Question

I am a relatively new R user, and most of the complex coding (and packages) looks like Greek to me. It has been a long time since I used a programming language (Java/Perl) and I have only used R for very simple manipulations in the past (basic loading data from file, subsetting, ANOVA/T-Test). However, I am working on a project where I had no control over the data layout and the data file is very lengthy.

In my data, I have 172 rows which feature the Participant to a survey and 158 columns, each which represents the question number. The answers for each are 1-5. The raw data includes the number “99” to indicate that a question was not answered. I need to exclude any questions where a Participant did not answer without excluding the entire participant.

Part  Q001  Q002  Q003  Q004
1      2      4    99    2
2      3      99   1     3
3      4      4    2     5
4      99     1    3     2
5      1      3    4     2

In the past I have used the subset feature to filter my data
data.filter <- subset(data, Q001 != 99)
Which works fine when I am working with sets where all my answers are contained in one column. Then this would just delete the whole row where the answer was not available.

However, with the answers in this set spread across 158 columns, if I subset out 99 in column 1 (Q001), I also filter out that entire Participant.

I’d like to know if there is a way to filter/subset the data such that my large data set would end up having ‘blanks’ when the “99” occured so that these 99’s would not inflate or otherwise interfere with the statistics I run of the rest of the numbers. I need to be able to calculate means per question and run ANOVAs and T-Tests on various questions.

Resp  Q001  Q002  Q003  Q004
1      2      4          2
2      3           1     3
3      4      4    2     5
4             1    3     2
5      1      3    4     2

Is this possible to do in R? I’ve tried to filter it before submitting to R, but it won’t read the data file in when I have blanks, and I’d like to be able to use the whole data set without creating a subset for each question (which I will do if I have to… it’s just time consuming if there is a better code or package to use)

Any assistance would be greatly appreciated!

Answer 1

You could replace the “99” by “NA” and the calculate the colMeans omitting NAs:

df <- replicate(20, sample(c(1,2,3,99), 4))
colMeans(df) # nono

dfc <- df
dfc[dfc == 99] <- NA
colMeans(dfc, na.rm = TRUE)