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<!DOCTYPE html>
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<meta name="author" content="Alison Hill" />
<title>Lab 03a: Animal Word Cleaning</title>
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<h1 class="title toc-ignore">Lab 03a: Animal Word Cleaning</h1>
<h3 class="subtitle"><em>CS631</em></h3>
<h4 class="author"><em>Alison Hill</em></h4>
</div>
<div id="data" class="section level1">
<h1>Data</h1>
<p><a href="http://langcog.github.io/wordbankr/" class="uri">http://langcog.github.io/wordbankr/</a></p>
</div>
<div id="packages" class="section level1">
<h1>Packages</h1>
<pre class="r"><code>library(tidyverse)
library(wordbankr)
library(here)</code></pre>
<pre class="r"><code>my_sounds <- c("meow", "woof woof", "cockadoodledoo")
# first get the items in the animal category
sounds <- get_item_data(language = "English (American)",
form = "WG") %>%
filter(definition %in% my_sounds)
# then get instrument data for those items
sounds_data <- get_instrument_data(
language = "English (American)",
form = "WG",
items = sounds$item_id,
administrations = TRUE,
iteminfo = TRUE
) %>%
mutate(produces = value == "produces",
understands = case_when(
produces == TRUE | value == "understands" ~ TRUE,
TRUE ~ FALSE
)) %>%
drop_na(produces) %>%
rename(sound = uni_lemma)
# what proportion of kids at each age understand/produce each word?
sounds_summary <- sounds_data %>%
group_by(age, sound) %>%
summarise(kids_produce = sum(produces, na.rm = TRUE),
kids_understand = sum(understands, na.rm = TRUE),
kids_respond = n_distinct(data_id),
prop_produce = kids_produce / kids_respond,
prop_understand = kids_understand / kids_respond)</code></pre>
<p>Now let’s export both data frames for the lab.</p>
<pre class="r"><code>write_csv(sounds_data, here::here("data",
"animal_sounds_data.csv"))
write_csv(sounds_summary, here::here("data",
"animal_sounds_summary.csv"))</code></pre>
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