![C A B
1 a t
2 b u
3 c v
1 a t
2 b u
3 c v
C A B
A B C
a t 1
b u 2
c v 3
1 a t
2 b u
3 c v
C A B
A.x B.x C A.y B.y
a t 1 d w
b u 2 b u
c v 3 a t
a t 1 d w
b u 2 b u
c v 3 a t
A1 B1 C A2 B2
A B.x C B.y D
a t 1 t 3
b u 2 u 2
c v 3 NA NA
A B D
a t 3
b u 2
d w 1
A B C D
a t 1 3
b u 2 2
c v 3 NA
d w NA 1
A B C D
a t 1 3
b u 2 2
a t 1 3
b u 2 2
d w NA 1
A B C D
A B C D
a t 1 3
b u 2 2
c v 3 NA
A B C A B D
a t 1 a t 3
b u 2 b u 2
c v 3 d w 1
A B C
c v 3
A B C
a t 1
b u 2
A B C
a t 1
b u 2
a t 1
b u 2
A B C
c v 3
d w 4
A B C
c v 3
DF A B C
x a t 1
x b u 2
x c v 3
z c v 3
z d w 4
Counts
dplyr::n() - number of values/rows
dplyr::n_distinct() - # of uniques
sum(!is.na()) - # of non-NA’s
Location
mean() - mean, also mean(!is.na())
median() - median
Logicals
mean() - Proportion of TRUE’s
sum() - # of TRUE’s
Position/Order
dplyr::first() - first value
dplyr::last() - last value
dplyr::nth() - value in nth location of vector
Rank
quantile() - nth quantile
min() - minimum value
max() - maximum value
Spread
IQR() - Inter-Quartile Range
mad() - mean absolute deviation
sd() - standard deviation
var() - variance
Offsets
dplyr::lag() - Offset elements by 1
dplyr::lead() - Offset elements by -1
Cumulative Aggregates
dplyr::cumall() - Cumulative all()
dplyr::cumany() - Cumulative any()
cummax() - Cumulative max()
dplyr::cummean() - Cumulative mean()
cummin() - Cumulative min()
cumprod() - Cumulative prod()
cumsum() - Cumulative sum()
Rankings
dplyr::cume_dist() - Proportion of all values <=
dplyr::dense_rank() - rank with ties = min, no
gaps
dplyr::min_rank() - rank with ties = min
dplyr::ntile() - bins into n bins
dplyr::percent_rank() - min_rank scaled to [0,1]
dplyr::row_number() - rank with ties = "first"
Math
+, - , *, /, ^, %/%, %% - arithmetic ops
log(), log2(), log10() - logs
<, <=, >, >=, !=, == - logical comparisons
Misc
dplyr::between() - x >= left & x <= right
dplyr::case_when() - multi-case if_else()
dplyr::coalesce() - first non-NA values by
element across a set of vectors
dplyr::if_else() - element-wise if() + else()
dplyr::na_if() - replace specific values with NA
pmax() - element-wise max()
pmin() - element-wise min()
dplyr::recode() - Vectorized switch()
dplyr::recode_factor() - Vectorized switch() for
factors
Summary FunctionsVectorized Functions Combine Tables
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", "tibble")) • dplyr 0.5.0 • tibble 1.2.0 • Updated: 2017-01
Combine Variables
bind_cols(…)
Returns tables placed side by
side as a single table.
BE SURE THAT ROWS ALIGN.
left_join(x, y, by = NULL,
copy=FALSE, suffix=c(“.x”,“.y”),…)
Join matching values from y to x.
right_join(x, y, by = NULL, copy =
FALSE, suffix=c(“.x”,“.y”),…)
Join matching values from x to y.
inner_join(x, y, by = NULL, copy =
FALSE, suffix=c(“.x”,“.y”),…)
Join data. Retain only rows with
matches.
full_join(x, y, by = NULL,
copy=FALSE, suffix=c(“.x”,“.y”),…)
Join data. Retain all values, all
rows.
Use a "Mutating Join" to join one table to columns
from another, matching values with the rows that
they correspond to. Each join retains a different
combination of values from the tables.
Use by = c("col1", "col2") to
specify the column(s) to match
on.
left_join(x, y, by = "A")
Use a named vector, by =
c("col1" = "col2"), to match on
columns with different names in
each data set.
left_join(x, y, by = c("C" = "D"))
Use suffix to specify suffix to give
to duplicate column names.
left_join(x, y, by = c("C" = "D"),
suffix = c("1", "2"))
Use bind_cols() to paste tables beside each other
as they are.
