Wrangling and Visualizing Historical Texts

By Laurie Baker

Acknowledgements

Slide Structure and Design inspired by Julia Silge

@juliasilge

What can you do with R? A snapshot

Influence of target species on fishing effort

Who is fishing?

Plot showing the vessel length and width as boats on a scatter plot where the size of the boat is proportional to the carrying capacity of the vessel

How are vessels fishing?

Plot showing the distribution of trip length by vessel

What are vessels targeting?

Plot showing the distribution of species in the catch by weight by vessel

Where are they fishing?

What else can R do?

Create open source learning resources

Code Mitten Patterns

Choose colors for mitten patterns

Monitor fish runs

Build websites!

Goals for this week

Get you coding and exploring text data.
Help you see ways you could use R in your projects.

Data Science Life Cycle

Learning Objectives:

Learn how to

Read in data using read_excel
Select columns using select
Create new variables using mutate
Summarize and arrange data using count and arrange
Group data to display and calculate multiple things
Correct entries using mutate and case_when
Visualize and map changes in space and time using ggplot and leaflet

Introducing Posit Cloud

Let’s install some packages

install.packages(c("tidyverse", # data wrangling
                   "lubridate", # dates
                   "readxl", # reading data
                   "DT")) # displaying data in tables

What are packages? You can think of them like toolboxes with different sets of tools (i.e. functions) we can apply to our data

The Journals (1870-1906)

12 journals (1871-1883) transcribed

library(readxl)
journal_1871_1872 <- read_excel("data/journal_1871_1872.xlsx")
journal_1873 <- read_excel("data/journal_1873.xlsx")
journal_1874 <- read_excel("data/journal_1874.xlsx")
journal_1875 <- read_excel("data/journal_1875.xlsx")
journal_1876 <- read_excel("data/journal_1876.xlsx")
journal_1877 <- read_excel("data/journal_1877.xlsx")
journal_1878 <- read_excel("data/journal_1878.xlsx")
journal_1879 <- read_excel("data/journal_1879.xlsx")
journal_1880 <- read_excel("data/journal_1880.xlsx")
journal_1881 <- read_excel("data/journal_1881.xlsx")
journal_1882 <- read_excel("data/journal_1882.xlsx")
journal_1883 <- read_excel("data/journal_1883.xlsx")

Keeping Track

library(tidyverse)

journal_1871_1872$journal <- 1
journal_1873$journal <- 2
journal_1874$journal <- 3
journal_1875$journal <- 4
journal_1876$journal <- 5
journal_1877$journal <- 6
journal_1878$journal <- 7
journal_1879$journal <- 8
journal_1880$journal <- 9
journal_1881$journal <- 10
journal_1882$journal <- 11
journal_1883$journal <- 12

journals <- dplyr::bind_rows(journal_1871_1872, journal_1873, journal_1874, 
                             journal_1875, journal_1876, journal_1877, 
                             journal_1878, journal_1879, journal_1880, 
                             journal_1881, journal_1882, journal_1883)

We have a lot of variables recorded!

glimpse(journals)
#> Rows: 5,509
#> Columns: 39
#> $ date_mdy                <chr> "12/23/1871", "12/24/1871", "12/25/1871", "12/…
#> $ month                   <chr> "December", "December", "December", "December"…
#> $ journal_entry           <chr> "Was married at home in evening by William Ran…
#> $ transcription           <chr> "confident", "confident", "confident", "confid…
#> $ location                <chr> "Winter Harbor", "NA", "Winter Harbor", "Winte…
#> $ location_accuracy       <chr> "mentioned", "NA", "assumed", "mentioned", "me…
#> $ local_place             <chr> "Home", "NA", "Hall", "Home", "Home", "Home", …
#> $ local_place_accuracy    <chr> "mentioned", "NA", "mentioned", "mentioned", "…
#> $ latitude                <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ latitude_origin         <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ longitude               <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ longitude_origin        <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ quantity                <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ unit                    <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ item                    <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ letter                  <chr> "no letter", "no letter", "no letter", "no let…
#> $ letter_from             <chr> "NA", "NA", "NA", "NA", "N. H. Higgins", "NA",…
#> $ letter_to               <chr> "NA", "NA", "NA", "NA", "NA", "Na", "Na", "Na"…
#> $ wind_direction_am       <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ wind_direction_pm       <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ wind_direction_night    <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ wind_speed_am           <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ wind_speed_pm           <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ wind_speed_night        <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ weather_condition_am    <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ weather_condition_pm    <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ weather_condition_night <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ temperature_am          <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ temperature_pm          <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ temperature_night       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ image_path              <chr> "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA"…
#> $ image_description       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ recorder                <chr> "NH", "NH", "NH", "NH", "NH", "NH", "NH", "NH"…
#> $ notes                   <chr> "Esqr? Esquire", "to?", "Christmas tree?", NA,…
#> $ noteworthy              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ second_check            <chr> "WD, EP", "EP", "WD, EP", "EP", "WD, EP", "EP"…
#> $ journal                 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ transcription_accuracy  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ ...1                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

