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).
# 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.
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.
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:
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", "...")
)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()
ftLanguage | 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.
Here we create a mock dataset with twenty languages (rows), their geographical coordinates and one hypothetical Feature with three levels: “present”, “absent” and “other”.
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:
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 |
First, we define a WALS-like colour palette (other colour palettes can be used):
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.
color_pal_WALS <- colorFactor(palette = col,
levels = c("present", "absent", "other"))We are now ready to create a leaflet map.
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)~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.
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 3.1.1). doi: 10.5281/zenodo.19332898.
@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 = {3.1.1}
doi = {10.5281/zenodo.19332898}
}sessionInfo()R version 4.6.1 (2026-06-24 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
time zone: Australia/Brisbane
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] leaflet_2.2.3 flextable_0.10.3 lubridate_1.9.5 forcats_1.0.1
[5] stringr_1.6.0 dplyr_1.2.1 purrr_1.2.2 readr_2.2.0
[9] tidyr_1.3.2 tibble_3.3.1 ggplot2_4.0.3 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] generics_0.1.4 fontLiberation_0.1.0 xml2_1.6.0
[4] stringi_1.8.9 hms_1.1.4 digest_0.6.39
[7] magrittr_2.0.5 evaluate_1.0.5 grid_4.6.1
[10] timechange_0.4.0 RColorBrewer_1.1-3 fastmap_1.2.0
[13] jsonlite_2.0.0 zip_3.0.2 crosstalk_1.2.2
[16] scales_1.4.0 fontBitstreamVera_0.1.1 jquerylib_0.1.4
[19] textshaping_1.0.5 codetools_0.2-20 cli_3.6.6
[22] rlang_1.3.0 fontquiver_0.2.1 withr_3.0.3
[25] yaml_2.3.12 otel_0.2.0 gdtools_0.5.2
[28] officer_0.7.7 tools_4.6.1 uuid_1.2-2
[31] tzdb_0.5.0 vctrs_0.7.3 R6_2.6.1
[34] lifecycle_1.0.5 htmlwidgets_1.6.4 ragg_1.5.2
[37] pkgconfig_2.0.3 pillar_1.11.1 gtable_0.3.6
[40] glue_1.8.1 data.table_1.18.6.1 Rcpp_1.1.2
[43] systemfonts_1.3.2 xfun_0.62 tidyselect_1.2.1
[46] rstudioapi_0.19.0 knitr_1.52 farver_2.1.2
[49] patchwork_1.3.2 htmltools_0.5.9 rmarkdown_2.32
[52] compiler_4.6.1 S7_0.2.2 askpass_1.2.1
[55] openssl_2.4.2 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.