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Learn R programming
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Module 3
Data Transformation with the Tidyverse
1
Piping with `dplyr`
Use the pipe operator (`%>%`) to chain `dplyr` functions into a readable workflow.
2
Column Selection and Renaming
Select and rename columns using `select()` and `rename()`.
3
Filtering Data with `filter()`
Filter rows based on one or more conditions using `filter()`.
Conditional Column Creation with `mutate()` and `case_when()`
Create or modify columns using `mutate()` and `case_when()`.
5
Sorting Data Frames with `arrange()`
Sort data frames using `arrange()`.
6
Grouping and Summarizing Data with `dplyr`
Generate grouped summaries using `group_by()` and `summarise()`.
7
Joining Data with `left_join()`
Join two data frames together using `left_join()`.
8
Removing Missing Values in Specific Columns with `tidyr::drop_na()`
Remove rows with missing values in specific columns using `tidyr::drop_na()`.
Previous module
Core Data Structures and Data Import
Next module
Data Reshaping and Functional Programming