library(dplyr) # 创建一个示例数据框 df <- data.frame(A = 1:5, B = 6:10) # 使用mutate()和across()为多个新列赋值 df <- df %>% mutate(across(c(A, B), ~ . * 2, .names = "new_{.col}")) # 打印结果 print(df) 在上面的代码中,我们首先加载了dplyr包,并创建了一个...
示例代码: # Create a character vector using the names of the columns to remove.# Note the quotes around the column names.to_remove=c('Col2','Col7','dplyrAA','Bdplyr')# Select the complement of the column names in the vector 'to_remove'.dplyr_df%>%select(!all_of(to_remove)) 输...
1. Summarize A. 语法 SUMMARIZE ( <Table>, <GroupBy_ColumnName> [, [<Name>] [, [<Expression...
# 6 columns should get dropped. dplyr_df %>% select(!starts_with(c('Co', 'B'))) 第一个和最后一个命令的输出:> # Look at the column names in our data frame. > names(dplyr_df) [1] "Col1" "Col2" "Col7" "Col8" "dplyrA" "dplyrAA" "Bdplyr" "BBdplyr" > dplyr_df %>...
The main downside of this form is that it forces you to name each intermediate element. If there are natural names, this is a good idea, and you should do it. But many times, like this in this example, there aren’t natural names, and you add numeric suffixes to make the names uniq...
# This will coalesce each of those into one column, without the suffix # Get columns names as symbols (needed for tidyeval stuff) cols_y_syms <- y |> select({{cols_y}}) |> names() |> rlang::syms() # Loop over each of the new columns for (i in seq_along(cols_y_syms)) ...
Is there a better solution for this, preferably usingdplyr? I think you can trymax.collike below mat_hard%>%mutate(Class=names(.)[max.col(.)]) which gives V1 V2 V3 V4 V5 Class12.125366026.427334513.728934861.75138736.2979783V221.832348910.24144095.308464840.334723529.2477737V536.14966785.68944...
# Student names name = c('Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie', 'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny', 'Jakeem','Helena','Ismat','Anila','Skye','Daniel', 'Aisha'), # Study hours study_hours ...
# If you have column names that don't work as attributes, you can use an # alternate "get item" notation with X. diamonds["column w/ spaces"] = range(len(diamonds)) diamonds >> select(X["column w/ spaces"]) >> head()
data.table's := requires column names to be provided, whereas dplyr generates it automatically. In case (b), dplyr's syntax is relatively straightforward. Improving aggregations/updates on multiple functions is on data.table's list. In case (c) though, dplyr would return n() ...