Regression analysis with separateing group in R - r

Question

In my dataset, there are two group variables shop and art
here data example
read.csv(reg.csv)
structure(list(shop = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("a", "c"), class = "factor"), art = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("b", "d"), class = "factor"),
Y = c(177L, 122L, 175L, 140L, 201L, 202L, 279L, 253L, 236L,
137L, 166L, 241L, 195L, 221L, 238L, 203L, 254L, 219L, 101L,
157L, 188L, 219L, 267L, 126L, 291L, 239L, 230L), x1 = c(1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L), x2 = c(0L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), x3 = c(0L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L), x4 = c(0L, 0L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L), x5 = c(0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 0L), x6 = c(0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L), x7 = c(1L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 0L), x8 = c(0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L), x9 = c(1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L,
1L, 1L, 0L)), .Names = c("shop", "art", "Y", "x1", "x2",
"x3", "x4", "x5", "x6", "x7", "x8", "x9"), class = "data.frame", row.names = c(NA,
-27L))
I need perform regression analysis for all groups separately.
The formula is simple
mymodel=lm(y~.,data=reg)
I.e. i must perform analysis for a+b group and c+d group separately.
In this example we have only 2 groups(a+b and c+d)
where a,c-mean name of shop, and b,d -mean name of vendor code.
How can i perform regression ananysis separately by groups, cause in real data, there are several ten groups, manually divide on the datasets it's impossible.

Answer 1

This is a relatively common analytical pattern called split - apply - combine and it is fairly easy to perform with R:
library(tidyverse)
library(broom)
create a function for lm:
my_lm <- function(df) {
lm(Y ~ ., data = df)
}
run the models on nested groups of data:
df %>%
group_by(art, shop) %>%
nest() %>%
mutate(fit = map(data, my_lm),
tidy = map(fit, tidy)) %>%
select(-fit, - data) %>%
unnest()
First you group by the variables by the desired variables, fit the lm model to the groups use tidy to extract the coefficients, remove unwanted columns and then unnest. The result is:
#output
art shop term estimate std.error statistic p.value
<fctr> <fctr> <chr> <dbl> <dbl> <dbl> <dbl>
1 b a (Intercept) 31.0 269 0.115 0.927
2 b a x1 109 153 0.714 0.605
3 b a x2 - 23.0 223 -0.103 0.934
4 b a x3 - 15.0 185 -0.0810 0.949
5 b a x4 31.0 333 0.0931 0.941
6 b a x5 81.0 457 0.177 0.888
7 b a x6 77.0 162 0.475 0.718
8 b a x7 - 17.0 310 -0.0548 0.965
9 b a x8 - 15.0 214 -0.0700 0.956
10 b a x9 54.0 349 0.155 0.902
11 d c (Intercept) 199 98.8 2.01 0.0907
12 d c x1 - 15.7 60.8 -0.259 0.804
13 d c x2 5.98 48.8 0.123 0.906
14 d c x3 7.34 57.8 0.127 0.903
15 d c x4 - 20.1 53.8 -0.373 0.722
16 d c x5 - 43.2 41.8 -1.03 0.342
17 d c x6 1.93 34.5 0.0560 0.957
18 d c x7 31.9 40.5 0.787 0.461
19 d c x8 36.0 45.9 0.786 0.462
20 d c x9 10.7 49.7 0.215 0.837
There are many tutorials using the same or similar approach like the one I posted in my comment.