The wordbankr package allows you to access data in the Wordbank database from R. This vignette shows some examples of how to use the data loading functions and what the resulting data look like.
There are three different data views that you can pull out of Wordbank: by-administration, by-item, and administration-by-item.
The get_administration_data function gives by-administration information, for either a specific language and form or for all instruments:
english_ws_admins <- get_administration_data("English", "WS")
head(english_ws_admins)## Source: local data frame [6 x 10]
##
## data_id age comprehension production language form birth_order
## (dbl) (int) (int) (int) (chr) (chr) (fctr)
## 1 1 24 337 337 English WS First
## 2 2 19 384 384 English WS Second
## 3 3 24 76 76 English WS First
## 4 4 18 19 19 English WS First
## 5 5 24 480 480 English WS First
## 6 6 19 313 313 English WS First
## Variables not shown: ethnicity (fctr), sex (fctr), mom_ed (fctr)all_admins <- get_administration_data()
head(all_admins)## Source: local data frame [6 x 10]
##
## data_id age comprehension production language form birth_order
## (dbl) (int) (int) (int) (chr) (chr) (fctr)
## 1 1 24 337 337 English WS First
## 2 2 19 384 384 English WS Second
## 3 3 24 76 76 English WS First
## 4 4 18 19 19 English WS First
## 5 5 24 480 480 English WS First
## 6 6 19 313 313 English WS First
## Variables not shown: ethnicity (fctr), sex (fctr), mom_ed (fctr)The get_item_data function gives by-item information, for either a specific language and form or for all instruments:
spanish_wg_items <- get_item_data("Spanish", "WG")
head(spanish_wg_items)## Source: local data frame [6 x 11]
##
## item_id definition language form type category lexical_category
## (chr) (chr) (chr) (chr) (chr) (chr) (chr)
## 1 item_1 am Spanish WG word sounds other
## 2 item_2 ay Spanish WG word sounds other
## 3 item_3 beemee Spanish WG word sounds other
## 4 item_4 cuacua Spanish WG word sounds other
## 5 item_5 guagua Spanish WG word sounds other
## 6 item_6 miau Spanish WG word sounds other
## Variables not shown: lexical_class (chr), uni_lemma (chr),
## complexity_category (chr), num_item_id (dbl)all_items <- get_item_data()
head(all_items)## Source: local data frame [6 x 11]
##
## item_id definition language form type category
## (chr) (chr) (chr) (chr) (chr) (chr)
## 1 item_1 be careful British Sign Language WG phrases NA
## 2 item_2 bring me British Sign Language WG phrases NA
## 3 item_3 change nappy British Sign Language WG phrases NA
## 4 item_4 come here British Sign Language WG phrases NA
## 5 item_5 daddy/mummy home British Sign Language WG phrases NA
## 6 item_6 donttouch British Sign Language WG phrases NA
## Variables not shown: lexical_category (chr), lexical_class (chr),
## uni_lemma (chr), complexity_category (chr), num_item_id (dbl)If you are only looking at total vocabulary size, admins is all you need, since it has both productive and receptive vocabulary sizes calculated. If you are looking at specific items or subsets of items, you need to load instrument data, using the get_instrument_data function. Pass it an instrument language and form, along with a list of items you want to extract (by item_id).
eng_ws_canines <- get_instrument_data(instrument_language = "English",
instrument_form = "WS",
items = c("item_26", "item_46"))
head(eng_ws_canines)## Source: local data frame [6 x 3]
##
## data_id value num_item_id
## (dbl) (chr) (dbl)
## 1 1 produces 26
## 2 2 produces 26
## 3 3 produces 26
## 4 4 produces 26
## 5 5 produces 26
## 6 6 produces 26By default get_instrument_table returns a data frame with columns of the administration’s data_id, the item’s num_item_id (numerical item_id), and the corresponding value. To include administration information, you can set the administrations argument to TRUE, or pass the result of get_administration_data as administrations (that way you can prevent the administration data from being loaded multiple times). Similarly, you can set the iteminfo argument to TRUE, or pass it result of get_item_data.
Loading the data is fast if you need only a handful of items, but the time scales about linearly with the number of items, and can get quite slow if you need many or all of them. So, it’s a good idea to filter down to only the items you need before calling get_instrument_data.
As an example, let’s say we want to look at the production of animal words on English Words & Sentences over age. First we get the items we want:
animals <- get_item_data("English", "WS") %>%
filter(category == "animals")Then we get the instrument data for those items:
animal_data <- get_instrument_data(instrument_language = "English",
instrument_form = "WS",
items = animals$item_id,
administrations = english_ws_admins)Finally, we calculate how many animals words each child produces and the median number of animals of each age bin:
animal_summary <- animal_data %>%
mutate(produces = value == "produces") %>%
group_by(age, data_id) %>%
summarise(num_animals = sum(produces, na.rm = TRUE)) %>%
group_by(age) %>%
summarise(median_num_animals = median(num_animals, na.rm = TRUE))
ggplot(animal_summary, aes(x = age, y = median_num_animals)) +
geom_point()