51861
Veatch	Frelin	37 28.186	37	28	186	37.51833
Veach-Nutty	NA	37 25.682	37	25	682	37.60611
Veach	John	37 25.681	37	25	681	37.60583
Veach	Rose	37 25.679	37	25	679	37.60528
Veach	Ruth	37 25.682	37	25	682	37.60611
Ware	Turner	37 52.856	37	52	856	38.10444
Ware	Martha	37 52.856	37	52	856	38.10444
Ware	Joseph	37 52.867	37	52	867	38.10750
Ware	Caroline	37 52.867	37	52	867	38.10750
Webber	Dick	37 49.829	37	49	829	38.04694
Webber	Pearl	37 49.826	37	49	826	38.04611
Weir	James	36 15.064	36	15	64	36.26778
Weir	Mary	36 15.064	36	15	64	36.26778
Wier	Leticia	37 49.208	37	49	208	37.87444
Whiteside	Lucinda	37 26.743	37	26	743	37.63972
Whiteside	John	37 26.743	37	26	743	37.63972
Willis	Matha	36 35.889	36	35	889	36.83028
Wilson	Jessie	36 26.350	36	26	350	36.53056
Wilson	Mary	36 26.361	36	26	361	36.53361
Wilson	Joseph	36 29.553	36	29	553	36.63694
Wilson	Elisha	36 28.812	36	28	812	36.69222
Wilson	Sallie	36 28.803	36	28	803	36.68972
Wilson	Lutetita	NA	NA	NA	NA	NA
Wilson	Thomas	37 48.034	37	48	34	37.80944
Wilson	Sarah	37 48.034	37	48	34	37.80944
Wilson	Elisha	36 26.351	36	26	351	36.53083
Wilson	Martha	36 26.351	36	26	351	36.53083
Wilson	Charles	36 26.351	36	26	351	36.53083
Wilson	Zack	36 26.350	36	26	350	36.53056
Wilson	Juritha	36 26.350	36	26	350	36.53056
Wilson	Elisha	36 26.351	36	26	351	36.53083
Wilson	Drury	NA	NA	NA	NA	NA
Wilson	Mary	NA	NA	NA	NA	NA
Wilson	Sandifer	NA	NA	NA	NA	NA
Wilson	Nancy	NA	NA	NA	NA	NA
Wise	Luvena	37 50.352	37	50	352	37.93111
Wollard	John	37 54.076	37	54	76	37.92111
Woolard	Nettie	37 54.075	37	54	75	37.92083
Woolard	Millie	37 58.721	37	58	721	38.16694
Woolard	Lawrence	37 58.721	37	58	721	38.16694
Woolard	Etta	37 58.721	37	58	721	38.16694
Woolard	John	37 58.723	37	58	723	38.16750
Woolard	James	37 58.721	37	58	721	38.16694
Woolard	C	37 58.720	37	58	720	38.16667
Woolard	Blanche	37 58.720	37	58	720	38.16667
Woolard	L	37 51.394	37	51	394	37.95944
Woolard	Ama	37 51.395	37	51	395	37.95972
Woolard	Robert	37 51.396	37	51	396	37.96000
Woolard	James	37 51.391	37	51	391	37.95861
Woolard	Romey	37 51.397	37	51	397	37.96028
Woolard	Anna	37 52.853	37	52	853	38.10361
Woolard	James	37 52.853	37	52	853	38.10361
Woolard	Francis	37 52.853	37	52	853	38.10361
Woolard	Turner	37 52.853	37	52	853	38.10361
Woolard	William	37 52.854	37	52	854	38.10389
Woolard	George	37 51.742	37	51	742	38.05611
Woolard	Nancy	37 51.742	37	51	742	38.05611

The weird coordinates like William Dorris had (524) were turned into NAs by this process, so I don’t need to worry about fixing them. The leading zeros were removed from the seconds data. For this application, it doesn’t matter. For others it might, and you could pad then back using str_pad(). So that’s it for cleaning this variable.

Cleaning up Dates (strings)

Next, I’m going to clean up the dates. They imported as also imported as strings. I don’t think I’m going to use the dates in the map, but I might use them when I’m working with the web scraping data. Like I did with the GPS Data, I’m first going to cleanup the typos then convert to the format I want.

Viewing the Dates

class(tombstones$DOB)

[1] "character"

