dt_mutate  R Documentation 
Subset of 'dplyr' verbs to work with data.table. Note that there is no
group_by
verb  use by
or keyby
argument when needed.
dt_mutate
adds new variables or modify existing variables. If
data
is data.table then it modifies inplace.
dt_summarize
computes summary statistics. Splits the data into
subsets, computes summary statistics for each, and returns the result in the
"data.table" form.
dt_summarize_all
is the same as dt_summarize
but work over all nongrouping variables.
dt_filter
selects rows/cases where conditions are true. Rows
where the condition evaluates to NA are dropped.
dt_select
selects column/variables from the data set. Range of
variables are supported, e. g. vs:carb. Characters which start with '^' or
end with '$' considered as Perlstyle regular expression patterns. For
example, '^Petal' returns all variables started with 'Petal'. 'Width$'
returns all variables which end with 'Width'. Pattern '^.' matches all
variables and pattern '^.*my_str' is equivalent to contains "my_str"
. See
examples.
dt_arrange
sorts dataset by variable(s). Use '' to sort in
descending order. If data
is data.table then it modifies inplace.
dt_mutate(data, ..., by) dt_summarize(data, ..., by, keyby, fun = NULL) dt_summarize_all(data, fun, by, keyby) dt_summarise(data, ..., by, keyby, fun = NULL) dt_summarise_all(data, fun, by, keyby) dt_select(data, ...) dt_filter(data, ...) dt_arrange(data, ..., na.last = FALSE)
data 
data.table/data.frame data.frame will be automatically converted
to data.table. 
... 
List of variables or namevalue pairs of summary/modifications
functions. The name will be the name of the variable in the result. In the

by 
unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table 
keyby 
Same as 
fun 
function which will be applied to all variables in

na.last 
logical. FALSE by default. If TRUE, missing values in the data are put last; if FALSE, they are put first. 
data.table
# examples from 'dplyr' # newly created variables are available immediately mtcars %>% dt_mutate( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # you can also use dt_mutate() to remove variables and # modify existing variables mtcars %>% dt_mutate( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped mutates mtcars %>% dt_mutate( rank = rank(mpg, ties.method = "min"), keyby = cyl) %>% print() # You can drop variables by setting them to NULL mtcars %>% dt_mutate(cyl = NULL) %>% head() # A summary applied without by returns a single row mtcars %>% dt_summarise(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% dt_summarise(mean = mean(disp), n = .N, by = cyl) # Multiple 'by'  variables mtcars %>% dt_summarise(cyl_n = .N, by = list(cyl, vs)) # Newly created summaries immediately # doesn't overwrite existing variables mtcars %>% dt_summarise(disp = mean(disp), sd = sd(disp), by = cyl) # You can group by expressions: mtcars %>% dt_summarise_all(mean, by = list(vsam = vs + am)) # filter by condition mtcars %>% dt_filter(am==0) # filter by compound condition mtcars %>% dt_filter(am==0, mpg>mean(mpg)) # select mtcars %>% dt_select(vs:carb, cyl) mtcars %>% dt_select(am, cyl) # regular expression pattern dt_select(iris, "^Petal") # variables which start from 'Petal' dt_select(iris, "Width$") # variables which end with 'Width' # move Species variable to the front. # pattern "^." matches all variables dt_select(iris, Species, "^.") # pattern "^.*i" means "contains 'i'" dt_select(iris, "^.*i") dt_select(iris, 1:4) # numeric indexing  all variables except Species # sorting dt_arrange(mtcars, cyl, disp) dt_arrange(mtcars, disp)
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