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Dplyr Cheat Sheet
dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
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)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.
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).
| < | 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 |
dplyr::select(iris, Sepal.Width, Petal.Length, Species)Select columns by name or helper function.
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.
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.
Mutate uses window functions, functions that take a vector of values and return another vector of values, such as:
dplyr::leadCopy with values shifted by 1.
dplyr::lagCopy with values lagged by 1.
dplyr::dense_rankRanks with no gaps.
dplyr::min_rankRanks. Ties get min rank.
dplyr::percent_rankRanks rescaled to [0, 1].
dplyr::row_numberRanks. Ties got to first value.
dplyr::ntileBin vector into n buckets.
dplyr::betweenAre values between a and b?
dplyr::cume_distCumulative distribution.
dplyr::cumallCumulative all
dplyr::cumanyCumulative any
dplyr::cummeanCumulative mean
cumsumCumulative sum
cummaxCumulative max
cumminCumulative min
cumprodCumulative prod
pmaxElement-wise max
pminElement-wise min
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).
Summarise uses summary functions, functions that take a vector of values and return a single value, such as:
dplyr::firstThe first value of a vector.
dplyr::lastLast value of a vector.
dplyr::nthNth value of a vector.
dplyr::ndplyr::n_distinctIQRIQR of a vector.
minThe minimum value in a vector.
maxThe maximum value in a vector.
meanMean value of a vector.
medianThe median value of a vector.
varThe variance of a vector.
sdThe standard deviation of a vector
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.
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.
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.
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.
null
dplyr::group_by(iris, Species)dplyr::ungroup(iris)iris %>% group_by(Species) %>% summarise(…)iris %>% group_by(Species) %>% mutate(…)library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)null
install.packages("dtplyr")54