Apply function to each column in a data frame observing each columns existing data type

Question

I'm trying to get the min/max for each column in a large data frame, as part of getting to know my data. My first try was:

apply(t,2,max,na.rm=1)

It treats everything as a character vector, because the first few columns are character types. So max of some of the numeric columns is coming out as " -99.5".

I then tried this:

sapply(t,max,na.rm=1)

but it complains about max not meaningful for factors. (lapply is the same.) What is confusing me is that apply thought max was perfectly meaningful for factors, e.g. it returned "ZEBRA" for column 1.

BTW, I took a look at Using sapply on vector of POSIXct and one of the answers says "When you use sapply, your objects are coerced to numeric,...". Is this what is happening to me? If so, is there an alternative apply function that does not coerce? Surely it is a common need, as one of the key features of the data frame type is that each column can be a different type.

Answer 1

If it were an "ordered factor" things would be different. Which is not to say I like "ordered factors", I don't, only to say that some relationships are defined for 'ordered factors' that are not defined for "factors". Factors are thought of as ordinary categorical variables. You are seeing the natural sort order of factors which is alphabetical lexical order for your locale. If you want to get an automatic coercion to "numeric" for every column, ... dates and factors and all, then try:

sapply(df, function(x) max(as.numeric(x)) )   # not generally a useful result

Or if you want to test for factors first and return as you expect then:

sapply( df, function(x) if("factor" %in% class(x) ) { 
            max(as.numeric(as.character(x)))
            } else { max(x) } )

@Darrens comment does work better:

 sapply(df, function(x) max(as.character(x)) )  

max does succeed with character vectors.