"Rolling" Regression in R - r

Question

Say I want to run regressions per group whereby I want to use the last 5 year data as input for that regression. Then, for each next year, I would like to "shift" the input for that regression by one year (i.e., 4 observations).
From those regressions I want to extract both the R2 and the fitted values/residuals, which I then need in subsequent regressions that follow similar notions.
I have some code working using loops, but it is not really elegant nor efficient for large datasets. I assume there must be a nice plyr way for resolving this issue.
# libraries #
library(dplyr)
library(broom)
# reproducible data #
df <- tibble(ID = as.factor(rep(c(1, 2), each = 40)),
YEAR = rep(rep(c(2001:2010), each = 4), 2),
QTR = rep(c(1:4), 20),
DV = rnorm(80),
IV = DV * rnorm(80))
# output vector #
output = tibble(ID = NA,
YEAR = NA,
R2 = NA)
# loop #
k = 1
for (i in levels(df$ID)){
n_row = df %>%
arrange(ID) %>%
filter(ID == i) %>%
nrow()
for (j in seq(1, (n_row - 19), by = 4)){
output[k, 1] = i
output[k, 2] = df %>%
filter(ID == i) %>%
slice((j + 19)) %>%
select(YEAR) %>%
unlist()
output[k, 3] = df %>%
filter(ID == i) %>%
slice(j:(j + 19)) %>%
do(model = lm(DV ~ IV, data = .)) %>%
glance(model) %>%
ungroup() %>%
select(r.squared) %>%
ungroup()
k = k + 1
}
}

Answer 1

Define a function which returns the year and R squared given a subset of rows of df (without ID) and then use rollapply with it.
library(dplyr)
library(zoo)
R2 <- function(x) {
x <- as.data.frame(x)
c(YEAR = tail(x$YEAR, 1), R2 = summary(lm(DV ~ IV, x))$r.squared)
}
df %>%
group_by(ID) %>%
do(data.frame(rollapply(.[-1], 20, by = 4, R2, by.column = FALSE))) %>%
ungroup
giving:
# A tibble: 12 x 3
ID YEAR R2
<fct> <dbl> <dbl>
1 1 2005 0.0133
2 1 2006 0.130
3 1 2007 0.0476
4 1 2008 0.0116
5 1 2009 0.00337
6 1 2010 0.00570
7 2 2005 0.0481
8 2 2006 0.00527
9 2 2007 0.0158
10 2 2008 0.0303
11 2 2009 0.235
12 2 2010 0.116