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childes-dbThe childesr package allows you to access data in the childes-db from R. This removes the need to write complex SQL queries in order to get the information you want from the database. This vignette shows some examples of how to use the data loading functions and what the resulting data look like.
There are several different get_ functions that you can use to extract different types of data from the childes-db:
get_transcripts()get_participants()get_tokens()get_types()get_utterances()get_speaker_statistics()get_sql_query()Technical note 1: You do not have to explicitly establish a connection to the childes-db since the childesr functions will manage these connections. But if you would like to establish your own connection, you can do so with connect_to_childes() and pass it as an argument to any of the get_ functions. If you do so, make sure to disconnect the connections you make by using DBI::dbDisconnect(), childesr::clear_connections(), or restarting your R session.
Technical note 2: We have tried to optimize the time it takes to get data from the database. But if you try to query and get all of the tokens, it will take a long time.
# load the library
library(childesr)
library(dplyr)The get_transcripts function returns high-level information about the transcripts that are available in the database. You can filter your query to get the transcripts for a specific collection, corpus, or child.
For example, you can run get_transcripts without any arguments to return all of the transcripts in the database.
d_transcripts <- get_transcripts()
head(d_transcripts)## # A tibble: 6 × 13
## transcript_id corpus_name language date filename target_child_na…
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 1 English-Wolf… eng <NA> Frogs/English-W… Margaret
## 2 2 English-Wolf… eng 1989-0… Frogs/English-W… Zachary
## 3 3 English-Wolf… eng 1989-0… Frogs/English-W… Andrew
## 4 4 English-Wolf… eng 1989-0… Frogs/English-W… Christina
## 5 5 English-Wolf… eng 1989-0… Frogs/English-W… Christopher
## 6 6 English-Wolf… eng 1989-0… Frogs/English-W… Alexandra
## # … with 7 more variables: target_child_age <dbl>, target_child_sex <chr>,
## # collection_name <chr>, pid <chr>, collection_id <int>, corpus_id <int>,
## # target_child_id <int>If you only want information about a specific collection, such as the English-American transcripts, then you can specify this in the collection argument.
d_eng_na <- get_transcripts(collection = "Eng-NA")
head(d_eng_na)## # A tibble: 6 × 13
## transcript_id corpus_name language date filename target_child_na…
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 3629 Garvey eng <NA> Eng-NA/Garvey/amyan… <NA>
## 2 3630 Garvey eng <NA> Eng-NA/Garvey/amywe… <NA>
## 3 3631 Garvey eng <NA> Eng-NA/Garvey/ariga… <NA>
## 4 3632 Garvey eng <NA> Eng-NA/Garvey/arike… <NA>
## 5 3633 Garvey eng <NA> Eng-NA/Garvey/bende… <NA>
## 6 3634 Garvey eng <NA> Eng-NA/Garvey/bevfl… <NA>
## # … with 7 more variables: target_child_age <dbl>, target_child_sex <chr>,
## # collection_name <chr>, pid <chr>, collection_id <int>, corpus_id <int>,
## # target_child_id <int>If you know the corpus that you want to analyze, then you can specify this in the corpus argument. The following function call will return information about all of the transcripts in the Brown corpus.
# returns all transcripts in the brown corpus
d_brown_transcripts <- get_transcripts(corpus = "Brown")
# print the number of rows
nrow(d_brown_transcripts)## [1] 214If you want more than one corpus, then you can pass a multiple corpus names. You can also pass more than one name to the collections and child arguments.
d_many_corpora <- get_transcripts(corpus = c("Brown", "Clark"))
# print the number of rows
nrow(d_many_corpora)## [1] 261If you want transcript information about a specific child from a corpus, then you pass their name to the child argument. Note that the following function call will not return any of the transcripts from the Brown corpus because the child Shem is not present in that corpus.
d_shem <- get_transcripts(corpus = c("Brown", "Clark"),
target_child = "Shem")
# print the number of rows
nrow(d_shem)## [1] 47The get_participants function returns background information about the speakers (both the children and the adults) in the database. This includes information about:
Again, if you run the function with no arguments, then you get all the background information for all speakers in the database.
