maditr: maditr: Pipe-Style Interface for 'data.table'
In hope-data-science/tidydt0: Tidy Verbs for `data.table`

Description Examples

Package provides pipe-style interface for data.table. It preserves all data.table features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks.

To select rows from data: take_if(mtcars, am==0)
To select columns from data: take(mtcars, am, vs, mpg)
To aggregate data: take(mtcars, mean_mpg = mean(mpg), by = am)
To aggregate all non-grouping columns: take(mtcars, fun = mean, by = am)
To aggregate several columns with one summary: take(mtcars, mpg, hp, fun = mean, by = am)
To get total summary skip 'by' argument: take(mtcars, fun = mean)

Use magrittr pipe '%>%' to chain several operations:

     mtcars %>%
        let(mpg_hp = mpg/hp) %>%
        take(mean(mpg_hp), by = am)

To modify variables or add new variables:

      mtcars %>%
         let(new_var = 42,
             new_var2 = new_var*hp) %>%
          head()

To drop variable assign NULL: let(mtcars, am = NULL) %>% head()

For parametric assignment use ':=':

     new_var = "my_var"
     old_var = "mpg"
     mtcars %>%
         let((new_var) := get(old_var)*2) %>%
         head()

For more sophisticated operations see 'query'/'query_if': these functions translates its arguments one-to-one to '[.data.table' method. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'.

# examples form 'dplyr' package
data(mtcars)

# Newly created variables are available immediately
mtcars %>%
    let(
        cyl2 = cyl * 2,
        cyl4 = cyl2 * 2
    ) %>% head()

# You can also use let() to remove variables and
# modify existing variables
mtcars %>%
    let(
        mpg = NULL,
        disp = disp * 0.0163871 # convert to litres
    ) %>% head()


# window functions are useful for grouped computations
mtcars %>%
    let(rank = rank(-mpg, ties.method = "min"),
        by = cyl) %>%
    head()

# You can drop variables by setting them to NULL
mtcars %>%
    let(cyl = NULL) %>%
    head()

# keeps all existing variables
mtcars %>%
    let(displ_l = disp / 61.0237) %>%
    head()

# keeps only the variables you create
mtcars %>%
    take(displ_l = disp / 61.0237)


# can refer to both contextual variables and variable names:
var = 100
mtcars %>%
    let(cyl = cyl * var) %>%
    head()

# filter by condition
mtcars %>%
    take_if(am==0)

# filter by compound condition
mtcars %>%
    take_if(am==0 & mpg>mean(mpg))


# A 'take' with summary functions applied without 'by' argument returns an aggregated data
mtcars %>%
    take(mean = mean(disp), n = .N)

# Usually, you'll want to group first
mtcars %>%
    take(mean = mean(disp), n = .N, by = am)

# grouping by multiple variables
mtcars %>%
    take(mean = mean(disp), n = .N, by = list(am, vs))

# parametric evaluation:
var = quote(mean(cyl))
take(mtcars, eval(var))


# You can group by expressions:
mtcars %>%
    take(
        fun = mean,
        by = list(vsam = vs + am)
    )