Source:
R/mutate.R
mutate()
adds new variables and preserves existing ones;transmute()
adds new variables and drops existing ones.New variables overwrite existing variables of the same name.Variables can be removed by setting their value to NULL
.Arguments
.data | A data frame, data frame extension (e.g. a tibble), or alazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, formore details. |
---|---|
.. | < data-masking > Name-value pairs.The name gives the name of the column in the output.The value can be:
|
.keep | This is an experimental argument that allows you to control which columnsfrom .data are retained in the output:
Grouping variables are always kept, unconditional to .keep . |
.before, .after | < tidy-select > Optionally, control where new columnsshould appear (the default is to add to the right hand side). Seerelocate() for more details. |
Data Wrangling with dplyr and tidyr Cheat Sheet- RStudio. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. Data wrangling with dplyr and tidyr cheat sheet tidy data foundation for wrangling in ma ma in tidy data set: each variable is saved in its own column syntax.
Value
The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Install the complete tidyverse with: install.packages ('tidyverse'). Tidyr::nest(data.,.key = data) For grouped data, moves groups into cells as data frames. Unnest a nested data frame with unnest: Species data setos versi virgini Species S.L S.W P.L P.W setosa 5.1 3.5 1.4 0.2 setosa 4.9 3.0 1.4 0.2 setosa 4.7 3.2 1.3 0.2 setosa 4.6 3.1 1.5 0.2 versi 7.0 3.2. R Syntax Comparison:: CHEAT SHEET Even within one syntax, there are o'en variations that are equally valid. As a case study, let’s look at the ggplot2 syntax. Ggplot2 is the plotting package that lives within the tidyverse. If you read down this column, all the code here produces the same graphic. Quickplot ggplot.
An object of the same type as
.data
. The output has the followingproperties:![Tidyr cheat sheet pdf Tidyr cheat sheet pdf](/uploads/1/3/7/2/137281882/706386923.jpeg)
- Rows are not affected.
- Existing columns will be preserved according to the
.keep
argument.New columns will be placed according to the.before
and.after
arguments. If.keep = 'none'
(as intransmute()
), the output orderis determined only by..
, not the order of existing columns. - Columns given value
NULL
will be removed - Groups will be recomputed if a grouping variable is mutated.
- Data frame attributes are preserved.
Useful mutate functions
+
,-
,log()
, etc., for their usual mathematical meaningslead()
,lag()
dense_rank()
,min_rank()
,percent_rank()
,row_number()
,cume_dist()
,ntile()
cumsum()
,cummean()
,cummin()
,cummax()
,cumany()
,cumall()
na_if()
,coalesce()
if_else()
,recode()
,case_when()
Grouped tibbles
Because mutating expressions are computed within groups, they mayyield different results on grouped tibbles. This will be the caseas soon as an aggregating, lagging, or ranking function isinvolved. Compare this ungrouped mutate:
With the grouped equivalent:
The former normalises
mass
by the global average whereas thelatter normalises by the averages within species levels.Methods
These function are generics, which means that packages can provideimplementations (methods) for other classes. See the documentation ofindividual methods for extra arguments and differences in behaviour.
Methods available in currently loaded packages:
mutate()
: dbplyr (tbl_lazy
), dplyr (data.frame
).transmute()
: dbplyr (tbl_lazy
), dplyr (data.frame
).
See also
Other single table verbs:
arrange()
,filter()
,rename()
,select()
,slice()
,summarise()