dt_mutate {maditr}

'dplyr'-like interface for data.table.

Description

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 in-place.

• 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 non-grouping 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 '$' considered as Perl-style regular expression patterns. For example, '^Petal' returns all variables started with 'Petal'. 'Width$' 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 in-place.

Usage

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)

Arguments

data

data.table/data.frame data.frame will be automatically converted to data.table. dt_mutate modify data.table object in-place.

...

List of variables or name-value pairs of summary/modifications functions. The name will be the name of the variable in the result. In the mutate function we can use a = b or a := b notation. Advantages of ⁠:=⁠ are multiassignment (c("a", "b") := list(1,2)) and parametric assignment ((a) := 2).

by

unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table

keyby

Same as by, but with an additional setkey() run on the by columns of the result, for convenience. It is common practice to use 'keyby=' routinely when you wish the result to be sorted. For details see data.table.

fun

function which will be applied to all variables in dt_summarize and dt_summarize_all.

na.last

logical. FALSE by default. If TRUE, missing values in the data are put last; if FALSE, they are put first.

Value

data.table

Examples

# 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)