10.67 Target as a Factor

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 base::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.

ds[[target]] %<>% as.factor()

# Confirm the distribution.

ds[target] %>% table()

## rain_tomorrow
##     no    yes 
## 208393  58924

We can visualise the distribution of the target variable using ggplot2 (Wickham, Chang, et al. 2026). 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 width= 0.2 and a fill= color of "grey". The resulting plot can be seen in Figure @ref(fig:data:plot_target_distribution).

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 per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Target variable distribution. Plotting the distribution is useful to gain an insight into the number of observations in each category. As is the case here we often see a skewed distribution.

(#fig:data:plot_target_distribution)Target variable distribution. Plotting the distribution is useful to gain an insight into the number of observations in each category. As is the case here we often see a skewed distribution.

References

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, Dewey Dunnington, and Teun van den Brand. 2026. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://ggplot2.tidyverse.org.

If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0