How to create a typological map in R with leaflet

Author

Ludovic De Cuypere

Published

January 1, 2025

Introduction

This tutorial will show you how to create a typological map using R.

To be able to follow this tutorial, we suggest you check out and familiarize yourself with the content of the following R Basics and Data Visualization tutorials:

Preparation and session set up

This tutorial is based on R. If you have not installed R or are new to it, you will find an introduction to and more information how to use R here. For this tutorials, we need to install certain packages from an R library so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead and ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time).

Code

# set options
options(stringsAsFactors = F)         # no automatic data transformation
options("scipen" = 100, "digits" = 4) # suppress math annotation
# install packages
install.packages("tidyverse")
install.packages("flextable")
install.packages("leaflet")

Next, we activate the packages.

Code

library(tidyverse)
library(flextable)
library(leaflet)

Once you have installed R, RStudio, and have also initiated the session by executing the code shown above, you are good to go.

Typological data

The objective of this tutorial is to use R to create a typological map as in the World Atlas of Linguistic Structures (WALS)(Dryer and Haspelmath 2013). The WALS is a large database that documents the structural features of languages around the world. It provides information on a wide range of linguistic characteristics, from phonological traits to grammatical structures. A typical WALS dataset includes information about various languages and specific linguistic features. Each row in the dataset represents a single language, and the columns contain metadata about the language and data about its structural features.

For instance, Comrie (1998) contains typological information about the “Order of Subject and Verb”, with three levels: “SV”, “VS”, and “No dominant order”. The related typological map can be found here.

To create a typological map, you need a dataset with four variables/columns, as illustrated in Table 1:

Code

data <- data.frame(
  Language = c("Tiranige", "Amharic", "Hungarian", "Vietnamese", "Indonesian", "..."),
  Longitude = c(3.70, 39.54, 19.66, 105.77, 109.72, NA),
  Latitude = c(14.76, 11.71, 46.91, 20.68, -7.33, NA),
  Feature = c("absent", "other", "absent", "absent", "other", "...")
)

Code

ft <- flextable(data) |> set_header_labels(
    Language = "Language",
    Longitude = "Longitude",
    Latitude = "Latitude",
    Feature = "Feature"
  ) |>
  set_table_properties(width = .5, layout = "autofit") |>
  theme_zebra() |>
  fontsize(size = 12) |>
  fontsize(size = 12, part = "header") |>
  align_text_col(align = "center") |>
  border_outer()
ft

Table 1: The first five rows of a WALS-like typological dataset

Language	Longitude	Latitude	Feature
Tiranige	3.70	14.76	absent
Amharic	39.54	11.71	other
Hungarian	19.66	46.91	absent
Vietnamese	105.77	20.68	absent
Indonesian	109.72	-7.33	other
...			...

The dataset should contain a column/variable with the languages, two columns/variables with the geographical coordinates for each language, i.e., Longitude and Latitude (in decimal numbers, not degrees!), and a columns/variable with the typological feature under analysis.

A mock dataset

Here we create a mock dataset with twenty languages (rows), their geographical coordinates and one hypothetical Feature with three levels: “present”, “absent” and “other”.

Code

Language <- c("Tiranige", "Amharic", "Hungarian", "Vietnamese", "Indonesian",
               "Kalaallisut (West Greenlandic)", "Magdalena Peñasco Mixtec", "Cayuga",
               "Laz", "Japanese", "Buwal", "Yucatec Maya", "Manx", "Forest Enets",
               "Lopit", "Mojeño Trinitario", "Ulwa (Yaul)", "Japhug",
               "West Circassian", "Kuuk Thayorre")
Longitude <- c(-3.70, 39.54, 19.66, 105.77, 109.72, -52.86, -97.54, -76.60,
                41.91, 135.00, 10.62, -88.96, -4.45, 86.50, 32.75, -65.41,
                143.99, 102.36, 39.33, 142.03)
Latitude <- c(14.76, 11.71, 46.91, 20.68, -7.33, 69.38, 17.28, 42.41,
               41.50, 35.00, 13.88, 18.78, 54.26, 68.60, 4.70, -15.33,
               -4.40, 32.10, 44.00, -14.82)
Feature <- c("absent", "other", "absent", "absent", "other", "absent", "absent", 
              "present", "other", "absent", "present", "present", "present", 
              "present", "present", "absent", "other", "present", "present", "present")
typ <- data.frame(Language, Longitude, Latitude, Feature)
typ$Feature <- as.factor(typ$Feature)

