lingtypology: easy mapping for Linguistic Typologylingtypology?The lingtypology package connects R with the Glottolog database (v. 2.7) and provides an additional functionality for linguistic typology. The Glottolog database contains a catalogue of the world’s languages. This package helps researchers to make linguistic maps, using the philosophy of the Cross-Linguistic Linked Data project, which is creating a uniform access to linguistic data across publications. This package is based on the leaflet package, so lingtypology is a package for interactive linguistic mapping. In addition, the package provides an ability to download data from typological databases such as WALS, AUTOTYP and others (see section 4).I would like to thank Natalya Tyshkevich, Samira Verhees and Eugenya Klyagina for reading and correcting some versions of this vignette.
Since lingtypology is an R package, you should install R (version >= 3.1.0) on your PC if you haven’t already done so. To install the lingtypology package, run the following command at your R IDE, so you get the stable version from CRAN:
install.packages("lingtypology", dependencies = TRUE)You can also get the development version from GitHub:
install.packages("devtools")
devtools::install_github("ropensci/lingtypology")Sometimes installation fails because of the absence of the package crosstalk or any other. Just install it using command install.packages("crosstalk").
Load the package:
library(lingtypology)This package is based on the Glottolog database (v. 2.7), so lingtypology has several functions for accessing data from that database.
Most of the functions in lingtypology have the same syntax: what you need.what you have. Most of them are based on language name.
Some of them help to define a vector of languages.
Additionally there are some functions to convert glottocodes to ISO 639-3 codes and vice versa:
The most important functionality of lingtypology is the ability to create interactive maps based on features and sets of languages (see the third section):
Glottolog database (v. 2.7) provides lingtypology with language names, ISO codes, genealogical affiliation, macro area, countries, coordinates, and much information. This set of functions doesn’t have a goal to cover all possible combinations of functions. Check out additional information that is preserved in the version of the Glottolog database used in lingtypology:
names(glottolog.original)## [1] "language" "iso" "glottocode"
## [4] "longitude" "latitude" "affiliation"
## [7] "area" "alternate names" "affiliation-HH"
## [10] "country" "dialects" "language status"
## [13] "language use" "location" "population numeric"
## [16] "typology" "writing"Using R functions for data manipulation you can create your own database for your purpose.
All functions introduced in the previous section are regular functions, so they can take the following objects as input:
iso.lang("Adyghe")## Adyghe
## "ady"lang.iso("ady")## ady
## "Adyghe"country.lang("Adyghe")## Adyghe
## "Turkey, United States, Israel, Australia, Egypt, Macedonia, France, Russia, Netherlands, Germany, Syria, Jordan, Iraq"lang.aff("West Caucasian")## [1] "Adyghe" "Abkhaz" "Abaza" "Ubykh" "Kabardian"I would like to point out that you can create strings in R using single or double quotes. Since inserting single quotes in a string created with single quotes causes an error in R, I use double quotes in my tutorial. You can use single quotes, but be careful and remember that 'Ma'ya' is an incorrect string in R.
area.lang(c("Adyghe", "Aduge"))## Adyghe Aduge
## "Eurasia" "Africa"lang <- c("Adyghe", "Russian")
aff.lang(lang)## Adyghe
## "North Caucasian, West Caucasian, Circassian"
## Russian
## "Indo-European, Slavic, East"iso.lang(lang.aff("Circassian"))## Adyghe Kabardian
## "ady" "kbd"If you are new to R, it is important to mention that you can create a table with languages, features and other parametres with any spreadsheet software you used to work. Then you can import the created file to R using standard tools.
