nse

I recently notices that rlang::sym doesn't seem to work in anonymous functions and I don't understand why. Here an example, it's pretty clumsy and ugly but I think it illustrates the point require(tidyverse) data <- tibble(x1 = letters[1:3], x2 = letters[4:6], val = 1:3) get_it <- function(a, b){ data %>% mutate(y1 = !!rlang::sym(a)) %>% mutate(y2 = !!rlang::sym(b)) %>% select(y1, y2, val) } get_it("x1", "x2") This defines some toy data and a (horrible) function that essentially renames the columns based on column names. Now I can do the same thing for different combinations of a and b: d <-

Hi following Programming with dplyr I noticed that one can add a name using quo_name. I was wondering how to do this for multiple columns, eg. like a quos_name of sorts. E.g.: my_mutate <- function(df, expr) { expr <- enquo(expr) mean_name <- paste0("mean_", quo_name(expr)) sum_name <- paste0("sum_", quo_name(expr)) mutate(df, !!mean_name := mean(!!expr), !!sum_name := sum(!!expr) ) } becomes my_mutate <- function(df, ...) { exprs <-quos(...) mean_names <- paste0("mean_", quos_name(exprs)) sum_names <- paste0("sum_", quos_name(exprs)) mutate(df, !!!mean_names := mean(!!!exprs), !!!sum_names :=

I am trying to assign a column name to a variable using mutate. df <-data.frame(x = sample(1:100, 50), y = rnorm(50)) new <- function(name){ df%>%mutate(name = ifelse(x <50, "small", "big")) } When I run new(name = "newVar") it doesn't work. I know mutate_() could help but I'm struggling in using it together with ifelse . Any help would be appreciated. Using dplyr 0.7.1 and its advances in NSE, you have to UQ the argument to mutate and then use := when assigning. There is lots of info on programming with dplyr and NSE here: https://cran.r-project.org/web/packages/dplyr/vignettes/programming

Non standard evaluation is really handy when using dplyr's verbs. But it can be problematic when using those verbs with function arguments. For example let us say that I want to create a function that gives me the number of rows for a given species. # Load packages and prepare data library(dplyr) library(lazyeval) # I prefer lowercase column names names(iris) <- tolower(names(iris)) # Number of rows for all species nrow(iris) # [1] 150 Example not working This function doesn't work as expected because species is interpreted in the context of the iris data frame instead of being interpreted in

This question already has an answer here: Reshaping multiple sets of measurement columns (wide format) into single columns (long format) 7 answers I am trying to write a function that will convert this data frame library(dplyr) library(rlang) library(purrr) df <- data.frame(obj=c(1,1,2,2,3,3,3,4,4,4), S1=rep(c("a","b"),length.out=10),PR1=rep(c(3,7),length.out=10), S2=rep(c("c","d"),length.out=10),PR2=rep(c(7,3),length.out=10)) obj S1 PR1 S2 PR2 1 1 a 3 c 7 2 1 b 7 d 3 3 2 a 3 c 7 4 2 b 7 d 3 5 3 a 3 c 7 6 3 b 7 d 3 7 3 a 3 c 7 8 4 b 7 d 3 9 4 a 3 c 7 10 4 b 7 d 3 In to this data frame df %>%