tombstones %>% 
  select(Surname, First.Name,DOB, DOD) %>%
  gt()  %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Surname	First.Name	DOB	DOD
Anderson	Abraham	10 Mar 1776	15 Aug 1838
Anderson	Elizabeth	29 Jan 1782	13 Oct 1869
Anderson	Zady	18 Apr 1812	12 Dec 1839
Anderson	Albert	28 Nov 1809	5 Nov 1882
Anderson	Adesia	17 Mar 1808	26 Spt 1864
Anderson	May	NA	9 Aug 1887
Anderson	E	23 Spt 1877	24 Oct 1899
Anderson	William	26 Feb 1836	31 Dec 1895
Anderson	Nancy	2 Spt 1836	18 Oct 1917
Appleton	Richard	1 Aug 1817	6 Oct 1897
Baldwin	John	25 Sep 1845	NA
Baldwin	William	NA	NA
Baggett	Mahalia	3 June 1832	6 June 1897
Beasley	E	13 May 1826	NA
Beasley	Josephine	23 Dec 1858	25 Aug 1899
Beasley	Fanning	NA	25 Aug 1899
Bell	John	1825	1872
Bell	Mary	1826	1859
Brazelton	Wm	NA	10 Dec 1858
Brazelton	Esther	NA	20 Sept 1840
Brown	Elizabeth	1 July 1807	2 Feb 1888
Brown	Joel	20 Dec 1803	NA
Bundy	Hope	1833	1918
Bundy	Clem	NA	NA
Bundy	Nancy	NA	NA
Bundy	W	NA	NA
Bundy	Charles	NA	NA
Bundy	Thomas	4 Feb 1881	22 Apr 1892
Bundy	Octavia	16 Apr 1839	10 Mar 1932
Bundy	George	1864	1949
Bundy	Lora	1870	1957
Burgess	W	1844	17 Dec 1907
Burgess	Alzada	1854	1931
Clayton	G	9 Apr 1846	9 Mar 1919
Clayton	Ellen	28 Mar 1852	23 May 1917
Clayton	L	NA	25 May 1909
Clayton	Mary	NA	18 Jan 1908
Chapman	Daniel	NA	NA
Chapman	Elizabeth	NA	NA
Chapman	Caroline	NA	24 Jan 1861
Chapman	Daniel	5 Jul 1863	Feb 1842
Chapman	Lucretia	23 Apr 1769	Aug 1849
Chapman	Samuel	14 May 1794	15 Apr 1863
Chapman	Elizabeth	23 May 1796	30 Dec 1859
Chapman	Laura	NA	14 June 1865
Chapman	Polly	NA	NA
Crockett	Mandy	3 Sep 1844	5 Nov 1914
Crockett	John	4 Oct 1844	25 July 1919
Davis	Ezra	1887	1949
Davis	Lizzie	1886	1955
Davis	Fred	NA	NA
Dolch	Catherine	11 Feb 1793	14 Feb 1867
Dolch	Christian	24 Dec 1794	29 Aug 1874
Dolch	Peter	NA	16 July 1862
Doley	George	25 July 1823	26 Jan 1868
Doley	Katie	8 Jan 1833	15 Spt 1927
Doley	Mary E	23 Aug 1851	18 July 1853
Doley	Henriettie	28 Feb 1853	18 July 1854
NA	MED	NA	NA
NA	HD	NA	NA
NA	GD	NA	NA
NA	Mother	NA	NA
NA	Father	NA	NA
Doley	George	14 Mar 1856	26 Jan 1860
Doley	James	21 Jan 1858	11 July 1926
Doley	May	1869	1940
Doley	John	1864	[1949?]
Doley	Maggie	1868	1917
Doley	William	20 Dec 1862	24 Nov 1934
Doley	Dora	17 Feb 1864	9 Apr 1934
Doley	L[eaman]	17 Apr 1889	19 Nov 1960
Doley	G[uilford]	5 Aug 1894	1 June 1960
Doley	D[ora]	1897	1983
Doley	Eugene	8 Nov 1886	12 June 1958
Doley	Lou	29 Oct 1895	26 June 1963
Dorris	J[oseph]	12 Feb 1821	8 Jan 1893
Dorris	Joseph	10 Feb 1779	5 Nov 1866
Dorris	Sarah	2 June 1790	23 June 1862
Dorris	W	2 June 1790	5 Nov 1873
Dorris	A	NA	7 Aug 1853
Dorris	J	15 Jan 1822	28 Nov 1911
Dorris	Elizabeth	7 Jan 1833	27 Aug 1895
Dorris	Robert	24 Mar 1818	22 Aug 1894
Dorris	Rebecca	22 mar 1829	24 Apr 1910
Dorris	Monroe	1848	1928
Dorris	Della	1884	1934
Dorris	Mary M	1855	1927
Dorris	Harve	13 Aug. 1889	6 Apr 1966
Dorris	Carrie	12 July 1892	7 Aug 1990
Dorris	Smith	NA	NA
Dorris	Ada	NA	NA
Dorris	William	NA	NA
Dorris	Harvey	NA	NA
Dorris	Cora	NA	NA
Dorris	John	NA	NA
Dorris	W	1857	1936
Dorris	Gustavus	2 Aug 1847	27 Feb 1923
Dorris	Sarah	NA	17 Spt 1880
Dorris	Joseph	NA	NA
Dorris	Della	NA	NA
Dorris	William	28 Nov 1818	17 Feb 1905
Dorris	Harriet	28 Nov 1818	17 Feb 1905
Dorris	William	1867	1942
Dorris	Mary	1872	1960
Dorris	James	NA	24 Mar 1877
Dorris	Sarah	1 June 1837	6 Dec 1908
Dorris	W[illiam]	2 Jan 1835	16 Feb 1898
Dorris	E[lisha]	17 Dec 1853	7 May 1907
Dorris	Sarah	28 Aug 1858	20 Feb 1922
Dorris	James	1861	1921
Dorris	Georgia	1860	1908
Dorris	William	NA	10 Aug 1857
Dorris	Malinda	NA	22 Mar 1850
Drake	Mary	1843	1909
Dreisbach	Catherina	21 Dec 1752	20 Spt 1819
Dreisbach	Johannes	21 Aug 1752	20 Spt 1825
Everett	Semantha	NA	Apr 1845
Farris	Elizabeth	NA	2 Feb 1866
Farris	Elizabeth	NA	28 Spt 1858
Finch	Isaac	NA	26 Nov 1813
Follis	Fawn	9 Feb 1907	17 Nov. 1909
Follis	Ralph	7 Apr 1901	27 Feb 1906
Follis	A	NA	17 Oct 1887
Follis	Christian	NA	21 Oct 1892
Follis	G	NA	20 Aug 1899
Follis	Ralph	6 Oct 1892	4 Nov 1892
Follis	E	NA	15 Mar 1890
Follis	William	20 Aug 1832	25 Aug 1900
Follis	Martha	10 Aug 1841	15 Aug 1881
Follis	Jeff	10 Sep 1873	25 July 1905
Ford	Florence	3 Spt 1866	1 July 1940
Fox	Frances	27 Spt 1904	18 Jan 1997
Frost	Ebenezer	NA	NA
NA	NA	NA	NA
Frost	NA	NA	NA
Fuqua	William	NA	NA
Gregory	Leonard	15 Mar 1887	13 June 1963
Gregory	Lucille	1899	1986
Hart	Parmelia	14 May 1847	17 Sep 1878
Hess	Amalphus	NA	27 Mar 1872
Hess	Adolphus	NA	23 June 1871
Hess	Samuel	23 Dec 1823	5 Mar 1901
Hess	Augusta	17 Jan 1828	NA
Hess	Ulysses	21 Dec 1867	16 Feb 1897
Hess	Ulysses	867	1897
Hess	William	9 Dec 1851	26 Apr 1900
Hess	William	1851	1900
Hess	Jerome	1849	1931
Hess	Franklin	1857	1935
Hess	Samuel	1854	1949
Hess	Bernice	20 Sep 1894	27 Sep 1978
Hess	Catherine	1856	1906
Hess	George	27 Feb 1864	3 Dec 1942
Holt	Lucinda	15 Aug 1832	28 Aug 1888
Holt	William	14 May 1818	26 Aug 1903
Horlacher	Daniel	4 Aug 1735	24 Spt 1804
Horlacher	Margaretha	4 Jan 1741	22 Apr 1806
Horrall	Polly	NA	NA
Horrall	James	NA	15 Apr 1848
Horrall	William	NA	NA
Hurt	Elizabeth	NA	NA
Jacobs	Jeremiah	NA	30 Dec 1824
Jacobs	Rebecca	NA	18 July 1813
Johnson	James	25 Feb 1837	NA
Johnson	Mary	5 Spt, 1844	12 Nov 1895
Jones	Levi	1828	1892
Jones	Hester	1841	1910
Jones	Ridley	NA	2 Mar 1863
Jones	James	1863	1933
Jones	Tina	1874	1918
Jones	Ezra	15 Spt 1867	19 May 1942
Jones	Nannie	18 July 1968	18 July 1957
Jones	Samuel	11 Feb 1876	22 Nov 1915
Jones	Melverda	1 Nov 1879	2 Apr 1924
Jones	John	NA	NA
Karnes	Willard	1 Dec 1911	27 Oct 1990
Karnes	Ruth	8 Jan 1917	8 Jan 2000
Keith	James	NA	6 Oct 1841
Keth	Nancy	NA	17 Nov 1827
Kleppinger	Anna	29 Spt 1748	19 June 1817
Lipsey	Joe	1888	1928
Lockwood	Eugenia	11 July 1910	5 Oct 1994
Lockwood	Leland	19 Feb. 1914	24 Dec 1970
Loomis	Jon	NA	NA
Mensch	Abraham	6 Apr 1754	16 Mar 1826
Merrell	Azariah	20 May 1777	25 Jan 1844
Merrell	Abigail	1781	11 June 1844
Meredith	Eleandra	NA	6 Oct 1875
Meredith	Micajah	NA	26 Oct 1822
Meredith	Samuel	8 Sept 1753	10 Oct 1825
Meredith	Elizabeth	22 Jan 1757	6 Apr 1824
Meredith	Ruth	NA	28 Aug 1856
Bell	Sarah	NA	1860
John	Bell	7 July 1812	4 July 1872
Meredith	Mary	16 Apr 1793	11 Dec 1873
Meredith	Clarence	1899	1979
Meredith	Cora	1900	1983
Meredith	W	1926	1945
Meredith	Susan	28 July 1833	2 Mar 1919
Meredith	Hannah	15 Nov 1830	21 Dec 1903
Meredith	Mary	NA	11 Nov 1882
Meredith	Samuel	NA	5 Jan 1884
Meredith	Belinda	NA	4 Aug 1889
Tipton	Susannah	NA	3 May 1852
Meredith	Thomas	NA	20 Apr 1840
Meredith	Sarah	NA	21 Mar 1830
Mildenberger	Anna	29 Spt 1739	11 Oct. 1777
Mildenberger	Nicolaus	15 Oct 1781	19 Oct 1856
Miller	Myrtie	18 July 1896	7 Dec 1991
Minnich	Elizabeth	NA	NA
Minnich	John	NA	NA
Mory	Catherina	8 May 1758	25 Aug 1837
Mory	Gotthard	20 Mar 1752	26 May 1843
Mory	Magdelena	17 Spt 1759	26 Nov 1827
Mory	Peter	3 May 1757	[grass blocks date]
Nagel	Anna	28 Jul 1761	27 Mar 1840
Nagel	Anna	9 Feb 1725	9 Spt 1790
Nagel	Caty	NA	4 May 1817
Nagel	Daniel	NA	7 May 1866
Nagel	Frederick	26 Apr 1759	10 Mar 1839
Nagel	Friedrich	1713	22 Nov 1779
Nagel	Johann	15 Feb 1746	3 June 1823
Nagel	Maria	NA	NA
Nagle	John	NA	23 Nov 1870
Nagle	Mary	NA	18 Mar 1870
Nagel	Henry	1834	1913
Nagel	Mary	1836	1921
Nagel	Will	1858	1916
Nagel	Adeline	1860	1941
NA	NA	NA	NA
Nutty	John	1907	1977
Nutty	Beatrice	1909	1989
Nutty	John	1907	1977
Ritter	NA	NA	NA
Ritter	NA	NA	NA
Odom	Archibald	20 Feb 1838	20 May 1915
Odom	Cynthia	NA	NA
Odom	G	1854	1943
Odom	Sarah	14 June 1854	24 Oct 1910
NA	Thomas	NA	15 Nov 1887
Odum	Britton	1794	1863
Odum	Wiley	21 May 1879	2 Mar 1937
Odum	Sallie A	15 May 1881	6 May 1946
Peters	Daniel	NA	20 Aug 1808
Peters	Charlotte	NA	20 June 1880
Pickard	William	19 Feb 1839	6 Jan 1907
Pickard	Harriet	1843	1916
Pickard	Louise	1868	1935
Pletz	Karl	1914	1975
Russell	Caroline	1917	1995
Pickard	William	1839	1907
Pickard	Harriet	1847	1916
Pickard	Louise	1868	1935
Pulliam	Frieda	NA	NA
Pulliam	Amos	NA	NA
Rex	William	NA	27 Apr 1909
Rex	Elmina	NA	25 Jan 1900
Rex	Mamie	NA	7 Aug 1892
Rex	George	NA	8 Dec 1891
Rex	Bertie	NA	5 Nov 1887
Rex	Lulie	10 Oct 1877	2 Oct 1878
Rex	Lily	21 Spt 1871	11 Oct 1872
Rex	Arthur	11 Aug 1870	25 Aug 1870
Rex	George	NA	NA
Rex	Jno	NA	NA
Rex	Guy	28 May 1889	14 Oct 1983
Rex	Harlie	9 Sep 1890	10 May 1979
Richardson	Annabelle	1916	1993
Richardson	Alfred	1915	1987
Riegel	Solomon	NA	18 May 1827
Riegel	Catherine	NA	27 Dec 1882
Ritter	J	NA	11 Nov 1917
Ritter	Mary	NA	10 Mat 1903
Rockel	Balzer	10 Nov 1707	9 June 1800
Rockel	Elisabetha	24 June 1719	16 Oct 1794
Rockel	Johannes	23 Mar 1749	4 Jan 1838
Rockel	Elizabeth	7 Oct 1764	1 Mar 1835
Ross	George	1 Aug 1885	8 June 1971
Ross	Euna	2 July 1885	19 Mar 1938
Ruckel	Mary	NA	NA
Ruckel	Melchir	27 may 1769	15 Apr 1832
Russell	James	NA	NA
Russell	Ana	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
Siliven	Jenniel	NA	30 Aug 1873
Sinks	A	NA	NA
Sinks	Francis	20 Nov 1837	17 Jan 1909
Sinks	Delphia	27 Dec 1838	11 Dec 1931
Sinks	Salem	NA	26 Oct 1869
Sinks	Daniel	1841	1923
Sinks	Martha	1856	1924
Sinks	Roy	1889	1908
Sinks	Elizabeth	10 Mar 1835	10 Aug 1911
Sinks	infant son	18 Dec. 1898	18 Dec. 1898
Sinks	John	NA	20 Nov 1893
Sinks	Mary	NA	23 Aug 1906
Sinks	William	NA	27 Spt 1953
Sinks	Charlotte	NA	31 Oct 1910
Sinks	Anna	NA	5 Aug 1909
Sinks	Leonard	NA	26 Oct 1919
Sinks	Etta Faye	31 Mar 1910	19 Mar 1989
Sinks	John	NA	28 Jan 1918
Sinks	Sena	18 Spt 1888	13 Dec 1972
Sinks	William	11 Spt 1914	30 Spt 1986
Sweet	Jewell	1912	1990
Sinks	Francis	1908	1988
Sinks	Arlie	1883	1951
Sinks	Viola	1882	1966
Sinks	Leonard	NA	NA
Sinks	Mae	NA	NA
Sinks	Bessie	21 Nov 1889	24 Apr 1979
Sinks	Caroline	NA	17 Apr 1876
Sinks	Arlie	27 July 1916	4 July 2009
Sinks	Eva	15 Jan 1914	8 Sep 1002
Solt	Conrad	29 Spt. 1758	24 Dec 1825
Solt	Conrad	20 Mar 1753	25 Aug 1830
Solt	Maria	1760	23 Dec 1839
Sfafford	Trice	NA	NA
Sfafford	Phebe	NA	NA
Steen	Richard	NA	NA
VanCleve	Martin	1860	1946
VanCleve	Florence	1867	1946
VanCleve	W	1 Jan 1813	20 May 1886
VanCleve	Nancy	26 Nov 1814	22 Feb 1902
VanCleve	J	20 Dec 1850	31 Oct 1872
VanCleave	W	29 June 1838	15 Jan 1914
VanCleave	Elizabeth	3 Sep 1840	25 May 1901
Veach	Pleasant	19 Dec 1845	23 Jan 1917
Veach	Victoria	4 Apr 1849	10 Apr 1921
Veach	Ward	1886	1916
Veach	Cynthia	1861	1921
Veach	James	1860	1890
Veach	James	1857	1939
Veach	Nannie	1863	1953
Veatch	John	11 Nov 1776	17 Spt 1844
Veatch	Eleanor	26 Oct 1773	22 July 1852
Veach	William	1882	1957
Veach	James	NA	NA
Veach	Rachel	NA	NA
Veach	Pleasant	1 Oct 1837	18 Jan 1895
Veach	Mary	NA	16 Nov 1876
Veatch	Parmelia	9 Feb 1808	31 Jan 1867
Veatch	Mary	5 Feb 1842	NA
Veatch	Elnor	NA	1834
Veatch	Frelin	21 Mar 1825	18 Jan 1848
Veach-Nutty	NA	NA	NA
Veach	John	1863	1950
Veach	Rose	1868	1948
Veach	Ruth	1900	1901
Ware	Turner	14 Feb 1817	5 Feb 1902
Ware	Martha	NA	23 Mar 1881
Ware	Joseph	NA	NA
Ware	Caroline	NA	NA
Webber	Dick	7 Aug 1881	3 Dec 1942
Webber	Pearl	17 Apr 1889	10 Apr 1967
Weir	James	1799	1879
Weir	Mary	1801	1886
Wier	Leticia	29 Dec 1836	26 Mar 1865
Whiteside	Lucinda	21 Apr 1858	31 Jan 1936
Whiteside	John	2 Apr 1852	30 Sep 1913
Willis	Matha	10 Mar 1830	13 Oct 1925
Wilson	Jessie	14 Apr 1924	16 May 1996
Wilson	Mary	7 Dec 1927	2 June 1978
Wilson	Joseph	26 Feb 1825	Jan 1862
Wilson	Elisha	24 May 1800	9 July 1873
Wilson	Sallie	15 Oct 1807	4 Apr 1866
Wilson	Lutetita	1826	1907
Wilson	Thomas	NA	1922
Wilson	Sarah	NA	NA
Wilson	Elisha	1841	1907
Wilson	Martha	1840	1929
Wilson	Charles	23 Jan 1908	19 Apr 1955
Wilson	Zack	1848	28 Sep 1918
Wilson	Juritha	6 Aug 1854	28 Sep 1916
Wilson	Elisha	1841	1907
Wilson	Drury	10 Dec 1827	5 Feb 1911
Wilson	Mary	17 Jan 1837	1 Mar 1900
Wilson	Sandifer	15 Oct 1834	8 Jan 1929
Wilson	Nancy	26 Oct 1842	1 Apr 1913
Wise	Luvena	NA	NA
Wollard	John	12 Jul 1846	9 Jan 1918
Woolard	Nettie	8 Spt 1874	28 Nov 1910
Woolard	Millie	13 Nov 1837	19 Apr 1932
Woolard	Lawrence	25 Dec 1877	12 May 1923
Woolard	Etta	14 July 1883	7 Dec 1945
Woolard	John	21 Jan 1872	19 Oct 1936
Woolard	James	30 June 1908	12 Oct 1966
Woolard	C	2 Oct 1882	9 Dec 1953
Woolard	Blanche	1 Jan 1885	25 July 1959
Woolard	L	15 Spt. 1811	9 May 1878
Woolard	Ama	23 May 1819	5 Dec 1891
Woolard	Robert	1863	1938
Woolard	James	12 Apr 1848	7 Spt 1888
Woolard	Romey	23 May 1922	12 Spt. 1944
Woolard	Anna	NA	30 Spt 1887
Woolard	James	NA	27 Spt 1878
Woolard	Francis	NA	20 Feb. 1867
Woolard	Turner	NA	20 Oct 1861
Woolard	William	22 Aug 1857	2 Jan 1858
Woolard	George	28 Mar 1867	5 Jan 1935
Woolard	Nancy	1869	1955

A few things to note on first glance. The majority of the dates use day month(abbreviated) year. Some entries only have the year. There are NAs. A few abbreviations have a period (Nov. in the Follis Fawn entry). September is abbreviated as both Spt and Sep. There are some dates that are guesses ([1949?] in the Doley John record.) I will replace Spt with Sep and the period with “” using str_replace_all() as above. I’m making the corrections in the original columns (DOB, DOD) but putting the date version in new variables. That way it is easy to check that the conversions were done correctly.

The date type must have a day, month, and year, so the years only records will become NAs. I don’t think this matters for my application, but if it did, I’d probably make an integer column for the year data. The dates would need to be split into numeric month, day, and year field or possibly date and numeric year fields.

Cleaning Typos and Converting to Dates

I’m using base R as.Date() for the conversion. This let’s you specify the format. A detailed explanation of specifying dates and the code can be found at the Epidemiologist R Handbook. Various functions from tidyverse package lubridate could also be used to parse the dates. If I were planning on doing more with the dates I’d probably use lubridate functions exclusively.

tombstones <- tombstones %>%
  mutate(DOB = str_replace_all(DOB, "Spt", "Sep")) %>%
  mutate(DOD = str_replace_all(DOD, "Spt", "Sep")) %>%
  mutate(DOB = str_replace_all(DOB, "\\.", "")) %>%
  mutate(DOD = str_replace_all(DOD, "\\.", "")) %>%
  mutate(DOB_date = as.Date(DOB, format = "%d %b %Y")) %>%
  mutate(DOD_date = as.Date(DOD, format = "%d %b %Y"))

Checking that everything worked.