The behavior of most functions is rather predictable, but the function country.lang has an additional feature. By default this function takes a vector of languages and returns a vector of countries. But if you set the argument intersection = TRUE, then the function returns a vector of countries where all languages from the query are spoken.
country.lang(c("Udi", "Laz"))## Udi
## "Russia, Georgia, Azerbaijan, Turkmenistan"
## Laz
## "Turkey, Georgia, France, United States, Germany, Belgium"country.lang(c("Udi", "Laz"), intersection = TRUE)## [1] "Georgia"There are some functions that take country names as input. Unfortunately, some countries have alternative names. In order to save users the trouble of having to figure out the exact name stored in the database (for example Ivory Coast or Cote d’Ivoire), all official country names and standard abbreviations are stored in the database:
lang.country("Cape Verde")## [1] "Kabuverdianu" "Portuguese"lang.country("Cabo Verde")## [1] "Kabuverdianu" "Portuguese"head(lang.country("USA"))## [1] "Holikachuk" "Hopi" "Palewyami Yokuts"
## [4] "Finnish" "Mbum" "Lower Sorbian"All functions which take a vector of languages are enriched with a kind of a spell checker. If a language from a query is absent in the database, functions return a warning message containing a set of candidates with the minimal Levenshtein distance to the language from the query.
aff.lang("Adyge")## Warning: Language Adyge is absent in our version of the Glottolog database.
## Did you mean Adyghe, Aduge?## Adyge
## NAUnfortunately, the Glottolog database (v. 2.7) is not perfect for all my tasks, so I changed it a little bit:
More detailed information about how our database was created can be seen from GitHub folder.
After Robert Forkel’s issue I decided to add an argument glottolog.source, so that everybody has access to “original” and “modified” (by default) glottolog versions:
is.glottolog(c("Abkhaz", "Abkhazian"), glottolog.source = "original")## [1] FALSE TRUEis.glottolog(c("Abkhaz", "Abkhazian"), glottolog.source = "modified")## [1] TRUE FALSEIt is common practice in R to reduce both function arguments and its values, so this can also be done with the following lingtypology functions.
is.glottolog(c("Abkhaz", "Abkhazian"), g = "o")## [1] FALSE TRUEis.glottolog(c("Abkhaz", "Abkhazian"), g = "m")## [1] TRUE FALSEmap.featureThe most important part of the lingtypology package is the function map.feature. This function allows you to produce maps similar to known projects within the Cross-Linguistic Linked Data philosophy, such as WALS and Glottolog:
map.feature(c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"))As shown in the picture above, this function generates an interactive Leaflet map. All specific points on the map have a pop-up box that appears when markers are clicked (see section 3.3 for more information about editing pop-up boxes). By default, they contain language names linked to the glottolog site.
If for some reasons you are not using RStudio or you want to automatically create and save a lot of maps, you can save a map to a variable and use the htmlwidgets package for saving created maps to an .html file. I would like to thank Timo Roettger for mentioning this problem.
m <- map.feature(c("Adyghe", "Korean"))
# install.packages("htmlwidgets")
library(htmlwidgets)
saveWidget(m, file="TYPE_FILE_PATH/m.html")There is an export button in RStudio, but for some reason it is not so easy to save a map as a .png or .jpg file using code. Here is a possible solution.
The goal of this package is to allow typologists (or any other linguists) to map language features. A list of languages and correspondent features can be stored in a data.frame as follows:
df <- data.frame(language = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
features = c("polysynthetic", "polysynthetic", "fusional", "fusional", "fusional"))
df## language features
## 1 Adyghe polysynthetic
## 2 Kabardian polysynthetic
## 3 Polish fusional
## 4 Russian fusional
## 5 Bulgarian fusionalNow we can draw a map:
map.feature(languages = df$language,
features = df$features)If you have a lot of features and they appear in the legend in a senseless order (by default it is ordered alphabetically), you can reorder them using factors (a vector with ordered levels, for more information see ?factor). For example, I want the feature polysynthetic to be listed first, followed by fusional:
df$features <- factor(df$features, levels = c("polysynthetic", "fusional"))
map.feature(languages = df$language, features = df$features)Like in most R functions, it is not necessary to name all arguments, so the same result can be obtained by:
map.feature(df$language, df$features)As shown in the picture above, all points are grouped by feature, colored and counted. As before, a pop-up box appears when markers are clicked. A control feature allows users to toggle the visibility of points grouped by feature.