Exploring a subset using `select`


(journals_sub <- journals %>%
    select(date_mdy, journal_entry, location))
#> # A tibble: 5,509 × 3
#>    date_mdy   journal_entry                                             location
#>    <chr>      <chr>                                                     <chr>   
#>  1 12/23/1871 Was married at home in evening by William Rand Esqr.      Winter …
#>  2 12/24/1871 Went to meeting.                                          NA      
#>  3 12/25/1871 Shooting match all day in the evening to Christmas tree … Winter …
#>  4 12/26/1871 About home at work jobbing.                               Winter …
#>  5 12/27/1871 Work about home reed letter from N. H. Higgins Ins agt.   Winter …
#>  6 12/28/1871 Work about home.                                          Winter …
#>  7 12/29/1871 To work in shop.                                          Winter …
#>  8 12/30/1871 To work in shop.                                          Winter …
#>  9 12/31/1871 Went to meeting.                                          Winter …
#> 10 1/1/1872   Work in shop.                                             Winter …
#> # ℹ 5,499 more rows

Creating new variables using `mutate`

library(lubridate)
(journals_sub <- journals_sub %>%
    mutate(date_mdy = mdy(date_mdy),
           year = year(date_mdy),
           month = month(date_mdy)))
#> # A tibble: 5,509 × 5
#>    date_mdy   journal_entry                                 location  year month
#>    <date>     <chr>                                         <chr>    <dbl> <dbl>
#>  1 1871-12-23 Was married at home in evening by William Ra… Winter …  1871    12
#>  2 1871-12-24 Went to meeting.                              NA        1871    12
#>  3 1871-12-25 Shooting match all day in the evening to Chr… Winter …  1871    12
#>  4 1871-12-26 About home at work jobbing.                   Winter …  1871    12
#>  5 1871-12-27 Work about home reed letter from N. H. Higgi… Winter …  1871    12
#>  6 1871-12-28 Work about home.                              Winter …  1871    12
#>  7 1871-12-29 To work in shop.                              Winter …  1871    12
#>  8 1871-12-30 To work in shop.                              Winter …  1871    12
#>  9 1871-12-31 Went to meeting.                              Winter …  1871    12
#> 10 1872-01-01 Work in shop.                                 Winter …  1872     1
#> # ℹ 5,499 more rows

The lubridate package contains useful functions for dates.

How often did Freeland write?

Where did Freeland go? Step by Step

Start with the dataset

journals_sub 
#> # A tibble: 5,509 × 5
#>    date_mdy   journal_entry                                 location  year month
#>    <date>     <chr>                                         <chr>    <dbl> <dbl>
#>  1 1871-12-23 Was married at home in evening by William Ra… Winter …  1871    12
#>  2 1871-12-24 Went to meeting.                              NA        1871    12
#>  3 1871-12-25 Shooting match all day in the evening to Chr… Winter …  1871    12
#>  4 1871-12-26 About home at work jobbing.                   Winter …  1871    12
#>  5 1871-12-27 Work about home reed letter from N. H. Higgi… Winter …  1871    12
#>  6 1871-12-28 Work about home.                              Winter …  1871    12
#>  7 1871-12-29 To work in shop.                              Winter …  1871    12
#>  8 1871-12-30 To work in shop.                              Winter …  1871    12
#>  9 1871-12-31 Went to meeting.                              Winter …  1871    12
#> 10 1872-01-01 Work in shop.                                 Winter …  1872     1
#> # ℹ 5,499 more rows

Where did Freeland go? Step by Step

And then filter for only known locations

journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA")
#> # A tibble: 3,164 × 5
#>    date_mdy   journal_entry                                 location  year month
#>    <date>     <chr>                                         <chr>    <dbl> <dbl>
#>  1 1871-12-23 Was married at home in evening by William Ra… Winter …  1871    12
#>  2 1871-12-25 Shooting match all day in the evening to Chr… Winter …  1871    12
#>  3 1871-12-26 About home at work jobbing.                   Winter …  1871    12
#>  4 1871-12-27 Work about home reed letter from N. H. Higgi… Winter …  1871    12
#>  5 1871-12-28 Work about home.                              Winter …  1871    12
#>  6 1871-12-29 To work in shop.                              Winter …  1871    12
#>  7 1871-12-30 To work in shop.                              Winter …  1871    12
#>  8 1871-12-31 Went to meeting.                              Winter …  1871    12
#>  9 1872-01-01 Work in shop.                                 Winter …  1872     1
#> 10 1872-01-02 Work in shop.                                 Winter …  1872     1
#> # ℹ 3,154 more rows

The function filter lets you filter rows based on their values.