tombstones %>%
  select(Surname, First.Name,DOB, DOD, DOB_date, DOD_date) %>%
  gt()  %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Surname	First.Name	DOB	DOD	DOB_date	DOD_date
Anderson	Abraham	10 Mar 1776	15 Aug 1838	1776-03-10	1838-08-15
Anderson	Elizabeth	29 Jan 1782	13 Oct 1869	1782-01-29	1869-10-13
Anderson	Zady	18 Apr 1812	12 Dec 1839	1812-04-18	1839-12-12
Anderson	Albert	28 Nov 1809	5 Nov 1882	1809-11-28	1882-11-05
Anderson	Adesia	17 Mar 1808	26 Sep 1864	1808-03-17	1864-09-26
Anderson	May	NA	9 Aug 1887	NA	1887-08-09
Anderson	E	23 Sep 1877	24 Oct 1899	1877-09-23	1899-10-24
Anderson	William	26 Feb 1836	31 Dec 1895	1836-02-26	1895-12-31
Anderson	Nancy	2 Sep 1836	18 Oct 1917	1836-09-02	1917-10-18
Appleton	Richard	1 Aug 1817	6 Oct 1897	1817-08-01	1897-10-06
Baldwin	John	25 Sep 1845	NA	1845-09-25	NA
Baldwin	William	NA	NA	NA	NA
Baggett	Mahalia	3 June 1832	6 June 1897	1832-06-03	1897-06-06
Beasley	E	13 May 1826	NA	1826-05-13	NA
Beasley	Josephine	23 Dec 1858	25 Aug 1899	1858-12-23	1899-08-25
Beasley	Fanning	NA	25 Aug 1899	NA	1899-08-25
Bell	John	1825	1872	NA	NA
Bell	Mary	1826	1859	NA	NA
Brazelton	Wm	NA	10 Dec 1858	NA	1858-12-10
Brazelton	Esther	NA	20 Sept 1840	NA	NA
Brown	Elizabeth	1 July 1807	2 Feb 1888	1807-07-01	1888-02-02
Brown	Joel	20 Dec 1803	NA	1803-12-20	NA
Bundy	Hope	1833	1918	NA	NA
Bundy	Clem	NA	NA	NA	NA
Bundy	Nancy	NA	NA	NA	NA
Bundy	W	NA	NA	NA	NA
Bundy	Charles	NA	NA	NA	NA
Bundy	Thomas	4 Feb 1881	22 Apr 1892	1881-02-04	1892-04-22
Bundy	Octavia	16 Apr 1839	10 Mar 1932	1839-04-16	1932-03-10
Bundy	George	1864	1949	NA	NA
Bundy	Lora	1870	1957	NA	NA
Burgess	W	1844	17 Dec 1907	NA	1907-12-17
Burgess	Alzada	1854	1931	NA	NA
Clayton	G	9 Apr 1846	9 Mar 1919	1846-04-09	1919-03-09
Clayton	Ellen	28 Mar 1852	23 May 1917	1852-03-28	1917-05-23
Clayton	L	NA	25 May 1909	NA	1909-05-25
Clayton	Mary	NA	18 Jan 1908	NA	1908-01-18
Chapman	Daniel	NA	NA	NA	NA
Chapman	Elizabeth	NA	NA	NA	NA
Chapman	Caroline	NA	24 Jan 1861	NA	1861-01-24
Chapman	Daniel	5 Jul 1863	Feb 1842	1863-07-05	NA
Chapman	Lucretia	23 Apr 1769	Aug 1849	1769-04-23	NA
Chapman	Samuel	14 May 1794	15 Apr 1863	1794-05-14	1863-04-15
Chapman	Elizabeth	23 May 1796	30 Dec 1859	1796-05-23	1859-12-30
Chapman	Laura	NA	14 June 1865	NA	1865-06-14
Chapman	Polly	NA	NA	NA	NA
Crockett	Mandy	3 Sep 1844	5 Nov 1914	1844-09-03	1914-11-05
Crockett	John	4 Oct 1844	25 July 1919	1844-10-04	1919-07-25
Davis	Ezra	1887	1949	NA	NA
Davis	Lizzie	1886	1955	NA	NA
Davis	Fred	NA	NA	NA	NA
Dolch	Catherine	11 Feb 1793	14 Feb 1867	1793-02-11	1867-02-14
Dolch	Christian	24 Dec 1794	29 Aug 1874	1794-12-24	1874-08-29
Dolch	Peter	NA	16 July 1862	NA	1862-07-16
Doley	George	25 July 1823	26 Jan 1868	1823-07-25	1868-01-26
Doley	Katie	8 Jan 1833	15 Sep 1927	1833-01-08	1927-09-15
Doley	Mary E	23 Aug 1851	18 July 1853	1851-08-23	1853-07-18
Doley	Henriettie	28 Feb 1853	18 July 1854	1853-02-28	1854-07-18
NA	MED	NA	NA	NA	NA
NA	HD	NA	NA	NA	NA
NA	GD	NA	NA	NA	NA
NA	Mother	NA	NA	NA	NA
NA	Father	NA	NA	NA	NA
Doley	George	14 Mar 1856	26 Jan 1860	1856-03-14	1860-01-26
Doley	James	21 Jan 1858	11 July 1926	1858-01-21	1926-07-11
Doley	May	1869	1940	NA	NA
Doley	John	1864	[1949?]	NA	NA
Doley	Maggie	1868	1917	NA	NA
Doley	William	20 Dec 1862	24 Nov 1934	1862-12-20	1934-11-24
Doley	Dora	17 Feb 1864	9 Apr 1934	1864-02-17	1934-04-09
Doley	L[eaman]	17 Apr 1889	19 Nov 1960	1889-04-17	1960-11-19
Doley	G[uilford]	5 Aug 1894	1 June 1960	1894-08-05	1960-06-01
Doley	D[ora]	1897	1983	NA	NA
Doley	Eugene	8 Nov 1886	12 June 1958	1886-11-08	1958-06-12
Doley	Lou	29 Oct 1895	26 June 1963	1895-10-29	1963-06-26
Dorris	J[oseph]	12 Feb 1821	8 Jan 1893	1821-02-12	1893-01-08
Dorris	Joseph	10 Feb 1779	5 Nov 1866	1779-02-10	1866-11-05
Dorris	Sarah	2 June 1790	23 June 1862	1790-06-02	1862-06-23
Dorris	W	2 June 1790	5 Nov 1873	1790-06-02	1873-11-05
Dorris	A	NA	7 Aug 1853	NA	1853-08-07
Dorris	J	15 Jan 1822	28 Nov 1911	1822-01-15	1911-11-28
Dorris	Elizabeth	7 Jan 1833	27 Aug 1895	1833-01-07	1895-08-27
Dorris	Robert	24 Mar 1818	22 Aug 1894	1818-03-24	1894-08-22
Dorris	Rebecca	22 mar 1829	24 Apr 1910	1829-03-22	1910-04-24
Dorris	Monroe	1848	1928	NA	NA
Dorris	Della	1884	1934	NA	NA
Dorris	Mary M	1855	1927	NA	NA
Dorris	Harve	13 Aug 1889	6 Apr 1966	1889-08-13	1966-04-06
Dorris	Carrie	12 July 1892	7 Aug 1990	1892-07-12	1990-08-07
Dorris	Smith	NA	NA	NA	NA
Dorris	Ada	NA	NA	NA	NA
Dorris	William	NA	NA	NA	NA
Dorris	Harvey	NA	NA	NA	NA
Dorris	Cora	NA	NA	NA	NA
Dorris	John	NA	NA	NA	NA
Dorris	W	1857	1936	NA	NA
Dorris	Gustavus	2 Aug 1847	27 Feb 1923	1847-08-02	1923-02-27
Dorris	Sarah	NA	17 Sep 1880	NA	1880-09-17
Dorris	Joseph	NA	NA	NA	NA
Dorris	Della	NA	NA	NA	NA
Dorris	William	28 Nov 1818	17 Feb 1905	1818-11-28	1905-02-17
Dorris	Harriet	28 Nov 1818	17 Feb 1905	1818-11-28	1905-02-17
Dorris	William	1867	1942	NA	NA
Dorris	Mary	1872	1960	NA	NA
Dorris	James	NA	24 Mar 1877	NA	1877-03-24
Dorris	Sarah	1 June 1837	6 Dec 1908	1837-06-01	1908-12-06
Dorris	W[illiam]	2 Jan 1835	16 Feb 1898	1835-01-02	1898-02-16
Dorris	E[lisha]	17 Dec 1853	7 May 1907	1853-12-17	1907-05-07
Dorris	Sarah	28 Aug 1858	20 Feb 1922	1858-08-28	1922-02-20
Dorris	James	1861	1921	NA	NA
Dorris	Georgia	1860	1908	NA	NA
Dorris	William	NA	10 Aug 1857	NA	1857-08-10
Dorris	Malinda	NA	22 Mar 1850	NA	1850-03-22
Drake	Mary	1843	1909	NA	NA
Dreisbach	Catherina	21 Dec 1752	20 Sep 1819	1752-12-21	1819-09-20
Dreisbach	Johannes	21 Aug 1752	20 Sep 1825	1752-08-21	1825-09-20
Everett	Semantha	NA	Apr 1845	NA	NA
Farris	Elizabeth	NA	2 Feb 1866	NA	1866-02-02
Farris	Elizabeth	NA	28 Sep 1858	NA	1858-09-28
Finch	Isaac	NA	26 Nov 1813	NA	1813-11-26
Follis	Fawn	9 Feb 1907	17 Nov 1909	1907-02-09	1909-11-17
Follis	Ralph	7 Apr 1901	27 Feb 1906	1901-04-07	1906-02-27
Follis	A	NA	17 Oct 1887	NA	1887-10-17
Follis	Christian	NA	21 Oct 1892	NA	1892-10-21
Follis	G	NA	20 Aug 1899	NA	1899-08-20
Follis	Ralph	6 Oct 1892	4 Nov 1892	1892-10-06	1892-11-04
Follis	E	NA	15 Mar 1890	NA	1890-03-15
Follis	William	20 Aug 1832	25 Aug 1900	1832-08-20	1900-08-25
Follis	Martha	10 Aug 1841	15 Aug 1881	1841-08-10	1881-08-15
Follis	Jeff	10 Sep 1873	25 July 1905	1873-09-10	1905-07-25
Ford	Florence	3 Sep 1866	1 July 1940	1866-09-03	1940-07-01
Fox	Frances	27 Sep 1904	18 Jan 1997	1904-09-27	1997-01-18
Frost	Ebenezer	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA
Frost	NA	NA	NA	NA	NA
Fuqua	William	NA	NA	NA	NA
Gregory	Leonard	15 Mar 1887	13 June 1963	1887-03-15	1963-06-13
Gregory	Lucille	1899	1986	NA	NA
Hart	Parmelia	14 May 1847	17 Sep 1878	1847-05-14	1878-09-17
Hess	Amalphus	NA	27 Mar 1872	NA	1872-03-27
Hess	Adolphus	NA	23 June 1871	NA	1871-06-23
Hess	Samuel	23 Dec 1823	5 Mar 1901	1823-12-23	1901-03-05
Hess	Augusta	17 Jan 1828	NA	1828-01-17	NA
Hess	Ulysses	21 Dec 1867	16 Feb 1897	1867-12-21	1897-02-16
Hess	Ulysses	867	1897	NA	NA
Hess	William	9 Dec 1851	26 Apr 1900	1851-12-09	1900-04-26
Hess	William	1851	1900	NA	NA
Hess	Jerome	1849	1931	NA	NA
Hess	Franklin	1857	1935	NA	NA
Hess	Samuel	1854	1949	NA	NA
Hess	Bernice	20 Sep 1894	27 Sep 1978	1894-09-20	1978-09-27
Hess	Catherine	1856	1906	NA	NA
Hess	George	27 Feb 1864	3 Dec 1942	1864-02-27	1942-12-03
Holt	Lucinda	15 Aug 1832	28 Aug 1888	1832-08-15	1888-08-28
Holt	William	14 May 1818	26 Aug 1903	1818-05-14	1903-08-26
Horlacher	Daniel	4 Aug 1735	24 Sep 1804	1735-08-04	1804-09-24
Horlacher	Margaretha	4 Jan 1741	22 Apr 1806	1741-01-04	1806-04-22
Horrall	Polly	NA	NA	NA	NA
Horrall	James	NA	15 Apr 1848	NA	1848-04-15
Horrall	William	NA	NA	NA	NA
Hurt	Elizabeth	NA	NA	NA	NA
Jacobs	Jeremiah	NA	30 Dec 1824	NA	1824-12-30
Jacobs	Rebecca	NA	18 July 1813	NA	1813-07-18
Johnson	James	25 Feb 1837	NA	1837-02-25	NA
Johnson	Mary	5 Sep, 1844	12 Nov 1895	NA	1895-11-12
Jones	Levi	1828	1892	NA	NA
Jones	Hester	1841	1910	NA	NA
Jones	Ridley	NA	2 Mar 1863	NA	1863-03-02
Jones	James	1863	1933	NA	NA
Jones	Tina	1874	1918	NA	NA
Jones	Ezra	15 Sep 1867	19 May 1942	1867-09-15	1942-05-19
Jones	Nannie	18 July 1968	18 July 1957	1968-07-18	1957-07-18
Jones	Samuel	11 Feb 1876	22 Nov 1915	1876-02-11	1915-11-22
Jones	Melverda	1 Nov 1879	2 Apr 1924	1879-11-01	1924-04-02
Jones	John	NA	NA	NA	NA
Karnes	Willard	1 Dec 1911	27 Oct 1990	1911-12-01	1990-10-27
Karnes	Ruth	8 Jan 1917	8 Jan 2000	1917-01-08	2000-01-08
Keith	James	NA	6 Oct 1841	NA	1841-10-06
Keth	Nancy	NA	17 Nov 1827	NA	1827-11-17
Kleppinger	Anna	29 Sep 1748	19 June 1817	1748-09-29	1817-06-19
Lipsey	Joe	1888	1928	NA	NA
Lockwood	Eugenia	11 July 1910	5 Oct 1994	1910-07-11	1994-10-05
Lockwood	Leland	19 Feb 1914	24 Dec 1970	1914-02-19	1970-12-24
Loomis	Jon	NA	NA	NA	NA
Mensch	Abraham	6 Apr 1754	16 Mar 1826	1754-04-06	1826-03-16
Merrell	Azariah	20 May 1777	25 Jan 1844	1777-05-20	1844-01-25
Merrell	Abigail	1781	11 June 1844	NA	1844-06-11
Meredith	Eleandra	NA	6 Oct 1875	NA	1875-10-06
Meredith	Micajah	NA	26 Oct 1822	NA	1822-10-26
Meredith	Samuel	8 Sept 1753	10 Oct 1825	NA	1825-10-10
Meredith	Elizabeth	22 Jan 1757	6 Apr 1824	1757-01-22	1824-04-06
Meredith	Ruth	NA	28 Aug 1856	NA	1856-08-28
Bell	Sarah	NA	1860	NA	NA
John	Bell	7 July 1812	4 July 1872	1812-07-07	1872-07-04
Meredith	Mary	16 Apr 1793	11 Dec 1873	1793-04-16	1873-12-11
Meredith	Clarence	1899	1979	NA	NA