d_participants <- get_participants()
head(d_participants)## # A tibble: 6 × 18
## id code name role corpus_name min_age max_age language group sex ses
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 1 CHI Marg… Targ… English-Wo… 80.0 105. eng <NA> fema… MC
## 2 2 CHI Chri… Targ… English-Wo… 77.0 105. eng <NA> fema… MC
## 3 3 INV Nina Inve… English-Wo… NA NA eng <NA> <NA> <NA>
## 4 4 CHI Andr… Targ… English-Wo… 77.0 104. eng <NA> male MC
## 5 5 CHI Chri… Targ… English-Wo… 79.0 106. eng <NA> male WC
## 6 6 CHI Alex… Targ… English-Wo… 79.0 103. eng <NA> fema… WC
## # … with 7 more variables: education <chr>, custom <chr>,
## # collection_name <chr>, collection_id <int>, corpus_id <int>,
## # target_child_id <int>, target_child_name <chr>The participants function introduces three new arguments: role, age, and sex. The role argument allows you to get information about a specific kind of speaker, such as the “target_child.”
d_target_child <- get_participants(role = "target_child")
head(d_target_child)## # A tibble: 6 × 18
## id code name role corpus_name min_age max_age language group sex ses
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 1 CHI Marg… Targ… English-Wo… 80.0 105. eng <NA> fema… MC
## 2 2 CHI Chri… Targ… English-Wo… 77.0 105. eng <NA> fema… MC
## 3 4 CHI Andr… Targ… English-Wo… 77.0 104. eng <NA> male MC
## 4 5 CHI Chri… Targ… English-Wo… 79.0 106. eng <NA> male WC
## 5 6 CHI Alex… Targ… English-Wo… 79.0 103. eng <NA> fema… WC
## 6 7 CHI Mich… Targ… English-Wo… 79.0 105. eng <NA> male WC
## # … with 7 more variables: education <chr>, custom <chr>,
## # collection_name <chr>, collection_id <int>, corpus_id <int>,
## # target_child_id <int>, target_child_name <chr>The age argument takes a number indicating the age(s) of children (in months) that you want to analyze. you can use this argument in two ways
For example, you can get the participant information for all of the children who had transcripts between the ages of 24 and 36 months.
d_age_range <- get_participants(age = c(24, 36))
head(d_age_range)## # A tibble: 6 × 18
## id code name role corpus_name min_age max_age language group sex ses
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 1598 CHI <NA> Targ… Chinese-Ta… 32.2 60.6 zho <NA> male <NA>
## 2 1669 CHI Ian Targ… Garvey 34.0 34.0 eng <NA> male <NA>
## 3 1679 CHI Nan Targ… Garvey 34.0 34.0 eng <NA> fema… <NA>
## 4 1688 NAN Nan Targ… Garvey 34.0 34.0 eng <NA> fema… <NA>
## 5 1701 CHI Sam Targ… Garvey 35.0 35.0 eng <NA> male <NA>
## 6 1702 CHI <NA> Targ… Valian 21.7 32.8 eng TD fema… <NA>
## # … with 7 more variables: education <chr>, custom <chr>,
## # collection_name <chr>, collection_id <int>, corpus_id <int>,
## # target_child_id <int>, target_child_name <chr>The get_tokens function returns a table with a row for each token based on a set of filtering criteria. The token argument allows you to pass a vector of one or more tokens that you want to analyze.