Where did Freeland go? Step by Step

And then separate_longer_delim so each location is on its own row

journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA") %>%
    separate_longer_delim(location, delim = ", ")
#> # A tibble: 4,138 × 5
#>    date_mdy   journal_entry                                 location  year month
#>    <date>     <chr>                                         <chr>    <dbl> <dbl>
#>  1 1871-12-23 Was married at home in evening by William Ra… Winter …  1871    12
#>  2 1871-12-25 Shooting match all day in the evening to Chr… Winter …  1871    12
#>  3 1871-12-26 About home at work jobbing.                   Winter …  1871    12
#>  4 1871-12-27 Work about home reed letter from N. H. Higgi… Winter …  1871    12
#>  5 1871-12-28 Work about home.                              Winter …  1871    12
#>  6 1871-12-29 To work in shop.                              Winter …  1871    12
#>  7 1871-12-30 To work in shop.                              Winter …  1871    12
#>  8 1871-12-31 Went to meeting.                              Winter …  1871    12
#>  9 1872-01-01 Work in shop.                                 Winter …  1872     1
#> 10 1872-01-02 Work in shop.                                 Winter …  1872     1
#> # ℹ 4,128 more rows

Where did Freeland go? Step by Step

And then count the number of times he was at that location

journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA") %>%
    separate_longer_delim(location, delim = ", ") %>%
    count(location)
#> # A tibble: 312 × 2
#>    location                     n
#>    <chr>                    <int>
#>  1 ""                           1
#>  2 " Northeast Harbor"          1
#>  3 "? Virgin"                   1
#>  4 "Albany Railroad Wharf"      9
#>  5 "Atlantic Wharf"             6
#>  6 "Azores/Western Islands"     1
#>  7 "Back Shore"                 1
#>  8 "Baker Island"              10
#>  9 "Bakers Island"              1
#> 10 "Bangor"                     5
#> # ℹ 302 more rows

Where did Freeland go? Step by Step

And then arrange the locations in descending order from most to least visited.

journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA") %>%
    separate_longer_delim(location, delim = ", ") %>%
    count(location) %>%
    arrange(desc(n))
#> # A tibble: 312 × 2
#>    location            n
#>    <chr>           <int>
#>  1 Winter Harbor    2368
#>  2 West Gouldsboro   132
#>  3 Ellsworth          68
#>  4 Rockland           55
#>  5 Bar Harbor         44
#>  6 Leghorn            43
#>  7 Boston             42
#>  8 Prospect Harbor    39
#>  9 Cranberry Isles    37
#> 10 Calais             36
#> # ℹ 302 more rows

Where did Freeland go less? Step by Step

And then arrange the locations in ascending order from least to most visited.

journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA") %>%
    separate_longer_delim(location, delim = ", ") %>%
    count(location) %>%
    arrange(n) 
#> # A tibble: 312 × 2
#>    location                     n
#>    <chr>                    <int>
#>  1 ""                           1
#>  2 " Northeast Harbor"          1
#>  3 "? Virgin"                   1
#>  4 "Azores/Western Islands"     1
#>  5 "Back Shore"                 1
#>  6 "Bakers Island"              1
#>  7 "Bass Habor"                 1
#>  8 "Bishop and Clerks"          1
#>  9 "Block Island"               1
#> 10 "Bog Brook"                  1
#> # ℹ 302 more rows

Plotting frequency of visits

(common_places <- journals_sub %>%
    filter(is.na(location) == FALSE, location != "NA") %>%
    separate_longer_delim(location, delim = ", ") %>%
    count(location) %>%
    slice_max(n, n = 15, with_ties = FALSE))
#> # A tibble: 15 × 2
#>    location            n
#>    <chr>           <int>
#>  1 Winter Harbor    2368
#>  2 West Gouldsboro   132
#>  3 Ellsworth          68
#>  4 Rockland           55
#>  5 Bar Harbor         44
#>  6 Leghorn            43
#>  7 Boston             42
#>  8 Prospect Harbor    39
#>  9 Cranberry Isles    37
#> 10 Calais             36
#> 11 Sullivan           35
#> 12 Bass Harbor        30
#> 13 East Sullivan      28
#> 14 Stave Island       26
#> 15 Jersey City        24

