I'm very new to R (and statistics) and I searched a lot for a possible solution, but couldn't find any.

I have a data set with around 18000 entries, which contain two columns: "rentals" and "season". I want to analyse if there is a difference between the mean of the rentals depending on the season using an one-way ANOVA.

My data looks like this:

First I calculate the SD and MEAN of the groups (season):

anova %>%
    group_by(season) %>%
    summarise(
        count_season = n(),
        mean_rentals = mean(rentals, na.rm = TRUE),
        sd_rentals = sd(rentals, na.rm = TRUE))

This is the result:

Then I perform the one-way ANOVA:

anova_one_way <- aov(season~as.factor(rentals), data = anova)
summary(anova_one_way)
<!-- I use "as.factor" on rentals, because otherwise I'm getting an error with TukeyHSD -->

Result:

Here comes the tricky part. I perform a TukeyHSD test:

TukeyHSD(anova_one_way) 

And the results are very disappointing. TukeyHSD returns 376896 rows, while I expect it to return just a few, comparing the seasons with each other. It looks like every single "rentals" row is being handled as a single group. This seems to be very wrong but I can't find the cause. Is this a common TukeyHSD behaviour considering the big data set or is there an error in my code or logic, which causes this enormous unreadable list of values as a return?

Here is a small insight on how it looks like (and it goes on until 376896).

CodePudding user response：

The terms are the wrong way around in your aov() call. Rentals is the outcome (dependent) variable, season is the predictor (independent) variable.

So you want:

anova_one_way <- aov(rentals ~ factor(season), data = anova)