tidyeval

I'm sorry for the confusion but eventually, the first example I posted (at the bottom of the page), did not help me to figure out how tidyeval works with mutate, so I'm adding a new example. I would like to create a function that takes three args: a dataframe the column(s) to mutate a variable (from the dataframe) to replace the values that are being mutated For instance, to replace the values in mpg with the values from carb I tried this: I tried this colToX <- function(dt, ..., repl) { cols <- quos(...) repl <- quo(repl) mutate_at(dt, vars(!!!cols), funs(function(x) !!repl)) } colToX(mtcars,

I am trying to write a function in R that summarizes a data frame according to grouping variables. The grouping variables are given as a list and passed to group_by_at , and I would like to parametrize them. What I am doing now is this: library(tidyverse) d = tribble( ~foo, ~bar, ~baz, 1, 2, 3, 1, 3, 5 4, 5, 6, 4, 5, 1 ) sum_fun <- function(df, group_vars, sum_var) { sum_var = enquo(sum_var) return( df %>% group_by_at(.vars = group_vars) %>% summarize(sum(!! sum_var)) ) } d %>% sum_fun(group_vars = c("foo", "bar"), baz) However, I would like to call the function like so: d %>% sum_fun(group

I cannot find in dplyr 0.7 a way to replace the mutate_ function which is going to be deprecated. The mutate_ function is useful in my use case : I store in a database (string format) many instructions (that can be filtered if needed) and apply these instructions to one or several data frames. For example : dplyr::tibble(test = "test@test") %>% dplyr::mutate_(.dots = list("test2" = "substr(test, 1, 5)", "test3" = "substr(test, 5, 5)")) Is there a way to do this with dplyr 0.7 keeping variables and instructions as character? To expand a little bit on MrFlick 's example, let's assume you have a

I would like to understand how to pass strings representing expressions into dplyr, so that the variables mentioned in the string are evaluated as expressions on columns in the dataframe. The main vignette on this topic covers passing in quosures, and doesn't discuss strings at all. It's clear that quosures are safer and clearer than strings when representing expressions, so of course we should avoid strings when quosures can be used instead. However, when working with tools outside the R ecosystem, such as javascript or YAML config files, one will often have to work with strings instead of

I want to scale my y-var by multiplying it by a number, say 10, in ggplot. The problem is this is in a Shiny app and the variable must be passed as a character string, i.e. input$variable . How can I multiply one of the variables in aes_string() the same way I would in aes() ? Here is an example of when it fails: library(ggplot2) ggplot(data = subset(mtcars, cyl == 4), aes_string(x = "wt", y = "mpg")) + geom_line(size = 1.5, color = "#00868B") + geom_line(data = subset(mtcars, cyl == 8), aes_string(x = "wt", y = "mpg" * 10)) Error in "mpg" * 10 : non-numeric argument to binary operator You can

I am trying to search a database and then label the ouput with a name derived from the original search, "derived_name" in the reproducible example below. I am using a dplyr pipe %>% , and I am having trouble with quasiquotation and/or non-standard evaluation. Specifically, using count_colname , a character object derived from "derived_name" , in the final top_n() function fails to subset the dataframe. search_name <- "derived_name" set.seed(1) letrs <- letters[rnorm(52, 13.5, 5)] letrs_count.df <- letrs %>% table() %>% as.data.frame() count_colname <- paste0(search_name, "_letr_count")

I am building a shiny app which needs to allow users to define new variables for plotting. Specifically I want to allow users to define an expression to be used in mutate verb. The server receives the expression as text and I am wondering how to make mutate execute it in dplyr 0.7. I can make it work (partially) using mutate_ but it is deprecated now. It also defines the new column name as the entire expression rather than the new variable Here is a reproducible example: input_from_shiny <- "Petal.ratio = Petal.Length/Petal.Width" iris_mutated <- iris %>% mutate_(input_from_shiny) This gives