20180726 We often build classification models. For such models we want to ensure the target is categoric. Often it is 0/1 and hence is loaded as numeric. We could tell our model algorithm of choice to explicitly do classification or else set the target using generics::as.factor() in the formula. Nonetheless it is generally cleaner to do this here and note that this code has no effect if the target is already categoric.
# Ensure the target is categoric. %<>% as.factor() ds[[target]] # Confirm the distribution. %>% table()ds[target]
## rain_tomorrow ## no yes ## 171165 48929
We can visualise the distribution of the target variable using
ggplot2 (Wickham, Chang, et al. 2023). The dataset is piped to
ggplot2::ggplot() whereby the target is associated through
ggplot2::aes_string() (the aesthetics) with the x-axis of the
plot. To this we add a graphics layer using
ggplot2::geom_bar() to produce the bar chart, with bars having
0.2 and a fill= color of
"grey". The resulting plot can be seen in
%>% ds ggplot(aes_string(x=target)) + geom_bar(width=0.2, fill="grey") + theme(text=element_text(size=14))
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0. ## ℹ Please use tidy evaluation idioms with `aes()`. ## ℹ See also `vignette("ggplot2-in-packages")` for more information. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
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