tidyeval

问题 How would I create a function that drops NA values in a column if I don't want to specify the column until the function is called? minimal_case <- function(column_name = "a") { enquo_name <- enquo(column_name) example <- tibble(a = c(NA, 1)) print(filter(example, !is.na(a))) print(filter(example, !is.na(rlang::UQ(enquo_name)))) } The output of the first print statement is: # A tibble: 1 x 1 a <dbl> 1 1 The output of the second print statement is: # A tibble: 2 x 1 a <dbl> 1 NA 2 1 How do I

问题 I had a hard time to figure out how I could join two tables using dplyr::left_join with NSE. The problem was that I could not supply the right value to 'by'. I think I have found a solution for now, but it feels like I am doing it in an extra complicated way. So, if you know an easier/more elegant solution, please let me know :) That's what I am doing: # some data df <- dplyr::tibble(x=1:10, y=LETTERS[1:10], z=LETTERS[11:20]) # some function test_fun <- function(df,id){ id <- rlang::enquo(id)

问题 I have a function x_expression() which prints the expression passed to argument x . pacman::p_load(magrittr, rlang) x_expression <- function(x) { print(enquo(x)) } y <- 1 x_expression(y) #> <quosure> #> expr: ^y #> env: global y %>% x_expression() #> <quosure> #> expr: ^. #> env: 0x7ff27c36a610 So you can see that it knows y was passed to it, but when y is piped in with %>% , the function returns prints . . Is there a way to recover the y in the case that it is piped in, or is it gone forever

问题 I have a function x_expression() which prints the expression passed to argument x . pacman::p_load(magrittr, rlang) x_expression <- function(x) { print(enquo(x)) } y <- 1 x_expression(y) #> <quosure> #> expr: ^y #> env: global y %>% x_expression() #> <quosure> #> expr: ^. #> env: 0x7ff27c36a610 So you can see that it knows y was passed to it, but when y is piped in with %>% , the function returns prints . . Is there a way to recover the y in the case that it is piped in, or is it gone forever

问题 The article discussing tidy evaluation in ggplot2 gives the impression that aes() now supports quasiquoation. However, I'm having problems getting it to work with the unquote-splice operator !!! . library( ggplot2 ) ## Predefine the mapping of symbols to aesthetics v <- rlang::exprs( x=wt, y=mpg ) ## Symbol-by-symbol unquoting works without problems ggplot( mtcars, aes(!!v$x, !!v$y) ) + geom_point() ## But unquote splicing doesn't... ggplot( mtcars, aes(!!!v) ) + geom_point() # Error: Can't

问题 Relatively new to tidy evaluation and while the functions I'm making work, I want to know why different helper functions are used. For example, what is the difference between enquo and ensym ? In the function I made below to capture daily average and moving average they're interchangeable: library(dplyr) library(lubridate) library(rlang) library(zoo) manipulate_for_ma <- function(data, group_var, da_col_name, summary_var, ma_col_name) { group_var <- ensym(group_var) summary_var <- enquo

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 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 I am trying to make multiple lags using the least amount of code possible in dplyr, while sticking to tidy eval. The following Standard Evaluation (SE) code works: #if(!require(dplyr)) install.packages("dplyr"); library(dplyr) a=as_tibble(c(1:100)) lags=3 lag_prefix=paste0("L", 1:lags, ".y") multi_lag=setNames(paste("lag(.,", 1:lags, ")"), lag_prefix) a %>% mutate_at(vars(value), funs_(multi_lag)) #final line # A tibble: 100 x 4 value L1.y L2.y L3.y <int> <int> <int> <int> 1 1 NA NA NA 2 2 1

问题 Trying to exclude multiple columns in a call to tidyr::gather() which are served as inputs to my function via a character vector argument (output of shiny::selectInput ) instead of via ... in a programmatic way How would I do that with tidy eval functionality? Since I pass multiple column names via a single function argument, I thought I needed to use !!! (unquote-splicing) instead of !! as layed out in Programming with dplyr. But that doesn't seem to play nicely with tidyselect::vars_select(