# Issue

I have a data frame with many Y and X variables. I would like to fit multiple single linear models with `lm()`

by iterating through all of the X and Y variables. I'm working my way to including the other Y variables, but I'm struggling just iterating through the X variables.

My data looks something like this:

```
set.seed(200)
df <- data.frame(y1 = c(rnorm(n=20, mean = 5)),
y2 = c(rnorm(n=20, mean = 5)),
x1 = c(rnorm(n=20, mean = 13)),
x2 = c(rnorm(n=20, mean = 14)),
x3 = c(rnorm(n=20, mean = 15)))
```

I have tried multiple ways of fitting these models, but the best way seems to be using a for loop.

```
models <- list() #creating an empty list
for (i in names(df)[3:5]){ #choosing just the x-variables from the df
models[[i]] <- lm(y1 ~ get(i), df)
}
```

My outputs are in the `models`

list, and I can access the statistics I want through `summary(models[[1]]`

but I don't want to have to do this for each model that was fit. Is there a way to extract the statistics I want using `do.call`

or `map_df`

or something? Specifically I want the `r.squared`

, `residual standard error`

, `p-value`

, and `f.statistic`

.

# Solution

This example is based on Chapter 25 of Wickham & Grolemund's "R for Data Science. Give it a read for the explanation.

```
library(dplyr)
library(modelr)
library(tidyverse)
set.seed(200)
df <- data.frame(y1 = c(rnorm(n=20, mean = 5)),
y2 = c(rnorm(n=20, mean = 5)),
x1 = c(rnorm(n=20, mean = 13)),
x2 = c(rnorm(n=20, mean = 14)),
x3 = c(rnorm(n=20, mean = 15)))
#Set up your data so that you nest each set of variables as dataframe within a dataframe
dfy <- df %>% select(starts_with("y"))
dfx <- df %>% select(starts_with("x"))
dat_all <- data.frame()
for (y in names(dfy)){
for(x in names(dfx)){
r <- paste(x,"_",y)
data = (data.frame(x = dfx[x], y = dfy[y]))
names(data) <- c("x", "y")
dd <- data.frame(vars = r, data = data) %>%
group_by(vars) %>%
nest()
dat_all <- rbind(dat_all, dd)
}
}
myModel <- function(df) {
lm(data.x ~ data.y, data = df)
}
dat_all <- dat_all %>%
mutate(model = map(data, myModel))
glance <- dat_all %>%
mutate(glance = map(model, broom::glance)) %>%
unnest(glance, .drop = TRUE)
glance %>%
select(r.squared, p.value)
#vars r.squared p.value
#<chr> <dbl> <dbl>
#1 x1 _ y1 0.00946 0.683
#2 x2 _ y1 0.00474 0.773
#3 x3 _ y1 0.00442 0.781
#4 x1 _ y2 0.106 0.162
#5 x2 _ y2 0.0890 0.201
#6 x3 _ y2 0.0000162 0.987
```

Answered By - stomper Answer Checked By - Katrina (PHPFixing Volunteer)

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