A B C
a t 1
b u 2
c v 3
+ =
x y
A B D
a t 3
b u 2
d w 1
Combine Cases
A B C
a t 1
b u 2
Use bind_rows() to paste tables below each other as
they are.
bind_rows(…, .id = NULL)
Returns tables one on top of the other
as a single table. Set .id to a column
name to add a column of the original
table names (as pictured)
intersect(x, y, …)
Rows that appear in both x and z.
setdiff(x, y, …)
Rows that appear in x but not z.
union(x, y, …)
Rows that appear in x or z. (Duplicates
removed). union_all() retains
duplicates.
Extract Rows
Use a "Filtering Join" to filter one table against the
rows of another.
semi_join(x, y, by = NULL, …)
Return rows of x that have a match in y.
USEFUL TO SEE WHAT WILL BE JOINED.
anti_join(x, y, by = NULL, …)
Return rows of x that do not have a
match in y. USEFUL TO SEE WHAT WILL
NOT BE JOINED.
A B C
a t 1
b u 2
c v 3
+
x
z
A B C
c v 3
d w 4
Use setequal() to test whether two data sets contain
the exact same rows (in any order).
A B C
a t 1
b u 2
c v 3
+ =
x y
A B D
a t 3
b u 2
d w 1
to use with summarise()to use with mutate()
mutate() and transmute() apply vectorized
functions to columns to create new columns.
Vectorized functions take vectors as input and
return vectors of the same length as output.
vectorized
function
summarise() applies summary functions to
columns to create a new table. Summary
functions take vectors as input and return single
values as output.
summary
function
Row names
Tidy data does not use rownames, which store
a variable outside of the columns. To work with
the rownames, first move them into a column.
rownames_to_column()
Move row names into col.
a <- rownames_to_column(iris,
var = "C")
column_to_rownames()
Move col in row names.
column_to_rownames(a,
var = "C")
Also has_rownames(), remove_rownames()
a t 1
b u 2
A B C
a t 1
b u 2
c v 3
A B C A B C
a t 3
b u 2
d w 1
a t 1
b u 2
c v 3
A B CA B C
a t 1
b u 2
c v 3
+ =
x y
A B D
a t 3
b u 2
d w 1
A B C
a t 1
b u 2
c v 3 + =
x y
A B D
a t 3
b u 2
d w 1](https://image.slidesharecdn.com/data-transformation-cheatsheet-170605180143/85/Data-transformation-cheatsheet-2-320.jpg)
Data transformation-cheatsheet
- 1. Group Cases group_by(.data, ..., add = FALSE) Returns copy of table grouped by … g_iris <- group_by(iris, Species) ungroup(x, ...) Returns ungrouped copy of table. ungroup(g_iris) wwwwww www Use group_by() to created a "grouped" copy of a table. dplyr functions will manipulate each "group" separately and then combine the results. mtcars %>% group_by(cyl) %>% summarise(avg = mean(mpg)) Summarise Cases These apply summary functions to columns to create a new table. Summary functions take vectors as input and return one value (see back). summary function Variations • summarise_all() - Apply funs to every column. • summarise_at() - Apply funs to specific columns. • summarise_if() - Apply funs to all cols of one type. www www summarise(.data, …) Compute table of summaries. Also summarise_(). summarise(mtcars, avg = mean(mpg)) count(x, ..., wt = NULL, sort = FALSE) Count number of rows in each group defined by the variables in … Also tally(). count(iris, Species) Manipulate Cases RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more with browseVignettes(package = c("dplyr", "tibble")) • dplyr 0.5.0 • tibble 1.2.0 • Updated: 2017-01 Extract Cases Add Cases Arrange Cases filter(.data, …) Extract rows that meet logical criteria. Also filter_(). filter(iris, Sepal.Length > 7) distinct(.data, ..., .keep_all = FALSE) Remove rows with duplicate values. Also distinct_(). distinct(iris, Species) sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = parent.frame()) Randomly select fraction of rows. sample_frac(iris, 0.5, replace = TRUE) sample_n(tbl, size, replace = FALSE, weight = NULL, .env = parent.frame()) Randomly select size rows. sample_n(iris, 10, replace = TRUE) slice(.data, …) Select rows by position. Also slice_(). slice(iris, 10:15) top_n(x, n, wt) Select and order top n entries (by group if grouped data). top_n(iris, 5, Sepal.Width) Row functions return a subset of rows as a new table. Use a variant that ends in _ for non-standard evaluation friendly code. wwwwww wwwwww wwwwww wwwwww Logical and boolean operators to use with filter() See ?base::logic and ?Comparison for help. > >= !is.na() ! & < <= is.na() %in% | xor() wwwwww arrange(.data, ...) Order rows by values of a column (low to high), use with desc() to order from high to low. arrange(mtcars, mpg) arrange(mtcars, desc(mpg)) wwwwww add_row(.data, ..., .before = NULL, .after = NULL) Add one or more rows to a table. add_row(faithful, eruptions = 1, waiting = 1) Manipulate Variables Extract Variables Make New Variables wwww www ww Column functions return a set of columns as a new table. Use a variant that ends in _ for non-standard evaluation friendly code. vectorized function These apply vectorized functions to columns. Vectorized funs take vectors as input and return vectors of the same length as output (see back). Data Transformation with dplyr Cheat Sheet wwwwww www wwww mutate(.data, …) Compute new column(s). mutate(mtcars, gpm = 1/mpg) transmute(.data, …) Compute new column(s), drop others. transmute(mtcars, gpm = 1/mpg) mutate_all(.tbl, .funs, ...) Apply funs to every column. Use with funs(). mutate_all(faithful, funs(log(.), log2(.))) mutate_at(.tbl, .cols, .funs, ...) Apply funs to specific columns. Use with funs(), vars() and the helper functions for select(). mutate_at(iris, vars( -Species), funs(log(.))) mutate_if(.tbl, .predicate, .funs, ...) Apply funs to all columns of one type. Use with funs(). mutate_if(iris, is.numeric, funs(log(.))) add_column(.data, ..., .before = NULL, .after = NULL) Add new column(s). add_column(mtcars, new = 1:32) rename(.data, …) Rename columns. rename(iris, Length = Sepal.Length) w ww Use these helpers with select(), e.g. select(iris, starts_with("Sepal")) contains(match) ends_with(match) matches(match) :, e.g. mpg:cyl -, e.g, -Species Each observation, or case, is in its own row A B C Each variable is in its own column A B C & dplyr functions work with pipes and expect tidy data. In tidy data: pipes x %>% f(y) becomes f(x, y) num_range(prefix, range) one_of(…) starts_with(match) select(.data, …) Extract columns by name. Also select_if() select(iris, Sepal.Length, Species) wwwwww
- 2. C A B 1 a t 2 b u 3 c v 1 a t 2 b u 3 c v C A B A B C a t 1 b u 2 c v 3 1 a t 2 b u 3 c v C A B A.x B.x C A.y B.y a t 1 d w b u 2 b u c v 3 a t a t 1 d w b u 2 b u c v 3 a t A1 B1 C A2 B2 A B.x C B.y D a t 1 t 3 b u 2 u 2 c v 3 NA NA A B D a t 3 b u 2 d w 1 A B C D a t 1 3 b u 2 2 c v 3 NA d w NA 1 A B C D a t 1 3 b u 2 2 a t 1 3 b u 2 2 d w NA 1 A B C D A B C D a t 1 3 b u 2 2 c v 3 NA A B C A B D a t 1 a t 3 b u 2 b u 2 c v 3 d w 1 A B C c v 3 A B C a t 1 b u 2 A B C a t 1 b u 2 a t 1 b u 2 A B C c v 3 d w 4 A B C c v 3 DF A B C x a t 1 x b u 2 x c v 3 z c v 3 z d w 4 Counts dplyr::n() - number of values/rows dplyr::n_distinct() - # of uniques sum(!is.na()) - # of non-NA’s Location mean() - mean, also mean(!is.na()) median() - median Logicals mean() - Proportion of TRUE’s sum() - # of TRUE’s Position/Order dplyr::first() - first value dplyr::last() - last value dplyr::nth() - value in nth location of vector Rank quantile() - nth quantile min() - minimum value max() - maximum value Spread IQR() - Inter-Quartile Range mad() - mean absolute deviation sd() - standard deviation var() - variance Offsets dplyr::lag() - Offset elements by 1 dplyr::lead() - Offset elements by -1 Cumulative Aggregates dplyr::cumall() - Cumulative all() dplyr::cumany() - Cumulative any() cummax() - Cumulative max() dplyr::cummean() - Cumulative mean() cummin() - Cumulative min() cumprod() - Cumulative prod() cumsum() - Cumulative sum() Rankings dplyr::cume_dist() - Proportion of all values <= dplyr::dense_rank() - rank with ties = min, no gaps dplyr::min_rank() - rank with ties = min dplyr::ntile() - bins into n bins dplyr::percent_rank() - min_rank scaled to [0,1] dplyr::row_number() - rank with ties = "first" Math +, - , *, /, ^, %/%, %% - arithmetic ops log(), log2(), log10() - logs <, <=, >, >=, !