Meredith	Cora	1900	1983	NA	NA
Meredith	W	1926	1945	NA	NA
Meredith	Susan	28 July 1833	2 Mar 1919	1833-07-28	1919-03-02
Meredith	Hannah	15 Nov 1830	21 Dec 1903	1830-11-15	1903-12-21
Meredith	Mary	NA	11 Nov 1882	NA	1882-11-11
Meredith	Samuel	NA	5 Jan 1884	NA	1884-01-05
Meredith	Belinda	NA	4 Aug 1889	NA	1889-08-04
Tipton	Susannah	NA	3 May 1852	NA	1852-05-03
Meredith	Thomas	NA	20 Apr 1840	NA	1840-04-20
Meredith	Sarah	NA	21 Mar 1830	NA	1830-03-21
Mildenberger	Anna	29 Sep 1739	11 Oct 1777	1739-09-29	1777-10-11
Mildenberger	Nicolaus	15 Oct 1781	19 Oct 1856	1781-10-15	1856-10-19
Miller	Myrtie	18 July 1896	7 Dec 1991	1896-07-18	1991-12-07
Minnich	Elizabeth	NA	NA	NA	NA
Minnich	John	NA	NA	NA	NA
Mory	Catherina	8 May 1758	25 Aug 1837	1758-05-08	1837-08-25
Mory	Gotthard	20 Mar 1752	26 May 1843	1752-03-20	1843-05-26
Mory	Magdelena	17 Sep 1759	26 Nov 1827	1759-09-17	1827-11-26
Mory	Peter	3 May 1757	[grass blocks date]	1757-05-03	NA
Nagel	Anna	28 Jul 1761	27 Mar 1840	1761-07-28	1840-03-27
Nagel	Anna	9 Feb 1725	9 Sep 1790	1725-02-09	1790-09-09
Nagel	Caty	NA	4 May 1817	NA	1817-05-04
Nagel	Daniel	NA	7 May 1866	NA	1866-05-07
Nagel	Frederick	26 Apr 1759	10 Mar 1839	1759-04-26	1839-03-10
Nagel	Friedrich	1713	22 Nov 1779	NA	1779-11-22
Nagel	Johann	15 Feb 1746	3 June 1823	1746-02-15	1823-06-03
Nagel	Maria	NA	NA	NA	NA
Nagle	John	NA	23 Nov 1870	NA	1870-11-23
Nagle	Mary	NA	18 Mar 1870	NA	1870-03-18
Nagel	Henry	1834	1913	NA	NA
Nagel	Mary	1836	1921	NA	NA
Nagel	Will	1858	1916	NA	NA
Nagel	Adeline	1860	1941	NA	NA
NA	NA	NA	NA	NA	NA
Nutty	John	1907	1977	NA	NA
Nutty	Beatrice	1909	1989	NA	NA
Nutty	John	1907	1977	NA	NA
Ritter	NA	NA	NA	NA	NA
Ritter	NA	NA	NA	NA	NA
Odom	Archibald	20 Feb 1838	20 May 1915	1838-02-20	1915-05-20
Odom	Cynthia	NA	NA	NA	NA
Odom	G	1854	1943	NA	NA
Odom	Sarah	14 June 1854	24 Oct 1910	1854-06-14	1910-10-24
NA	Thomas	NA	15 Nov 1887	NA	1887-11-15
Odum	Britton	1794	1863	NA	NA
Odum	Wiley	21 May 1879	2 Mar 1937	1879-05-21	1937-03-02
Odum	Sallie A	15 May 1881	6 May 1946	1881-05-15	1946-05-06
Peters	Daniel	NA	20 Aug 1808	NA	1808-08-20
Peters	Charlotte	NA	20 June 1880	NA	1880-06-20
Pickard	William	19 Feb 1839	6 Jan 1907	1839-02-19	1907-01-06
Pickard	Harriet	1843	1916	NA	NA
Pickard	Louise	1868	1935	NA	NA
Pletz	Karl	1914	1975	NA	NA
Russell	Caroline	1917	1995	NA	NA
Pickard	William	1839	1907	NA	NA
Pickard	Harriet	1847	1916	NA	NA
Pickard	Louise	1868	1935	NA	NA
Pulliam	Frieda	NA	NA	NA	NA
Pulliam	Amos	NA	NA	NA	NA
Rex	William	NA	27 Apr 1909	NA	1909-04-27
Rex	Elmina	NA	25 Jan 1900	NA	1900-01-25
Rex	Mamie	NA	7 Aug 1892	NA	1892-08-07
Rex	George	NA	8 Dec 1891	NA	1891-12-08
Rex	Bertie	NA	5 Nov 1887	NA	1887-11-05
Rex	Lulie	10 Oct 1877	2 Oct 1878	1877-10-10	1878-10-02
Rex	Lily	21 Sep 1871	11 Oct 1872	1871-09-21	1872-10-11
Rex	Arthur	11 Aug 1870	25 Aug 1870	1870-08-11	1870-08-25
Rex	George	NA	NA	NA	NA
Rex	Jno	NA	NA	NA	NA
Rex	Guy	28 May 1889	14 Oct 1983	1889-05-28	1983-10-14
Rex	Harlie	9 Sep 1890	10 May 1979	1890-09-09	1979-05-10
Richardson	Annabelle	1916	1993	NA	NA
Richardson	Alfred	1915	1987	NA	NA
Riegel	Solomon	NA	18 May 1827	NA	1827-05-18
Riegel	Catherine	NA	27 Dec 1882	NA	1882-12-27
Ritter	J	NA	11 Nov 1917	NA	1917-11-11
Ritter	Mary	NA	10 Mat 1903	NA	NA
Rockel	Balzer	10 Nov 1707	9 June 1800	1707-11-10	1800-06-09
Rockel	Elisabetha	24 June 1719	16 Oct 1794	1719-06-24	1794-10-16
Rockel	Johannes	23 Mar 1749	4 Jan 1838	1749-03-23	1838-01-04
Rockel	Elizabeth	7 Oct 1764	1 Mar 1835	1764-10-07	1835-03-01
Ross	George	1 Aug 1885	8 June 1971	1885-08-01	1971-06-08
Ross	Euna	2 July 1885	19 Mar 1938	1885-07-02	1938-03-19
Ruckel	Mary	NA	NA	NA	NA
Ruckel	Melchir	27 may 1769	15 Apr 1832	1769-05-27	1832-04-15
Russell	James	NA	NA	NA	NA
Russell	Ana	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA
Siliven	Jenniel	NA	30 Aug 1873	NA	1873-08-30
Sinks	A	NA	NA	NA	NA
Sinks	Francis	20 Nov 1837	17 Jan 1909	1837-11-20	1909-01-17
Sinks	Delphia	27 Dec 1838	11 Dec 1931	1838-12-27	1931-12-11
Sinks	Salem	NA	26 Oct 1869	NA	1869-10-26
Sinks	Daniel	1841	1923	NA	NA
Sinks	Martha	1856	1924	NA	NA
Sinks	Roy	1889	1908	NA	NA
Sinks	Elizabeth	10 Mar 1835	10 Aug 1911	1835-03-10	1911-08-10
Sinks	infant son	18 Dec 1898	18 Dec 1898	1898-12-18	1898-12-18
Sinks	John	NA	20 Nov 1893	NA	1893-11-20
Sinks	Mary	NA	23 Aug 1906	NA	1906-08-23
Sinks	William	NA	27 Sep 1953	NA	1953-09-27
Sinks	Charlotte	NA	31 Oct 1910	NA	1910-10-31
Sinks	Anna	NA	5 Aug 1909	NA	1909-08-05
Sinks	Leonard	NA	26 Oct 1919	NA	1919-10-26
Sinks	Etta Faye	31 Mar 1910	19 Mar 1989	1910-03-31	1989-03-19
Sinks	John	NA	28 Jan 1918	NA	1918-01-28
Sinks	Sena	18 Sep 1888	13 Dec 1972	1888-09-18	1972-12-13
Sinks	William	11 Sep 1914	30 Sep 1986	1914-09-11	1986-09-30
Sweet	Jewell	1912	1990	NA	NA
Sinks	Francis	1908	1988	NA	NA
Sinks	Arlie	1883	1951	NA	NA
Sinks	Viola	1882	1966	NA	NA
Sinks	Leonard	NA	NA	NA	NA
Sinks	Mae	NA	NA	NA	NA
Sinks	Bessie	21 Nov 1889	24 Apr 1979	1889-11-21	1979-04-24
Sinks	Caroline	NA	17 Apr 1876	NA	1876-04-17
Sinks	Arlie	27 July 1916	4 July 2009	1916-07-27	2009-07-04
Sinks	Eva	15 Jan 1914	8 Sep 1002	1914-01-15	1002-09-08
Solt	Conrad	29 Sep 1758	24 Dec 1825	1758-09-29	1825-12-24
Solt	Conrad	20 Mar 1753	25 Aug 1830	1753-03-20	1830-08-25
Solt	Maria	1760	23 Dec 1839	NA	1839-12-23
Sfafford	Trice	NA	NA	NA	NA
Sfafford	Phebe	NA	NA	NA	NA
Steen	Richard	NA	NA	NA	NA
VanCleve	Martin	1860	1946	NA	NA
VanCleve	Florence	1867	1946	NA	NA
VanCleve	W	1 Jan 1813	20 May 1886	1813-01-01	1886-05-20
VanCleve	Nancy	26 Nov 1814	22 Feb 1902	1814-11-26	1902-02-22
VanCleve	J	20 Dec 1850	31 Oct 1872	1850-12-20	1872-10-31
VanCleave	W	29 June 1838	15 Jan 1914	1838-06-29	1914-01-15
VanCleave	Elizabeth	3 Sep 1840	25 May 1901	1840-09-03	1901-05-25
Veach	Pleasant	19 Dec 1845	23 Jan 1917	1845-12-19	1917-01-23
Veach	Victoria	4 Apr 1849	10 Apr 1921	1849-04-04	1921-04-10
Veach	Ward	1886	1916	NA	NA
Veach	Cynthia	1861	1921	NA	NA
Veach	James	1860	1890	NA	NA
Veach	James	1857	1939	NA	NA
Veach	Nannie	1863	1953	NA	NA
Veatch	John	11 Nov 1776	17 Sep 1844	1776-11-11	1844-09-17
Veatch	Eleanor	26 Oct 1773	22 July 1852	1773-10-26	1852-07-22
Veach	William	1882	1957	NA	NA
Veach	James	NA	NA	NA	NA
Veach	Rachel	NA	NA	NA	NA
Veach	Pleasant	1 Oct 1837	18 Jan 1895	1837-10-01	1895-01-18
Veach	Mary	NA	16 Nov 1876	NA	1876-11-16
Veatch	Parmelia	9 Feb 1808	31 Jan 1867	1808-02-09	1867-01-31
Veatch	Mary	5 Feb 1842	NA	1842-02-05	NA
Veatch	Elnor	NA	1834	NA	NA
Veatch	Frelin	21 Mar 1825	18 Jan 1848	1825-03-21	1848-01-18
Veach-Nutty	NA	NA	NA	NA	NA
Veach	John	1863	1950	NA	NA
Veach	Rose	1868	1948	NA	NA
Veach	Ruth	1900	1901	NA	NA
Ware	Turner	14 Feb 1817	5 Feb 1902	1817-02-14	1902-02-05
Ware	Martha	NA	23 Mar 1881	NA	1881-03-23
Ware	Joseph	NA	NA	NA	NA
Ware	Caroline	NA	NA	NA	NA
Webber	Dick	7 Aug 1881	3 Dec 1942	1881-08-07	1942-12-03
Webber	Pearl	17 Apr 1889	10 Apr 1967	1889-04-17	1967-04-10
Weir	James	1799	1879	NA	NA
Weir	Mary	1801	1886	NA	NA
Wier	Leticia	29 Dec 1836	26 Mar 1865	1836-12-29	1865-03-26
Whiteside	Lucinda	21 Apr 1858	31 Jan 1936	1858-04-21	1936-01-31
Whiteside	John	2 Apr 1852	30 Sep 1913	1852-04-02	1913-09-30
Willis	Matha	10 Mar 1830	13 Oct 1925	1830-03-10	1925-10-13
Wilson	Jessie	14 Apr 1924	16 May 1996	1924-04-14	1996-05-16
Wilson	Mary	7 Dec 1927	2 June 1978	1927-12-07	1978-06-02
Wilson	Joseph	26 Feb 1825	Jan 1862	1825-02-26	NA
Wilson	Elisha	24 May 1800	9 July 1873	1800-05-24	1873-07-09
Wilson	Sallie	15 Oct 1807	4 Apr 1866	1807-10-15	1866-04-04
Wilson	Lutetita	1826	1907	NA	NA
Wilson	Thomas	NA	1922	NA	NA
Wilson	Sarah	NA	NA	NA	NA
Wilson	Elisha	1841	1907	NA	NA
Wilson	Martha	1840	1929	NA	NA
Wilson	Charles	23 Jan 1908	19 Apr 1955	1908-01-23	1955-04-19
Wilson	Zack	1848	28 Sep 1918	NA	1918-09-28
Wilson	Juritha	6 Aug 1854	28 Sep 1916	1854-08-06	1916-09-28
Wilson	Elisha	1841	1907	NA	NA
Wilson	Drury	10 Dec 1827	5 Feb 1911	1827-12-10	1911-02-05
Wilson	Mary	17 Jan 1837	1 Mar 1900	1837-01-17	1900-03-01
Wilson	Sandifer	15 Oct 1834	8 Jan 1929	1834-10-15	1929-01-08
Wilson	Nancy	26 Oct 1842	1 Apr 1913	1842-10-26	1913-04-01
Wise	Luvena	NA	NA	NA	NA
Wollard	John	12 Jul 1846	9 Jan 1918	1846-07-12	1918-01-09
Woolard	Nettie	8 Sep 1874	28 Nov 1910	1874-09-08	1910-11-28
Woolard	Millie	13 Nov 1837	19 Apr 1932	1837-11-13	1932-04-19
Woolard	Lawrence	25 Dec 1877	12 May 1923	1877-12-25	1923-05-12
Woolard	Etta	14 July 1883	7 Dec 1945	1883-07-14	1945-12-07
Woolard	John	21 Jan 1872	19 Oct 1936	1872-01-21	1936-10-19
Woolard	James	30 June 1908	12 Oct 1966	1908-06-30	1966-10-12
Woolard	C	2 Oct 1882	9 Dec 1953	1882-10-02	1953-12-09
Woolard	Blanche	1 Jan 1885	25 July 1959	1885-01-01	1959-07-25
Woolard	L	15 Sep 1811	9 May 1878	1811-09-15	1878-05-09
Woolard	Ama	23 May 1819	5 Dec 1891	1819-05-23	1891-12-05
Woolard	Robert	1863	1938	NA	NA
Woolard	James	12 Apr 1848	7 Sep 1888	1848-04-12	1888-09-07
Woolard	Romey	23 May 1922	12 Sep 1944	1922-05-23	1944-09-12
Woolard	Anna	NA	30 Sep 1887	NA	1887-09-30
Woolard	James	NA	27 Sep 1878	NA	1878-09-27
Woolard	Francis	NA	20 Feb 1867	NA	1867-02-20
Woolard	Turner	NA	20 Oct 1861	NA	1861-10-20
Woolard	William	22 Aug 1857	2 Jan 1858	1857-08-22	1858-01-02
Woolard	George	28 Mar 1867	5 Jan 1935	1867-03-28	1935-01-05
Woolard	Nancy	1869	1955	NA	NA