For example, if you wanted to get all of the production data for a specific token(s), then you could run the following call to get all instances of “dog” and “ball” for Adam in the Brown corpus.
d_adam_prod <- get_tokens(corpus = "Brown",
role = "target_child",
target_child = "Adam",
token = c("dog", "ball"))
# view the structure of the data
str(d_adam_prod)## tibble [265 × 28] (S3: tbl_df/tbl/data.frame)
## $ id : int [1:265] 7557235 7557298 7557590 7557593 7557787 7557840 7558259 7558391 7558393 7559092 ...
## $ gloss : chr [1:265] "dog" "ball" "ball" "ball" ...
## $ language : chr [1:265] "eng" "eng" "eng" "eng" ...
## $ token_order : int [1:265] 3 2 2 3 1 3 2 1 1 1 ...
## $ prefix : chr [1:265] "" "" "" "" ...
## $ part_of_speech : chr [1:265] "n" "n" "n" "n" ...
## $ stem : chr [1:265] "dog" "ball" "ball" "ball" ...
## $ actual_phonology : chr [1:265] "" "" "" "" ...
## $ model_phonology : chr [1:265] "" "" "" "" ...
## $ suffix : chr [1:265] "" "" "" "" ...
## $ num_morphemes : int [1:265] 1 1 1 1 1 1 1 1 1 1 ...
## $ english : chr [1:265] "" "" "" "" ...
## $ clitic : chr [1:265] "" "" "" "" ...
## $ utterance_type : chr [1:265] "declarative" "declarative" "declarative" "declarative" ...
## $ corpus_name : chr [1:265] "Brown" "Brown" "Brown" "Brown" ...
## $ speaker_code : chr [1:265] "CHI" "CHI" "CHI" "CHI" ...
## $ speaker_name : chr [1:265] "Adam" "Adam" "Adam" "Adam" ...
## $ speaker_role : chr [1:265] "Target_Child" "Target_Child" "Target_Child" "Target_Child" ...
## $ target_child_name: chr [1:265] "Adam" "Adam" "Adam" "Adam" ...
## $ target_child_age : num [1:265] 27.1 27.1 27.1 27.1 27.1 ...
## $ target_child_sex : chr [1:265] "male" "male" "male" "male" ...
## $ collection_name : chr [1:265] "Eng-NA" "Eng-NA" "Eng-NA" "Eng-NA" ...
## $ collection_id : int [1:265] 2 2 2 2 2 2 2 2 2 2 ...
## $ corpus_id : int [1:265] 60 60 60 60 60 60 60 60 60 60 ...
## $ speaker_id : int [1:265] 3327 3327 3327 3327 3327 3327 3327 3327 3327 3327 ...
## $ target_child_id : int [1:265] 3327 3327 3327 3327 3327 3327 3327 3327 3327 3327 ...
## $ transcript_id : int [1:265] 7263 7263 7263 7263 7263 7263 7265 7265 7265 7264 ...
## $ utterance_id : int [1:265] 1759848 1759936 1760126 1760127 1760262 1760293 1763420 1763944 1763967 1764045 ...# print the unique tokens
unique(d_adam_prod$gloss)## [1] "dog" "ball"The get_types() function works like the get_tokens() function, returning a table with a row for each type based on set of filtering criteria. The type argument allows you to pass a vector of one or more types that you want to analyze. The main difference is that you now have a single row for each type (i.e., a concept) and a variable count that tracks the number of times that type appeared in a particular transcript.
For example, if you wanted to get all of the production data for a specific type(s), then you could run the following call to get counts of “dog” and “ball” for all of Adam’s transcripts in the Brown corpus.
d_adam_types <- get_types(corpus = "Brown",
target_child = "Adam",
role = "target_child",
type = c("dog", "ball"))
# print the number of times ball appears in the first transcript
c(d_adam_types$gloss[1], d_adam_types$count[1])## [1] "ball" "26"The get_utterances function returns a table with a row for each utterance based on user-defined filtering criteria. For example, the following function will get you all of the utterances in the Brown Corpus for the child Adam.
