Condition: Logical vector. True, false: Values to use for TRUE and FALSE values of condition.They must be either the same length as condition, or length 1.They must also be the same type: ifelse checks that they have the same type and same class. All other attributes are taken from true. A consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with 'NA's and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.
Subsetting using the tidyverse
You can also subset
tibbles
using tidyverse functions from package dplyr
. dplyr
verbs are inspired by SQL vocabulary and designed to be more intuitive.The first argument of the main
dplyr
functions is a tibble
(or data.frame)Filtering rows with filter()
filter()
allows us to subset observations (rows) based on their values. The first argument is the name of the data frame. The second and subsequent arguments are the expressions that filter the data frame.dplyr
executes the filtering operation by generating a logical vector and returns a new tibble
of the rows that match the filtering conditions. You can therefore use any logical operators we learnt using [
.Slicing rows with slice()
![R Tidyverse Cheat Sheet R Tidyverse Cheat Sheet](/uploads/1/3/7/6/137651163/760678504.jpg)
Using
slice()
is similar to subsetting using element indices in that we provide element indices to select rows.Selecting columns with select()
select()
allows us to subset columns in tibbles using operations based on the names of the variables.Tidyr Cheat Sheet Pdf
In
dplyr
we use unquoted column names (ie Volume
rather than 'Volume'
).R Tidyverse Cheat Sheet 2018
Behind the scenes,
select
matches any variable arguments to column names creating a vector of column indices. This is then used to subset the tibble
. As such we can create ranges of variables using their names and :
There’s also a number of helper functions to make selections easier. For example, we can use
one_of()
to provide a character vector of column names to select.