R For Loop anomaly when expanding the range - r

Question

Assume the following dataframe:
Application <- c('A','A','B','B','B','C','C','D')
Rating <- c('0','0.6','0.6','2.0','2.0','3.8','3.8','3.9')
DF <- data.frame(Application,Rating)
DF
#Application Score
#1 A 0
#2 A 0.6
#3 B 0.6
#4 B 2.0
#5 B 2.0
#6 C 3.8
#7 C 3.8
#8 D 3.9
I want to create an empty results table to be populated through a loop:
1st column - to show the rating being counted (e.g. 0.6)
2nd column - to show the number of times that rating occurs in DF
3rd column - to list total number of ratings in DF (i.e. 8)
4th column - to calculate the proportion of the applications with that rating relative to the overall
#create empty results table
results_rating_bins <- as.data.frame(matrix(nrow = 1, ncol = 4))
#initiate row count
rownr = 1
#Loop:
for (rating in seq(from = 0, to = 4.0, by = 0.1)) {
this_rating <- subset(DF, DF$Score == rating)
results_rating_bins[rownr, 1] = rating
results_rating_bins[rownr, 2] = nrow(this_rating)
results_rating_bins[rownr, 3] = nrow(DF)
results_rating_bins[rownr, 4] = nrow(this_rating) / nrow(DF)
rownr <- rownr + 1
}
The final result is what I expect, except for rating 2.0 where the count is 0 even though it should be 2.
This illustrates at small scale, what I see at larger scale with a 30k line dataset. I have a list of apps with ratings going from 0 to 4.9, so the range in my loop would be set to 0 to 4.9 instead of 0.6 to 4.0 in my example. However, when I run the loop on the large dataset I end up with a number of instances where the rating count is 0 even though it shouldn't be. What's even more odd, is that by playing around with the ranges, the ratings where the anomaly (i.e. count = 0) happens varies completely randomly.
Any idea what may justify this type of behaviour?
Amnesty

Answer 1

Typically I answer the questions as asked, trying to work through the logic a question poster is already using. However, in this case, it is so much easier to use dplyr to aggregate into the new table that I am breaking with tradition.
require(dplyr)
Application <- c('A','A','B','B','B','C','C','D')
Rating <- c('0','0.6','0.6','2.0','2.0','3.8','3.8','3.9')
DF <- data.frame(Application,Rating)
df2<-DF%>%
group_by(Application, Rating)%>%
summarize(ratio=(n()/nrow(DF)))
The first part is the same as yours, but with the library call added
where it starts df2 you are setting the df2 data frame equal to a grouped version of your initial data frame based on the combinations of Application and Rating. In the summarize statement, for each possible combination we tell it to count the number n() and divide it by the total number of rows in the original data frame nrow(DF), This creates the third row of your new the percent of total each pair represents.
It looks like this and you could add the column with the number of rows with another summarize statement if you need it, but to perform this function, it is not necessary.
Application Rating ratio
1 A 0 0.125
2 A 0.6 0.125
3 B 0.6 0.125
4 B 2.0 0.250
5 C 3.8 0.250
6 D 3.9 0.125
This will absolutely catch every combination of Application and Rating and calculate the ratio relative to the whole data frame.
EDIT: If you do not care about the Application letter, you cans imply remove it from the group_by function and still get what you want.
And add
%>%
summarise(rows=nrow(DF))
if you want the total number of rows in the frame on each row