Some summary statistics

Use dplyrs group_by() and summarize() to compute summary statistics for both years.

This exercise is part of the course

Communicating with Data in the Tidyverse

Exercise instructions

Have a look at the structure of the ilo_data set with str().
After this, group the data by year using the group_by() function.
Then, calculate the mean of both variables hourly_compensation and working_hours using the summarize() function.

Hands-on interactive exercise

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

# Examine the structure of ilo_data
___(ilo_data)

# Group and summarize the data
ilo_data %>%
  group_by(___) %>%
  ___(mean_hourly_compensation = ___(___),
            mean_working_hours = ___(___))

Edit and Run Code

This exercise is part of the course

Communicating with Data in the Tidyverse

BeginnerSkill Level

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In this chapter, you will have a first look at the data you're going to work with throughout this course: the relationship between weekly working hours and monetary compensation in European countries, according to the International Labour Organization (ILO). After that, you'll dive right in and discover a stunning correlation by employing an exploratory visualization. You will then apply a custom look to that graphic – you'll turn an ordinary plot into an aesthetically pleasing and unique data visualization.

Exercise 1: Introduction to the data Exercise 2: Join the two data sets together Exercise 3: Change variable types Exercise 4: Filtering and plotting the data Exercise 5: Filter the data for plotting Exercise 6: Some summary statistics

Current Exercise

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