You can install rcldf directly from GitHub using devtools:
library(devtools)
install_github("SimonGreenhill/rcldf", dependencies = TRUE)You create a cldf object by giving either a path to the directory the CLDF
is stored in or a URL where we can find the CLDF dataset.
(i.e. where the metadata.json file lives).
> df <- cldf('/path/to/dir/wals_1a_cldf')
> df <- cldf('/path/to/dir/wals_1a_cldf/StructureDataset-metadata.json')
> df <- cldf("https://zenodo.org/record/7844558/files/grambank/grambank-v1.0.3.zip?download=1")
> df <- cldf('https://github.com/phlorest/greenhill_et_al2023')Or you can use the datasets functionality to browse available datasets:
datasets()...and load them:
wals <- load_dataset('wals')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: CodeTable (4 columns, 5 rows)
2/4: LanguageTable (9 columns, 563 rows)
3/4: ParameterTable (6 columns, 1 rows)
4/4: ValueTable (7 columns, 563 rows)
Sources: 947Each table is attached to the df$tables list:
> names(df$tables)
[1] "ValueTable" "LanguageTable" "ParameterTable" "CodeTable" ...and we can access these tables:
> df$tables$LanguageTable
# 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
# OR
> df$tables$ParameterTable
# 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…
# OR
> df$tables$ValueTable
# 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
> schema(df)
values.csv
----------
name link property
1 Code_ID codes.csv:ID CLDF:codeReference
2 Comment CLDF:comment
3 ID CLDF:id
4 Language_ID languages.csv:ID CLDF:languageReference
5 Parameter_ID parameters.csv:ID CLDF:parameterReference
6 Source CLDF:source
7 Value CLDF:value
languages.csv
-------------
name link property
1 ID CLDF:id
2 Name CLDF:name
3 Macroarea CLDF:macroarea
4 Latitude CLDF:latitude
5 Longitude CLDF:longitude
6 Glottocode CLDF:glottocode
7 ISO639P3code CLDF:iso639P3code
8 Genus <NA>
9 Family <NA>
parameters.csv
--------------
name link property
1 ID CLDF:id
2 Name CLDF:name
3 Description CLDF:description
4 Authors <NA>
5 Url <NA>
6 Area <NA>
codes.csv
---------
name link property
1 Description CLDF:description
2 ID CLDF:id
3 Name CLDF:name
4 Parameter_ID parameters.csv:ID CLDF:parameterReferenceYou can extract subset of a dataset (which deletes all non-relevant rows)
cldf.small <- subset_cldf(df, Glottocode=='chem1251')CLDF datasets have sources stored in BibTeX format. We don't load them by default, as it can take a long time to parse the BibTeX file correctly.
You can load them like this:
o <- cldf('/path/to/dir/wals_1a_cldf', load_bib=TRUE)
# or if you loaded the CLDF without sources the first time.
o <- read_bib(o)...and then access them by:
o$sourcesSometimes people want to have all the data from a CLDF dataset as one dataframe.
Use as.cldf.wide to do this, passing it the name of a table to act as the base.
This will take the base table, and resolve all foreign keys (usually *_ID) into
their own columns.
For example, this dataset has a CodeTable which connects to the ParameterTable
via Parameter_ID:
> df$tables$CodeTable
# A tibble: 5 x 4
ID Parameter_ID Name Description
<chr> <chr> <chr> <chr>
1 1A-1 1A Small NA
2 1A-2 1A Moderately small NAUsing as.cldf.wide we can combine all the information from ParameterTable into
the CodeTable:
> as.cldf.wide(df, 'CodeTable')
# A tibble: 5 x 9
ID Parameter_ID Name Description Name.ParameterTable Description.Par… Authors
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1A-1 1A Small A small thing Consonant Inve… NA Ian Ma…
2 1A-2 1A Moderatel… a moderately sm… Consonant Inve… NA Ian Ma…
3 1A-3 1A Average an average thing Consonant Inve… NA Ian Ma…
4 1A-4 1A Moderatel… a moderately la… Consonant Inve… NA Ian Ma…
5 1A-5 1A Large a large thing Consonant Inve… NA Ian Ma…
# … with 2 more variables: Url <chr>, Area <chr>Note that name clashes between the two tables are resolved by appending the tablename
(e.g. the column Name in the original CodeTable is still Name but the Name column from
ParameterTable is now Name.ParameterTable`).
Sometimes you just want to get one table:
df <- get_table_from('LanguageTable', '/path/to/dir/wals_1a_cldf')print(df$citation)Sometimes you want to know which version of a dataset you have without loading the whole dataset:
> get_details('/path/to/examples/wals_1A_cldf')
Title Path Size Citation ConformsTo
1 The Dataset /path/to/examples/wals_1A_cldf 5432 (...) http://cldf.clld.org/v1.0/terms.rdf#StructureDatasetWhen you load a dataset from a URL, rcldf downloads the dataset and unpacks it to a cache directory. By default this is a temporary directory which will be deleted when you close R.
However, by specifying a directory or using tools::R_user_dir("rcldf", which = "cache")
you can re-use the dataset later.
To see where downloads will be saved:
> get_cache_dir()
[1] "/Users/simon/Library/Caches/org.R-project.R/R/rcldf"To see what datasets you've downloaded:
> list_cache_files()
Title
1 glottolog/glottolog: Glottolog database 5.0 as CLDF
2 Grambank v1.0
3 hueblerstability
4 McElhanon 1967 Huon Peninsula data
5 World Atlas of Classifier Languages
6 The World Atlas of Language Structures Online
Path Size
1 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/glottolog-cldf-5.0 544
2 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/grambank-1.0.3 640
3 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/hueblerstability-1.1 512
4 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/huon 288
5 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/wacl-1.0.0 544
6 /Users/simon/Library/Caches/org.R-project.R/R/rcldf/wals-2020.3 608
Citation
1 ✂
2 ✂
3 ✂
4 ✂
5 ✂
6 ✂
ConformsTo
1 http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
2 http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
3 http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
4 http://cldf.clld.org/v1.0/terms.rdf#Wordlist
5 http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
6 http://cldf.clld.org/v1.0/terms.rdf#StructureDataset
>
You can re-use datasets in your cache:
cldf('/Users/simon/Library/Caches/org.R-project.R/R/rcldf/glottolog-cldf-5.0', load_bib=FALSE)
A CLDF dataset with 7 tables (CodeTable, LanguageTable, MediaTable, names.csv, ParameterTable, TreeTable, ValueTable)For a full tutorial see the vignette here: https://github.com/SimonGreenhill/rcldf/blob/main/vignettes/using-rcldf.Rmd
logger::log_threshold(DEBUG)
o <- rcldf(...)library(dplyr)
dplace <- cldf('https://github.com/D-PLACE/dplace-dataset-ea')
ea66 <- dplace$tables$ValueTable %>% filter(Var_ID=='EA066')