d_adam_utts <- get_utterances(corpus = "Brown",
target_child = "Adam")
# view the structure of the data
str(d_adam_utts)## tibble [73,431 × 27] (S3: tbl_df/tbl/data.frame)
## $ id : int [1:73431] 1759250 1759256 1759261 1759264 1759269 1759274 1759279 1759284 1759289 1759294 ...
## $ gloss : chr [1:73431] "play checkers" "big drum" "big drum" "big drum" ...
## $ stem : chr [1:73431] "play checker" "big drum" "big drum" "big drum" ...
## $ actual_phonology : chr [1:73431] "" "" "" "" ...
## $ model_phonology : chr [1:73431] "" "" "" "" ...
## $ type : chr [1:73431] "declarative" "declarative" "question" "declarative" ...
## $ language : chr [1:73431] "eng" "eng" "eng" "eng" ...
## $ num_morphemes : int [1:73431] 3 2 2 2 2 2 1 1 2 4 ...
## $ num_tokens : int [1:73431] 2 2 2 2 2 2 1 1 2 3 ...
## $ utterance_order : int [1:73431] 1 2 3 4 5 6 7 8 9 10 ...
## $ corpus_name : chr [1:73431] "Brown" "Brown" "Brown" "Brown" ...
## $ part_of_speech : chr [1:73431] "n n" "adj n" "adj n" "adj n" ...
## $ speaker_code : chr [1:73431] "CHI" "CHI" "MOT" "CHI" ...
## $ speaker_name : chr [1:73431] "Adam" "Adam" NA "Adam" ...
## $ speaker_role : chr [1:73431] "Target_Child" "Target_Child" "Mother" "Target_Child" ...
## $ target_child_name: chr [1:73431] "Adam" "Adam" "Adam" "Adam" ...
## $ target_child_age : num [1:73431] 27.1 27.1 27.1 27.1 27.1 ...
## $ target_child_sex : chr [1:73431] "male" "male" "male" "male" ...
## $ media_start : num [1:73431] NA NA NA NA NA NA NA NA NA NA ...
## $ media_end : num [1:73431] NA NA NA NA NA NA NA NA NA NA ...
## $ media_unit : chr [1:73431] NA NA NA NA ...
## $ collection_name : chr [1:73431] "Eng-NA" "Eng-NA" "Eng-NA" "Eng-NA" ...
## $ collection_id : int [1:73431] 2 2 2 2 2 2 2 2 2 2 ...
## $ corpus_id : int [1:73431] 60 60 60 60 60 60 60 60 60 60 ...
## $ speaker_id : int [1:73431] 3327 3327 3329 3327 3327 3327 3327 3329 3327 3329 ...
## $ target_child_id : int [1:73431] 3327 3327 3327 3327 3327 3327 3327 3327 3327 3327 ...
## $ transcript_id : int [1:73431] 7263 7263 7263 7263 7263 7263 7263 7263 7263 7263 ...# print the first five utterances
d_adam_utts$gloss[1:5]## [1] "play checkers" "big drum" "big drum" "big drum"
## [5] "big drum"The get_speaker_statistics() function returns a table with a row for each transcript and columns that contain a set of summary statistics for that transcript. The summary statistics include:
num_utterances)num_types)num_tokens)num_morphemes)mlu_w)mlu_m)For example, if we wanted to get the summary statistics for Adam’s production data, we could run the following call.
d_adam_stats <- get_speaker_statistics(corpus = "Brown",
target_child = "Adam",
role = "target_child")
# get the average mlu across all Adam's transcripts
mean(d_adam_stats$mlu_w)## [1] 3.559118The get_sql_query() function returns a table from a SQL query run on the specified database. For example, if you wanted to see the top 10 corpora in the Eng-NA collection with the highest count of token for “dog”, you could run the following call.
d_na_dog <- get_sql_query("SELECT corpus_name, COUNT(id) AS count FROM token WHERE collection_name = 'Eng-NA' AND gloss = 'dog' GROUP BY corpus_name")
dplyr::arrange(d_na_dog, desc(count))## # A tibble: 55 × 2
## corpus_name count
## <chr> <dbl>
## 1 HSLLD 1261
## 2 Providence 862
## 3 Brown 546
## 4 Gelman 537
## 5 Weist 437
## 6 NewmanRatner 433
## 7 Suppes 421
## 8 Hall 337
## 9 Brent 277
## 10 Davis 241
## # … with 45 more rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.