=, == - logical comparisons Misc dplyr::between() - x >= left & x <= right dplyr::case_when() - multi-case if_else() dplyr::coalesce() - first non-NA values by element across a set of vectors dplyr::if_else() - element-wise if() + else() dplyr::na_if() - replace specific values with NA pmax() - element-wise max() pmin() - element-wise min() dplyr::recode() - Vectorized switch() dplyr::recode_factor() - Vectorized switch() for factors Summary FunctionsVectorized Functions Combine Tables RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more with browseVignettes(package = c("dplyr", "tibble")) • dplyr 0.5.0 • tibble 1.2.0 • Updated: 2017-01 Combine Variables bind_cols(…) Returns tables placed side by side as a single table. BE SURE THAT ROWS ALIGN. left_join(x, y, by = NULL, copy=FALSE, suffix=c(“.x”,“.y”),…) Join matching values from y to x. right_join(x, y, by = NULL, copy = FALSE, suffix=c(“.x”,“.y”),…) Join matching values from x to y. inner_join(x, y, by = NULL, copy = FALSE, suffix=c(“.x”,“.y”),…) Join data. Retain only rows with matches. full_join(x, y, by = NULL, copy=FALSE, suffix=c(“.x”,“.y”),…) Join data. Retain all values, all rows. Use a "Mutating Join" to join one table to columns from another, matching values with the rows that they correspond to. Each join retains a different combination of values from the tables. Use by = c("col1", "col2") to specify the column(s) to match on. left_join(x, y, by = "A") Use a named vector, by = c("col1" = "col2"), to match on columns with different names in each data set. left_join(x, y, by = c("C" = "D")) Use suffix to specify suffix to give to duplicate column names. left_join(x, y, by = c("C" = "D"), suffix = c("1", "2")) Use bind_cols() to paste tables beside each other as they are. A B C a t 1 b u 2 c v 3 + = x y A B D a t 3 b u 2 d w 1 Combine Cases A B C a t 1 b u 2 Use bind_rows() to paste tables below each other as they are. bind_rows(…, .id = NULL) Returns tables one on top of the other as a single table. Set .id to a column name to add a column of the original table names (as pictured) intersect(x, y, …) Rows that appear in both x and z. setdiff(x, y, …) Rows that appear in x but not z. union(x, y, …) Rows that appear in x or z. (Duplicates removed). union_all() retains duplicates. Extract Rows Use a "Filtering Join" to filter one table against the rows of another. semi_join(x, y, by = NULL, …) Return rows of x that have a match in y. USEFUL TO SEE WHAT WILL BE JOINED. anti_join(x, y, by = NULL, …) Return rows of x that do not have a match in y. USEFUL TO SEE WHAT WILL NOT BE JOINED. A B C a t 1 b u 2 c v 3 + x z A B C c v 3 d w 4 Use setequal() to test whether two data sets contain the exact same rows (in any order). A B C a t 1 b u 2 c v 3 + = x y A B D a t 3 b u 2 d w 1 to use with summarise()to use with mutate() mutate() and transmute() apply vectorized functions to columns to create new columns. Vectorized functions take vectors as input and return vectors of the same length as output. vectorized function summarise() applies summary functions to columns to create a new table. Summary functions take vectors as input and return single values as output. summary function Row names Tidy data does not use rownames, which store a variable outside of the columns. To work with the rownames, first move them into a column. rownames_to_column() Move row names into col. a <- rownames_to_column(iris, var = "C") column_to_rownames() Move col in row names. column_to_rownames(a, var = "C") Also has_rownames(), remove_rownames() a t 1 b u 2 A B C a t 1 b u 2 c v 3 A B C A B C a t 3 b u 2 d w 1 a t 1 b u 2 c v 3 A B CA B C a t 1 b u 2 c v 3 + = x y A B D a t 3 b u 2 d w 1 A B C a t 1 b u 2 c v 3 + = x y A B D a t 3 b u 2 d w 1