You can install rcldf directly from GitHub using devtools:
library(devtools)
install_github("SimonGreenhill/rcldf", dependencies = TRUE)# create a `cldf` object by giving either a path to the directory
# or the metadata.json file
> df <- cldf('/path/to/dir/wals_1a_cldf')
> df <- cldf('/path/to/dir/wals_1a_cldf/StructureDataset-metadata.json')
# a cldf object has various bits of information
> summary(df)
A Cross-Linguistic Data Format (CLDF) dataset:
Name: My Dataset
Type: http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
Tables:
1/4: codes (4 columns, 5 rows)
2/4: languages (9 columns, 563 rows)
3/4: parameters (6 columns, 1 rows)
4/4: values (7 columns, 563 rows)
Sources: 947
# each table is attached to the df$tables list.
> names(df$tables)
[1] values" "languages" "parameters" "codes"
> df$tables$languages
# A tibble: 563 x 9
ID Name Macroarea Latitude Longitude Glottocode ISO639P3code Genus Family
<chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
1 abi Abipón NA -29 -61 abip1241 axb South Gu… Guaicuru…
2 abk Abkhaz NA 43.1 41 abkh1244 abk Northwes… Northwes…
3 ach Aché NA -25.2 -55.2 ache1246 guq Tupi-Gua… Tupian
> df$tables$parameters
# A tibble: 1 x 6
ID Name Description Authors Url Area
<chr> <chr> <chr> <chr> <chr> <chr>
1 1A Consonant Inventori… NA Ian Maddieson http://wals.info/featur… Phonolo…
> df$tables$values
# A tibble: 563 x 7
ID Language_ID Parameter_ID Value Code_ID Comment Source
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1A-abi abi 1A 2 1A-2 NA Najlis-1966
2 1A-abk abk 1A 5 1A-5 NA Hewitt-1979
3 1A-ach ach 1A 1 1A-1 NA Susnik-1974
4 1A-acm acm 1A 2 1A-2 NA Olmsted-1966;Olmsted-1964
> df$tables$codes
# A tibble: 5 x 4
ID Parameter_ID Name Description
<chr> <chr> <chr> <chr>
1 1A-1 1A Small NA
2 1A-2 1A Moderately small NA
3 1A-3 1A Average NA
4 1A-4 1A Moderately large NA
5 1A-5 1A Large NA
# You can extract a "wide" table, with all foreign key entries filled in:
> as.cldf.wide(df, 'codes')
# A tibble: 5 x 9
ID Parameter_ID Name.codes Description.cod… Name.parameters Description.par… Authors
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1A-1 1A Small A small thing Consonant Inve… NA Ian Ma…
2 1A-2 1A Moderatel… a moderately sm… Consonant Inve… NA Ian Ma…
3 1A-3 1A Average an average thing Consonant Inve… NA Ian Ma…
4 1A-4 1A Moderatel… a moderately la… Consonant Inve… NA Ian Ma…
5 1A-5 1A Large a large thing Consonant Inve… NA Ian Ma…
# … with 2 more variables: Url <chr>, Area <chr>
# Or:
> as.cldf.wide(df, 'values')
# A tibble: 9 x 23
ID Language_ID Parameter_ID.va… Value Code_ID Comment Source Name.languages
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1A-a… abi 1A 2 1A-2 NA Najli… Abipón
2 1A-a… abk 1A 5 1A-5 NA Hewit… Abkhaz
3 1A-a… ach 1A 1 1A-1 NA Susni… Aché
4 1A-a… acm 1A 2 1A-2 NA Olmst… Achumawi
5 1A-a… aco 1A 5 1A-5 NA Mille… Acoma
6 1A-a… adz 1A 2 1A-2 NA Holzk… Adzera
7 1A-a… agh 1A 3 1A-3 NA Hyman… Aghem
8 1A-a… aht 1A 4 1A-4 NA Kari-… Ahtna
9 1A-a… aik 1A 3 1A-3 NA Hanke… Aikaná
# … with 15 more variables: Macroarea <chr>, Latitude <dbl>, Longitude <dbl>,
# Glottocode <chr>, ISO639P3code <chr>, Genus <chr>, Family <chr>,
# Name.parameters <chr>, Description.parameters <chr>, Authors <chr>, Url <chr>,
# Area <chr>, Parameter_ID.codes <chr>, Name.codes <chr>, Description.codes <chr>