There are several types of variables in R and map.feature works differently depending on the variable type. I will use a build in data set ejective_and_n_consonants that contains 27 languages from LAPSyD database. This dataset have two variables: the categorical variable ejectives indicates whether some language has any ejective sound, the numeric variable consonants contains information about the number of consonants (based on LAPSyD database). We can create two maps with categorical variable and with numeric variable:
map.feature(ejective_and_n_consonants$language,
ejective_and_n_consonants$ejectives) # categoricalmap.feature(ejective_and_n_consonants$language,
ejective_and_n_consonants$consonants) # numericDefault colors are not perfect for this goal, but the main point is clear. For creating a correct map, you should correctly define the type of the variable.
This dataset also can be used to show one other parameter of the map.feature function. There are two possible ways to show the World map: with the Atlantic sea or with the Pacific sea in the middle. If you don’t need default Pacific view use the map.orientation parameter (thanks @languageSpaceLabs and @tzakharko for that idea):
map.feature(ejective_and_n_consonants$language,
ejective_and_n_consonants$consonants,
map.orientation = "Atlantic")Sometimes it is a good idea to add some additional information (e.g. language affiliation, references or even examples) to pop-up boxes that appear when points are clicked. In order to do so, first of all we need to create an extra vector of strings in our dataframe:
df$popup <- aff.lang(df$language)The function aff.lang() creates a vector of genealogical affiliations that can be easily mapped:
map.feature(languages = df$language, features = df$features, popup = df$popup)Pop-up strings can contain HTML tags, so it is easy to insert a link, a couple of lines, a table or even a video and sound. Here is how pop-up boxes can demonstrate language examples:
# change a df$popup vector
df$popup <- c ("sɐ s-ɐ-k'ʷɐ<br> 1sg 1sg.abs-dyn-go<br>'I go'",
"sɐ s-o-k'ʷɐ<br> 1sg 1sg.abs-dyn-go<br>'I go'",
"id-ę<br> go-1sg.npst<br> 'I go'",
"ya id-u<br> 1sg go-1sg.npst <br> 'I go'",
"id-a<br> go-1sg.prs<br> 'I go'")
# create a map
map.feature(df$language,
features = df$features,
popup = df$popup)How to say moon in Sign Languages? Here is an example:
# Create a dataframe with links to video
sign_df <- data.frame(languages = c("American Sign Language", "Russian Sign Language", "French Sign Language"),
popup = c("https://media.spreadthesign.com/video/mp4/13/48600.mp4", "https://media.spreadthesign.com/video/mp4/12/17639.mp4", "https://media.spreadthesign.com/video/mp4/10/17638.mp4"))
# Change popup to an HTML code
sign_df$popup <- paste("<video width='200' height='150' controls> <source src='",
as.character(sign_df$popup),
"' type='video/mp4'></video>", sep = "")
# create a map
map.feature(languages = sign_df$languages, popup = sign_df$popup)An alternative way to add some short text to a map is to use the label option.
map.feature(df$language, df$features,
label = df$language)There are some additional arguments for customization: label.fsize for setting font size, label.position for controlling the label position, and label.hide to control the appearance of the label: if TRUE, the labels are displayed on mouse over (as on the previous map), if FALSE, the labels are always displayed (as on the next map).
map.feature(df$language, df$features,
label = df$language,
label.fsize = 20,
label.position = "left",
label.hide = FALSE)There is an additional tool for emphasis of some points on the map. The argument label.emphasize allows to emphasize selected points with the color specified by a user.
map.feature(df$language, df$features,
label = df$language,
label.fsize = 20,
label.position = "left",
label.hide = FALSE,
label.emphasize = list(2:4, "red"))In this example the first vector of the list in the label.emphasize argument is vector 2:4 that produce elements 2, 3 and 4. You can create your own selected rows. e. g. c(1, 3, 4). The second vector of the list is the string with a color.
