maditr (version 0.7.4)

maditr: maditr: Pipe-Style Interface for 'data.table'

Description

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_all(mtcars, mean, by = am)

• To conditionally aggregate all non-grouping columns: take_all(iris, if(is.numeric(.x)) mean(.x))

• To aggregate several columns with one summary: take(mtcars, mpg, hp, fun = mean, by = am)

• To get total summary skip 'by' argument: take_all(mtcars, 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 modify all non-grouping variables:

      iris %>%
         let_all(
             scaled = (.x - mean(.x))/sd(.x),
             by = Species) %>%
          head()

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

• To aggregate all variables conditionally on name:

      iris %>%
          take_all(
              mean = if(startsWith(.name, "Sepal")) mean(.x),
              median = if(startsWith(.name, "Petal")) median(.x),
              by = Species
          )

• 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

# NOT RUN {
# 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 = cyl)

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

# modify all non-grouping variables in-place
mtcars %>%
    let_all((.x - mean(.x))/sd(.x), by = am) %>%
    head()

# modify all non-grouping variables to new variables
mtcars %>%
    let_all(scaled = (.x - mean(.x))/sd(.x), by = am) %>%
    head()

# conditionally modify all variables
iris %>%
    let_all(mean = if(is.numeric(.x)) mean(.x)) %>%
    head()

# modify all variables conditionally on name
iris %>%
    let_all(
        mean = if(startsWith(.name, "Sepal")) mean(.x),
        median = if(startsWith(.name, "Petal")) median(.x),
        by = Species
    ) %>%
    head()

# aggregation with 'take_all'
mtcars %>%
    take_all(mean = mean(.x), sd = sd(.x), n = .N, by = am)

# conditionally aggregate all variables
iris %>%
    take_all(mean = if(is.numeric(.x)) mean(.x))

# aggregate all variables conditionally on name
iris %>%
    take_all(
        mean = if(startsWith(.name, "Sepal")) mean(.x),
        median = if(startsWith(.name, "Petal")) median(.x),
        by = Species
    )

# parametric evaluation:
var = quote(mean(cyl))
mtcars %>%
    let(mean_cyl = eval(var)) %>%
    head()
take(mtcars, eval(var))

# all together
new_var = "mean_cyl"
mtcars %>%
    let((new_var) := eval(var)) %>%
    head()
take(mtcars, (new_var) := eval(var))

# }