Package index • dtplyr

Getting data in and out

lazy_dt(): Create a "lazy" data.table for use with dplyr verbs

collect(<dtplyr_step>) compute(<dtplyr_step>) as.data.table(<dtplyr_step>) as.data.frame(<dtplyr_step>) as_tibble(<dtplyr_step>): Force computation of a lazy data.table

Single table verbs

arrange(<dtplyr_step>): Arrange rows by column values

count(<dtplyr_step>): Count observations by group

distinct(<dtplyr_step>): Subset distinct/unique rows

filter(<dtplyr_step>): Subset rows using column values

group_by(<dtplyr_step>) ungroup(<dtplyr_step>): Group and ungroup

group_modify(<dtplyr_step>) group_map(<dtplyr_step>): Apply a function to each group

head(<dtplyr_step>) tail(<dtplyr_step>): Subset first or last rows

mutate(<dtplyr_step>): Create and modify columns

transmute(<dtplyr_step>): Create new columns, dropping old

relocate(<dtplyr_step>): Relocate variables using their names

rename(<dtplyr_step>) rename_with(<dtplyr_step>): Rename columns using their names

reframe(<dtplyr_step>): Summarise each group to one row

select(<dtplyr_step>): Subset columns using their names

slice(<dtplyr_step>) slice_head(<dtplyr_step>) slice_tail(<dtplyr_step>) slice_min(<dtplyr_step>) slice_max(<dtplyr_step>): Subset rows using their positions

summarise(<dtplyr_step>): Summarise each group to one row

Two table verbs

left_join(<dtplyr_step>): Join data tables

intersect(<dtplyr_step>) union(<dtplyr_step>) union_all(<dtplyr_step>) setdiff(<dtplyr_step>): Set operations

tidyr verbs

complete(<dtplyr_step>): Complete a data frame with missing combinations of data

drop_na(<dtplyr_step>): Drop rows containing missing values

expand(<dtplyr_step>): Expand data frame to include all possible combinations of values.

fill(<dtplyr_step>): Fill in missing values with previous or next value

nest(<dtplyr_step>): Nest

pivot_wider(<dtplyr_step>): Pivot data from long to wide

pivot_longer(<dtplyr_step>): Pivot data from wide to long

replace_na(<dtplyr_step>): Replace NAs with specified values

separate(<dtplyr_step>): Separate a character column into multiple columns with a regular expression or numeric locations

unite(<dtplyr_step>): Unite multiple columns into one by pasting strings together.