20 Advanced Data Manipulation in R with dplyr and data.table

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Both dplyr and data.table are powerful R packages for data manipulation, each with its own syntax and advantages. Below, I'll provide an overview of advanced data manipulation techniques using both packages:

I. Advanced Data Manipulation with dplyr:

1. Chaining Operations (%>%):

dplyr allows you to chain operations using the pipe operator (%>%). This facilitates a more readable and concise code structure.

library(dplyr)

df %>%
  filter(condition) %>%
  group_by(group_var) %>%
  summarize(mean_value = mean(value))

2. Window Functions (window_*):

The dplyr package provides window functions for working with rolling and cumulative operations on data frames.

library(dplyr)

df %>%
  arrange(date) %>%
  mutate(rolling_mean = zoo::rollmean(value, k = 3, fill = NA))

3. Advanced Grouping:

dplyr allows for advanced grouping with functions like group_by_at, group_by_all, and group_by_if. These functions enable more dynamic grouping.

library(dplyr)

df %>%
  group_by_at(vars(starts_with("group_"))) %>%
  summarize(mean_value = mean(value))