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

btd
4 min readNov 17, 2023

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))

4. Conditional Mutate with case_when:

The case_when function allows you to perform conditional mutations based on multiple conditions.

library(dplyr)

df %>%
mutate(category = case_when(
value > 0 ~ "positive",
value < 0 ~ "negative",
TRUE ~ "zero"
))

5. Joins with anti_join and semi_join:

dplyr provides anti_join and semi_join for finding rows that do not have a match and finding rows with at least one match, respectively.

library(dplyr)

df1 %>%
anti_join(df2, by = "id") # Rows in df1 with no match in df2

df1 %>%
semi_join(df2, by = "id") # Rows in df1 with at least one match in df2

6. Row-wise Operations with rowwise and c_across:

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