Mastering R: 100 Different Ways for Transforming Data

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Data transformation in R refers to the process of modifying or restructuring the original dataset to make it more suitable for analysis, modeling, or visualization. This process involves applying a series of operations to the data, such as cleaning, aggregating, merging, and reshaping, to prepare it for further exploration or statistical analysis.

1. Subset Data:

new_data <- old_data[old_data$column_name == value, ]

2. Filter with dplyr:

library(dplyr)
new_data <- filter(old_data, column_name == value)

3. Sort Data:

new_data <- arrange(old_data, column_name)

4. Select Columns:

new_data <- old_data[, c("column1", "column2")]

5. Rename Columns:

names(new_data)[names(new_data) == "old_name"] <- "new_name"

6. Create New Variables:

new_data$sum_column <- new_data$column1 + new_data$column2

7. Convert Data Types:

new_data$numeric_column <- as.numeric(new_data$character_column)

8. Aggregate Data: