df %>%mutate(z = x + y, .keep ="all")# the default#> # A tibble: 1 × 5#> x y a b z#> <dbl> <dbl> <chr> <chr> <dbl>#> 1 1 2 a b 3df %>%mutate(z = x + y, .keep ="used")#> # A tibble: 1 × 3#> x y z#> <dbl> <dbl> <dbl>#> 1 1 2 3df...
下面的代码和数据源主要来自:https://stackoverflow.com/questions/51063842/create-multiple-columns-in-summarize,以计算分位数为例。 > library(dplyr) > library(tidyr) > > Z<-data.frame(x=runif(1000,min=0,max=20))%>% + mutate(y = rnorm(n(), mean = sin(x))) %>% + group_by(x.cat...
因此照原格式输出4spread(test, grade) %>%5#spread:Spread a key-value pair across multiple columns.6mutate(class=parse_number(class)) %>%7#parse_numeric Extract numeric component of variable.8print
group_by(g) %>%reframe(x = intersect(x, table))#> # A tibble: 5 × 2#> g x#> <dbl> <chr>#> 1 1 a#> 2 1 b#> 3 2 f#> 4 2 d#> 5 2 a# You can add multiple columns at once using a single expression by returning# a data frame.quantile_df <-function(x, probs ...
我们可以使用map
(ArrDelay,na.rm=TRUE))#summarise_each allows you to apply the same summary function to multiple columns at once#Note: mutate_each is also available# for each carrier, calculate the percentage of flights cancelled or divertedflights%>%group_by(UniqueCarrier)%>%summarise_each(funs(mean),...
dplyr::mutate(price_per_carat = price / carat) pryr::object_size(diamonds) #> 3.46 MB pryr::object_size(diamonds2) #> 3.89 MB pryr::object_size(diamonds, diamonds2) #> 3.89 MB pryr::object_size() gives the memoryoccupiedby all of its arguments. The results seem counterintuitive at ...
summarise_eachSummariseandmutatemultiplecolumns. Description Applyoneormorefunctionstooneormorecolumns.Groupingvariablesarealwaycludedfrom modification. Usage summarise_each(tbl,funs,...) summarise_each_(tbl,funs,vars) summarize_each(tbl,funs,...) summarize_each_(tbl,funs,vars) mutate_each(tbl,funs...
2)Example 1: Sums of Columns Using dplyr Package 3)Example 2: Sums of Rows Using dplyr Package 4)Video & Further Resources Let’s do this: Example Data & Add-On Packages First, we have to create some example data: data<-data.frame(x1=1:5,# Example datax2=c(NA,5,1,1, NA), ...
list columns motivation subsetting modeling repeated function calls simulations multiple combinations varying functions dplyr介绍 tidyverse系列应该算是R语言数据分析中的瑞士军刀了,统一的格式,简洁的代码,管道符便于阅读的形式,都能让大家快速上手。R数据科学就是专门讲这个系列的,但是对于很多函数的用法和细节问题,...