Reference
dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
Syntax
dplyr::tbl_df(iris)
Converts data to tbl class. tbl’s are easier to examine than data frames. R displays only the data that fits onscreen:
dplyr::glimpse(iris)
Information dense summary of tbl data.
utils::View(iris)
View data set in the spreadsheet-like display (note capital V).
dplyr::%>%
Passes object on left hand side as the first argument (or .argument) of function on the righthand side.
x %>% f(y) is the same as f(x, y)
y %>% f(x, ., z) is the same as f(x, y, z )
"Piping" with %>% makes code more readable, e.g.
iris %>%
group_by(Species) %>%
summarise(avg = mean(Sepal.Width)) %>%
arrange(avg)
Reshaping Data
tidyr::gather(cases, "year", "n", 2:4)
Gather columns into rows.
tidyr::unite(data, col, ..., sep)
Unite several columns into one.
dplyr::data_frame(a = 1:3, b = 4:6)
Combine vectors into data frame (optimized).
dplyr::arrange(mtcars, mpg)
Order rows by values of a column (low to high).
dplyr::arrange(mtcars, desc(mpg))
Order rows by values of a column (high to low).
dplyr::rename(tb, y = year)
Rename the columns of a data frame.
tidyr::spread(pollution, size, amount)
Spread rows into columns.
tidyr::separate(storms, date, c("y", "m", "d"))
Separate one column into several.
Subset Observations (Rows)
dplyr::filter(iris, Sepal.Length > 7)
Extract rows that meet logical criteria.
dplyr::distinct(iris)
Remove duplicate rows.
dplyr::sample_frac(iris, 0.5, replace = TRUE)
Randomly select fraction of rows.
dplyr::sample_n(iris, 10, replace = TRUE)
Randomly select n rows.
dplyr::slice(iris, 10:15)
Select rows by position.
dplyr::top_n(storms, 2, date)
Select and order top n entries (by group if grouped data).
Logic in R
| < | Less than | != | Not equal to |
| > | Greater than | %in% | Group membership |
| == | Equal to | is.na | Is NA |
| <= | Less than or equal to | !is.na | Is not NA |
| >= | Greater than or equal to | &,\ | ,!,xor,any,all |
Subset Variables (Columns)
dplyr::select(iris, Sepal.Width, Petal.Length, Species)
Select columns by name or helper function.
Helper functions for select
select(iris, contains("."))
Select columns whose name contains a character string.
select(iris, ends_with("Length"))
Select columns whose name ends with a character string.
select(iris, everything())
Select every column.
select(iris, matches(".t."))
Select columns whose name matches a regular expression.
select(iris, num_range("x", 1:5))
Select columns named x1, x2, x3, x4, x5.
select(iris, one_of(c("Species", "Genus")))
Select columns whose names are in a group of names.
select(iris, starts_with("Sepal"))
Select columns whose name starts with a character string.
select(iris, Sepal.Length:Petal.Width)
Select all columns between Sepal.Length and Petal.Width (inclusive).
select(iris, -Species)
Select all columns except Species.
Make New Variables
dplyr::mutate(iris, sepal = Sepal.Length + Sepal. Width)
Compute and append one or more new columns.
dplyr::mutate_each(iris, funs(min_rank))
Apply window function to each column.
dplyr::transmute(iris, sepal = Sepal.Length + Sepal. Width)
Compute one or more new columns. Drop original columns.
Window function
Mutate uses window functions, functions that take a vector of values and return another vector of values, such as:
dplyr::lead
Copy with values shifted by 1.
dplyr::lag
Copy with values lagged by 1.
dplyr::dense_rank
Ranks with no gaps.
dplyr::min_rank
Ranks. Ties get min rank.
dplyr::percent_rank
Ranks rescaled to [0, 1].
dplyr::row_number
Ranks. Ties got to first value.
dplyr::ntile
Bin vector into n buckets.
dplyr::between
Are values between a and b?
dplyr::cume_dist
Cumulative distribution.
dplyr::cumall
Cumulative all
dplyr::cumany
Cumulative any
dplyr::cummean
Cumulative mean
cumsum
Cumulative sum
cummax
Cumulative max
cummin
Cumulative min
cumprod
Cumulative prod
pmax
Element-wise max
pmin
Element-wise min
Summarise Data
dplyr::summarise(iris, avg = mean(Sepal.Length))
Summarise data into single row of values.
dplyr::summarise_each(iris, funs(mean))
Apply summary function to each column.
dplyr::count(iris, Species, wt = Sepal.Length)
Count the number of rows with each unique value of a variable (with or without weights).
Summary function
Summarise uses summary functions, functions that take a vector of values and return a single value, such as:
dplyr::first
The first value of a vector.
dplyr::last
Last value of a vector.
dplyr::nth
Nth value of a vector.
dplyr::n
of values in a vector.
dplyr::n_distinct
of distinct values in a vector.
IQR
IQR of a vector.
min
The minimum value in a vector.
max
The maximum value in a vector.
mean
Mean value of a vector.
median
The median value of a vector.
var
The variance of a vector.
sd
The standard deviation of a vector
Combine Data Sets
Mutating Joins
dplyr::lef_join(a, b, by = "x1")
Join matching rows from b to a.
dplyr::right_join(a, b, by = "x1")
Join matching rows from a to b.
dplyr::inner_join(a, b, by = "x1")
Join data. Retain only rows in both sets.
dplyr::full_join(a, b, by = "x1")
Join data. Retain all values, all rows.
Filtering Joins
dplyr::semi_join(a, b, by = "x1")
All rows in a that have a match in b.
dplyr::anti_join(a, b, by = "x1")
All rows in a that do not have a match in b.
Set Operations
dplyr::intersect(y, z)
Rows that appear in both y and z.
dplyr::union(y, z)
Rows that appear in either or both y and z.
dplyr::setdiff(y, z)
Rows that appear in y but not z.
Binding
dplyr::bind_rows(y, z)
Append z to y as new rows.
dplyr::bind_cols(y, z)
Append z to y as new columns.
Caution: matches rows by position.
Group Data
null
Group data into rows with the same value of Species.
dplyr::group_by(iris, Species)
Remove grouping information from data frame.
dplyr::ungroup(iris)
Compute separate summary row for each group.
iris %>% group_by(Species) %>% summarise(…)
Compute new variables by group.
iris %>% group_by(Species) %>% mutate(…)
Usage
library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)
Installation
null
You can install from CRAN with
install.packages("dtplyr")
Top comments (0)