Cleaning up Cemetery Names (strings)

The string/ character data is more difficult to clean, since the possibilities are endless. Almost anything could be correct. Correcting this data requires some subject matter expertise. I think I would like to group tombstones by cemetery in my map, so I do want to clean this up. However, I’m generally going to take a light touch with this.

Figuring Out the Types of Typos

Here, I expect that there is a limited set of cemeteries, so I can group the data to look for typos.

cems_unique <- tombstones %>%
  distinct(Cemetery) %>%
  arrange(Cemetery)

cems_unique %>%
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett Cem
Baldwin Cem
Bethlehem
Bethlehem Baptist Church
Bethlehem Cem
Blockhouse
Boner Cem
Britton Odum Farm Commerce
Campground Cem
Casey Springs
Casey Springs Cem
Chapman-Veatch Cemeteryem
Christ Church
Cole Cem
Corinth Zion Meth Cem
Crocker Cem
Denning
Ebenezer Bapt. Ch. Cem.
Ebenezer Cumberland Presbyterian Church Cem
Eld SM Williams
Ewing Cem
Follis Cemetery
Fredonia
Friedensville Evangelican Lutheran Church
Gladdish-Anderson
Goshen Cumberland Presbyterian Church Cem
Grapevine Cem
Green Cem
Greenbrier Cem
Greenlawn
Hanover Green
Harmony Free Will Baptist Ch Cem
Harmony Free Will Baptist Church
Hartwell
Hidlay Cem.
Hudgens Cem.
Jersey Baptis Ch
Johnson Cem
Johnston City Cem
Lake Creek Cem.
Lebanon Cem
Lebanon Cumberland Presbyterian Ch
Lebanon Cumberland Presbyterian Church
Liberty Meth Cem
Maplewood Cem
Masonic
Masonic & Oddfellows Cem
Miller Cem
Mt Olive Cem
Mt Olive Meth Ch Cem
Mt. Olive Cem.
Nashville National Cem.
New Chapel Methodist Church
Oak Grove Cem
Oddfellows
Oddfellows Cem
Old Bethel Cem
Orlinda
Orlinda Cem
Pleasant Valley Bapt. Church Cem.
Robinson Cem
Rose Hill
Russell Cem
Spillertown Cem.
Spring Hill
St. John's Lutheran Church
St. Paul's Lutheran "Blue" Church
Trinity
Trinity Methodist Church
Union Grove Cem
Vicksburg National Cem
Vienna Fraternal Cem
Webber Campground
Weber-Campground
Weir Cem.
White Ash
Williams Prairie Baptist Ch Cem
Zion Stone Ch
NA

There are 79 unique names. I see some obvious issues. Sometimes the word cemetery is added to the end (or an abbreviation like Cem and Cem.). Sometimes it isn’t. Church is rendered as Ch. in some cases.

Cleaning the Easy Typos and Inconsistencies

Since all of these are cemeteries, I’m just going to remove that from the name. I will change all Ch. to Church and I will clean out all periods. Using str_replace_all() as usual.

Order of operation matters here, I think. I want to replace Ch and Ch. with Church but I don’t want the Church to be replaced. So I either need a regex or I need to include something else in my pattern. I think I can use “Ch” and “Ch.” as the pattern. I can’t just clear out the periods and replace them with spaces and use “Ch” because some of the periods occur in the middle of the string and I’ll end up with extra spaces I need to clear out. Similarly, Cemetary should be replaced before Cem, otherwise I’ll have to clean out etary. Meth should be Methodist. Bapt. should be Baptist. There is a Cemeterem, which is clearly a typo

(The fact that I’m putting this in a new dataframe is a hint that I’m not as clever as I thought. Can you spot what I did wrong?)

tombstones2 <- tombstones %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Ch ", "Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Ch.", "Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "\\.", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cemeteryem", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cemetery", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cem", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Meth ", "Methodist ")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Bapt ", "Baptist ")) 

See how I did…

cems_unique <- tombstones2 %>%
  distinct(Cemetery) %>%
  arrange(Cemetery)

cems_unique %>%
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett
Baldwin
Bethlehem
Bethlehem
Bethlehem Baptist Churchrch
Blockhouse
Boner
Britton Odum Farm Commerce
Campground
Casey Springs
Casey Springs
Churchist Churchrch
Churchpman-Veatch
Cole
Corinth Zion Methodist
Crocker
Denning
Ebenezer Baptist Church
Ebenezer Cumberland Presbyterian Churchrch
Eld SM Williams
Ewing
Follis
Fredonia
Friedensville Evangelican Lutheran Churchrch
Gladdish-Anderson
Goshen Cumberland Presbyterian Churchrch
Grapevine
Green
Greenbrier
Greenlawn
Hanover Green
Harmony Free Will Baptist Churchrch
Hartwell
Hidlay
Hudgens
Jersey Baptis Ch
Johnson
Johnston City
Lake Creek
Lebanon
Lebanon Cumberland Presbyterian Ch
Lebanon Cumberland Presbyterian Churchrch
Liberty Methodist
Maplewood
Masonic
Masonic & Oddfellows
Miller
Mt Olive
Mt Olive Methodist Churchrch
Nashville National
New Churchpel Methodist Churchrch
Oak Grove
Oddfellows
Oddfellows
Old Bethel
Orlinda
Orlinda
Pleasant Valley Baptist Churchrch
Robinson
Rose Hill
Russell
Spillertown
Spring Hill
St John's Lutheran Churchrch
St Paul's Lutheran "Blue" Churchrch
Trinity
Trinity Methodist Churchrch
Union Grove
Vicksburg National
Vienna Fraternal
Webber Campground
Weber-Campground
Weir
White Ash
Williams Prairie Baptist Churchrch
Zion Stone Ch
NA

Badly! Almost like I should read my own digression about regex and escape characters. Even the period in Ch. needs to be escaped, not just single periods or leading periods. So "Ch\\." not "Ch." as the pattern.

tombstones3 <- tombstones %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Ch ", "Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Ch\\.", "Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "\\.", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cemeteryem", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cemetery", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Cem", "")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Meth ", "Methodist ")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Bapt ", "Baptist "))

Did that work?

cems_unique <- tombstones3 %>%
  distinct(Cemetery) %>%
  arrange(Cemetery)

cems_unique %>% 
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett
Baldwin
Bethlehem
Bethlehem
Bethlehem Baptist Church
Blockhouse
Boner
Britton Odum Farm Commerce
Campground
Casey Springs
Casey Springs
Chapman-Veatch
Christ Church
Cole
Corinth Zion Methodist
Crocker
Denning
Ebenezer Baptist Church
Ebenezer Cumberland Presbyterian Church
Eld SM Williams
Ewing
Follis
Fredonia
Friedensville Evangelican Lutheran Church
Gladdish-Anderson
Goshen Cumberland Presbyterian Church
Grapevine
Green
Greenbrier
Greenlawn
Hanover Green
Harmony Free Will Baptist Church
Hartwell
Hidlay
Hudgens
Jersey Baptis Ch
Johnson
Johnston City
Lake Creek
Lebanon
Lebanon Cumberland Presbyterian Ch
Lebanon Cumberland Presbyterian Church
Liberty Methodist
Maplewood
Masonic
Masonic & Oddfellows
Miller
Mt Olive
Mt Olive Methodist Church
Nashville National
New Chapel Methodist Church
Oak Grove
Oddfellows
Oddfellows
Old Bethel
Orlinda
Orlinda
Pleasant Valley Baptist Church
Robinson
Rose Hill
Russell
Spillertown
Spring Hill
St John's Lutheran Church
St Paul's Lutheran "Blue" Church
Trinity
Trinity Methodist Church
Union Grove
Vicksburg National
Vienna Fraternal
Webber Campground
Weber-Campground
Weir
White Ash
Williams Prairie Baptist Church
Zion Stone Ch
NA

Better, but some names that look the same are coming up as distinct entries, like Bethlehem. This probably means that there are extraneous spaces floating around. These can be removed from the front and back of a string using the stringr function str_trim() with the side set to both.

tombstones3 <- tombstones3 %>%
  mutate(Cemetery = str_trim(Cemetery, side = c("both")))

Checking…

cems_unique <- tombstones3 %>%
  distinct(Cemetery) %>%
  arrange(Cemetery)

cems_unique %>% 
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett
Baldwin
Bethlehem
Bethlehem Baptist Church
Blockhouse
Boner
Britton Odum Farm Commerce
Campground
Casey Springs
Chapman-Veatch
Christ Church
Cole
Corinth Zion Methodist
Crocker
Denning
Ebenezer Baptist Church
Ebenezer Cumberland Presbyterian Church
Eld SM Williams
Ewing
Follis
Fredonia
Friedensville Evangelican Lutheran Church
Gladdish-Anderson
Goshen Cumberland Presbyterian Church
Grapevine
Green
Greenbrier
Greenlawn
Hanover Green
Harmony Free Will Baptist Church
Hartwell
Hidlay
Hudgens
Jersey Baptis Ch
Johnson
Johnston City
Lake Creek
Lebanon
Lebanon Cumberland Presbyterian Ch
Lebanon Cumberland Presbyterian Church
Liberty Methodist
Maplewood
Masonic
Masonic & Oddfellows
Miller
Mt Olive
Mt Olive Methodist Church
Nashville National
New Chapel Methodist Church
Oak Grove
Oddfellows
Old Bethel
Orlinda
Pleasant Valley Baptist Church
Robinson
Rose Hill
Russell
Spillertown
Spring Hill
St John's Lutheran Church
St Paul's Lutheran "Blue" Church
Trinity
Trinity Methodist Church
Union Grove
Vicksburg National
Vienna Fraternal
Webber Campground
Weber-Campground
Weir
White Ash
Williams Prairie Baptist Church
Zion Stone Ch
NA

Using Regex Anchors to Help Clean

The abbreviation Ch at the end of the line doesn’t have a space after it, so it isn’t replaced. Here we can use an anchor in our regex to specify that we want to match the pattern at the end of a string. “Ch$” will match at the end and “^Ch” will match at the start of a string.

tombstones3 <- tombstones3 %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Ch$", "Church")) 

Checking again.

cems_unique <- tombstones3 %>%
  distinct(Cemetery) %>% 
  arrange(Cemetery)

cems_unique %>% 
  gt() %>% 
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett
Baldwin
Bethlehem
Bethlehem Baptist Church
Blockhouse
Boner
Britton Odum Farm Commerce
Campground
Casey Springs
Chapman-Veatch
Christ Church
Cole
Corinth Zion Methodist
Crocker
Denning
Ebenezer Baptist Church
Ebenezer Cumberland Presbyterian Church
Eld SM Williams
Ewing
Follis
Fredonia
Friedensville Evangelican Lutheran Church
Gladdish-Anderson
Goshen Cumberland Presbyterian Church
Grapevine
Green
Greenbrier
Greenlawn
Hanover Green
Harmony Free Will Baptist Church
Hartwell
Hidlay
Hudgens
Jersey Baptis Church
Johnson
Johnston City
Lake Creek
Lebanon
Lebanon Cumberland Presbyterian Church
Liberty Methodist
Maplewood
Masonic
Masonic & Oddfellows
Miller
Mt Olive
Mt Olive Methodist Church
Nashville National
New Chapel Methodist Church
Oak Grove
Oddfellows
Old Bethel
Orlinda
Pleasant Valley Baptist Church
Robinson
Rose Hill
Russell
Spillertown
Spring Hill
St John's Lutheran Church
St Paul's Lutheran "Blue" Church
Trinity
Trinity Methodist Church
Union Grove
Vicksburg National
Vienna Fraternal
Webber Campground
Weber-Campground
Weir
White Ash
Williams Prairie Baptist Church
Zion Stone Church
NA

Using the Geographic Data to Match Cemeteries

Now, I do have both geographic data and a subject matter expert (my father) to further refine this list. Let’s start with geography. Bethlehem and Bethlehem Baptist Church could be the same place.

tombstones3 %>%
  filter(Cemetery == "Bethlehem" |
           Cemetery == "Bethlehem Baptist Church") %>%
  select(Cemetery, City, State, lat, long) %>%
  gt() %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery	City	State	lat	long
Bethlehem	Robertson Co	TN	36.58917	-87.02361
Bethlehem	Robertson Co	TN	36.59000	-87.02333
Bethlehem	Springfield	TN	36.68833	-86.76972
Bethlehem	Springfield	TN	36.69194	-86.76889
Bethlehem	Springfield	TN	36.69222	-86.76889
Bethlehem	Springfield	TN	36.69222	-86.76889
Bethlehem	Springfield	TN	36.69250	-86.76861
Bethlehem	Springfield	TN	NA	NA
Bethlehem	Springfield	TN	NA	NA
Bethlehem Baptist Church	Springfield	TN	36.69000	-86.76861
Bethlehem Baptist Church	Springfield	TN	36.69222	-86.77306
Bethlehem Baptist Church	Springfield	TN	36.68972	-86.76861
Bethlehem Baptist Church	Springfield	TN	NA	NA
Bethlehem	Robertson Co.	TN	NA	NA
Bethlehem	Robertson Co.	TN	NA	NA

It turns out that Roberston County’s county seat is Springfield, so these entries are all the same. I’d say this is a pretty common type of problem that you might run into. The spreadsheet column says City, but what it really means is something like “the local level of geography that is meaningful to me”. So thinking about what the data represents rather than how it is labeled can help guide how you handle it.

So, perhaps you could check if cemeteries were the same by calculating the distance between the two sets of coordinates. There is a calculator here, which gives a distance as <0.2 mi for one comparison between Bethlehem and Bethlehem Baptist Church. You could also calculate the distance programmatically, see code block 14 in this blog post. You could pull in other geographic data and correct/ add data so you have both city and county if applicable.

For now, I’m going to make the correction so all entries say Bethlehem Baptist Church. I’m not going to correct the city/county issue.

tombstones3 <- tombstones3 %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Bethlehem$", "Bethlehem Baptist Church"))


tombstones3 %>% filter(Cemetery == "Bethlehem" |
                         Cemetery == "Bethlehem Baptist Church") %>% 
  select(Cemetery, City, State, lat, long) %>% 
  gt() %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery	City	State	lat	long
Bethlehem Baptist Church	Robertson Co	TN	36.58917	-87.02361
Bethlehem Baptist Church	Robertson Co	TN	36.59000	-87.02333
Bethlehem Baptist Church	Springfield	TN	36.68833	-86.76972
Bethlehem Baptist Church	Springfield	TN	36.69194	-86.76889
Bethlehem Baptist Church	Springfield	TN	36.69222	-86.76889
Bethlehem Baptist Church	Springfield	TN	36.69222	-86.76889
Bethlehem Baptist Church	Springfield	TN	36.69250	-86.76861
Bethlehem Baptist Church	Springfield	TN	NA	NA
Bethlehem Baptist Church	Springfield	TN	NA	NA
Bethlehem Baptist Church	Springfield	TN	36.69000	-86.76861
Bethlehem Baptist Church	Springfield	TN	36.69222	-86.77306
Bethlehem Baptist Church	Springfield	TN	36.68972	-86.76861
Bethlehem Baptist Church	Springfield	TN	NA	NA
Bethlehem Baptist Church	Robertson Co.	TN	NA	NA
Bethlehem Baptist Church	Robertson Co.	TN	NA	NA

There aren’t that many possible duplicates.

