R: Replacing a for-loop with an apply function - r

Question

I managed to apply a linear regression for each subject of my data frame and paste the values into a new dataframe using a for-loop. However, I think there should be a more readable way of achieving my result using an apply function, but all my attempts fail. This is how I do it:
numberOfFiles <- length(resultsHick$subject)
intslop <- data.frame(matrix(0,numberOfFiles,4))
intslop <- rename(intslop,
subject = X1,
intercept = X2,
slope = X3,
Rsquare = X4)
cond <- c(0:3)
allSubjects <- resultsHick$subject
for (i in allSubjects)
{intslop[i,1] <- i
yvalues <- t(subset(resultsHick,
subject == i,
select = c(H0meanRT, H1meanRT, H2meanRT, H258meanRT)))
fit <- lm(yvalues ~ cond)
intercept <- fit$coefficients[1]
slope <- fit$coefficients[2]
rsquared <- summary(fit)$r.squared
intslop[i,2] <- intercept
intslop[i,3] <- slope
intslop[i,4] <- rsquared
}
The result should look the same as
> head(intslop)
subject intercept slope Rsquare
1 1 221.3555 54.98290 0.9871209
2 2 259.4947 66.33344 0.9781499
3 3 227.8693 47.28699 0.9537868
4 4 257.7355 80.71935 0.9729132
5 5 197.4659 49.57882 0.9730409
6 6 339.1649 61.63161 0.8213179
...
Does anybody know a more readable way of writing this code using an apply function?

Answer 1

One common pattern I use to replace for loops that aggregate data.frames is:
do.call(
rbind,
lapply(1:numberOfDataFrames,
FUN = function(i) {
print(paste("Processing index:", i)) # helpful to see how slow/fast
temp_df <- do_some_work[i]
temp_df$intercept <- 1, etc.
return(temp_df) # key is to return a data.frame for each index.
}
)
)