maditr is on CRAN, so for installation you can print in the console
install.packages("maditr").
Package provides pipe-style interface for data.table package. 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:
rows(mtcars, am==0) - To select columns from data:
columns(mtcars, mpg, vs:carb) - To aggregate data:
take(mtcars, mean_mpg = mean(mpg), by = am) - To aggregate all non-grouping columns:
take_all(mtcars, mean, by = am) - To aggregate several columns with one summary:
take(mtcars, mpg, hp, fun = mean, by = am) - To get total summary skip
byargument: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 drop variable assign NULL:
let(mtcars, am = NULL) %>% head() - To modify all non-grouping variables:
iris %>%
let_all(
scaled = (.x - mean(.x))/sd(.x),
by = Species) %>%
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()
# or,
expr = quote(mean(cyl))
mtcars %>%
let((new_var) := eval(expr)) %>%
head()
# the same with `take`
by_var = "vs,am"
take(mtcars, (new_var) := eval(expr), by = by_var)query_if function translates its arguments one-to-one to [.data.table method. Additionally there are some conveniences such as automatic data.frame conversion to data.table.
Let's make datasets for lookups:
library(maditr)
workers = fread("
name company
Nick Acme
John Ajax
Daniela Ajax
")
positions = fread("
name position
John designer
Daniela engineer
Cathie manager
")
# xlookup
workers = let(workers,
position = xlookup(name, positions$name, positions$position)
)
# vlookup
# by default we search in the first column and return values from second column
workers = let(workers,
position = vlookup(name, positions, no_match = "Not found")
)
# the same
workers = let(workers,
position = vlookup(name, positions,
result_column = "position",
no_match = "Not found") # or, result_column = 2
)
head(workers)
We will use for demonstartion well-known mtcars dataset and some examples from dplyr package.
library(maditr)
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) %>%
head()
# can refer to both contextual variables and variable names:
var = 100
mtcars %>%
let(cyl = cyl * var) %>%
head()
# select rows
mtcars %>%
rows(am==0) %>%
head()
# select rows with compound condition
mtcars %>%
rows(am==0 & mpg>mean(mpg))
# select columns
mtcars %>%
columns(vs:carb, cyl)
mtcars %>%
columns(-am, -cyl)
# regular expression pattern
columns(iris, "^Petal") # variables which start from 'Petal'
columns(iris, "Width$") # variables which end with 'Width'
# move Species variable to the front
# pattern "^." matches all variables
columns(iris, Species, "^.")
# pattern "^.*al" means "contains 'al'"
columns(iris, "^.*al")
# numeric indexing - all variables except Species
columns(iris, 1:4)
# 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))
# 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))
You can use 'columns' inside expression in the 'take'/'let'. 'columns' will be replaced with data.table with selected columns. In 'let' in the expressions with ':=', 'cols' or '%to%' can be placed in the left part of the expression. It is usefull for multiple assignment. There are four ways of column selection:
- Simply by column names
- By variable ranges, e. g. vs:carb. Alternatively, you can use '%to%' instead of colon: 'vs %to% carb'.
- With regular expressions. 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"'.
- By character variables with interpolated parts. Expression in the curly
brackets inside characters will be evaluated in the parent frame with
'text_expand' function. For example,
a{1:3}will be transformed to the names 'a1', 'a2', 'a3'. 'cols' is just a shortcut for 'columns'.
# range selection
iris %>%
let(
avg = rowMeans(Sepal.Length %to% Petal.Width)
) %>%
head()
# multiassignment
iris %>%
let(
# starts with Sepal or Petal
multipled1 %to% multipled4 := cols("^(Sepal|Petal)")*2
) %>%
head()
mtcars %>%
let(
# text expansion
cols("scaled_{names(mtcars)}") := lapply(cols("{names(mtcars)}"), scale)
) %>%
head()
# range selection in 'by'
# selection of range + additional column
mtcars %>%
take(
res = sum(cols(mpg, disp %to% drat)),
by = vs %to% gear
)Here we use the same datasets as with lookups:
workers = fread("
name company
Nick Acme
John Ajax
Daniela Ajax
")
positions = fread("
name position
John designer
Daniela engineer
Cathie manager
")
workers
positionsDifferent kinds of joins:
workers %>% dt_inner_join(positions)
workers %>% dt_left_join(positions)
workers %>% dt_right_join(positions)
workers %>% dt_full_join(positions)
# filtering joins
workers %>% dt_anti_join(positions)
workers %>% dt_semi_join(positions)To suppress the message, supply by argument:
workers %>% dt_left_join(positions, by = "name")Use a named by if the join variables have different names:
positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions'
workers %>% dt_inner_join(positions2, by = c("name" = "worker"))There are a small 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_mutateadds new variables or modify existing variables. If data is data.table then it modifies in-place.dt_summarizecomputes summary statistics. Splits the data into subsets, computes summary statistics for each, and returns the result in the "data.table" form.dt_summarize_allthe same asdt_summarizebut work over all non-grouping variables.dt_filterSelects rows/cases where conditions are true. Rows where the condition evaluates to NA are dropped.dt_selectSelects 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_arrangesorts dataset by variable(-s). Use '-' to sort in desending order. If data is data.table then it modifies in-place.
# 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 "^.*al" means "contains 'al'"
dt_select(iris, "^.*al")
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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