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Data-Centric Statistical Inference Using R and Tidyverse

EDA for a Numerical Explanatory Variable

Explore techniques for exploratory data analysis of numerical explanatory variables using R and the Tidyverse. Learn to summarize data with skimr, calculate correlation coefficients, and visualize relationships through scatterplots and regression lines to understand linear associations and data distribution.

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Typing out all these summary statistic functions in summarize() would be long and tedious. Instead, let’s use the convenient skim() function from the skimr package. This function takes in a data frame, skims it, and returns the commonly used summary statistics. Let’s take our evals_ch5 data frame, select() only the outcome and explanatory variables teaching score and bty_avg, and pipe them into the skim() function:

R
evals_ch5 %>% 
    select(score, bty_avg) %>% skim()

For the numerical variables teaching score and bty_avg, it returns:

  • n_missing: This is the number of missing values.

  • complete_rate: This is the number of non-missing or complete values.

  • mean: This is the average.

  • sd: ...