Lebanon

Lebanon Cumberland Presbyterian Church

Mt Olive

Mt Olive Methodist Church

Trinity

Trinity Methodist Church

Webber Campground

Weber-Campground

Talking to the Subject Matter Expert

Sometimes it is easier just to go talk to the subject matter expert than it is to come up with complicated programmatic solutions. Sometimes that isn’t possible; maybe you don’t even know who generated the data. Or perhaps their time is much more valuable than yours. That’s why I also came up with a solution (using geographic data to match cemeteries) that could be done independently, even though I can ask the expert.

For me, a 5 minute conversation eliminated the need to write lots more code. It turns out that Webber (correct spelling) Campground Cemetery, Weber Campground and Campground are all the same. I wouldn’t have caught Campground as being a match, but my father mentioned it. These entries also switch between city and county in the location part of the table, making it even trickier. It turns out all of these pairs are the same, so I’m going to correct them. I’m also writing this back to the main tombstones dataframe, rather than continuing with intermediate variables. My father also told me that Johnston City Cemetery is actually Shakerag Masonic Cemetery. He also stated that Oddfellows was Oddfellow and Masonic and Oddfellows was Masonic and Odd Fellows. So I’ll correct these too.

tombstones <- tombstones3 %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Lebanon$", "Lebanon Cumberland Presbyterian Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Mt Olive$", "Mt Olive Methodist Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Trinity$", "Trinity Methodist Church")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Weber-Campground", "Webber Campground")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "^Campground", "Webber Campground")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Johnston City", "Shakerag Masonic")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Masonic & Oddfellows", "Masonic & Odd Fellows")) %>%
  mutate(Cemetery = str_replace_all(Cemetery, "Oddfellows", "Oddfellow"))

Final check.

cems_unique <- tombstones %>%
  distinct(Cemetery) %>%
  arrange(Cemetery)

cems_unique %>%
  gt() %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

Cemetery
Baggett
Baldwin
Bethlehem Baptist Church
Blockhouse
Boner
Britton Odum Farm Commerce
Casey Springs
Chapman-Veatch
Christ Church
Cole
Corinth Zion Methodist
Crocker
Denning
Ebenezer Baptist Church
Ebenezer Cumberland Presbyterian Church
Eld SM Williams
Ewing
Follis
Fredonia
Friedensville Evangelican Lutheran Church
Gladdish-Anderson
Goshen Cumberland Presbyterian Church
Grapevine
Green
Greenbrier
Greenlawn
Hanover Green
Harmony Free Will Baptist Church
Hartwell
Hidlay
Hudgens
Jersey Baptis Church
Johnson
Lake Creek
Lebanon Cumberland Presbyterian Church
Liberty Methodist
Maplewood
Masonic
Masonic & Odd Fellows
Miller
Mt Olive Methodist Church
Nashville National
New Chapel Methodist Church
Oak Grove
Oddfellow
Old Bethel
Orlinda
Pleasant Valley Baptist Church
Robinson
Rose Hill
Russell
Shakerag Masonic
Spillertown
Spring Hill
St John's Lutheran Church
St Paul's Lutheran "Blue" Church
Trinity Methodist Church
Union Grove
Vicksburg National
Vienna Fraternal
Webber Campground
Weir
White Ash
Williams Prairie Baptist Church
Zion Stone Church
NA

Lastly, I’m going to replace the NAs with a blank. This will make things nicer if I use this for a label.

tombstones <- tombstones %>%
  mutate(Cemetery = ifelse(is.na(Cemetery), "", Cemetery))

Cleaning Up Names (strings)

In addition to cleaning up typos and such from the names, I’m going to construct a some compound names following my father’s photo naming pattern.

My father’s photo naming convention is mostly “last name first name middle initial”. He often includes other information, like if the photo was a close up or distance or if the stone was rubbed with chalk to enhance. So I can’t use exact pattern matching, but rather need to use partial pattern matching.

This is another situation where having some clear guidelines about how data will be handled is important. I have decided to prioritize correct matches over more matches. Take my name- Louise Elaine Sinks. It could be rendered as LE Sinks, L E Sinks, L Sinks, Louise E Sinks, and Louise Sinks. I could capture all occurrences of my name by matching on all of these variations, but I’m also more likely to get false matches. In the context of this dataset, names and name patterns are often reused within a family, making false matches even more likely. (I inherited my grandfather’s nameplate and have it on my desk- L E Sinks Jr- since I share the same name pattern with him.)

I will create the following patterns for matching:

1. Full Name with whatever is in the Middle Name column (e.g. Louise Elaine Sinks)
2. If First and Middle name are just initials, I will create a version with and without spaces, since I see it done both ways in the photo names (e.g. LE Sinks and L E Sinks)
3. If no middle name is available, I will use First and Last Name (e.g. Louise Sinks)

I will not:

1. Omit the middle name if it is available. (e.g. Louise Elaine Sinks will never be Louise Sinks)
2. Truncate a name to an initial. (e.g. If the entry says Louise Elaine Sinks, I will not use the pattern L E Sinks or Louise E Sinks)

These rules seem complicated, but when you have a set of unique individuals with names like A T Sinks, Arlie T Sinks and Arlie Sinks, you need to think very carefully about how you will distinguish them when you are doing partial matching.

With that said, let’s clean up some names.

Cleaning up First Names

Look at what we are dealing with.

tombstones_raw %>%
  select(First.Name) %>%
  gt() %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

First.Name
Abraham
Elizabeth
Zady
Albert
Adesia
May
E
William
Nancy
Richard
John
William
Mahalia
E
Josephine
Fanning
John
Mary
Wm
Esther
Elizabeth
Joel
Hope
Clem
Nancy
W
Charles
Thomas
Octavia
George
Lora
W
Alzada
G
Ellen
L
Mary
Daniel
Elizabeth
Caroline
Daniel
Lucretia
Samuel
Elizabeth
Laura
Polly
Mandy
John
Ezra
Lizzie
Fred
Catherine
Christian
Peter
George
Katie
Mary E
Henriettie
MED
HD
GD
Mother
Father
George
James
May
John
Maggie
William
Dora
L[eaman]
G[uilford]
D[ora]
Eugene
Lou
J[oseph]
Joseph
Sarah
W
A
J
Elizabeth
Robert
Rebecca
Monroe
Della
Mary M
Harve
Carrie
Smith
Ada
William
Harvey
Cora
John
W
Gustavus
Sarah
Joseph
Della
William
Harriet
William
Mary
James
Sarah
W[illiam]
E[lisha]
Sarah
James
Georgia
William
Malinda
Mary
Catherina
Johannes
Semantha
Elizabeth
Elizabeth
Isaac
Fawn
Ralph
A
Christian
G
Ralph
E
William
Martha
Jeff
Florence
Frances
Ebenezer
NA
NA
William
Leonard
Lucille
Parmelia
Amalphus
Adolphus
Samuel
Augusta
Ulysses
Ulysses
William
William
Jerome
Franklin
Samuel
Bernice
Catherine
George
Lucinda
William
Daniel
Margaretha
Polly
James
William
Elizabeth
Jeremiah
Rebecca
James
Mary
Levi
Hester
Ridley
James
Tina
Ezra
Nannie
Samuel
Melverda
John
Willard
Ruth
James
Nancy
Anna
Joe
Eugenia
Leland
Jon
Abraham
Azariah
Abigail
Eleandra
Micajah
Samuel
Elizabeth
Ruth
Sarah
Bell
Mary
Clarence
Cora
W
Susan
Hannah
Mary
Samuel
Belinda
Susannah
Thomas
Sarah
Anna
Nicolaus
Myrtie
Elizabeth
John
Catherina
Gotthard
Magdelena
Peter
Anna
Anna
Caty
Daniel
Frederick
Friedrich
Johann
Maria
John
Mary
Henry
Mary
Will
Adeline
NA
John
Beatrice
John
NA
NA
Archibald
Cynthia
G
Sarah
Thomas
Britton
Wiley
Sallie A
Daniel
Charlotte
William
Harriet
Louise
Karl
Caroline
William
Harriet
Louise
Frieda
Amos
William
Elmina
Mamie
George
Bertie
Lulie
Lily
Arthur
George
Jno
Guy
Harlie
Annabelle
Alfred
Solomon
Catherine
J
Mary
Balzer
Elisabetha
Johannes
Elizabeth
George
Euna
Mary
Melchir
James
Ana
NA
NA
NA
Jenniel
A
Francis
Delphia
Salem
Daniel
Martha
Roy
Elizabeth
infant son
John
Mary
William
Charlotte
Anna
Leonard
Etta Faye
John
Sena
William
Jewell
Francis
Arlie
Viola
Leonard
Mae
Bessie
Caroline
Arlie
Eva
Conrad
Conrad
Maria
Trice
Phebe
Richard
Martin
Florence
W
Nancy
J
W
Elizabeth
Pleasant
Victoria
Ward
Cynthia
James
James
Nannie
John
Eleanor
William
James
Rachel
Pleasant
Mary
Parmelia
Mary
Elnor
Frelin
NA
John
Rose
Ruth
Turner
Martha
Joseph
Caroline
Dick
Pearl
James
Mary
Leticia
Lucinda
John
Matha
Jessie
Mary
Joseph
Elisha
Sallie
Lutetita
Thomas
Sarah
Elisha
Martha
Charles
Zack
Juritha
Elisha
Drury
Mary
Sandifer
Nancy
Luvena
John
Nettie
Millie
Lawrence
Etta
John
James
C
Blanche
L
Ama
Robert
James
Romey
Anna
James
Francis
Turner
William
George
Nancy

Sometimes the tombstone only has the initial, but my father knows the full name through other means. This would be rendered as L[ouise]. The photos will usually use L not Louise, so I’m just removing this extra data and storing it elsewhere. I do this with str_extract() and then I replace it with a blank using str_replace_all(). There are also extra spaces that I will trim off as before with str_trim().

Using Regex Quantity Codes

The regex for getting rid of the bracketed information is a bit more complicated than what I’ve used before. I want something that looks for [ followed by any number of other characters followed by ]. The brackets are special characters, so they need to be escaped. So that would be “\\[ \\]” for the front and back of the pattern. The middle can be anything. This is where regex is so powerful. As I mentioned before, an un-escaped period will match to any character. There are also quantity codes- a plus sign will match one or more. So I use “\\[.+\\]” which will match one or more characters inside brackets. (I could use the quantity symbol * which matches 0 or more, but there will never be empty brackets in this dataset.)

tombstones <- tombstones %>%
  mutate(Extra_First_Name = str_extract(First.Name, "\\[.+\\]")) %>%
  mutate(First.Name = str_replace(First.Name, "\\[.+\\]", "")) %>%
  mutate(First.Name = str_replace(First.Name, "\\.", "")) %>%
  mutate(First.Name = str_trim(First.Name, side = c("both"))) %>%
  drop_na(First.Name)

Look at the data.

tombstones %>%
  select(First.Name) %>%
  gt() %>%
  tab_options(container.height = px(300), container.padding.y = px(24))

First.Name
Abraham
Elizabeth
Zady
Albert
Adesia
May
E
William
Nancy
Richard
John
William
Mahalia
E
Josephine
Fanning
John
Mary
Wm
Esther
Elizabeth
Joel
Hope
Clem
Nancy
W
Charles
Thomas
Octavia
George
Lora
W
Alzada
G
Ellen
L
Mary
Daniel
Elizabeth
Caroline
Daniel
Lucretia
Samuel
Elizabeth
Laura
Polly
Mandy
John
Ezra
Lizzie
Fred
Catherine
Christian
Peter
George
Katie
Mary E
Henriettie
MED
HD
GD
Mother
Father
George
James
May
John
Maggie
William
Dora
L
G
D
Eugene
Lou
J
Joseph
Sarah
W
A
J
Elizabeth
Robert
Rebecca
Monroe
Della
Mary M
Harve
Carrie
Smith
Ada
William
Harvey
Cora
John
W
Gustavus
Sarah
Joseph
Della
William
Harriet
William
Mary
James
Sarah
W
E
Sarah
James
Georgia
William
Malinda
Mary
Catherina
Johannes
Semantha
Elizabeth
Elizabeth
Isaac
Fawn
Ralph
A
Christian
G
Ralph
E
William
Martha
Jeff
Florence
Frances
Ebenezer
William
Leonard
Lucille
Parmelia
Amalphus
Adolphus
Samuel
Augusta
Ulysses
Ulysses
William
William
Jerome
Franklin
Samuel
Bernice
Catherine
George
Lucinda
William
Daniel
Margaretha
Polly
James
William
Elizabeth
Jeremiah
Rebecca
James
Mary
Levi
Hester
Ridley
James
Tina
Ezra
Nannie
Samuel
Melverda
John
Willard
Ruth
James
Nancy
Anna
Joe
Eugenia
Leland
Jon
Abraham
Azariah
Abigail
Eleandra
Micajah
Samuel
Elizabeth
Ruth
Sarah
Bell
Mary
Clarence
Cora
W
Susan
Hannah
Mary
Samuel
Belinda
Susannah
Thomas
Sarah
Anna
Nicolaus
Myrtie
Elizabeth
John
Catherina
Gotthard
Magdelena
Peter
Anna
Anna
Caty
Daniel
Frederick
Friedrich
Johann
Maria
John
Mary
Henry
Mary
Will
Adeline
John
Beatrice
John
Archibald
Cynthia
G
Sarah
Thomas
Britton
Wiley
Sallie A
Daniel
Charlotte
William
Harriet
Louise
Karl
Caroline
William
Harriet
Louise
Frieda
Amos
William
Elmina
Mamie
George
Bertie
Lulie
Lily
Arthur
George
Jno
Guy
Harlie
Annabelle
Alfred
Solomon
Catherine
J
Mary
Balzer
Elisabetha
Johannes
Elizabeth
George
Euna
Mary
Melchir
James
Ana
Jenniel
A
Francis
Delphia
Salem
Daniel
Martha
Roy
Elizabeth
infant son
John
Mary
William
Charlotte
Anna
Leonard
Etta Faye
John
Sena
William
Jewell
Francis
Arlie
Viola
Leonard
Mae
Bessie
Caroline
Arlie
Eva
Conrad
Conrad
Maria
Trice
Phebe
Richard
Martin
Florence
W
Nancy
J
W
Elizabeth
Pleasant
Victoria
Ward
Cynthia
James
James
Nannie
John
Eleanor
William
James
Rachel
Pleasant
Mary
Parmelia
Mary
Elnor
Frelin
John
Rose
Ruth
Turner
Martha
Joseph
Caroline
Dick
Pearl
James
Mary
Leticia
Lucinda
John
Matha
Jessie
Mary
Joseph
Elisha
Sallie
Lutetita
Thomas
Sarah
Elisha
Martha
Charles
Zack
Juritha
Elisha
Drury
Mary
Sandifer
Nancy
Luvena
John
Nettie
Millie
Lawrence
Etta
John
James
C
Blanche
L
Ama
Robert
James
Romey
Anna
James
Francis
Turner
William
George
Nancy

There is an unnamed child (infant son) and maybe a typo’d name (Jno). I’ll keep these cases in mind as I move forward.

