purrr

I am trying to do an exercise to become more familiar with how to use the map function in purrr. I am creating some random data (10 columns of 10 datapoints) and then I wanted to use map to perform a series of regressions (i.e. lm(y ~ x, data = )) over the resulting columns in the data frame. If I just repeatedly use the first column as 'y', I want to perform 10 regressions with each column from 1 to 10 as 'x'. Obviously the results are unimportant - it's just the method. I want to end up with a list of 10 linear model objects. list_of_vecs <- list() for (i in 1:10){ list_of_vecs[[paste('vec_'

I'm trying to make a list of lm object using purrr::map. use mtcars as an example: vars <- c('hp', 'wt', 'disp') map(vars, ~lm(mpg~.x, data=mtcars)) error: Error in model.frame.default(formula = mpg ~ .x, data = mtcars, drop.unused.levels = TRUE) : variable lengths differ (found for '.x') I also tried: map(vars, function(x) {x=sym(x); lm(mpg~!!x, data=mtcars)}) I got error message: Error in !x : invalid argument type Can anyone tell what I did wrong? Thanks in advance. The usual way is to paste together formulas as strings, convert them by map ping as.formula (you can't make a vector of

I just came across the the purrr package and I think this would help me out a bit in terms of what I want to do - I just can't put it together. I think this is going to be along post but goes over a common use case I think many others run into so hopefully this is of use to them as well. This is what I'm aiming for: From one big dataset run multiple models on each of the different subgroups. Have these models readily available so I can examine - for coeffients, accuracy, etc. From this saved model list for each of the different groupings, be able to apply the corresponding model to the

I currently have the following R code that runs multiple regression models with different predictors, across different subsets, and returns tidied output using the broom package. library(dplyr) library(purrr) library(broom) cars <- mtcars preds<-c("disp", "drat", "wt") model_fits <- map_df(preds, function(pred) { model_formula <- sprintf("mpg ~ %s", pred) cars %>% group_by(cyl) %>% do(tidy(lm(model_formula, data = .), conf.int = T)) %>% filter(term == pred) %>% mutate(outcome = "mpg") %>% select(outcome, cyl:estimate, starts_with("conf.")) }) This results in the following data frame: > model

Please help me 1) Why does map_if not work within a list 2) Is there a way to make it work 3) If not, what are the alternatives Thanks in advance. library(dplyr) library(purrr) cyl <- split(mtcars, mtcars$cyl) # This works map_if(mtcars, is.numeric, mean) # This does not work map_if(cyl, is.numeric, mean) Because you need to map to one lever lower, the columns are at level 2. So you can do: map(cyl, ~map_if(., is.numeric, mean)) Or: map(cyl, map_if, is.numeric, mean) Without the if one could do map_depth(cyl, 2, mean) count You can try lapply : lapply(cyl, function(x) map_if(x, is.numeric,

I'm trying to use map() of purrr package to apply filter() function to the data stored in a nested data frame. "Why wouldn't you filter first, and then nest? - you might ask. That will work (and I'll show my desired outcome using such process), but I'm looking for ways to do it with purrr . I want to have just one data frame, with two list-columns, both being nested data frames - one full and one filtered. I can achieve it now by performing nest() twice: once on all data, and second on filtered data: library(tidyverse) df <- tibble( a = sample(x = rep(c('x','y'),5), size = 10), b = sample(c(1