Check the first rows:

Code

head(typ) |>
  flextable() |>
  flextable::set_table_properties(width = .5, layout = "autofit") |>
  flextable::theme_zebra() |>
  flextable::fontsize(size = 12) |>
  flextable::fontsize(size = 12, part = "header") |>
  flextable::align_text_col(align = "center") |>
  flextable::set_caption(caption = "")  |>
  flextable::border_outer()

Language	Longitude	Latitude	Feature
Tiranige	-3.70	14.76	absent
Amharic	39.54	11.71	other
Hungarian	19.66	46.91	absent
Vietnamese	105.77	20.68	absent
Indonesian	109.72	-7.33	other
Kalaallisut (West Greenlandic)	-52.86	69.38	absent

Create map

First, we define a WALS-like colour palette (other colour palettes can be used):

Code

col <- c("#0000dd", # colour for "present"
         "#dd0000", # colour for "absent"
         "#ff66ff") # colour for "other"
# at this stage the colours are not yet assigned to the levels of Feature!

We use leaflet::colorFactor to map the colour palette to the levels of the variable Feature.

Code

color_pal_WALS <- colorFactor(palette = col, 
                         levels = c("present", "absent", "other"))

We are now ready to create a leaflet map.

Code

1leaflet(typ) |>
2  addTiles()  |>
3  addCircleMarkers(
    ~Longitude, ~Latitude, 
    fillColor = ~color_pal_WALS(Feature),
    color = "white", 
    radius = 8,      
    fillOpacity = 1, 
    stroke = TRUE,   
    weight = 1,       
    label = ~Language, 
    popup = ~paste("<strong>Language:</strong>", Language,
                   "<br><strong>Coexpression:</strong>", Feature)
  ) |>
4  addLegend("topright",
            pal = color_pal_WALS, 
            values = ~Feature, 
            title = "Feature",
            opacity = 0.9)

1: create a leaflet object
2: add Open streetmap tile
3: Set option for the visualisation of the markers (points) on the map. We set the following options: ~Longitude, ~Latitude: based on the coordinate variables in the dataset, fillColor: apply the defined color palette, color the color of the border of the circles, radius: changes the width of the circles, fillOpacity: changes the opacity, with 1 removing the transparency completely, stroke = TRUE: adds a circle around the circles, weight: the width of the circles, label = ~Language: adds the languages as labels to the circles on the map, which appear when hovering over the circles, popup: what appears when hovering over the circles.
4: Adds a legend on the upper left corner, with the correct labels (languages) and specified colours.

Citation & Session Info

Citation

Ludovic De Cuypere. 2025. How to create a typological map in R with leaflet. Vrije Universiteit Brussel & Ghent University. url: https://ladal.edu.au/tutorials/leaflet/leaflet.html (Version 2025.04.02), doi: .

@manual{ludovicdecuypere2025how,
  author       = {Ludovic De Cuypere},
  title        = {How to create a typological map in R with leaflet},
  year         = {2025},
  note         = {https://ladal.edu.au/tutorials/leaflet/leaflet.html},
  organization = {Vrije Universiteit Brussel & Ghent University},
  edition      = {2025.04.02}
  doi      = {}
}

Code

sessionInfo()

