library(dplyr)
library(L2TDatabase)
# Are you connected to the UMN network or VPN?
# Connection information is stored in .cnf files. Tristan probably created one
# for you already. To connect to the default database, point to the file.
my_cnf_file <- "./inst/l2t_db.cnf"
l2t <- l2t_connect(my_cnf_file)
# This is a database connection. You can see all the tables available to you.
l2t
# You can point to a table in the database with tbl().
tbl(l2t, "EVT")
# But to download a table, you need to collect() it.
df_evt <- tbl(l2t, "EVT") %>% collect()
df_evt
# You can manipulate the table
df_evt %>% filter(Study == "DialectSwitch")
df_evt %>% select(Study, ResearchID, EVT_GSV)
# Or plot it
library(ggplot2)
ggplot(df_evt) +
aes(x = EVT_Age, y = EVT_GSV, color = Study) +
geom_point()
# Many scores from a Study will be bundled together in the tables that start
# with "Scores_".
tp3 <- tbl(l2t, "Scores_TimePoint3") %>%
collect()
# Don't worry about the warnings! Some numbers are computed on the fly whenever
# the table is requested, so R has to a conversion on those numbers.
# There are lots and lots of scores
tp3
ggplot(tp3) +
aes(x = EVT_Standard, y = GFTA_Standard) +
stat_smooth(method = "lm") +
geom_point()
# Some tables are queries that target specific research designs. This one lets
# us compare how children who received multiple administrations of the minimal
# pairs task in different dialect performed.
df_minp <- tbl(l2t, "MinPair_Dialect_Summary") %>%
collect()
df_minp
ggplot(df_minp) +
aes(x = MinPair_AAE_ProportionCorrect, color = Child_Dialect) +
geom_density()
ggplot(df_minp) +
aes(x = MinPair_SAE_ProportionCorrect, color = Child_Dialect) +
geom_density()
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