Converting string to numeric [duplicate]

I suspect you are having a problem with factors. For example,

> x = factor(4:8)> x[1] 4 5 6 7 8Levels: 4 5 6 7 8> as.numeric(x)[1] 1 2 3 4 5> as.numeric(as.character(x))[1] 4 5 6 7 8

Some comments:

• You mention that your vector contains the characters "Down" and "NoData". What do expect/want as.numeric to do with these values?
• In read.csv, try using the argument stringsAsFactors=FALSE
• Are you sure it's sep="/t and not sep="\t"
• Use the command head(pitchman) to check the first fews rows of your data
• Also, it's very tricky to guess what your problem is when you don't provide data. A minimal working example is always preferable. For example, I can't run the command pichman <- read.csv(file="picman.txt", header=TRUE, sep="/t") since I don't have access to the data set.

As csgillespie said. stringsAsFactors is default on TRUE, which converts any text to a factor. So even after deleting the text, you still have a factor in your dataframe.

Now regarding the conversion, there's a more optimal way to do so. So I put it here as a reference :

> x <- factor(sample(4:8,10,replace=T))> x [1] 6 4 8 6 7 6 8 5 8 4Levels: 4 5 6 7 8> as.numeric(levels(x))[x] [1] 6 4 8 6 7 6 8 5 8 4

To show it works.

The timings :

> x <- factor(sample(4:8,500000,replace=T))> system.time(as.numeric(as.character(x)))   user  system elapsed    0.11    0.00    0.11 > system.time(as.numeric(levels(x))[x])   user  system elapsed       0       0       0 

It's a big improvement, but not always a bottleneck. It gets important however if you have a big dataframe and a lot of columns to convert.