R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Australia/Brisbane
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] leaflet_2.2.2   flextable_0.9.7 lubridate_1.9.4 forcats_1.0.0  
 [5] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.4     readr_2.1.5    
 [9] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] generics_0.1.3          fontLiberation_0.1.0    renv_1.1.1             
 [4] xml2_1.3.6              stringi_1.8.4           hms_1.1.3              
 [7] digest_0.6.37           magrittr_2.0.3          evaluate_1.0.3         
[10] grid_4.4.2              timechange_0.3.0        fastmap_1.2.0          
[13] jsonlite_1.9.0          zip_2.3.2               crosstalk_1.2.1        
[16] scales_1.3.0            fontBitstreamVera_0.1.1 jquerylib_0.1.4        
[19] codetools_0.2-20        textshaping_1.0.0       cli_3.6.4              
[22] rlang_1.1.5             fontquiver_0.2.1        munsell_0.5.1          
[25] withr_3.0.2             yaml_2.3.10             gdtools_0.4.1          
[28] tools_4.4.2             officer_0.6.7           uuid_1.2-1             
[31] tzdb_0.4.0              colorspace_2.1-1        vctrs_0.6.5            
[34] R6_2.6.1                lifecycle_1.0.4         htmlwidgets_1.6.4      
[37] ragg_1.3.3              pkgconfig_2.0.3         pillar_1.10.1          
[40] gtable_0.3.6            glue_1.8.0              data.table_1.17.0      
[43] Rcpp_1.0.14             systemfonts_1.2.1       xfun_0.51              
[46] tidyselect_1.2.1        rstudioapi_0.17.1       knitr_1.49             
[49] farver_2.1.2            htmltools_0.5.8.1       rmarkdown_2.29         
[52] compiler_4.4.2          askpass_1.2.1           openssl_2.3.2

AI Transparency Statement

This tutorial was revised and substantially expanded with the assistance of Claude (claude.ai), a large language model created by Anthropic. Claude was used to restructure the document into Quarto format, expand the theoretical introduction, add the new sections and accompanying callouts, expand interpretation guidance across all sections, write the new quiz questions and detailed answer explanations, and produce the comparison summary table. All content was reviewed, edited, and approved by the author (Martin Schweinberger), who takes full responsibility for its accuracy.

Back to HOME

References

Comrie, Bernard. 1998. “Rethinking the Typology of Relative Clauses.” Language Design 1: 59–86.

Dryer, Matthew S., and Martin Haspelmath, eds. 2013. WALS Online (V2020.4). Data set. Zenodo. https://doi.org/10.5281/zenodo.13950591.

