2

I have a dataset Comorbidity in RStudio, where I have added columns such as MDDOnset, and if the age at onset of MDD < the onset of OUD, it equals 1, and if the opposite is true, then it equals 2. I also have another column PhysDis that has values 0-100 (numeric in nature).

What I want to do is make a new column that includes the values of PhysDis, but only if MDDOnset == 1, and another if MDDOnset==2. I want to make these columns so that I can run a t-test on them and compare the two groups (those with MDD prior OUD, and those who had MDD after OUD with regards to which group has a greater physical disability score). I want any case where MDDOnset is not 1 to be NA. 

ttest1 <-t.test(Comorbidity$MDDOnset==1, Comorbidity$PhysDis)
ttest2 <-t.test(Comorbidity$MDDOnset==2, Comorbidity$PhysDis)

When I did the t test twice, once where MDDOnset = 1 and another when it equaled 2, the mean for y (Comorbidity$PhysDis) was the same, and when I looked into the original csv file, it turned out that this mean was the mean of the entire column, and not just cases where MDDOnset had a value of one or two. If there is a different way to run the t-tests that would have the mean of PhysDis only when MDDOnset = 1, and another with the mean of PhysDis only when MDDOnset == 2 that does not require making new columns, then please tell me.. Sorry if there are any similar questions or if my approach is way off, I'm new to R and programming in general, and thanks in advance.

Here's a smaller data frame where I tried to replicate the error where the new columns have switched lengths. The issue would be that the length of C would be 4, and the length of D would be 6 if I could replicate the error. 

> A <- sample(1:10)
> B <-c(25,34,14,76,56,34,23,12,89,56)
> alphabet <-data.frame(A,B)
> alphabet$C <-ifelse(alphabet$A<7, alphabet$B, NA)
> alphabet$D <-ifelse(alphabet$A>6, alphabet$B, NA)
> print(alphabet)
    A  B  C  D
1   7 25 NA 25
2   9 34 NA 34
3   4 14 14 NA
4   2 76 76 NA
5   5 56 56 NA
6  10 34 NA 34
7   8 23 NA 23
8   6 12 12 NA
9   1 89 89 NA
10  3 56 56 NA
> length(which(alphabet$C>0))
[1] 6
> length(which(alphabet$D>0))
[1] 4
Thaneson Balachandran
  • 21
  • 2
  • 1
    If you can include a subset of you data it makes it easier to reproduce the example to answer the question. See https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example – austensen Jun 02 '17 at 16:16

1 Answers1

0

I would use the mutate command from the dplyr package.

Comorbidity <- mutate(Comorbidity, newColumn = (ifelse(MDDOnset == 1, PhysDis, "")), newColumn2 = (ifelse(MDDOnset == 2, PhysDis, "")))
Alex Dometrius
  • 812
  • 7
  • 20