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https://github.com/gdemin/maditr

Fast Data Aggregation, Modification, and Filtering
https://github.com/gdemin/maditr

data-table magrittr pipes r

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Fast Data Aggregation, Modification, and Filtering

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# maditr: Fast Data Aggregation, Modification, and Filtering

[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/maditr)](https://cran.r-project.org/package=maditr)
[![](https://cranlogs.r-pkg.org/badges/maditr)](https://cran.rstudio.com/web/packages/maditr/index.html)
[![](https://cranlogs.r-pkg.org/badges/grand-total/maditr)](https://cran.rstudio.com/web/packages/maditr/index.html)
[![Coverage Status](https://img.shields.io/codecov/c/github/gdemin/maditr/master.svg)](https://codecov.io/github/gdemin/maditr?branch=master)

### Links

- [maditr on CRAN](https://cran.r-project.org/package=maditr)
- [maditr on Github](https://github.com/gdemin/maditr)
- [Issues](https://github.com/gdemin/maditr/issues)

### Installation

`maditr` is on CRAN, so for installation you can print in the console
`install.packages("maditr")`.

## Overview

Package provides pipe-style interface for [data.table](https://cran.r-project.org/package=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 `by` argument: `take_all(mtcars, mean)`
- Use magrittr pipe `%>%` to chain several operations:
```R
mtcars %>%
let(mpg_hp = mpg/hp) %>%
take(mean(mpg_hp), by = am)
```
- To modify variables or add new variables:
```R
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:
```R
iris %>%
let_all(
scaled = (.x - mean(.x))/sd(.x),
by = Species) %>%
head()
```
- To aggregate all variables conditionally on name:
```R
iris %>%
take_all(
mean = if(startsWith(.name, "Sepal")) mean(.x),
median = if(startsWith(.name, "Petal")) median(.x),
by = Species
)
```
- For parametric assignment use `:=`:
```R
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`.

## vlookup & xlookup

Let's make datasets for lookups:
```{r include=FALSE}
library(maditr)
```

```{r}

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)
```

### More examples

We will use for demonstartion well-known `mtcars` dataset and some examples from `dplyr` package.

```R
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))

```

## Variable selection in the expressions

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:

1. Simply by column names
2. By variable ranges, e. g. vs:carb. Alternatively, you can use '%to%'
instead of colon: 'vs %to% carb'.
3. 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"'.
4. 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'.

```R
# 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
)
```

## Joins

Here we use the same datasets as with lookups:

```R
workers = fread("
name company
Nick Acme
John Ajax
Daniela Ajax
")

positions = fread("
name position
John designer
Daniela engineer
Cathie manager
")

workers
positions
```

Different kinds of joins:

```R
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:
```R
workers %>% dt_left_join(positions, by = "name")
```

Use a named `by` if the join variables have different names:
```R
positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions'
workers %>% dt_inner_join(positions2, by = c("name" = "worker"))
```

## 'dplyr'-like interface for data.table.

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_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` 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.

```R
# 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

```