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...
#gather Gather columns into key-value pairs;students2为数据集,grade为第一列,不参与gather;剩下的列名及数据作为键值对放入sex_class(key)和count(value)下。 接下来seperate将sex_class列分成俩列。separate :Separate one column into multiple columns. (2)spread(): # spread:Spread a key-value pair ac...
Issue described (with solutions) here but I feel like this would be an excellent feature, contingent on it not being too nightmarish to program. Currently summarise_at/summarise_if/summarize_at/summarize_if can only be used once, on a si...
summarise(across(height:mass, ~ mean(.x, na.rm =TRUE)))#> # A tibble: 1 × 2#> height mass#> <dbl> <dbl>#> 1 174. 97.3# The _if() variants apply a predicate function (a function that# returns TRUE or FALSE) to determine the relevant subset of# columns. Here we apply mean...
The reason for the message “`summarise()` has grouped output by ‘X’. You can override using the `.groups` argument.” is that the dplyr package drops the last group variable that was specified in the group_by function, in case we are using multiple columns to group our data before ...
(group) %>% summarize(value = mean(value)) %>% as.data.frame() # sum, grouping by multiple columns df %>% group_by(group,subgroup) %>% summarize(value = sum(value)) %>% as.data.frame() # custom function, grouping by one column # in this example we want the sum of all ...
或者，如果我们只需要使用tidyverse函数，则将其转换为'long'格式，其中包含gather，summarize和row，并获得...
使用rowSums对每行求和（rowwise适用于任何聚合，但速度较慢）
summarise_each() and mutate_each() make it easy to apply one or more functions to multiple columns in a tbl (#178).Minor improvementsIf you load plyr after dplyr, you'll get a message suggesting that you load plyr first (#347). as.tbl_cube() gains a method for matrices (#359, @...
4. Using dplyr group_by() on Multiple Columns The group_by() and summarise() also supportgroup by on multiple columnsand summarise on multiple columns. # Group by on multiple columns # & multiple aggregations agg_tbl <- df %>% group_by(department, state) %>% ...