Excluding columns from a dataframe based on column sums

Question

I'm working on a data set that includes community data, and many of the columns (species) have a lot of zeroes. I would like to be able to drop these columns for some of the analyses I'm doing, based on the sum of the whole column. I'm tempted to do this with a for loop, but I hear that the apply and by functions are better when you're using R. My goal is to remove all columns with a sum of less than 15. I have used `which()` to remove rows by factors, e.g., ``` September<-which(data$Time_point=="September") data<-data[-September,] ``` and the two ways I've tried removing columns is by using `apply()`: ``` data<-data[,apply(data,2,function(x)sum(x<=15))] ``` and by using a messy for loop/if else combo: ``` for (i in 6:length(data)){ if (sum(data[,i])<=15) data[,i]<-NULL else data[,i]<-data[,i] } ``` Neither of these methods has been working. Surely there is an elegant way to get rid of columns based on logical criteria? ``` str(head(data,10)) 'data.frame': 10 obs. of 23 variables: $ C…

Accepted Answer

What about a simple subset? First, we create a simple data frameL

```
R> dd = data.frame(x = runif(5), y = 20*runif(5), z=20*runif(5))
```

Then select the columns where the sum is greater than 15

```
R> dd1 = dd[,colSums(dd) > 15]
R> ncol(dd1)
[1] 2
```

In your data set, you only want to subset columns 6 onwards, so something like:

```
 ##Drop the first five columns
 dd[,colSums(dd[,6:ncol(dd)]) > 15]
```

or

```
 #Keep the first six columns
 cols_to_drop = c(rep(TRUE, 5), dd[,6:ncol(dd)]>15)
 dd[,cols_to_drop]
```

should work.

The key part to note is that in the square brackets, we want a vector of logicals, i.e. a vector of TRUE and FALSE. So if you wanted to subset using something a bit more complicated, then create a function that returns TRUE or FALSE and subset as usual.

Excluding columns from a dataframe based on column sums

Solution

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