I have a number of space separated files of weather data, with some
equivalent column names, and differing number of fields in each file. Some
of the files have 40 or more vars, but I only want a subset of the fields.
I can use colClasses with read.table to drop some of the fields, but only if
I know where those columns are in the first place, and they're not always in
the same place. So I would like to be able to drop all unwanted columns on
import, by name.
In addition, most fields have a "Q" (quality) field next to them, and I need
to read of those as well, each "Q" next to its relevant field, such as
"Temp", and rename to e.g., "Temp.Q".
Some example data:
Date HrMn I Type Dir Q I Spd Q Visby Q I Q Temp Q Dewpt Q Slp Q Pr Amt I Q
19450101 0900 4 SAO 315 1 N 1.0 1 024000 1 N 1 -37.0 1 -45.9 1 1031.8 1 99
999.9 9 9
19450101 1000 4 SAO 315 1 N 1.0 1 024000 1 N 1 -35.9 1 -43.1 1 1032.2 1 99
999.9 9 9
19450101 1100 4 SAO 360 1 N 1.0 1 024000 1 N 1 -35.9 1 -43.1 1 1032.5 1 99
999.9 9 9
19450101 1200 4 SAO 315 1 N 1.0 1 024000 1 N 1 -36.4 1 -50.9 1 1032.9 1 99
999.9 9 9
19450101 1300 4 SAO 360 1 N 1.0 1 024000 1 N 1 -36.4 1 -43.1 1 1032.9 1 99
999.9 9 9
19450101 1400 4 SAO 315 1 N 1.0 1 016000 1 N 1 -36.4 1 -42.0 1 1032.5 1 99
999.9 9 9
19450101 1500 4 SAO 180 1 N 1.0 1 016000 1 N 1 -36.4 1 -45.3 1 1032.5 1 99
999.9 9 9
19450101 1600 4 SAO 360 1 N 1.0 1 024000 1 N 1 -37.5 1 -45.9 1 1032.9 1 99
999.9 9 9
So if I want to extract Date, HrMn, Temp, and the Q following Temp:
tmp1<-read.table("ex.dat", sep=" ", strip.white=TRUE,
colClasses=c("character","character",
rep("NULL",11),"numeric","factor",rep("NULL",8)),na.strings="999.9",
header=T)
But having to alter colClasses for every file, the fields of which may
change when next year's data is retrieved, is no fun. And is there a way to
specify na.strings per column?
--
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