Plotting frequency of visits

 common_places %>%
    ggplot(aes(x = n, y = location)) +
    geom_col() +
    labs(x = "Number of mentions", y = "Location")

Learn about ggplot in these slides and with these interactive tutorials

Plotting frequency of visits

 common_places %>%
    ggplot(aes(x = n, y = fct_reorder(location, n))) +
    geom_col() +
    labs(x = "Number of mentions", y = "Location")

Use `distinct` to detect mispellings and look at unique entries

journals_sub %>%
    separate_longer_delim(location, delim = ", ") %>%
    filter(str_detect(string = location, pattern = "Cranberry")) %>%
    distinct(location)
#> # A tibble: 4 × 1
#>   location                       
#>   <chr>                          
#> 1 "Cranberry Island"             
#> 2 "Cranberry Isles"              
#> 3 "Winter Harbor,Cranberry Isles"
#> 4 "Cranberry Isles "

Tidying words using `case_when`

journals_sub <- journals_sub %>%
    separate_rows(location, sep = ",") %>%
    mutate(location = case_when(location %in% c("Cranberry Isle", "Cranberry Island") ~ "Cranberry Isles",
                            TRUE ~ location)) 


journals_sub %>%
    filter(str_detect(string = location, pattern = "Cranberry")) %>%
    distinct(location)
#> # A tibble: 3 × 1
#>   location           
#>   <chr>              
#> 1 "Cranberry Isles"  
#> 2 " Cranberry Isles" 
#> 3 " Cranberry Isles "

Why are places still coming up as different?

We can use str_trim() to get rid of white space.

journals_sub <- journals_sub %>%
    mutate(location = str_trim(location, side = "both"))


journals_sub %>%
    filter(str_detect(string = location, pattern = "Cranberry")) %>%
    distinct(location)
#> # A tibble: 1 × 1
#>   location       
#>   <chr>          
#> 1 Cranberry Isles

Your Turn: Fix Bass Harbor misspellings

journals_sub %>%
    separate_longer_delim(location, delim = ", ") %>%
    filter(str_detect(string = location, pattern = "Bass|bass")) %>%
    distinct(location)
#> # A tibble: 2 × 1
#>   location   
#>   <chr>      
#> 1 Bass Harbor
#> 2 Bass Habor


journals_loc <- journals_loc %>%
    mutate(location = case_when(location %in% c(__________) ~ _______,
                            TRUE ~ location))

Where did Freeland visit

How often did Freeland visit over time?

Use `group_by` to count yearly visits

(journals_loc_count <- journals_sub %>%
    filter(location %in% c("West Gouldsboro", "Ellsworth", "Rockland", "Bar Harbor", "Sullivan", "Calais", "Prospect Harbor", "East Sullivan", "Boston", "Bass Harbor", "Cranberry Isles")) %>%
    group_by(year) %>%
    count(location))
#> # A tibble: 76 × 3
#> # Groups:   year [12]
#>     year location            n
#>    <dbl> <chr>           <int>
#>  1  1872 Bass Harbor         3
#>  2  1872 Cranberry Isles     7
#>  3  1872 Ellsworth           8
#>  4  1872 Prospect Harbor     2
#>  5  1872 Rockland            9
#>  6  1872 Sullivan            3
#>  7  1872 West Gouldsboro    14
#>  8  1873 Bar Harbor          9
#>  9  1873 Bass Harbor         9
#> 10  1873 Boston              2
#> # ℹ 66 more rows

Plot Code

journals_loc_count %>%
    ggplot(aes(x = year, y = n, color = location)) +
    geom_line() +
    labs(x = "Year", 
         y = "Number of mentions", 
         color = "Location", 
         title = "Number of times place visited each year by Freeland Bunker", subtitle = "Locations don't include home, shop, factory, and Winter Harbor")

Your Turn

Where did Freeland visit during your journal year? Fill in the blanks to find out.

