Join the two data sets together

In the video, you have learned that the inner_join() function of dplyr needs to be given a "key" on which two data frames are joined. Actually, multiple keys that need to match can be specified. In this first exercise, you are going to join two data sets by two keys. The data frames ilo_hourly_compensation and ilo_working_hours are already loaded for you and are available in your workspace.

This course touches on a lot of concepts you may have forgotten, so if you ever need a quick refresher, download the tidyverse Cheat Sheet and keep it handy!

This exercise is part of the course

Communicating with Data in the Tidyverse

Exercise instructions

Combine both ILO data frames ilo_hourly_compensation and ilo_working_hours using the inner_join() function of dplyr.
Join both data frames by the variables "country" and "year".

Hands-on interactive exercise

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

# Join both data frames
ilo_data <- ilo_hourly_compensation %>%
  ___(___, by = c(___, ___))

# Count the resulting rows
ilo_data  %>% 
    count()

# Examine ilo_data
ilo_data

Edit and Run Code

This exercise is part of the course

Communicating with Data in the Tidyverse

BeginnerSkill Level

4.8+

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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

Current Exercise

Exercise 3: Change variable types Exercise 4: Filtering and plotting the data Exercise 5: Filter the data for plotting Exercise 6: Some summary statistics Exercise 7: A basic scatter plot Exercise 8: Add labels to the plot Exercise 9: Custom ggplot2 themes Exercise 10: Apply a default theme Exercise 11: Change the appearance of titles Exercise 12: Alter background color and add margins

Barcharts, scatter plots, and histograms are probably the most common and effective data visualizations. Yet, sometimes, there are even better ways to visually highlight the finding you want to communicate to your audience. So-called "dot plots" make us better grasp and understand changes in data: development over time, for example. In this chapter, you'll build a custom and unique visualization that emphasizes and explains exactly one aspect of the story you want to tell.

Exercise 1: Visualizing aspects of data with facets Exercise 2: Prepare the data set for the faceted plot Exercise 3: Add facets to the plot Exercise 4: Define your own theme function Exercise 5: Apply the new theme function to the plot Exercise 6: A custom plot to emphasize change Exercise 7: A basic dot plot Exercise 8: Add arrows to the lines in the plot Exercise 9: Add some labels to each country Exercise 10: Polishing the dot plot Exercise 11: The fct_reorder function Exercise 12: Reordering elements in the plot Exercise 13: Correct ugly label positions Exercise 14: Finalizing the plot for different audiences and devices Exercise 15: Change the viewport so labels don't overlap with plot border Exercise 16: Optimizing the plot for mobile devices

Back in the old days, researchers and data analysts used to generate plots in R and then tediously copy them into their LaTeX or Word documents. Nowadays, whole reports can be produced and reproduced from within R and RStudio, using the RMarkdown language – combining R chunks, formatted prose, tables and plots. In this chapter, you'll take your previous findings, results, and graphics and integrate them into such a report to tell the story that needs to be told.

Exercise 1: What is RMarkdown?Exercise 2: When is a document reproducible?Exercise 3: Introduction to the RMarkdown exercise interface Exercise 4: Formatting with Markdown Exercise 5: Give your document a structure Exercise 6: Change formatting of text snippets Exercise 7: R code in RMarkdown documents Exercise 8: Specify packages in the first code chunk Exercise 9: R code chunk options Exercise 10: Inline code statements Exercise 11: Images in RMarkdown files Exercise 12: Adjusting figure options, optimizing them for mobile devices.Exercise 13: Add auxiliary images

Your boss, your client, or your professor usually expects your results to be accurate and presented in a clear and concise structure. However, coming up with a nicely formatted and unique report on top of that is certainly a plus and RMarkdown can be customized to accomplish this. In this last chapter, you'll take your report from the last chapter and brand it with your own custom and unique style.

Exercise 1: Advanced YAML settings Exercise 2: Change the overall appearance of your report Exercise 3: Add a table of contents Exercise 4: More YAML hacks Exercise 5: Custom stylesheets Exercise 6: CSS selectors Exercise 7: Change style attributes of text elements Exercise 8: Reference the style sheet Exercise 9: Beautiful tables Exercise 10: Beautify a table with kable Exercise 11: Repetition: CSS Exercise 12: Summary