arrange.dtplyr_step     Arrange rows by column values
collect.dtplyr_step     Force computation of a lazy data.table
complete.dtplyr_step    Complete a data frame with missing combinations
                        of data
count.dtplyr_step       Count observations by group
distinct.dtplyr_step    Subset distinct/unique rows
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
filter.dtplyr_step      Subset rows using column values
group_by.dtplyr_step    Group and ungroup
group_modify.dtplyr_step
                        Apply a function to each group
head.dtplyr_step        Subset first or last rows
intersect.dtplyr_step   Set operations
lazy_dt                 Create a "lazy" data.table for use with dplyr
                        verbs
left_join.dtplyr_step   Join data tables
mutate.dtplyr_step      Create and modify columns
nest.dtplyr_step        Nest
pivot_longer.dtplyr_step
                        Pivot data from wide to long
pivot_wider.dtplyr_step
                        Pivot data from long to wide
relocate.dtplyr_step    Relocate variables using their names
rename.dtplyr_step      Rename columns using their names
replace_na.dtplyr_step
                        Replace NAs with specified values
select.dtplyr_step      Subset columns using their names
separate.dtplyr_step    Separate a character column into multiple
                        columns with a regular expression or numeric
                        locations
slice.dtplyr_step       Subset rows using their positions
summarise.dtplyr_step   Summarise each group to one row
transmute.dtplyr_step   Create new columns, dropping old
unite.dtplyr_step       Unite multiple columns into one by pasting
                        strings together.