CODEWITHSUNDEEP
rtidyversebroom
TanZor
TanZor

Reputation: 237

alternative to broom::tidy?

In my analysis code I've been using broom::tidy to extract a regression slope per group in a data frame:

E1.first_trial_df <- data.frame( 
  search_type  = factor(rep(1:3, each = 10)),
  set_size    = rep(1:10,3),
  RT     = runif(300, min = 0, max = 2500)
)

E1.search_slopes_first_trial <- E1.first_trial_df %>%
  group_by(search_type) %>%
  do(model=lm(RT~set_size,data=.)) %>%
  broom::tidy(model) %>%
  filter(term=='set_size')

I had an issue with R so I reinstalled R, Rstudio and all my packages, and I guess on the way I've upgraded to a new version of broom. So I'm now getting the following error message:

Error in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm) : Calling var(x) on a factor x is defunct. Use something like 'all(duplicated(x)[-1L])' to test for a constant vector. In addition: Warning messages: 1: Data frame tidiers are deprecated and will be removed in an upcoming release of broom. 2: In mean.default(X[[i]], ...) : argument is not numeric or logical: returning NA 3: In mean.default(X[[i]], ...) : argument is not numeric or logical: returning NA

Any idea what's happening here, and how to fix it? Thanks!

Upvotes: 2

Views: 1197

Answers (2)

Nad Pat
Nad Pat

Reputation: 3173

An alternate to tidyr using base package,

df = E1.first_trial_df
df$search_type = as.factor(df$search_type)
#Splitting data frame 
df = split(df, df$search_type)

#Using `lm` to fit the model
lmr = lapply(df, function(x){
  lmr = lm(x$RT ~ x$set_size, x)
})

#Extract regression co-efficient for each model
rslope = lapply(lmr, function(x) x[["coefficients"]][["(Intercept)"]])
rslope = unlist(rslope)
#Extract intercept for each model
intercept = lapply(lmr, function(x) x[["coefficients"]][["x$set_size"]])
intercept = unlist(intercept)

Upvotes: 3

Anoushiravan R
Anoushiravan R

Reputation: 21938

You could modify your code in the following way:

library(tidyverse)
library(broom)

E1.first_trial_df %>%
  group_by(search_type) %>%
  nest() %>%
  mutate(model = map(data, ~ lm(RT ~ set_size, data = .x)), 
         tidy = map(model, ~ tidy(.x))) %>%
  unnest(tidy) %>%
  filter(term == 'set_size') %>%
  select(!c(data, model))

# A tibble: 3 x 6
# Groups:   search_type [3]
  search_type term     estimate std.error statistic p.value
  <fct>       <chr>       <dbl>     <dbl>     <dbl>   <dbl>
1 1           set_size   -0.898      23.9   -0.0376   0.970
2 2           set_size  -27.0        24.4   -1.11     0.271
3 3           set_size   -2.00       26.0   -0.0768   0.939

Upvotes: 1

Related Questions