If you’ve struggled to categorize dates, clean strings, or order bars in ggplot, this course is for you. Learn the basics of splitting and combining data, variable cleaning and creation, grouping and summarizing data, and creating visualizations.
Summarizing statistics across groups is invaluable for comparing categories of observations. In this course, Splitting and Combining Data with R, you'll explore splitting data into groups based on some criteria, applying functions or calculations to each group independently, and combining the results into a data structure. To begin, you’ll learn how to create custom categorical variables for grouping, and custom numeric variables to which you can apply functions. Next, with the criteria for grouping created, you will split the data, apply functions, and combine the data into a data structure. Finally, with the raw data transformed, you’ll discover how a grouped dataframe can then be ungrouped with summary statistics maintained, or keep the grouped dataframe intact with plotting functions for visualizing variation between groups. By the end of this course, you’ll have a better understanding of how to use R to build data pipelines with dplyr, manipulate strings and dates for feature engineering, and create customized ggplot charts. .
Summarizing statistics across groups is invaluable for comparing categories of observations. In this course, Splitting and Combining Data with R, you'll explore splitting data into groups based on some criteria, applying functions or calculations to each group independently, and combining the results into a data structure. To begin, you’ll learn how to create custom categorical variables for grouping, and custom numeric variables to which you can apply functions. Next, with the criteria for grouping created, you will split the data, apply functions, and combine the data into a data structure. Finally, with the raw data transformed, you’ll discover how a grouped dataframe can then be ungrouped with summary statistics maintained, or keep the grouped dataframe intact with plotting functions for visualizing variation between groups. By the end of this course, you’ll have a better understanding of how to use R to build data pipelines with dplyr, manipulate strings and dates for feature engineering, and create customized ggplot charts. .