--- title: "How to create a typological map in R with leaflet" author: "Ludovic De Cuypere" date: "2025" params: title: "How to create a typological map in R with leaflet" author: "Ludovic De Cuypere" year: "2025" version: "2025.04.02" url: "https://ladal.edu.au/tutorials/leaflet/leaflet.html" institution: "Vrije Universiteit Brussel & Ghent University" description: "This showcase tutorial demonstrates how to create interactive typological and geographical maps in R using the leaflet package, covering the plotting of linguistic data on maps, the addition of markers and popups, and the customisation and publication of interactive map visualisations. It is aimed at researchers in linguistic typology, dialectology, and sociolinguistics." doi: "10.5281/zenodo.19332899" format: html: toc: true toc-depth: 4 code-fold: show code-tools: true theme: cosmo --- ![](/images/uq1.jpg){ width=100% } # Introduction {#introduction .unnumbered} This tutorial will show you how to create a typological map using R. <div class="warning"> To be able to follow this tutorial, we suggest you check out and familiarize yourself with the content of the following **R Basics** and **Data Visualization** tutorials: <ul> <li>[Getting started with R](/tutorials/intror/intror.html) </li> <li>[Loading, saving, and generating data in R](/tutorials/load/load.html) </li> <li>[String Processing in R](/tutorials/string/string.html) </li> <li>[Regular Expressions in R](/tutorials/regex/regex.html) </li> <li>[Handling Tables in R](/tutorials/table/table.html) </li> <li>[Introduction to Data Viz](/tutorials/introviz/introviz.html) </li> <li>[Data Visualization with R](/tutorials/dviz/dviz.html) </li> </ul> </div> **Preparation and session set up** This tutorial is based on R. If you have not installed R or are new to it, you will find an introduction to and more information how to use R [here](https://slcladal.github.io/intror.html). For this tutorials, we need to install certain *packages* from an R *library* so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead and ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time). ```{r} #| label: prep1 #| echo: TRUE #| eval: FALSE #| message: FALSE #| warning: FALSE # set options options(stringsAsFactors = F) # no automatic data transformation options("scipen" = 100, "digits" = 4) # suppress math annotation # install packages install.packages("tidyverse") install.packages("flextable") install.packages("leaflet") ``` Next, we activate the packages. ```{r} #| label: prep2 #| message: FALSE #| warning: FALSE library(tidyverse) library(flextable) library(leaflet) ``` Once you have installed R, RStudio, and have also initiated the session by executing the code shown above, you are good to go. ## Typological data The objective of this tutorial is to use R to create a typological map as in the World Atlas of Linguistic Structures (WALS)[@wals]. The WALS is a large database that documents the structural features of languages around the world. It provides information on a wide range of linguistic characteristics, from phonological traits to grammatical structures. A typical WALS dataset includes information about various languages and specific linguistic features. Each row in the dataset represents a single language, and the columns contain metadata about the language and data about its structural features. For instance, Comrie [-@Comrie-1998] contains typological information about the "Order of Subject and Verb", with three levels: "SV", "VS", and "No dominant order". The related typological map can be found [here](https://wals.info/feature/82A#2/18.0/152.9). To create a typological map, you need a dataset with four variables/columns, as illustrated in @tbl-type: ```{r} data <- data.frame( Language = c("Tiranige", "Amharic", "Hungarian", "Vietnamese", "Indonesian", "..."), Longitude = c(3.70, 39.54, 19.66, 105.77, 109.72, NA), Latitude = c(14.76, 11.71, 46.91, 20.68, -7.33, NA), Feature = c("absent", "other", "absent", "absent", "other", "...") ) ``` ```{r} #| label: tbl-type #| tbl-cap: "The first five rows of a WALS-like typological dataset" ft <- flextable(data) |> set_header_labels( Language = "Language", Longitude = "Longitude", Latitude = "Latitude", Feature = "Feature" ) |> set_table_properties(width = .5, layout = "autofit") |> theme_zebra() |> fontsize(size = 12) |> fontsize(size = 12, part = "header") |> align_text_col(align = "center") |> border_outer() ft ``` The dataset should contain a column/variable with the languages, two columns/variables with the geographical coordinates for each language, i.e., Longitude and Latitude (in decimal numbers, not degrees!), and a columns/variable with the typological feature under analysis. ## A mock dataset Here we create a mock dataset with twenty languages (rows), their geographical coordinates and one hypothetical `Feature` with three levels: "present", "absent" and "other". ```{r} Language <- c("Tiranige", "Amharic", "Hungarian", "Vietnamese", "Indonesian", "Kalaallisut (West Greenlandic)", "Magdalena Peñasco Mixtec", "Cayuga", "Laz", "Japanese", "Buwal", "Yucatec Maya", "Manx", "Forest Enets", "Lopit", "Mojeño Trinitario", "Ulwa (Yaul)", "Japhug", "West Circassian", "Kuuk Thayorre") Longitude <- c(-3.70, 39.54, 19.66, 105.77, 109.72, -52.86, -97.54, -76.60, 41.91, 135.00, 10.62, -88.96, -4.45, 86.50, 32.75, -65.41, 143.99, 102.36, 39.33, 142.03) Latitude <- c(14.76, 11.71, 46.91, 20.68, -7.33, 69.38, 17.28, 42.41, 41.50, 35.00, 13.88, 18.78, 54.26, 68.60, 4.70, -15.33, -4.40, 32.10, 44.00, -14.82) Feature <- c("absent", "other", "absent", "absent", "other", "absent", "absent", "present", "other", "absent", "present", "present", "present", "present", "present", "absent", "other", "present", "present", "present") typ <- data.frame(Language, Longitude, Latitude, Feature) typ$Feature <- as.factor(typ$Feature) ``` Check the first rows: ```{r} head(typ) |> flextable() |> flextable::set_table_properties(width = .5, layout = "autofit") |> flextable::theme_zebra() |> flextable::fontsize(size = 12) |> flextable::fontsize(size = 12, part = "header") |> flextable::align_text_col(align = "center") |> flextable::set_caption(caption = "") |> flextable::border_outer() ``` ## Create map First, we define a WALS-like colour palette (other colour palettes can be used): ```{r} col <- c("#0000dd", # colour for "present" "#dd0000", # colour for "absent" "#ff66ff") # colour for "other" # at this stage the colours are not yet assigned to the levels of Feature! ``` We use `leaflet::colorFactor` to map the colour palette to the levels of the variable `Feature`. ```{r} color_pal_WALS <- colorFactor(palette = col, levels = c("present", "absent", "other")) ``` We are now ready to create a leaflet map. ```{r} leaflet(typ) |> # <1> addTiles() |> # <2> addCircleMarkers( # <3> ~Longitude, ~Latitude, fillColor = ~color_pal_WALS(Feature), color = "white", radius = 8, fillOpacity = 1, stroke = TRUE, weight = 1, label = ~Language, popup = ~paste("Language:", Language, " Coexpression:", Feature) ) |> addLegend("topright", # <4> pal = color_pal_WALS, values = ~Feature, title = "Feature", opacity = 0.9) ``` 1. create a leaflet object 2. add Open streetmap tile 3. Set option for the visualisation of the markers (points) on the map. We set the following options: `~Longitude, ~Latitude`: based on the coordinate variables in the dataset, `fillColor`: apply the defined color palette, `color` the color of the border of the circles, `radius:` changes the width of the circles, `fillOpacity`: changes the opacity, with 1 removing the transparency completely, `stroke = TRUE`: adds a circle around the circles, `weight`: the width of the circles, `label = ~Language`: adds the languages as labels to the circles on the map, which appear when hovering over the circles, `popup`: what appears when hovering over the circles. 4. Adds a legend on the upper left corner, with the correct labels (languages) and specified colours. # Citation & Session Info {.unnumbered} ::: {.callout-note} ## Citation ```{r citation-callout, echo=FALSE, results='asis'} cat( params$author, ". ", params$year, ". *", params$title, "*. ", params$institution, ". ", "url: ", params$url, " ", "(Version ", params$version, "), ", "doi: ", params$doi, ".", sep = "" ) ``` ```{r citation-bibtex, echo=FALSE, results='asis'} key <- paste0( tolower(gsub(" ", "", gsub(",.*", "", params$author))), params$year, tolower(gsub("[^a-zA-Z]", "", strsplit(params$title, " ")[[1]][1])) ) cat("```\n") cat("@manual{", key, ",\n", sep = "") cat(" author = {", params$author, "},\n", sep = "") cat(" title = {", params$title, "},\n", sep = "") cat(" year = {", params$year, "},\n", sep = "") cat(" note = {", params$url, "},\n", sep = "") cat(" organization = {", params$institution, "},\n", sep = "") cat(" edition = {", params$version, "}\n", sep = "") cat(" doi = {", params$doi, "}\n", sep = "") cat("}\n```\n") ``` ::: ```{r fin} sessionInfo() ``` ::: {.callout-note} ## AI Transparency Statement This tutorial was revised and substantially expanded with the assistance of **Claude** (claude.ai), a large language model created by Anthropic. Claude was used to restructure the document into Quarto format, expand the theoretical introduction, add the new sections and accompanying callouts, expand interpretation guidance across all sections, write the new quiz questions and detailed answer explanations, and produce the comparison summary table. All content was reviewed, edited, and approved by the author (Martin Schweinberger), who takes full responsibility for its accuracy. ::: [Back to top](#introduction) [Back to HOME](https://slcladal.github.io/index.html) # References {.unnumbered}