purrr

I want to use ellipsis parameters inside map function of purrr package. this is a toy example: f1<-function(x,a=NA,b=NA,prs=seq(0, 1, 0.25),SW=T){ if(SW){ res<-data.frame(name1=a,name2=b,t(quantile(x, prs, na.rm = T)), mean=mean(x, na.rm = T), sd=sd(x, na.rm = T), NAs=length(x[is.na(x)]),n=length(x[!is.na(x)]),SWp=shapiro.test(x)$p.value,stringsAsFactors =F) }else { res<-data.frame(name1=a,name2=b,t(quantile(x, prs, na.rm = T)), mean=mean(x, na.rm = T), sd=sd(x, na.rm = T), NAs=length(x[is.na(x)]),n=length(x[!is.na(x)]),stringsAsFactors =F) } return(res) } f1(c(NA,rnorm(25),NA),SW=F) f1(c(NA

I hope I haven't missed it, but I haven't been able to find a working solution to this problem. I have a set of data frames with a shared column. These columns contain multiple and varying transcription errors, some of which are shared, others not, for multiple values. I would like replace/recode the transcription errors (bad_values) with the correct values (good_values) across all data frames. I have tried nesting the map*() family of functions across lists of data frames, bad_values, and good_values to do this, among other things. Here is an example: df1 = data.frame(grp = c("a1","a.","a."

Is there a "tidyverse" way to join a list of data.frames (a la full_join() , but for >2 data.frames)? I have a list of data.frames as a result of a call to map() . I've used Reduce() to do something like this before, but would like to merge them as part of a pipeline - just haven't found an elegant way to do that. Toy example: library(tidyverse) ## Function to make a data.frame with an ID column and a random variable column with mean = df_mean make.df <- function(df_mean){ data.frame(id = 1:50, x = rnorm(n = 50, mean = df_mean)) } ## What I'd love: my.dfs <- map(c(5, 10, 15), make.df) #%>% # <

When using both pipes and the map() function from purrr, I am confused about how data and variables are passed along. For instance, this code works as I expect: library(tidyverse) cars %>% select_if(is.numeric) %>% map(~hist(.)) Yet, when I try something similar using ggplot, it behaves in a strange way. cars %>% select_if(is.numeric) %>% map(~ggplot(cars, aes(.)) + geom_histogram()) I'm guessing this is because the "." in this case is passing a vector to aes(), which is expecting a column name. Either way, I wish I could pass each numeric column to a ggplot function using pipes and map().

I am working on a spatial problem using the sf package in conjunction with dplyr and purrr . I would prefer to perform spatial operations inside a mutate call, like so: simple_feature %>% mutate(geometry_area = map_dbl(geometry, ~ as.double(st_area(.x)))) I like that this approach allows me to run a series of spatial operations using %>% and mutate . I dislike that this approach seems to significantly increase the run-time of the sf functions (sometimes prohibitively) and I would appreciate hearing suggestions about how to overcome this speed loss. Here is a reprex that illustrates the speed

Take this simple dataset and function (representative of more complex problems): x <- data.frame(a = 1:3, b = 2:4) mult <- function(a,b,n) (a + b) * n Using base R's Map I could do this to add 2 new columns in a vectorised fashion: ns <- 1:2 x[paste0("new",seq_along(ns))] <- Map(mult, x["a"], x["b"], n=ns) x # a b new1 new2 #1 1 2 3 6 #2 2 3 5 10 #3 3 4 7 14 purrr attempt via pmap gets close with a list output: library(purrr) library(dplyr) x %>% select(a,b) %>% pmap(mult, n=1:2) #[[1]] #[1] 3 6 # #[[2]] #[1] 5 10 # #[[3]] #[1] 7 14 My attempts from here with pmap_dfr etc all seem to error out