Creating maps using `leaflet`

We have location coordinates

location_coordinates <- read_excel(path = "data/location_coordinates.xlsx")

location_coordinates <- location_coordinates %>%
    rename(location = location_list)

We have a `count` of how often Freeland visited that location

(loc_count <- journals_sub %>%
    count(location) %>%
    arrange(desc(n)))
#> # A tibble: 308 × 2
#>    location            n
#>    <chr>           <int>
#>  1 Winter Harbor    2369
#>  2 West Gouldsboro   132
#>  3 Ellsworth          68
#>  4 Rockland           55
#>  5 Bar Harbor         44
#>  6 Leghorn            43
#>  7 Boston             42
#>  8 Cranberry Isles    39
#>  9 Prospect Harbor    39
#> 10 Calais             36
#> # ℹ 298 more rows

We can combine two data sets using a `join`

(loc_df <- loc_count %>%
    left_join(location_coordinates, by = "location") %>%
    drop_na(latitude) %>%
    mutate(latitude = as.numeric(latitude),
           longitude = as.numeric(longitude))
           )
#> # A tibble: 99 × 16
#>    location            n Instructions  official_name_check.…¹ latitude longitude
#>    <chr>           <int> <chr>         <chr>                     <dbl>     <dbl>
#>  1 Winter Harbor    2369 <NA>          yes                        44.4     -68.1
#>  2 West Gouldsboro   132 <NA>          yes                        44.5     -68.1
#>  3 Ellsworth          68 <NA>          yes                        44.5     -68.4
#>  4 Rockland           55 <NA>          yes                        44.1     -69.1
#>  5 Bar Harbor         44 <NA>          yes                        44.4     -68.2
#>  6 Leghorn            43 <NA>          yes                        43.6      10.3
#>  7 Boston             42 location_dro… yes                        42.4     -71.1
#>  8 Cranberry Isles    39 <NA>          yes                        44.3     -68.3
#>  9 Prospect Harbor    39 <NA>          yes                        44.4     -68.0
#> 10 Calais             36 <NA>          yes                        45.2     -67.3
#> # ℹ 89 more rows
#> # ℹ abbreviated name: ¹official_name_check...3
#> # ℹ 10 more variables: date_mentioned_location <chr>, location_notes <chr>,
#> #   initials...8 <chr>, local_place_list <chr>, official_name_check...10 <chr>,
#> #   local_place_latitude <dbl>, local_place_longitude <dbl>,
#> #   date_mentioned_local <chr>, local_place_notes <chr>, initials...15 <chr>

Where did Freeland go?

library(leaflet)

leaflet(data = loc_df) %>%
     setView(lng = -66, lat = 43.5, zoom = 9) %>%
     addProviderTiles(providers$OpenStreetMap) %>%
     addCircleMarkers(lng = ~longitude, 
                      lat = ~latitude, 
                      label = ~location, 
                      radius = ~n/100)

Learn more leaflet in these slides and these tutorials

Homework

Double check a location’s coordinates or
Look up two locations and add 2 more sets of coordinates to the location excel file. Add your initials to the excel spreadsheet.
Re-upload the location data

Rerun the leaflet map to see the map expand

Thanks!

@LaurieLBaker

lauriebaker@rbind.io

Wrangling and Visualizing Historical Texts

Acknowledgements

What can you do with R? A snapshot

Influence of target species on fishing effort

Who is fishing?

How are vessels fishing?

What are vessels targeting?

Where are they fishing?

What else can R do?

Create open source learning resources

Code Mitten Patterns

Choose colors for mitten patterns

Monitor fish runs

Build websites!

Goals for this week

Data Science Life Cycle

Data Science Life Cycle

Data Science Life Cycle

Data Science Life Cycle

Data Science Life Cycle

Data Science Life Cycle

Data Science Life Cycle

Learning Objectives:

Introducing Posit Cloud

Let’s install some packages

The Journals (1870-1906)

12 journals (1871-1883) transcribed

Keeping Track

We have a lot of variables recorded!

Exploring a subset using select

Creating new variables using mutate

How often did Freeland write?

Where did Freeland go? Step by Step

Where did Freeland go? Step by Step

Where did Freeland go? Step by Step

Where did Freeland go? Step by Step

Where did Freeland go? Step by Step

Where did Freeland go less? Step by Step

Plotting frequency of visits

Plotting frequency of visits

Plotting frequency of visits

Use distinct to detect mispellings and look at unique entries

Tidying words using case_when

Why are places still coming up as different?

Your Turn: Fix Bass Harbor misspellings

Where did Freeland visit

How often did Freeland visit over time?

Use group_by to count yearly visits

Plot Code

Your Turn

Creating maps using leaflet

We have location coordinates

We have a count of how often Freeland visited that location

We can combine two data sets using a join

Where did Freeland go?

Where did Freeland go?

Homework

Thanks!

Exploring a subset using `select`

Creating new variables using `mutate`

Use `distinct` to detect mispellings and look at unique entries

Tidying words using `case_when`

Use `group_by` to count yearly visits

Creating maps using `leaflet`

We have a `count` of how often Freeland visited that location

We can combine two data sets using a `join`