An easier way to get average of a table with some conditions in R - r

Question

I am trying to get the average of all 6 quizzes for each male student.
Here is part of the code that I've tried:
a<-subset(mydf,Sex=="M")
b<-a[4:9]
b
sum(b[1:6])
My logic is to get a table only contains male students with each of their 6 quizzes, then sum the table and divide by the number of male student. But I think there should be an easier way to do this.
Sample data:
df <- data.frame(Section=c(rep('A',9)),
Degree=c(rep('MBA',4),'MS','MBA','MBA','MS','MBA'),
Sex=c(rep('M',5),'F','M','M','F'),
Quiz1=c(0,10,2,2,8,6,6,2,3),
Quiz2=c(0,1,4,4,1,5,0,3,9),
Quiz3=c(6,5,6,6,4,2,7,9,3),
Quiz4=c(5,4,5,5,10,5,7,7,3),
Quiz5=c(7,3,6,3,10,7,6,10,5),
Quiz6=c(3,8,6,6,5,8,10,10,5))

Answer 1

How about this:
data.frame(df[which(df$Sex=='M'),],QuizMeans=rowMeans(df[which(df$Sex=='M'),c(4:9)]))
Note: "c(4:9)" in the code above is takes the row average for quiz columns 4-9.
So we're calculating quiz scores for each individual this way.
Output:
Section Degree Sex Quiz1 Quiz2 Quiz3 Quiz4 Quiz5 Quiz6 QuizMeans
1 A MBA M 0 0 6 5 7 3 3.500000
2 A MBA M 10 1 5 4 3 8 5.166667
3 A MBA M 2 4 6 5 6 6 4.833333
4 A MBA M 2 4 6 5 3 6 4.333333
5 A MS M 8 1 4 10 10 5 6.333333
7 A MBA M 6 0 7 7 6 10 6.000000
8 A MS M 2 3 9 7 10 10 6.833333
Then if you wanted to take the mean of their means (i.e. the grand mean), you could store the above as something like "df", then use mean() to calculate the mean of the column QuizMeans, like this:
df <- data.frame(df[which(df$Sex=='M'),],QuizMeans=rowMeans(df[which(df$Sex=='M'),c(4:9)]))
mean(df$QuizMeans)
[1] 5.285714
If there are missing values in your data, you'll need to add na.rm=TRUE to either the mean() or rowMeans() function, like this:
mean(df$QuizMeans, na.rm=TRUE)
[1] 5.285714

Answer 2

You could use the following without specifying column positions
ans <- sum(df[df$Sex=="M", grepl("Quiz",names(df))])/sum(df$Sex=="M")
# 31.71429
If you know the column positions
ans <- sum(df[df$Sex=="M", 4:9])/sum(df$Sex=="M")
# 31.71429
Data
df <- data.frame(Section=c(rep('A',9)),
Degree=c(rep('MBA',4),'MS','MBA','MBA','MS','MBA'),
Sex=c(rep('M',5),'F','M','M','F'),
Quiz1=c(0,10,2,2,8,6,6,2,3),
Quiz2=c(0,1,4,4,1,5,0,3,9),
Quiz3=c(6,5,6,6,4,2,7,9,3),
Quiz4=c(5,4,5,5,10,5,7,7,3),
Quiz5=c(7,3,6,3,10,7,6,10,5),
Quiz6=c(3,8,6,6,5,8,10,10,5))

Answer 3

Use dplyr.
library(dplyr)
mydf %>% filter(Sex == "Male") %>%
summarise(avg_q6 = mean(Quiz6))