tidyeval

In the following example, why should we favour using f1 over f2 ? Is it more efficient in some sense? For someone used to base R, it seems more natural to use the "substitute + eval" option. library(dplyr) d = data.frame(x = 1:5, y = rnorm(5)) # using enquo + !! f1 = function(mydata, myvar) { m = enquo(myvar) mydata %>% mutate(two_y = 2 * !!m) } # using substitute + eval f2 = function(mydata, myvar) { m = substitute(myvar) mydata %>% mutate(two_y = 2 * eval(m)) } all.equal(d %>% f1(y), d %>% f2(y)) # TRUE In other words, and beyond this particular example, my question is: can I get get away

Consider this simple example library(dplyr) library(ggplot2) dataframe <- data_frame(id = c(1,2,3,4), group = c('a','b','c','c'), value = c(200,400,120,300)) # A tibble: 4 x 3 id group value <dbl> <chr> <dbl> 1 1 a 200 2 2 b 400 3 3 c 120 4 4 c 300 Here I want to write a function that takes the dataframe and the grouping variable as input. Ideally, after grouping and aggregating I would like to print a ggpplot chart. This works: get_charts2 <- function(data, mygroup){ quo_var <- enquo(mygroup) df_agg <- data %>% group_by(!!quo_var) %>% summarize(mean = mean(value, na.rm = TRUE), count = n()) %

I'm confused about how to pass function argument into dplyr and ggplot codes. I'm using the newest version of dplyr and ggplot2 Here is my code to produce a barplot (clarity vs mean price) diamond.plot<- function (data, group, metric) { group<- quo(group) metric<- quo(metric) data() %>% group_by(!! group) %>% summarise(price=mean(!! metric)) %>% ggplot(aes(x=!! group,y=price))+ geom_bar(stat='identity') } diamond.plot(diamonds, group='clarity', metric='price') error: Error in UseMethod("group_by_") : no applicable method for 'group_by_' applied to an object of class "packageIQR" For the newest