10.52 Rain
20180723 The two remaining character variables are:
rain_today
, rain_tomorrow
. Their distributions are generated by
dplyr::select()ing from the dataset those variables that
start with rain_
and then build a vctrs::table() over
those variables. We use base::sapply() to apply
vctrs::table() to the selected columns to count the
frequency of the occurrence of each value of a variable within the
dataset.
# Review the distribution of observations across levels.
ds %>%
select(starts_with("rain_")) %>%
sapply(table)
## rain_today rain_tomorrow
## No 171174 171165
## Yes 48919 48929
Noting that No
and Yes
are the only values these two
variables will take it makes sense to convert them both to
factors. We will keep the ordering as alphabetic and so a simple call
to base::factor() will to convert from character to factor.
# Note the names of the rain variables.
ds %>%
select(starts_with("rain_")) %>%
names() ->
vnames
# Confirm these are currently character variables.
ds[vnames] %>% sapply(class)
## rain_today rain_tomorrow
## "factor" "factor"
# Convert these variables from character to factor.
ds[vnames] %<>%
lapply(factor) %>%
data.frame() %>%
as_tibble()
# Confirm they are now factors.
ds[vnames] %>% sapply(class)
## rain_today rain_tomorrow
## "factor" "factor"
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