After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Construct effective scatterplots and apply additional aesthetics to explore relationships in data
- Explore and transform data using dplyr verbs to prepare datasets for analysis
- Produce a variety of visualizations (line, bar, histogram, boxplot) to communicate distributional and temporal patterns
- Summarize and aggregate data using group_by() and summarize() to reveal patterns across groups
Prerequisites
There are no prerequisites for this course
Curriculum
Course outline
1
Data wrangling
In this chapter, you'll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You'll see how each of these steps allows you to answer questions about your data.
- The gapminder dataset50 XP
- Loading the gapminder and dplyr packages100 XP
- Understanding a data frame50 XP
- The filter verb50 XP
- Filtering for one year100 XP
- Filtering for one country and one year100 XP
- The arrange verb50 XP
- Arranging observations by life expectancy100 XP
- Filtering and arranging100 XP
- The mutate verb50 XP
- Using mutate to change or create a column100 XP
- Combining filter, mutate, and arrange100 XP
2
Data visualization
Often a better way to understand and present data as a graph. In this chapter, you'll learn the essential skills of data visualization using the ggplot2 package, and you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.
3
Grouping and summarizing
So far you've been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you'll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.
4
Types of visualizations
In this chapter, you'll learn how to create line plots, bar plots, histograms, and boxplots. You'll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.
R
Introduction to the Tidyverse
Course
Complete
