Description Major differences from dplyr Standard dplyr-like functions Parallel functions Additional data frame functions Data manipulation adjuncts
Provides a new form of data frame backed by shared memory matrices and
a way to manipulate them. Upon creation these data frames are shared
across multiple local nodes to allow for simple parallel processing. Run the
following command for a more thorough explanation: vignette("basics")
summarise
with dplyr will return a single number, but here it
will return N values depending on how many nodes there are. Typically
you should follow summarise
with reduce
, which is
run locally.
arrange | Sort data |
distinct | Select unique rows or unique combinations of variables |
filter | Filter data |
group_by | Group data |
group_sizes | Return size of groups |
groupwise | Use grouped data (also known as ungroup ) |
mutate | Change values of existing variables (and create new ones) |
n_groups | Return number of groups |
rename | Rename variables |
rowwise | Use data as individual rows |
select | Retain only specified variables |
slice | Select rows by position |
summarise | Summarise data |
transmute | Change variables and drop all others |
partition_even | Partition data evenly amongst cluster nodes |
partition_group | Partition data so that each group is wholly on a node |
within_group | Execute code within a group |
within_node | Execute code within a group |
Multiplyr | Create new parallel data frame |
define | Define new variables |
nsa | No strings attached mode |
reduce | Summarise locally only |
regroup | Return to grouped data |
undefine | Delete variables |
between | Tests whether elements of a vector lie between two values (inclusively) |
cumall | Cumulative all |
cumany | Cumulative any |
cummean | Cumulative mean |
first | Returns first value in vector |
last | Returns last value in vector |
lag | Offset x backwards by n |
lead | Offset x forwards by n |
n | Number of items in current group |
nth | Return the nth item from a vector |
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