Cleaning up Middle Names

The middle name column is very problematic. Sometimes it is the middle name. Sometimes it is a note like “shared family tombstone”. Sometimes it is the middle initial from the gravestone, but with the full name filled in with brackets like A[lvis]. Sometimes it is a maiden name or the name of the spouse.

Most of this stuff is similar to what I’ve done with other fields. I’ll strip out extra spaces, deal with the bracketed info, periods and the abbreviation ux (Latin for wife). I’m taking out everything after ux, since that should be the name of the wife.

I’m also changing all blanks to NAs and I will use this to partition my dataset when I am doing the photo matching. na_if() from dplyr will let you replace any specific value with NA.

tombstones <- tombstones %>%
  mutate(Extra_Middle_Name1 = str_extract(Middle.Name, "ux .+")) %>%
  mutate(Middle.Name = str_replace(Middle.Name, "ux .+", "")) %>%
  mutate(Extra_Middle_Name2 = str_extract(Middle.Name, "\\[.+\\]")) %>%
  mutate(Middle.Name = str_replace(Middle.Name, "\\[.+\\]", "")) %>%
  mutate(Middle.Name = str_replace(Middle.Name, "\\.", "")) %>%
  mutate(Middle.Name = str_trim(Middle.Name, side = c("both"))) %>%
  mutate(Middle.Name = na_if(Middle.Name, "")) 

This fixes most of the issues, but not all. I’m going to see how the rest of this going and then come back and fix problem cases if needed. (Turns out this is fine.)

Now I’m making the full name with Middle Name/initial (e.g. Sinks Louise Elaine). His photo naming format is almost always last name first. Nothing too exotic here. I do use paste() to glue together my pieces, using space as the separator.

tombstones <- tombstones %>%
  mutate(full_name_MI = ifelse(
    is.na(Middle.Name) == TRUE,
    Middle.Name,
    paste(Surname, First.Name, Middle.Name, sep = " ")
  )) %>%
  mutate(full_name = paste(Surname, First.Name, sep = " "))

Now to deal with the case where first and middle names that are only letters, sometimes the photo name is Sinks LE rather than Sinks L E (which is created above) . So I need a second middle name column for these cases. (There are also cases with initials in the middle name itself. I don’t know if I need to clean that up. See Hess Ulysses S G.)

This may not be elegant, but I’m just slamming together first and middle name without spaces. So I’m getting names like Sinks LouiseElaine also.

tombstones <- tombstones %>%
  mutate(full_name_MI_no_space = ifelse(
    is.na(Middle.Name) == TRUE,
    Middle.Name,
    paste(Surname, paste0(First.Name, Middle.Name), sep = " ")
  )) 

And here I filter out first + middle name combo that is longer than three characters and setting the middle name without spaces column to NA. So LouiseElaine gets set to NA but LE remains. I chose 3 because I did see some folks with double initials in the middle name (e.g. Ulysses SG Hess) and I wasn’t sure if there were some fully initialed names that might be like that. I think in practice 2 is fine. This whole thing is a bit sloppy. The more rigorous way to do this is to check the patterns of the first and middle name and only created the combined name if they matched the pattern of being a single letter or two single letters separated by a space. Sometimes though, quick and dirty gets the job done.

tombstones <- tombstones %>%
  mutate(full_name_MI_no_space = ifelse(
    nchar(paste0(First.Name, Middle.Name)) > 3,
    " ",
    full_name_MI_no_space
  )) %>%
  mutate(full_name_MI_no_space = na_if(full_name_MI_no_space, " "))

There is another part of this project that I’m working on separately, so I’m saving a copy of the cleaned dataset to be used in that module.

saveRDS(tombstones, "tombstones_cleaned.RDS")

Matching to Photos

As I mentioned above, I decided that it was more important to have completely correct matching, rather than more complete matching. The order of the matching matters too. I’m starting with the most complete names, matching them, and then moving them out of the unmatched photo folder. Since I’m doing partial matching, Sinks A will match to both Sinks A (correct) and Sinks A T (incorrect). So I need to match Sinks A T first, so it is not available for matching by the time I get to matching with Sinks A. This took a lot of iterations to get the correct logic and resulting flow. I also prototyped by just matching full names and not using any functions. I’m not showing that here, but I think it is often easier to do that, and then go back and convert certain chunks to functions.

I’m not showing all the iterations and mistakes in full here like I did with the Cemetery cleaning section, because this section is pretty slow to run. But don’t think I am super clever and came up with the logic/scheme first go. I did also try the methods that I said I wouldn’t do, just to see how many false matches I got and how many more (probably) correct matches I got.

Reset Photo Folders

Testing the matching is an iterative process. I also ended up going back and adding more cleaning steps based on what I was seeing during the matching. There are about 500 photos and they get sorted into various folders. I was manually resetting all the folders, but that got old quickly. I wrote a code chunk to reset the folders. I move everything into a folder called trash- please do this rather than actually deleting the files until you are sure your code works! Then I copy the originals into a starting folder (Unmatched Photos)

How this works is I generate a list of files in each folder using list.files() and then I pass that list to the file.rename() or file.copy() functions. The file functions report TRUE for each file they correct find an act on and FALSE otherwise. The output should be all TRUEs.

(I thought I could make this a function, since I end up using it again below, but it doesn’t work. I didn’t return which is invalid and I ended up locking up Studio. Then I returned TRUE, but that seems to not work either. I ended up terminating R. It seems to hang when copying the files back in.)

# first moving all the photos to the trash folder, folder by folder
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Unmatched Photos"))
file.rename(
  from = here(blog_folder, photo_folder, "Unmatched Photos", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Archived Photos"))
file.rename(
  from = here(blog_folder, photo_folder, "Archived Photos", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round1"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round1", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round2"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round2", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round3"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round3", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round4"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round4", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Map"))
file.rename(
  from = here(blog_folder, photo_folder, "Map", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[196] TRUE TRUE TRUE TRUE

# now I move a copy of all the photos into the starting folder.
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Original Photos"))

file.copy(
  from = here(blog_folder, photo_folder, "Original Photos", my_file_list),
  to = here(blog_folder, photo_folder, unmatched_folder, my_file_list)
)

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[496] TRUE TRUE TRUE TRUE

Split out Middle Names and No Middle Names

Okay, now I am creating the two main data sets- with and without middle name entries.

unmatched_middle <- tombstones %>%
  filter(is.na(Surname) == FALSE) %>%
  filter(is.na(Middle.Name) == FALSE)

unmatched_no_middle <-  tombstones %>%
  filter(is.na(Surname) == FALSE) %>%
  filter(is.na(Middle.Name) == TRUE)

Now a function to do the matching.

Matching_Photos() Function

This function is a beast and should probably be broken into smaller functions. When you have to comment all the different steps in a function, then it is likely doing too many things! As I mentioned, I originally just tested this in non-function form, and I just took the whole chunk and put it in a function. I might clean this up later, but since this is a one off project, it may not be worth the time.

There are a few things to note about this function. First, for debugging inside a quarto file, use browser() inside the function to allow you to step through and observe the variables in the function. Not all of the regular debugging tools work in quarto (compared to a regular R file).

Secondly, accessing the value of the parameters inside the function required using functions like get(). Filter on name_to_match and nothing matches, because nothing has that name. Using get(name_to_match) actual returns the column name to match on.

Oh, and I use loops for everything! If I were to clean this up, I’d definitely get rid of loops using purrr or something.

So what does this function do?

It takes in a dataframe (df_to_match) and what column of names it should be matching against (name_to_match). It also takes in which match round this is.

1. Generates the list of unmatched photos.
2. Finds duplicated names.
3. and filters them out so that matching only occurs on the unique names in the set.
4. Does the matching with a fuzzyjoin. More about fuzzyjoin here and here.
5. Move all the photos that matched to the appropriate folder for the round.
6. Remove all the unmatched names from the dataframe.
7. Generate a list of people who have multiple photos associated with their name.
8. Make a panel/composite photo from the individual photos for each person with multiples and move the original to the folder archive. The panel photos are made using the magick package. I got started using this solution from stack overflow.
9. Return the dataframe with the matches. This is useful for troubleshooting, but it is not the final results dataframe because it has the original (multiple) photos listed and not the new panel photos. Functions in R must return something, and I did use this returned dataframe extensively when troubleshooting, but in other cases, I’d probably silence the output or just return TRUE/FALSE (with some error checking) to reflect if the function executed properly. It could also return the duplicate names df which could be sent to my father to manually match photos to those names. Or it could call Update_Photo_Names() as the last part, which would then let Matching_Photos() return the dataframe with the correct file names.

Matching_Photos <-
  function(df_to_match, name_to_match, match_folder) {
    # browser()
    #Step 1: list of unmatched photos
    photo_names = list.files(here(blog_folder, photo_folder, unmatched_folder))
    photo_df = as.data.frame(photo_names)
    #Step 2: generate duplicate names
    duplicate_names <- df_to_match %>%
      group_by_at(name_to_match) %>% count(sort = TRUE) %>% filter(n > 1)
    # return(duplicate_names) #this was for troubleshooting
    #Step 3: remove duplicate names
    tombstones_unique_names <- df_to_match %>%
      anti_join(duplicate_names)
    #step 4: do the matching
    tombstones_merged <-
      fuzzy_right_join(
        photo_df,
        tombstones_unique_names,
        by = c("photo_names" = name_to_match),
        match_fun = str_detect
      )
    #step 5: Moving all the photos that match to the correct match folder
    matched_this_round <- inner_join(photo_df, tombstones_merged)
    index <- 1
    for (index in seq(1:nrow(matched_this_round))) {
      file.rename(
        from = here(
          blog_folder,
          photo_folder,
          unmatched_folder,
          matched_this_round$photo_names[index]
        ),
        to = here(
          blog_folder,
          photo_folder,
          match_folder,
          matched_this_round$photo_names[index]
        )
      )
    }
    #step 6: Remove any unmatched names
    tombstones_merged <- tombstones_merged %>%
      drop_na(photo_names)
    #step 7: generate the list of folks with multiple photos
    multiple_photos <-
      tombstones_merged %>% group_by_at(name_to_match) %>% count(sort = TRUE) %>% filter(n > 1)
    #step 8: make the panel photos and move the originals to archive. (function?)
    if (nrow(multiple_photos) > 0) {
      index <- 1
      for (index in seq(1:nrow(multiple_photos))) {
        df_temp <- tombstones_merged %>%
          filter(get(name_to_match) == multiple_photos[[name_to_match]][index])
        these <-
          as.list(here(
            blog_folder,
            photo_folder,
            match_folder,
            df_temp$photo_names
          ))
        photo_panel <-
          image_append(do.call("c", lapply(these, image_read)))
        image_write(
          photo_panel,
          path =  here(
            blog_folder,
            photo_folder,
            match_folder,
            paste0(df_temp[[name_to_match]][1], "_panel.png")
            #paste0(df_temp$full_name_MI[1], "_panel.png")
          ),
          format = "jpeg"
        )
        index2 <- 1
        for (index2 in seq(1:nrow(df_temp))) {
          file.rename(
            from = here(
              blog_folder,
              photo_folder,
              match_folder,
              df_temp$photo_names[index2]
            ),
            to = here(
              blog_folder,
              photo_folder,
              archive_folder,
              df_temp$photo_names[index2]
            )
          )
        }
      }
    }
    #Step 9: return
    return(tombstones_merged)
  }

A False Start

Does the function work?

tester <- Matching_Photos(unmatched_middle, "full_name_MI", match1)

Joining with `by = join_by(full_name_MI)`
Joining with `by = join_by(photo_names)`

Warning in file.rename(from = here(blog_folder, photo_folder, unmatched_folder,
: cannot rename file 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Unmatched
Photos/Jones Levi A Jones Hester J.JPG' to 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Matched_Round1/Jones
Levi A Jones Hester J.JPG', reason 'The system cannot find the file specified'

Warning in file.rename(from = here(blog_folder, photo_folder, unmatched_folder,
: cannot rename file 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Unmatched
Photos/Pickard William Epps.JPG' to 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Matched_Round1/Pickard
William Epps.JPG', reason 'The system cannot find the file specified'

Yes, it works! However, there are a couple of warnings kicked up. The warnings are generated when the file to be moved doesn’t exist. This means that it has matched to another name and has already been moved. So these aren’t a problem with the function, but rather a problem with my logic/ partitioning of data into the different rounds.

The first message is about “Jones Levi A Jones Hester J.JPG”. The tombstone is for both the husband and wife and it should match to two names- Levi A Jones and Hester J Jones. So this is okay and not an error. The second one error arises from “Pickard William Epps.jpg”. This is a problem. There is apparently both a William E Pickard and a William Epps Pickard. The strings that are being matched against the photos are Pickard William E and Pickard William Epps. The first will match to the second when doing a partial match. Notice that if the naming convention were First Middle Last and not Last First Middle there would not be a problem. William E Pickard and William Epps Pickard will not match with each other in my method. This also means I don’t need to worry about a similar scenario for the first name, only the middle name/initial because it is at the end of the string.

Reset Photos Again

I’m suppressing the output, so you won’t see pages of TRUES.

