![]() ![]() quantile() was hard to use previously because it returns multiple values. To demonstrate this new flexibility in a more useful situation, let’s take a look at quantile(). This is a big change to summarise() but it should have minimal impact on existing code because it broadens the interface: all existing code will continue to work, and a number of inputs that would have previously errored now work. The R MASS package contains a data frame, anorexia, containing weight change data for young female anorexia patients. Summarise multiple columns summariseall dplyr Summarise multiple columns Source: R/colwise-mutate.R Scoped verbs ( if, at, all) have been superseded by the use of pick () or across () in an existing verb. ![]() filter () picks cases based on their values. ![]() To put this another way, before dplyr 1.0.0, each summary had to be a single value (one row, one column), but now we’ve lifted that restriction so each summary can generate a rectangle of arbitrary size. dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges: mutate () adds new variables that are functions of existing variables select () picks variables based on their names. It performs faster for in-memory data by writing key. (This isn’t very useful when used directly, but as you’ll see shortly, it’s really useful inside of functions.) 6.1.1 dplyr package It identifies the most important data manipulations and make them easy to use from R. Df %>% group_by ( grp ) %>% summarise ( tibble ( min = min ( x ), mean = mean ( x ))) #> `summarise()` ungrouping output (override with `.groups` argument) #> # A tibble: 2 x 3 #> grp min mean #> * #> 1 1 -2.69 -0.843 #> 2 2 -2.73 -0.434 ![]()
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