You can set your own coordinates using the arguments latitude and longitude. It is important to note, that lingtypology works only with decimal degrees (something like this: 0.1), not with degrees, minutes and seconds (something like this: 0° 06′ 0″). I will illustrate this with the dataset circassian built into the lingtypology package. This dataset comes from fieldwork collected during several expeditions in the period 2011-2016 and contains a list of Circassian villages:
map.feature(languages = circassian$language,
features = circassian$dialect,
popup = circassian$village,
latitude = circassian$latitude,
longitude = circassian$longitude)By default the color palette is created by the rainbow() function, but you can set your own colors using the argument color:
df <- data.frame(language = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
features = c("polysynthetic", "polysynthetic", "fusional", "fusional", "fusional"))
map.feature(languages = df$language,
features = df$features,
color = c("yellowgreen", "navy"))Arguments from RColorBrewer or viridis also can be used as a color argument:
map.feature(ejective_and_n_consonants$language,
ejective_and_n_consonants$consonants,
color = "magma")The package can generate a control box that allows users to toggle the visibility of points and features. To enable it, there is an argument control in the map.feature function:
map.feature(languages = df$language,
features = df$features,
control = TRUE)The map.feature function has an additional argument stroke.features. Using this argument it becomes possible to show two independent sets of features on one map. By default strokes are colored in grey (so for two levels it will be black and white, for three — black, grey, white end so on), but you can set your own colors using the argument stroke.color:
map.feature(circassian$language,
features = circassian$dialect,
stroke.features = circassian$language,
latitude = circassian$latitude,
longitude = circassian$longitude)It is important to note that stroke.features can work with NA values. The function won’t plot anything if there is an NA value. Let’s set a language value to NA in all Baksan villages from the circassian dataset.
# create newfeature variable
newfeature <- circassian[,c(5,6)]
# set language feature of the Baksan villages to NA and reduce newfeature from dataframe to vector
newfeature <- replace(newfeature$language, newfeature$language == "Baksan", NA)
# create a map
map.feature(circassian$language,
features = circassian$dialect,
latitude = circassian$latitude,
longitude = circassian$longitude,
stroke.features = newfeature)All markers have their own radius and opacity, so you can set it. Just use the arguments radius, stroke.radius, opacity and stroke.opacity:
map.feature(circassian$language,
features = circassian$dialect,
stroke.features = circassian$language,
latitude = circassian$latitude,
longitude = circassian$longitude,
width = 7, stroke.radius = 13)map.feature(circassian$language,
features = circassian$dialect,
stroke.features = circassian$language,
latitude = circassian$latitude,
longitude = circassian$longitude,
opacity = 0.7, stroke.opacity = 0.6)By default the legend appears in the bottom left corner. If there are stroke features, two legends are generated. There are additional arguments that control the appearence and the title of the legends.
map.feature(circassian$language,
features = circassian$dialect,
stroke.features = circassian$language,
latitude = circassian$latitude,
longitude = circassian$longitude,
legend = FALSE, stroke.legend = TRUE)map.feature(circassian$language,
features = circassian$dialect,
stroke.features = circassian$language,
latitude = circassian$latitude,
longitude = circassian$longitude,
title = "Circassian dialects", stroke.title = "Languages")legend.position and stroke.legend.position allow you to change a legend’s position using “topright”, “bottomright”, “bottomleft” or “topleft” strings.
A scale bar is automatically added to a map, but you can control its appearance (set scale.bar argument to TRUE or FALSE) and its position (use scale.bar.position argument values “topright”, “bottomright”, “bottomleft” or “topleft”).
map.feature(c("Adyghe", "Polish", "Kabardian", "Russian"),
scale.bar = TRUE,
scale.bar.position = "topright")It is possible to use different tiles on the same map using the tile argument. For more tiles see here.
df <- data.frame(lang = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
feature = c("polysynthetic", "polysynthetic", "fusion", "fusion", "fusion"),
popup = c("Adyghe", "Adyghe", "Slavic", "Slavic", "Slavic"))
map.feature(df$lang, df$feature, df$popup,
tile = "Thunderforest.OpenCycleMap")It is possible to use different map tiles on the same map. Just add a vector with tiles.
df <- data.frame(lang = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
feature = c("polysynthetic", "polysynthetic", "fusion", "fusion", "fusion"),
popup = c("Adyghe", "Adyghe", "Slavic", "Slavic", "Slavic"))
map.feature(df$lang, df$feature, df$popup,
tile = c("OpenStreetMap.BlackAndWhite", "Thunderforest.OpenCycleMap"))It is possible to name tiles using the tile.name argument.
