rlang

问题 I've been using quosures with dplyr: library(dplyr) library(ggplot2) thing <- quo(clarity) diamonds %>% select(!!thing) print(paste("looking at", thing)) [1] "looking at ~" "looking at clarity" I really want to print out the string value put into the quo, but can only get the following: print(thing) <quosure: global> ~clarity print(thing[2]) clarity() substr(thing[2],1, nchar(thing[2])) [1] "clarity" is there a simpler way to "unquote" a quo()? 回答1: We can use quo_name print(paste("looking at

问题 I've been trying for awhile to combine mutate_at with coalesce in case in which names of columns are generated dynamically. In my example there are only five columns, but in the real data there are much more (and not all columns should be included in coalesce step). Example DF: data_example <- data.frame( aa = c(1, NA, NA), bb = c(NA, NA, 2), cc = c(6, 7, 8), aa_extra = c(2, 2, NA), bb_extra = c(1, 2, 3) ) Expected output: aa bb cc aa_extra bb_extra 1 1 1 6 2 1 2 2 2 7 2 2 3 NA 2 8 NA 3

问题 I've been trying for awhile to combine mutate_at with coalesce in case in which names of columns are generated dynamically. In my example there are only five columns, but in the real data there are much more (and not all columns should be included in coalesce step). Example DF: data_example <- data.frame( aa = c(1, NA, NA), bb = c(NA, NA, 2), cc = c(6, 7, 8), aa_extra = c(2, 2, NA), bb_extra = c(1, 2, 3) ) Expected output: aa bb cc aa_extra bb_extra 1 1 1 6 2 1 2 2 2 7 2 2 3 NA 2 8 NA 3

问题 I used to do it, using group_by_ library(dplyr) group_by <- c('cyl', 'vs') mtcars %>% group_by_(.dots = group_by) %>% summarise(gear = mean(gear)) but now group_by_ is deprecated. I don't know how to do it using the tidy evaluation framework. 回答1: There is group_by_at variant of group_by : library(dplyr) group_by <- c('cyl', 'vs') mtcars %>% group_by_at(group_by) %>% summarise(gear = mean(gear)) Above it's simplified version of generalized: mtcars %>% group_by_at(vars(one_of(group_by))) %>%

问题 Using the rlang package, I wonder what is the difference between sym() and parse_expr() . Consider for example the following expressions: ex1 = sym('a') ex2 = parse_expr('a') They both return a identical(ex1, ex2) [1] TRUE Suppose now I need a quosure: ex3 = quo(!!sym('a')) ex4 = quo(!!parse_expr('a')) In both case, the result is: <quosure> expr: ^a env: global identical(ex3, ex4) [1] TRUE However, the two following are not the same for some reasons. ex5 = quo(!!sym('a - b')) ex6 = quo(!

问题 Sometimes when working with dplyr one has a character vector of column names that's to be used to operate on data, for instance: cols_of_interest <- c("Petal.Width", "Petal.Length") In dplyr 0.5.0 and earlier, the recommended approach to this problem was to use the verb_ underscore construct as follows: library("tidyverse") my_cols <- c("Petal.Width", "Petal.Length") iris %>% select_(.dots = my_cols) The verb_ functions are now deprecated in favour of the new tidy evaluation framework (dplyr

问题 Related to this question. I'd like to build a custom pipe %W>% that would silence warnings for one operation library(magrittr) data.frame(a= c(1,-1)) %W>% mutate(a=sqrt(a)) %>% cos will be equivalent to : w <- options()$warn data.frame(a= c(1,-1)) %T>% {options(warn=-1)} %>% mutate(a=sqrt(a)) %T>% {options(warn=w)} %>% cos These two tries don't work : `%W>%` <- function(lhs,rhs){ w <- options()$warn on.exit(options(warn=w)) options(warn=-1) lhs %>% rhs } `%W>%` <- function(lhs,rhs){ lhs <-

问题 Here is the data: library(tidyverse) data <- tibble::tribble( ~var1, ~var2, ~var3, ~var4, ~var5, "a", "d", "g", "hello", 1L, "a", "d", "h", "hello", 2L, "b", "e", "h", "k", 4L, "b", "e", "h", "k", 7L, "c", "f", "i", "hello", 3L, "c", "f", "i", "hello", 4L ) and the vectors, I want to use: filter_var <- c("hello") groupby_vars1 <- c("var1", "var2", "var3") groupby_vars2 <- c("var1", "var2") joinby_vars1 <- c("var1", "var2") joinby_vars2 <- c("var1", "var2", "var3") 2nd & 5th, and 3rd & 4th

问题 This is a follow-up question to (using `rlang` for conditional labelling in `ggplot` using `ggrepel`), which solved the problem I was facing in a custom function using an expression to filter out data while labeling data points. But the answer raised another issue that I don't know how to solve. Here is the custom function that uses rlang to evaluate the user-entered expression to filter out data while attaching labels to data points. And this function works fine when not used inside list

问题 This is a follow-up question to (using `rlang` for conditional labelling in `ggplot` using `ggrepel`), which solved the problem I was facing in a custom function using an expression to filter out data while labeling data points. But the answer raised another issue that I don't know how to solve. Here is the custom function that uses rlang to evaluate the user-entered expression to filter out data while attaching labels to data points. And this function works fine when not used inside list