George Moroz

As all other types of visualization, linguistic mapping has two main
goals: data presentation and data analysis. The most common purpose for
which linguistic maps are used, is simply pointing to the location of
one or more languages of interest (presentation). A more sophisticated
task is showing the distribution of particular linguistic features or
their combination among languages of a certain area (presentation and
analysis). There are three linguistic subdisciplines that use maps for
visualization: linguistic typology, areal linguistics and dialectology.
`lingtypology` makes it easier to create all kinds of linguistic maps
simplifying both: data presentation and data analysis.

The `lingtypology` package connects the [Glottolog database (v.
2.7)](https://glottolog.org/), which is a catalogue of the world's
languages with the [leaflet package](https://rstudio.github.io/leaflet/)
for interactive maps. This package helps researchers to make linguistic
maps, using the philosophy of [the Cross-Linguistic Linked Data
project](https://clld.org/), which is creating uniform access to
linguistic data. Let me walk you through some of its functionalities!

## 1. Installation

The package is available via
[CRAN](https://CRAN.R-project.org/package=lingtypology):

::: {.highlight}
``` {.chroma tabindex="0"}
install.packages("lingtypology", dependencies = TRUE)
```
:::

However, as the package is still under construction, I would suggest
users to download the development version instead, which is available at
Github and can be accessed using
[devtools](https://CRAN.R-project.org/package=devtools):

::: {.highlight}
``` {.chroma tabindex="0"}
devtools::install_github("ropensci/lingtypology")
```
:::

Since `lingtypology` depends on the [`leaflet`
package](https://rstudio.github.io/leaflet/), it will work only with R
version greater than or equal 3.1.0.

Load the package:

::: {.highlight}
``` {.chroma tabindex="0"}
library("lingtypology")
```
:::

## 2. Simple maps

The main function for creating maps is `map.feature()`. Let's create a
map with some languages:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(languages = c("Irish", "Danish", "Rusyn", "Bebe"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/01.png)

By using just one line we have created an interactive leaflet map. Each
point on the map has a pop-up box that appears when the marker is
clicked. The pop-up box by default contains the name of the language
with a link to its page on Glottolog.  
There are several functions in `lingtypology` for finding a language by
affiliation, ISO-code or Glottolog code. For example, if you are
interested in Lezgic languages and don't want to list all of them
separately, you can access them through the database with the following
function:

::: {.highlight}
``` {.chroma tabindex="0"}
lang.aff("Lezgic")
```
:::

``` {tabindex="0"}
## [1] "Tsakhur"   "Aghul"     "Lezgian"   "Kryz"      "Rutul"    ## [6] "Archi"     "Udi"       "Budukh"    "Tabasaran"
```

Most of the functions in `lingtypology` have the same syntax: **what you
need.what you have**. So you can search for additional information such
as area, country name, ISO code or glottolog code using the same syntax.
See the [package
manual](https://docs.ropensci.org/lingtypology/#2_glottolog_functions)
for a full list of functions.

::: {.highlight}
``` {.chroma tabindex="0"}
area.lang(c("Bebe", "Irish")) #area
```
:::

``` {tabindex="0"}
##      Bebe     Irish ##  "Africa" "Eurasia"
```

::: {.highlight}
``` {.chroma tabindex="0"}
iso.lang(c("Adyghe", "Koasati")) # ISO code
```
:::

``` {tabindex="0"}
##  Adyghe Koasati ##   "ady"   "cku"
```

::: {.highlight}
``` {.chroma tabindex="0"}
gltc.lang(c("Nihali", "Malali")) # Glottolog code
```
:::

``` {tabindex="0"}
##     Nihali     Malali ## "niha1238" "mala1432"
```

Using these functions you can do incredible things and easily create
maps that haven't been created before. Here is my favourite map with
showing all Sign languages from the database.

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(lang.aff("Sign"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/02.png)

If you have a map with a lot of points it can be tiresome to have to
click on each point in order to know which language it respresents. For
this purpose, the 'label' argument can be used: it labels each point
with a certain parameter, for example the name of the languages.

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(lang.aff("Sign"), label = lang.aff("Sign"))
```
:::

On this map, the labels are displayed on mouse over, but it is also
possible to make the labels visible at all times. In this case I used
the same vector as a language set, but it is possible to use any other
vector (see example in the end of section 3.2).

## 3. Show language features with a map

The main goal of the `lingtypology` package is showing different
language features on a map. Let's make an example using the built-in
data set `ejective_and_n_consonants`, which contains 27 languages from
the [LAPSyD database](http://www.lapsyd.ddl.cnrs.fr/lapsyd//). The
dataset has two variables: the categorical variable `ejectives`
indicates whether languages have any ejective
sounds^[1](#fn:1){.footnote-ref role="doc-noteref"}^, and the numeric
variable `consonants` contains information about the number of
consonants (based on [LAPSyD
database](http://www.lapsyd.ddl.cnrs.fr/lapsyd//)). I'll make a little
change in a dataset for my purposes:

::: {.highlight}
``` {.chroma tabindex="0"}
df <- ejective_and_n_consonantsdf$ejectives <- ifelse(df$ejectives == "yes", "with ejectives", "without ejectives")
```
:::

### 3.1 Colors

There is a `feature` argument that automatically colors points:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$ejectives,            label = df$language)
```
:::

![](/img/blog-images/2017-05-16-lingtypology/04.png)

You can change colors to your taste by the `color` argument:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$ejectives,            label = df$language,            color = c("blue", "tomato"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/05.png)

The color scheme works differently depending on the variable type. Let's
create a map based on a numeric variable, for example:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$consonants,            label = df$language)
```
:::

![](/img/blog-images/2017-05-16-lingtypology/06.png)

Again, the colors can be customized:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$consonants,            label = df$language,            color = c("blue", "tomato"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/07.png)

There are two possible ways to show the World map: either with the
Atlantic Ocean or with the Pacific Ocean in the middle. If you don't
want the default Pacific view, use the `map.orientation` parameter
(thanks rOpenSci reviewers for this idea):

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$consonants,            label = df$language,            color = c("blue", "tomato"),            map.orientation = "Atlantic")
```
:::

![](/img/blog-images/2017-05-16-lingtypology/08.png)

### 3.2 Strokes

It is possible to show two independent sets of features on one map using
an additional argument `stroke.features`.

