Converting string to numeric

Question

I ve imported a test file and tried to make a histogram   pichman  lt - read csv file  picman txt   header TRUE  sep   t      hist  lt - as numeric pichman WS        However  I get different numbers from values in my dataset  Originally I thought that this  because I had text  so I deleted the text   table pichman WS      ws  lt - pichman WS pichman WS   Down   amp  pichman WS   NoData         However  I am still getting very high numbers does anyone have an idea

User · Accepted Answer

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

> x = factor(4:8)
> x
[1] 4 5 6 7 8
Levels: 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.

User · Answer

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     gt  x  lt - factor sample 4 8 10 replace T    gt  x   1  6 4 8 6 7 6 8 5 8 4 Levels  4 5 6 7 8  gt  as numeric levels x   x    1  6 4 8 6 7 6 8 5 8 4   To show it works   The timings     gt  x  lt - factor sample 4 8 500000 replace T    gt  system time as numeric as character x       user  system elapsed     0 11    0 00    0 11   gt  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

[string] Converting string to numeric

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