df <- data.frame(lang = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
feature = c("polysynthetic", "polysynthetic", "fusion", "fusion", "fusion"),
popup = c("Adyghe", "Adyghe", "Slavic", "Slavic", "Slavic"))
map.feature(df$lang, df$feature, df$popup,
tile = c("OpenStreetMap.BlackAndWhite", "Thunderforest.OpenCycleMap"),
tile.name = c("b & w", "colored"))It is possible to combine the tiles’ control box with the features’ control box.
df <- data.frame(lang = c("Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"),
feature = c("polysynthetic", "polysynthetic", "fusion", "fusion", "fusion"),
popup = c("Adyghe", "Adyghe", "Slavic", "Slavic", "Slavic"))
map.feature(df$lang, df$feature, df$popup,
tile = c("OpenStreetMap.BlackAndWhite", "Thunderforest.OpenCycleMap"),
control = TRUE)It is possible to add a minimap to a map.
map.feature(c("Adyghe", "Polish", "Kabardian", "Russian"),
minimap = TRUE)You can control its appearance (by setting the minimap argument to TRUE or FALSE), its position (by using the values “topright”, “bottomright”, “bottomleft” or “topleft” of the minimap.position argument) and its height and width (with the arguments minimap.height and minimap.width).
map.feature(c("Adyghe", "Polish", "Kabardian", "Russian"),
minimap = TRUE,
minimap.position = "topright",
minimap.height = 100,
minimap.width = 100)This part is created using the beutifull leaflet.minicharts library. The argument minichart allows you to add piechart or barplot instead of standard point markers. In this part I will use a build in data set ejective_and_n_consonants that contains 19 languages from UPSyD database. Here is an example of barplot:
map.feature(languages = ejective_and_n_consonants$language,
minichart.data = ejective_and_n_consonants[, c("vowels", "consonants")],
minichart = "bar")Here is an example of piechart:
map.feature(languages = ejective_and_n_consonants$language,
minichart.data = ejective_and_n_consonants[, c("vowels", "consonants")],
minichart = "pie")Colors and opacity could be changed, legend moved:
map.feature(languages = ejective_and_n_consonants$language,
minichart.data = ejective_and_n_consonants[, c("vowels", "consonants")],
minichart = "bar",
color = c("yellowgreen", "navy"),
opacity = 0.7,
label = ejective_and_n_consonants$language,
legend.position = "topleft")It is possible to add values using argument minichart.labels:
map.feature(languages = ejective_and_n_consonants$language,
minichart.data = ejective_and_n_consonants[, c("vowels", "consonants")],
minichart = "pie",
minichart.labels = TRUE)It is possible to highlight some part of your map with a rectangle. You need to provide a latitude and longitude of the diagonal (rectangle.lat and rectangel.lng) and color of the rectangle (rectangle.color):
map.feature(circassian$language,
circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
rectangle.lng = c(42.7, 45),
rectangle.lat = c(42.7, 44.3),
rectangle.color = "blue")Sometimes it is easier to look at a density contour plot. It can be created using density.estimation argument:
map.feature(circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = circassian$language)Density estimation plot can be colored by :
map.feature(circassian$language,
features = circassian$dialect,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = circassian$language)It is possible to remove points and display only the kernal density estimation plot, using the density.points argument:
map.feature(circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = circassian$language,
density.points = FALSE)It is possible to change kernal density estimation plot opacity using thedensity.estimation.opacity argument:
map.feature(circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = circassian$language,
density.estimation.opacity = 0.9)Since this type of visualisation is based on the kernal density estimation, there are parameters density.longitude.width and density.latitude.width that increase/decrease area:
map.feature(circassian$language,
features = circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = "Circassian",
density.longitude.width = 0.3,
density.latitude.width = 0.3,
color = c("darkgreen", "blue"))map.feature(circassian$language,
features = circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = "Circassian",
density.longitude.width = 0.7,
density.latitude.width = 0.7,
color = c("darkgreen", "blue"))map.feature(circassian$language,
features = circassian$language,
longitude = circassian$longitude,
latitude = circassian$latitude,
density.estimation = "Circassian",
density.longitude.width = 1.3,
density.latitude.width = 0.9,
color = c("darkgreen", "blue"))It is important to note, that this type of visualization have some shortcomings. The kernel density estimation is calculated without any adjustment, so longitude and latitude values used as a values in Cartesian coordinate system. To reduce consequences of that solution it is better to use a different coordinate projection. That allows not to treat Earth as a flat object.
lingtypology provides an ability to download data from these typological databases
All database function names have identical structure: database_name.feature. All functions have as first argument feature. All functions create dataframe with column language that can be used in map.feature() function. It should be noted that all functions cut out the data that can’t be maped, so if you want to prevent functions from this behaviour set argument na.rm to FALSE.
