## ----include=FALSE------------------------------------------------------------
library(wordbankr)
library(dplyr)
library(ggplot2)
knitr::opts_chunk$set(message = FALSE, warning = FALSE, cache = FALSE)
theme_set(theme_minimal())

# CRAN machines must not execute network code (and vignette builds there
# must be fast): data chunks only evaluate when NOT_CRAN=true, e.g. locally
# or on CI jobs that opt in
knitr::opts_chunk$set(eval = identical(Sys.getenv("NOT_CRAN"), "true"))

## -----------------------------------------------------------------------------
# get_administration_data(language = "English (American)", form = "WS")
# get_administration_data()

## -----------------------------------------------------------------------------
# get_item_data(language = "Italian", form = "WG")
# get_item_data()

## -----------------------------------------------------------------------------
# get_instrument_data(
#   language = "English (American)",
#   form = "WS",
#   items = c("item_26", "item_46")
# )

## ----fig.width=6, fig.height=4------------------------------------------------
# items <- get_item_data(language = "English (American)", form = "WS")
# animals <- if (!is.null(items)) items %>% filter(category == "animals")

## -----------------------------------------------------------------------------
# animal_data <- if (!is.null(animals)) {
#   get_instrument_data(language = "English (American)",
#                       form = "WS",
#                       items = animals$item_id,
#                       administration_info = TRUE,
#                       item_info = TRUE)
# }

## ----fig.width=6, fig.height=4------------------------------------------------
# if (!is.null(animal_data)) {
#   animal_summary <- animal_data %>%
#     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() +
#     labs(x = "Age (months)", y = "Median animal words producing")
# }

## -----------------------------------------------------------------------------
# get_instruments()

## -----------------------------------------------------------------------------
# get_datasets(form = "WG")
# get_datasets(language = "Spanish (Mexican)", admin_data = TRUE)

## -----------------------------------------------------------------------------
# if (!is.null(animal_data)) {
#   fit_aoa(animal_data)
#   fit_aoa(animal_data, method = "glmrob", proportion = 1/3)
# }

## -----------------------------------------------------------------------------
# get_crossling_items()

## ----eval=FALSE---------------------------------------------------------------
# get_crossling_data(uni_lemmas = c("hat", "nose")) %>%
#   select(language, uni_lemma, item_definition, age, n_children, comprehension,
#          production, comprehension_sd, production_sd) %>%
#   arrange(uni_lemma)

## -----------------------------------------------------------------------------
# instruments_v2 <- get_instruments(version = "v2.0")

## -----------------------------------------------------------------------------
# if (!is.null(instruments_v2)) unique(instruments_v2$dataset_version)

## ----eval=FALSE---------------------------------------------------------------
# versions <- wb_dataset()$list_versions()
# sapply(versions, function(v) v$properties$tag)