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$consonants,            stroke.features = df$ejectives,            label = df$language,            color = c("white", "red"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/09.png)

It looks like languages with ejective sounds tend to have more
consonants! Information about the number of consonants can be added to
the labels:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(df$language,            features = df$consonants,            stroke.features = df$ejectives,            label = paste(df$language, df$consonants),            color = c("white", "red"))
```
:::

![](/img/blog-images/2017-05-16-lingtypology/10.png)

Strokes are a really nice tool, because at a certain density of
parameters they merge and create a larger area with the same color.

### 3.3 Pop-ups

Sometimes it is a good idea to add some additional information (e.g.
language affiliation, references or even examples) to the pop-up boxes
that appear when the points are clicked. Pop-ups are like tiny HTML
pages, so we can even add videos!  
How to say *moon* in different Sign Languages for example? In order to
show this on a map, first of all we need to create a dataframe with
links to videos:

::: {.highlight}
``` {.chroma tabindex="0"}
sign_df <- data.frame(languages = c("American Sign Language",                                    "Russian Sign Language",                                    "French Sign Language"),                      popup = c("https://goo.gl/La2Oab",                                "https://goo.gl/lYVYbx",                                "https://goo.gl/YRsqva"))
```
:::

Then we need to change the links to HTML codes:

::: {.highlight}
``` {.chroma tabindex="0"}
sign_df$popup <- paste0("<video width='200' height='150' controls> <source src='",                        as.character(sign_df$popup),                        "' type='video/mp4'></video>")
```
:::

Now we can create a map:

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(sign_df$languages,            popup = sign_df$popup)
```
:::

![](/img/blog-images/2017-05-16-lingtypology/11.png)

### 3.4 Contour plot

It is possible to add isoglosses to a map using contour plots that are
based on a kernel density estimation.  
I will use a built-in dataset of Circassian villages in Russia that
contains the genealogical affiliation of the language spoken in the
respective villages, their geographical coordinates and the names of the
districts in which they are located. Now let's divide the villages into
two regions based on the branch of Circassian that is spoken.

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(circassian$language,            features = circassian$language,            popup = circassian$village,            latitude = circassian$latitude,            longitude = circassian$longitude,            color = c("darkgreen", "navy"),            density.estimation = "Circassian")
```
:::

![](/img/blog-images/2017-05-16-lingtypology/12.png)

It is possible to change what is treated as the same area using
`density.longitude.width` and `density.latitude.width` arguments.

::: {.highlight}
``` {.chroma tabindex="0"}
map.feature(circassian$language,            features = circassian$language,            longitude = circassian$longitude,            latitude = circassian$latitude,            color = c("darkgreen", "navy"),            density.estimation = "Circassian",            density.longitude.width = 2,            density.latitude.width =0.8)
```
:::

![](/img/blog-images/2017-05-16-lingtypology/13.png)

This example shows that it is also possible to add your own coordinates
to the `map.feature()`.

## 4. Conclusions

In this post I showed the main fucntions of `lingtypology` for creating
interactive linguistic maps, but there are a lot of other functions that
make the visualizations more flexible, so take a look at the [manual
page](https://docs.ropensci.org/lingtypology/) for more information on
functionalities.If you have any suggestions, please create an issue on
[GitHub](https://github.com/ropensci/lingtypology/issues).

## 5. Acknowledgments

I would like to thank [Misha
Daniel](https://www.hse.ru/en/org/persons/6635821), [Samira
Verhees](https://www.hse.ru/en/staff/samiraverhees), [Calle
Börstell](https://borstell.github.io/#name), [Timo
Roettger](http://phonetik.phil-fak.uni-koeln.de/troettger.html) and
[Robert Forkel](https://www.shh.mpg.de/person/45369/25522) for their
ideas and comments. I sincerely thank the reviewers for their
constructive criticism and valuable comments during the rOpenSci
reviewing: [Kenton Russell](https://github.com/timelyportfolio), [Taras
Zahkarko](https://github.com/tzakharko),
[@languageSpaceLabs](https://github.com/languagespacelabs), [Scott
Chamberlain](https://github.com/sckott), and [Stefanie
Butland](https://github.com/stefaniebutland).

::: {.footnotes role="doc-endnotes"}

------------------------------------------------------------------------

1.  ::: {#fn:1}
    You can listen to the [ejective
    p](https://www.youtube.com/watch?v=mfrAlv-5P1c), [ejective
    t](https://www.youtube.com/watch?v=Xg_MVMixhQY), [ejective
    k](https://www.youtube.com/watch?v=UEDqwSQ64VA) or [ejective
    q](https://www.youtube.com/watch?v=6pL_zGR7d7A) via these links. If
    you are interested in the distribution of glottolized consonants,
    see the [WALS chapter](https://wals.info/feature/7A#2/28.6/141.5) by
    Ian Maddieson. [↩︎](#fnref:1){.footnote-backref role="doc-backlink"}
    :::
:::


As all other types of visualization, linguistic mapping has two main goals: data presentation and data analysis.

Messaggi di Rogue Scholar

Easy linguistic mapping with lingtypology