Data Wrangling with dplyr and tidyr Cheat Sheet

Data Wrangling
with dplyr and tidyr
Cheat Sheet
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com
Syntax - Helpful conventions for wrangling
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 spreadsheet-like display (note capital V).
Source: local data frame [150 x 5]
Sepal.Length Sepal.Width Petal.Length
1 5.1 3.5 1.4
2 4.9 3.0 1.4
3 4.7 3.2 1.3
4 4.6 3.1 1.5
5 5.0 3.6 1.4
.. ... ... ...
Variables not shown: Petal.Width (dbl),
Species (fctr)
dplyr::%>%
Passes object on left hand side as first argument (or .
argument) of function on righthand side.
"Piping" with %>% makes code more readable, e.g.
iris %>%
group_by(Species) %>%
summarise(avg = mean(Sepal.Width)) %>%
arrange(avg)
x %>% f(y) is the same as f(x, y)
y %>% f(x, ., z) is the same as f(x, y, z )
Reshaping Data - Change the layout of a data set
Subset Observations (Rows) Subset Variables (Columns)
F M A
Each variable is saved
in its own column
F M A
Each observation is
saved in its own row
In a tidy
data set: &
Tidy Data - A foundation for wrangling in R
Tidy data complements R’s vectorized
operations. R will automatically preserve
observations as you manipulate variables.
No other format works as intuitively with R.
FAM
M * A
*
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.
wwwwwwA1005A1013A1010A1010
wwp110110100745451009
wwp110110100745451009 wwp110110100745451009wwp110110100745451009
wppw11010071007110451009100945
wwwww110110110110110 wwww
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 Boolean operators
Logic in R - ?Comparison, ?base::Logic
dplyr::select(iris, Sepal.Width, Petal.Length, Species)
Select columns by name or helper function.
Helper functions for select - ?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.
Learn more with browseVignettes(package = c("dplyr", "tidyr")) • dplyr 0.4.0• tidyr 0.2.0 • Updated: 1/15
wwwwwwA1005A1013A1010A1010
devtools::install_github("rstudio/EDAWR") for data sets

dplyr::group_by(iris, Species)
Group data into rows with the same value of Species.
dplyr::ungroup(iris)
Remove grouping information from data frame.
iris %>% group_by(Species) %>% summarise(…)
Compute separate summary row for each group.
Combine Data Sets
Group Data
Summarise Data Make New Variables
ir ir
C
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 number of rows with each unique value of
variable (with or without weights).
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.
Summarise uses summary functions, functions that
take a vector of values and return a single value, such as:
Mutate uses window functions, functions that take a vector of
values and return another vector of values, such as:
window
function
summary
function
dplyr::first
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
Minimum value in a vector.
max
Maximum value in a vector.
mean
Mean value of a vector.
median
Median value of a vector.
var
Variance of a vector.
sd
Standard deviation of a
vector.
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
iris %>% group_by(Species) %>% mutate(…)
Compute new variables by group.
x1 x2
A 1
B 2
C 3
x1 x3
A T
B F
D T+ =
x1 x2 x3
A 1 T
B 2 F
C 3 NA
x1 x3 x2
A T 1
B F 2
D T NA
x1 x2 x3
A 1 T
B 2 F
x1 x2 x3
A 1 T
B 2 F
C 3 NA
D NA T
x1 x2
A 1
B 2
C 3
x1 x2
B 2
C 3
D 4+ =
x1 x2
B 2
C 3
x1 x2
A 1
B 2
C 3
D 4
x1 x2
A 1
x1 x2
A 1
B 2
C 3
B 2
C 3
D 4
x1 x2 x1 x2
A 1 B 2
B 2 C 3
C 3 D 4
Mutating Joins
Filtering Joins
Binding
Set Operations
dplyr::left_join(a, b, by = "x1")
Join matching rows from b to a.
a b
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.
x1 x2
A 1
B 2
x1 x2
C 3
y z
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.
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com Learn more with browseVignettes(package = c("dplyr", "tidyr")) • dplyr 0.4.0• tidyr 0.2.0 • Updated: 1/15devtools::install_github("rstudio/EDAWR") for data sets

Data Wrangling with dplyr and tidyr Cheat Sheet

Recommended

More Related Content

What's hot (20)

Similar to Data Wrangling with dplyr and tidyr Cheat Sheet (20)

More from Dr. Volkan OBAN (20)

Recently uploaded (20)

Data Wrangling with dplyr and tidyr Cheat Sheet