Summarizing gender discrimination

As the first step of any analysis, you should look at and summarize the data. Categorical variables are often summarized using proportions, and it is always important to understand the denominator of the proportion.

Do you want the proportion of women who were promoted or the proportion of promoted individuals who were women? Here, you want the first of these, so in your R code it's necessary to group_by() the sex variable.

The discrimination study data are available in your workspace as disc.

This exercise is part of the course

Foundations of Inference in R

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Exercise instructions

Using the count() function from dplyr, tabulate the variables sex and promote.
Summarize the data by using group_by() on the sex variable.
Find the proportion who were promoted. Call this variable promoted_prop. Note that with binary variables, the proportion of either value can be found using the mean() function (e.g. mean(variable == "value")).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a contingency table summarizing the data
disc %>%
  # Count the rows by sex, promote
  ___

# Find proportion of each sex who were promoted
disc %>%
  # Group by sex
  ___
  # Calculate proportion promoted summary stat
  ___

Edit and Run Code