Suppose I have a data frame called pop, and I wish to split this data frame by a categorical variable called replicate. This replicate consists out of 110 categories, and I wish to perform analyses on each data frame then the output of each must be combined to create a new data frame. In other words suppose it is replicate i then I wish to create data frame i and perform a logistic regression on i and save beta 0 for i. All the beta 0 will be combined to create a table with all the beta 0 for replicate 1-110. I know that's A mouth full but thanks in advance.
Since you didn't give some sample data I will use mtcars. You can use split to split a data.frame on a categorical value. Combining this with map and tidy from the purrr and broom packages you can create a dataframe with all the beta's in one go.
So what happens is 1: split data.frame, 2: run regression model 3: tidy data to get the coefficients out and create a data.frame of the data.
You will need to adjust this to your data.frame and replicate variable. Broom can handle logistic regression so everything should work out.
library(purrr)
library(broom)
my_lms <- mtcars %>%
split(.$cyl) %>%
map(~ lm(mpg ~ wt, data = .x)) %>%
map_dfr(~ tidy(.))
my_lms
term estimate std.error statistic p.value
1 (Intercept) 39.571196 4.3465820 9.103980 7.771511e-06
2 wt -5.647025 1.8501185 -3.052251 1.374278e-02
3 (Intercept) 28.408845 4.1843688 6.789278 1.054844e-03
4 wt -2.780106 1.3349173 -2.082605 9.175766e-02
5 (Intercept) 23.868029 3.0054619 7.941551 4.052705e-06
6 wt -2.192438 0.7392393 -2.965803 1.179281e-02
EDIT
my_lms <- lapply(split(mtcars, mtcars$cyl), function(x) lm(mpg ~ wt, data = x))
my_coefs <- as.data.frame(sapply(my_lms, coef))
my_coefs
4 6 8
(Intercept) 39.571196 28.408845 23.868029
wt -5.647025 -2.780106 -2.192438
#Or transpose the coefficents if you want column results.
t(my_coefs)
(Intercept) wt
4 39.57120 -5.647025
6 28.40884 -2.780106
8 23.86803 -2.192438