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Summarise 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)
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Use group_by() to create 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))
These apply summary functions to columns to create a new
table of summary statistics. Summary functions take vectors as
input and return one value (see back).
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.
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summarise(.data, …)!
Compute table of summaries. !
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)
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Each observation, or
case, is in its own row
Each variable is in
its own column
&
dplyr functions work with pipes and expect tidy data. In tidy data:
pipes
x %>% f(y)
becomes f(x, y)
filter(.data, …) Extract rows that meet logical
criteria. filter(iris, Sepal.Length > 7)
distinct(.data, ..., .keep_all = FALSE) Remove
rows with duplicate values. !
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.
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.
See ?base::logic and ?Comparison for help.
>
>=
!is.na()
!
&
<
<=
is.na()
%in%
|
xor()
arrange(.data, …) Order rows by values of a
column or columns (low to high), use with
desc() to order from high to low.
arrange(mtcars, mpg)
arrange(mtcars, desc(mpg))
add_row(.data, ..., .before = NULL, .aer = NULL)
Add one or more rows to a table.
add_row(faithful, eruptions = 1, waiting = 1)
Group Cases
Manipulate Cases
EXTRACT VARIABLES
ADD CASES
ARRANGE CASES
Logical and boolean operators to use with filter()
Column functions return a set of columns as a new vector or table.
contains(match)
ends_with(match)
matches(match)
:, e.g. mpg:cyl
-, e.g, -Species
num_range(prefix, range)
one_of(…)
starts_with(match)
pull(.data, var = -1) Extract column values as
a vector. Choose by name or index.
pull(iris, Sepal.Length)
Manipulate Variables
Use these helpers with select (),
e.g. select(iris, starts_with("Sepal"))
These apply vectorized functions to columns. Vectorized funs take
vectors as input and return vectors of the same length as output
(see back).
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(). Also mutate_if().!
mutate_all(faithful, funs(log(.), log2(.)))
mutate_if(iris, is.numeric, funs(log(.)))
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(.)))
add_column(.data, ..., .before = NULL, .aer =
NULL) Add new column(s). Also add_count(),
add_tally(). add_column(mtcars, new = 1:32)
rename(.data, …) Rename columns.!
rename(iris, Length = Sepal.Length)
MAKE NEW VARIABLES
EXTRACT CASES
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summary function
vectorized function
Data Transformation with dplyr : : CHEAT SHEET
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select(.data, …)
Extract columns as a table. Also select_if().
select(iris, Sepal.Length, Species)
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dplyr
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
dplyr::between() - x >= le & x <= right
dplyr::near() - safe == for floating point
numbers
MISC
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
mutate()andtransmute()apply vectorized
functions to columns to create new columns.
Vectorized functions take vectors as input and
return vectors of the same length as output.
Vector Functions
TO USE WITH MUTATE ()
vectorized function
Summary Functions
TO USE WITH SUMMARISE ()
summarise() applies summary functions to
columns to create a new table. Summary
functions take vectors as input and return single
values as output.
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() - median absolute deviation
sd() - standard deviation
var() - variance
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.
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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")
summary function
Also has_rownames(), remove_rownames()
Combine Tables
COMBINE VARIABLES COMBINE CASES
Use bind_cols() to paste tables beside each
other as they are.
bind_cols(…) Returns tables placed side by
side as a single table.
BE SURE THAT ROWS ALIGN.
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.
le_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 by = c("col1", "col2", …) to
specify one or more common
columns to match on.
le_join(x, y, by = "A")
Use a named vector, by = c("col1" =
"col2"), to match on columns that
have different names in each table.
le_join(x, y, by = c("C" = "D"))
Use suffix to specify the suffix to
give to unmatched columns that
have the same name in both tables.
le_join(x, y, by = c("C" = "D"), suffix =
c("1", "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 y.
setdiff(x, y, …)
Rows that appear in x but not y.
union(x, y, …)
Rows that appear in x or y. !
(Duplicates removed). union_all()
retains duplicates.
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.
Use setequal() to test whether two data sets
contain the exact same rows (in any order).
EXTRACT ROWS
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dplyr