R语言 使用Dplyr将一个函数应用到每个组中在这篇文章中,我们将学习如何使用R编程语言中的dplyr对每个组应用一个函数。R语言中的dplyr包用于数据操作和修改。该包可以通过以下命令下载并安装到工作空间。install.packages("dplyr") R Copy什么是tibbletibble是R语言中一种类似于数据框架的结构,它包含以表格结构排列的...
quantile_df <- function(x, probs = c(0.25, 0.5, 0.75)) { tibble( value = quantile(x, probs, na.rm = TRUE), prob = probs ) } df |> reframe(quantile_df(x), .by = g) # # A tibble: 6 × 3 # g value prob # <dbl> <dbl> <dbl> # 1 1 3.5 0.25 # 2 1 4 0.5 ...
Intersection of two data frames can be easily achieved by using intersect Function in R Dplyr package . Dplyr package in R is provided with intersect() function intersect function in R: intersect function in R takes the rows that appear in both the tables R intersect Function example: First ...
值得一提的是,管道函数还可以用在自定义函数(function)中,比如我们定义一个对向量中的数求整后取绝对值求和的函数。 一般代码: > f1=function(x)sum(abs(round(x))) 管道函数代码 > f2=.%>%round%>abs%>%sum 我们来看看源码的显示: > f1
Mutate Function in R is used to create new variable or column to the dataframe in R. Dplyr package in R is provided with mutate(), mutate_all(), mutate_at()
rowAny <- function(x) rowSums(x, na.rm = TRUE) > 0 df[, 4:6] %>% filter(rowAny(across(everything(), ~. > 90))) # 选择在所有列存在值>90的行 注:从数学逻辑上来说,【存在值>90】等价于【非“所有值都≤90”】,这在 iris 上测试成功,但本例只返回 6 个观测,不知道是不是bug。
Data Manipulation in R With dplyr Package. Grouping Pipe Operator Sample() It is used to generate a sample of a specific size from a vector or a dataset, either with or without replacement. The basic syntax of sample() function is as follows: sample(data, size, replace = FALSE, prob =...
(3, mean = 12, sd = 5), ) my_abs <- function(x) if (x < 0) -x else x df %>% mutate(my_abs(x)) ## --- df %>% mutate(abs(x)) ## --- ifelse_abs <- function(x) ifelse(x < 0, -
Filtering data using dplyr function in R I have a data set of company directors. For example, for company X in 2005 they have 3 directors. So for company x in 2005 have three observations. Each director has a unique ID. Now I want to filter only those observations in which this year ...
join_by() helper function uses unquoted column names and the == boolean operator, which package authors say makes more sense in an R context than c("col1" = "col2"), since = is meant for assigning a value to a variable, not testing for equality.A left join keeps all rows in the...