The names of the WALS features can be typed in a lower case. This function preserves coordinates from WALS, so you can map coordinates from the WALS or use coordinates from lingtypology.
df <- wals.feature(c("1a", "20a"))
head(df)## wals.code latitude longitude 1a
## 1 abi -29.00000 -61.00000 Moderately small
## 2 abk 43.08333 41.00000 Large
## 3 abm 32.33333 -87.41667 Small
## 4 ach -25.25000 -55.16667 Small
## 5 acm 41.50000 -121.00000 Moderately small
## 6 aco 34.91667 -107.58333 Large
## 20a glottocode language
## 1 Exclusively concatenative abip1241 Abipon
## 2 Exclusively concatenative abkh1244 Abkhaz
## 3 <NA> alab1237 Alabama
## 4 <NA> ache1246 Ache (Tupian)
## 5 <NA> achu1247 Achumawi
## 6 Exclusively concatenative west2632 Western Keresmap.feature(df$language,
features = df$`1a`,
latitude = df$latitude,
longitude = df$longitude,
label = df$language,
title = "Consonant Inventories")The AUTOTYP features are listed on the GitHub page. You can use more human way with spaces.
df <- autotyp.feature(c('Gender', 'Numeral classifiers'))
head(df)## LID Gender.n Gender.binned4 Gender.Presence NumClass.n NumClass.Presence
## 1 6 2 2 genders TRUE 0 FALSE
## 2 7 NA FALSE 0 FALSE
## 3 9 NA FALSE 9 TRUE
## 4 10 NA FALSE 0 FALSE
## 5 12 NA FALSE 2 TRUE
## 6 14 NA TRUE 0 FALSE
## Glottocode language
## 1 ambu1247 Ambulas
## 2 abkh1244 Abkhaz
## 3 achi1257 Achinese
## 4 west2632 Western Keres
## 5 ainu1240 Hokkaido Ainu
## 6 alam1246 Alamblakmap.feature(df$language,
features = df$NumClass.Presence,
label = df$language,
title = "Presence of Numeral Classifiers")## Warning: There is no coordinates for languages Akkadian, Hittite, West !
## XoonI used only four features from PHOIBLE: the number of phonemes, the number of consonants, the number of tones and the number of vowels. If you need only a set of them, just specify it in the features argument. Since there is a lot of doubling information in the PHOIBLE database, there is an argument source.
df <- phoible.feature(source = "UPSID")
head(df)## source phonemes consonants tones vowels language
## 198 UPSID 36 27 0 9 Afade
## 199 UPSID 23 17 0 6 Achumawi
## 200 UPSID 25 18 0 7 Adzera
## 201 UPSID 30 27 0 3 Eastern Arrernte
## 202 UPSID 20 15 0 5 Amele
## 203 UPSID 21 12 0 9 Angaatahamap.feature(df$language,
features = df$phonemes,
label = df$language,
title = "Number of Phonemes")## Warning: There is no coordinates for languages Dafla, Garrwan, GelaoThe AfBo database has a lot of features that distinguish affix functions, but again you can use a bare function without any arguments to download the whole database. There will be no difference in time, since this function downloads the whole database to your PC. The main destinction is that this database provides recipient and donor languages, so other column names should be used.