# first moving all the photos to the trash folder, folder by folder
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Unmatched Photos"))
file.rename(
  from = here(blog_folder, photo_folder, "Unmatched Photos", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Archived Photos"))
file.rename(
  from = here(blog_folder, photo_folder, "Archived Photos", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round1"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round1", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round2"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round2", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round3"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round3", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Matched_Round4"))
file.rename(
  from = here(blog_folder, photo_folder, "Matched_Round4", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

my_file_list <-
  list.files(here(blog_folder, photo_folder, "Map"))
file.rename(
  from = here(blog_folder, photo_folder, "Map", my_file_list),
  to = here(blog_folder, photo_folder, "Trash", my_file_list)
)

# now I move a copy of all the photos into the starting folder.
my_file_list <-
  list.files(here(blog_folder, photo_folder, "Original Photos"))

file.copy(
  from = here(blog_folder, photo_folder, "Original Photos", my_file_list),
  to = here(blog_folder, photo_folder, unmatched_folder, my_file_list)
)

The Full Scheme: A Flowchart

So to fix this, I can split this into two new middle name dataframes. One with middle names longer than one character (or maybe 2) and those with initials for middle names. Again, the more complete version needs to be matched first.

I made a nice flowchart to explain. (I worked off of handwritten charts while I was coding. This was too complex not to have a reference.) Dark blue squares are the various dataframes as they are filtered and split before matching. Light blue squares are the results of the various rounds of matching. The magenta boxes are the matching protocols for each round. At the end, all the results_r# dataframes will be merged.

Round 1: Long Middle Names

Splitting : Long and Initials Middle Names

middle_names_long <- unmatched_middle %>%
  filter(nchar(Middle.Name) > 2)
  
middle_names_short <- unmatched_middle %>%
  filter(nchar(Middle.Name) <= 2)

Matching Round 1: Full Middle Names

tester <- Matching_Photos(middle_names_long, "full_name_MI", match1)

Joining with `by = join_by(full_name_MI)`
Joining with `by = join_by(photo_names)`

No errors or warnings. Now I’m generating the new list of photos. This will have the panel photos instead of the individual photos. I’m matching back to the original input dataframe to the match function, not the output (tester). The output of the renaming function is our results dataframe for this round.

Update_Photo_Names() : Rename and Moves Photos Function

This function updates the photo list to include the panel photos instead of the original files. It also moves all the photos from this round into the proper folder for the round.

Update_Photo_Names <- function(df, name_to_match, match_folder) {
  photo_names <-
    list.files(here(blog_folder, photo_folder, match_folder))
  photo_df = as.data.frame(photo_names)
  df_updated <- fuzzy_right_join(photo_df,
                                 df,
                                 by = c("photo_names" = name_to_match),
                                 match_fun = str_detect)
  #new_name <- paste("photos", match_folder, sep = "_") this was for debugging
  
  df_updated <- df_updated %>%
    rename_with(.fn = ~ paste("photos", match_folder, sep = "_"),
                .cols = photo_names)
  return(df_updated)
}

Rename Round 1: Generate first results DF

results_r1 <- Update_Photo_Names(middle_names_long, "full_name_MI", match1)

And now we just go through this again for each round.

Round 2: Short Middle Names

tester2 <- Matching_Photos(middle_names_short, "full_name_MI", match2)

Joining with `by = join_by(full_name_MI)`
Joining with `by = join_by(photo_names)`

Warning in file.rename(from = here(blog_folder, photo_folder, unmatched_folder,
: cannot rename file 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Unmatched
Photos/Jones Levi A Jones Hester J.JPG' to 'C:/Users/drsin/OneDrive/Documents/R
Projects/lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/Photos/Matched_Round2/Jones
Levi A Jones Hester J.JPG', reason 'The system cannot find the file specified'

Only get the Hester and Levi Jones warning, which is fine.

results_r2 <- Update_Photo_Names(middle_names_short, "full_name_MI", match2)

Round 3: Middle Names with No Space

Now I need to pull out the names that are only initials and match the no space version. These should only be contained in the results_r2 dataframe (since the other dataframe (r1) only has long middle names anyway.)

middle_name_initials <- results_r2 %>%
  mutate(full_name_MI_no_space = na_if(full_name_MI_no_space, " ")) %>%
  filter(is.na(full_name_MI_no_space) == FALSE) %>%
  filter(is.na(photos_Matched_Round2) == TRUE)

Okay, 17 didn’t match.

tester <- Matching_Photos(middle_name_initials, "full_name_MI_no_space", match3)

Joining with `by = join_by(full_name_MI_no_space)`
Joining with `by = join_by(photo_names)`

No warnings or errors.

results_r3 <-
  Update_Photo_Names(middle_name_initials, "full_name_MI_no_space", match3)

Round 4: No Middle Initials

Almost done. Now I’m matching the folks with no middle names. This group has more panel pictures than the others and it runs much slower the other match rounds.

tester3 <- Matching_Photos(unmatched_no_middle, "full_name", match4)

Joining with `by = join_by(full_name)`
Joining with `by = join_by(photo_names)`

Now we generate the new names.

results_r4 <- Update_Photo_Names(unmatched_no_middle, "full_name", match4)

Join all the Results

Now I full_join all the results dataframes together.

tombstones_matched <- results_r1 %>%
  full_join(results_r2) %>%
  full_join(results_r3) %>%
  full_join(results_r4)

Now generate the final photo list. Because of the way I segmented the data and how careful I was about order of matching, there should be only one match per name. And this is true.

However, by also being careful about what order I create the final photo list, I can put in a safeguard against incorrect matching for future work. The file name should always be taking from the earliest match round if for some reason it matches in multiple rounds. This might be cleaner written as a case statement, but I wrote this section when I only had two rounds of matching and the nested ifelse was very clear.

tombstones_matched_final <- tombstones_matched %>%
  mutate(photo_list = ifelse(
    is.na(photos_Matched_Round1) == TRUE,
    ifelse(
      is.na(photos_Matched_Round2) == TRUE,
      ifelse(
        is.na(photos_Matched_Round3) == TRUE,
        photos_Matched_Round4,
        photos_Matched_Round3
      ),
      photos_Matched_Round2
    )
    ,
    photos_Matched_Round1
  ))

Now I just double check that the photo_list was generated properly.

tombstones_matched_checker <- tombstones_matched_final %>%
  select(photo_list, contains("photos_Matched"))

Cleaning Up in Prep for Mapping

Move to all Photos to the Map Folder

Lastly, I move all the matched photos to a single folder. I’m putting it in a folder called “Map” since I’m using this to make my leaflet map. Again, I’ve suppressed the output for this block, but when troubleshooting you do want to make sure you are getting all trues.

my_file_list <-
  list.files(here(blog_folder, photo_folder, match1))

file.copy(
  from = here(blog_folder, photo_folder, match1, my_file_list),
  to = here(blog_folder, photo_folder, "Map", my_file_list)
)

my_file_list <-
  list.files(here(blog_folder, photo_folder, match2))

file.copy(
  from = here(blog_folder, photo_folder, match2, my_file_list),
  to = here(blog_folder, photo_folder, "Map", my_file_list)
)

my_file_list <-
  list.files(here(blog_folder, photo_folder, match3))

file.copy(
  from = here(blog_folder, photo_folder, match3, my_file_list),
  to = here(blog_folder, photo_folder, "Map", my_file_list)
)

my_file_list <-
  list.files(here(blog_folder, photo_folder, match4))

file.copy(
  from = here(blog_folder, photo_folder, match4, my_file_list),
  to = here(blog_folder, photo_folder, "Map", my_file_list)
)

Making Labels for the Map

Making a Complete Name Field

Now I need to make a column of the most complete name. This will be used as the label in the map. If the person has a middle name entry, I use the full name with middle name, and if not, I use the first and last. Note that I’m generating this by pasting together the individual name parts rather than using all the name variations I generated to match with. I want this label to be in conventional order and not the last name first format that I used for matching.

tombstones_matched_final <- tombstones_matched_final %>%
  mutate(complete_name = ifelse(
    is.na(full_name_MI) == FALSE,
    paste(First.Name, Middle.Name, Surname, sep = " "),
    paste(First.Name, Surname, sep = " "))
  )

Making a Cemetery Label

tombstones_matched_final <- tombstones_matched_final %>%
  mutate(cemetery_name = ifelse(Cemetery == "", "", paste(Cemetery, "Cemetery", sep = " "))
  )

Saving the file for use in another part of this project.

saveRDS(tombstones_matched_final, "tombstones_matched_final.RDS")

Converting to Geo Data

First, I’m dropping any NAs in lat/long. I convert it to a SF geo object. I talk about that process in a TidyTuesday post, where I also generate a map.

I’m adding some jitter to the coordinates with sf_jitter(); tombstones that are very close to each other have the same coordinates and show up as overlapping on the map. I’m not really happy with how far apart they are jittered, but I’ll play with that in the leaflet tutorial.

tombstones_matched_final <- tombstones_matched_final %>% drop_na(lat) %>% drop_na(long)


tombstones_geo <- st_as_sf(tombstones_matched_final, coords = c("long", "lat"), crs = 4326)

tombstones_geo <- tombstones_geo %>% drop_na(photo_list)

tombstones_geo_jittered <- st_jitter(tombstones_geo, factor = 0.0001)

Making the Leaflet Map

This is just a draft. I’d like to link to some other material and color and group by cemetery. I’ll post a detailed tutorial on that later. I do have a simple walk through of making an interactive leaflet map here. I found datacamp’s course on leaflet very good also.

The draft map is another opportunity to check for obvious errors or bugs. I’m commenting out the section that generates the photo pop-up window. This creates a file larger than github prefers. I’ll have a link to the final map with photos in the leaflet tutorial.

image_list <- tombstones_geo_jittered$photo_list

leaflet() %>%
  addTiles() %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addScaleBar() %>%
  addCircleMarkers(
    data = tombstones_geo,
    label = ~ (paste(complete_name, cemetery_name, sep = " ")), 
    clusterOptions = markerClusterOptions(),
    opacity = 1,
    radius = 10,
    color = "blue",
    stroke = NA,
    group = "group1"
  ) #  %>%

  #  leafpop::addPopupImages(
  #   image = paste0(here(blog_folder, photo_folder, "Map" ),"/", image_list),
  #     src = local,
  #    group = "group1", width = 400,
  # maxHeight = 300, maxWidth = 400
  #  )

Why is blockhouse cemetery in the ocean?

tombstones_geo_jittered %>% filter(Cemetery == "Blockhouse") %>%
  select(Cemetery, City, State, N, W, geometry)

Simple feature collection with 1 feature and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -37.48555 ymin: 44.75355 xmax: -37.48555 ymax: 44.75355
Geodetic CRS:  WGS 84
    Cemetery              City State          N           W
1 Blockhouse Peru, Clinton Co.    NY 44 34.662  37  27.129 
                    geometry
1 POINT (-37.48555 44.75355)

A quick Google reveals that “The latitude and longitude coordinates (GPS waypoint) of Peru are 44.578379 (North), -73.5268028 (West) and the approximate elevation is 335 feet (102 meters)”.

So, this is a simple transposition typo- instead of 73 he typed 37. I could have put some sort of error handling in the cleaning GPS data section. For example, I could have flagged lats or longs that were outside the US.

I’m not entirely happy with the sf_jitter parameters, so I’m correcting this in two places- the jittered dataframe I use for the mapping here, and the unjittered version as well. I will be optimizing on the unjittered version when I create the final map. Making the same correction twice isn’t great, but I wanted to produce a final map here independent of making the pretty leaflet map.

st_geometry(tombstones_geo_jittered[tombstones_geo$Cemetery == "Blockhouse", ]) <-  st_sfc(st_point(c(-73.48583,44.75056)))

tombstones_geo_jittered %>% filter(Cemetery == "Blockhouse") %>%
  select(Cemetery, N, W, geometry)

Simple feature collection with 1 feature and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -73.48583 ymin: 44.75056 xmax: -73.48583 ymax: 44.75056
Geodetic CRS:  WGS 84
    Cemetery          N           W                   geometry
1 Blockhouse 44 34.662  37  27.129  POINT (-73.48583 44.75056)

st_geometry(tombstones_geo[tombstones_geo$Cemetery == "Blockhouse", ]) <-  st_sfc(st_point(c(-73.48583,44.75056)))

tombstones_geo %>% filter(Cemetery == "Blockhouse") %>%
  select(Cemetery, N, W, geometry)

Simple feature collection with 1 feature and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -73.48583 ymin: 44.75056 xmax: -73.48583 ymax: 44.75056
Geodetic CRS:  WGS 84
    Cemetery          N           W                   geometry
1 Blockhouse 44 34.662  37  27.129  POINT (-73.48583 44.75056)

Now re-check the map.

leaflet() %>%
  addTiles() %>%
  addScaleBar() %>%
  addCircleMarkers(
    data = tombstones_geo,
    label = ~ (paste(complete_name, "-", Cemetery, "Cemetery", sep = " ")), 
    clusterOptions = markerClusterOptions(),
    opacity = 1,
    radius = 10,
    color = "blue",
    stroke = NA,
    group = "group1"
  ) # %>%

  #  leafpop::addPopupImages(
  #   image = paste0(here(blog_folder, photo_folder, "Map" ),"/", image_list),
  #     src = local,
  #    group = "group1", width = 400,
  # maxHeight = 300, maxWidth = 600
  #  )

Blockhouse is now correctly located in upstate New York.

I assume I can save this file as a RDS file for use in the leaflet mapper module, but if not, I’ll let you know.

saveRDS(tombstones_geo, "tombstones_geo.RDS")

Conclusions

Despite fairly intensive efforts with data cleaning, only 194 names and photos were included in the final map. A handful of matched names and photos didn’t make it on the map because there was a problem with the geographic data and got dropped for not having latitude or longitude.

Hand correction of the files names would be needed to increase the matches in the current dataset. Moving forward, photos should include an additional identifier, perhaps birth or death year to disambiguate people with identical names.

Citation

BibTeX citation:

@online{sinks2023,
  author = {Sinks, Louise E.},
  title = {Data {Cleaning} for the {Tombstone} {Project}},
  date = {2023-08-04},
  url = {https://lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/tombstone_data_cleaning.html},
  langid = {en}
}

For attribution, please cite this work as:

Sinks, Louise E. 2023. “Data Cleaning for the Tombstone Project.” August 4, 2023. https://lsinks.github.io/posts/2023-08-04-data-cleaning-tombstone/tombstone_data_cleaning.html.