I have a dataset with 26 variables and 4662 observation over one year. I want to analyse differences which may occur after a specific date. There is the variable time which is 0 if it is before the date and 1 if it is after. Another variable categories my different types of observation.
I would like to examine if there are significant differences between each categories before and after the specific date. But the differences which I want to look at are saved in another variable number_trackers. c4 is a placeholder for all other unimprtant variables I wont need for this t.test
reproduceable Dataframe
Dataset <- data.frame = category=c("tools", "finance", "business", "education","tools","education"),
number_trackers = c(10, 12, 1, 30, 7, 21),
c4 = c("url1.com","ur2.com","url3.com","url4.com","url5.com","url6.com"),
time = c(1,0,0,0,1,1))
It would be best if the output would be a t-test for each category with the two different time periods.
A loop with categories might help:
#taking the list of unique categories
categories <- unique(Dataset$category)
#Creating an empty list
output_list <- list()
#Lopping the t-test for different categories and creating a list of output
for (i in categories) {
output_list[[i]] <- t.test(number_trackers ~ time,
data = Dataset[Dataset$category == i,],
paired = FALSE)
}
If you want to see the summary of the first category:
output_list[[categories[1]]]
Edit:
For generating a summary table of the output
sum_tab <- as.data.frame(matrix(nrow = length(categories), ncol = 7))
colnames(sum_tab) <- c("t", "df", "p.value", "ConfIntLower",
"ConfIntUpper", "Mean in Gr 0", "Mean in Gr 1")
rownames(sum_tab) <- categories
for (i in categories) {
sum_tab[i, ] <- with(output_list[[i]],
c(statistic, parameter, p.value, conf.int, estimate))
}
write.csv(sum_tab, "Summary.csv", row.names = TRUE)
P.S.: Since the reproducible example is not sufficient, I couldn't run this to show the output.