df <- afbo.feature(c("adjectivizer", "adverbializer"))
head(df)## Recipient.name Donor.name reliability adjectivizer adverbializer
## 4 Sakha Halh Mongolian high 4 1
## 5 Vlax Romani Romanian high 1 NA
## 7 Tajik Northern Uzbek mid NA 1
## 13 Gheg Albanian Serbian high 4 NA
## 14 Cajamarca Quechua Spanish high 3 NA
## 22 Gheg Albanian Turkish high 1 NAmap.feature(df$Recipient.name,
features = df$adjectivizer,
label = df$Recipient.name,
title = "Borrowed adjectivizer affixes")The SAILS database provide a lot of features, so the function work with their ids:
df <- sails.feature(features = "ics10")
head(df)## language ics10_value ics10_description latitude longitude
## 1 Achagua 1 Yes 4.386490 -72.20050
## 2 Apolista 1 Yes -14.830000 -68.66000
## 3 Apurina 1 Yes -8.216920 -66.77141
## 4 Arhuaco 0 No 10.701840 -73.63629
## 5 Ashaninka 0 No -11.931090 -73.90728
## 6 Awa-Cuaiquer 0 No 1.216525 -78.34014map.feature(df$language,
features = df$ics10_description,
longitude = df$longitude,
latitude = df$latitude,
label = df$language,
title = "Are there numeral classifiers?")The ABVD database is a lexical database, so it is different from clld databases. First of all, ABVD has its own language classification ids. The information about the same language from different sources can be received from these database different ids. So I select several languages and map them coloring by word with the meaning ‘hand’.
df <- abvd.feature(50:55)
head(df)## word_id word item language
## 1 1 hand fahan Buru (Indonesia)
## 2 2 left eʔbali Buru (Indonesia)
## 3 3 right eʔwana Buru (Indonesia)
## 4 4 leg/foot kadan Buru (Indonesia)
## 5 5 to walk iko Buru (Indonesia)
## 6 6 road/path tohon Buru (Indonesia)new_df <- df[df$word == "hand",]
map.feature(new_df$language,
features = new_df$item,
label = new_df$language)dplyr integrationIt is possible to use dplyr functions and pipes with lingtypology. It is widely used, so I will give some examples, how to use it with thelingtypology package. Using query “list of languages csv” I found Vincent Garnier’s languages-list repository. Let’s download and map all the languages from that set. First download the data:
new_data <- read.csv("https://goo.gl/GgscBE")
tail(new_data)## X639.1 X639.2.T X639.2.B Language.name Native.name
## 180 xh xho xho Xhosa isiXhosa
## 181 yi yid yid Yiddish ייִדיש
## 182 yo yor yor Yoruba Yorùbá
## 183 za zha zha Zhuang, Chuang Saɯ cueŋƅ, Saw cuengh
## 184 zh zho chi Chinese 中文 (Zhōngwén), 汉语, 漢語
## 185 zu zul zul Zulu isiZuluAs we see, some values of the Language.name variable contain more than one language name. Some of the names probably have different names in our database. Imagine that we want to map all languages from Africa. So that the following examples work correctly, use library(dplyr).
library(dplyr)
new_data %>%
mutate(Language.name = gsub(pattern = " ", replacement = "", Language.name)) %>%
filter(is.glottolog(Language.name) == TRUE) %>%
filter(area.lang(Language.name) == "Africa") %>%
select(Language.name) %>%
map.feature()We start with a dataframe, here a new_data. First we remove spaces at the end of each string. Then we check, whether the language names are in the glottolog database. Then we select only rows that contain languages of Africa. Then we select the Language.name variable. And the last line maps all selected languages.
By default, the values that came from the pipe are treated as the first argument of a function. But when there are some additional arguments, point sign specify what exact position should be piped to. Let’s produce the same map with a minimap.
new_data %>%
mutate(Language.name = gsub(pattern = " ", replacement = "", Language.name)) %>%
filter(is.glottolog(Language.name) == TRUE) %>%
filter(area.lang(Language.name) == "Africa") %>%
select(Language.name) %>%
map.feature(., minimap = TRUE)lingtyplogyIt is important to cite R and R packages when you use them. For this purpose use the citation function:
citation("lingtypology")##
## Moroz G (2017). _lingtypology: easy mapping for Linguistic
## Typology_. <URL: https://CRAN.R-project.org/package=lingtypology>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {lingtypology: easy mapping for Linguistic Typology},
## author = {George Moroz},
## year = {2017},
## url = {https://CRAN.R-project.org/package=lingtypology},
## }