Referring to objects using variable strings in R

Question

Edit: Thanks to those who have responded so far; I'm very much a beginner in R and have just taken on a large project for my MSc dissertation so am a bit overwhelmed with the initial processing. The data I'm using is as follows (from WMO publically available rainfall data): ` 120 6272100 KHARTOUM 15.60 32.55 382 1899 1989 0.0 1899 0.03 0.03 0.03 0.03 0.03 1.03 13.03 12.03 9999 6.03 0.03 0.03 1900 0.03 0.03 0.03 0.03 0.03 23.03 80.03 47.03 23.03 8.03 0.03 0.03 1901 0.03 0.03 0.03 0.03 0.03 17.03 23.03 17.03 0.03 8.03 0.03 0.03 (...) `120 6272101 JEBEL AULIA 15.20 32.50 380 1920 1988 0.0 1920 0.03 0.03 0.03 0.00 0.03 6.90 20.00 108.80 47.30 1.00 0.01 0.03 1921 0.03 0.03 0.03 0.00 0.03 0.00 88.00 57.00 35.00 18.50 0.01 0.03 1922 0.03 0.03 0.03 0.00 0.03 0.00 87.50 102.30 10.40 15.20 0.01 0.03 (...) There are ~100 observation stations that I'm interested in, each of which has a varying start and end date for rainfall measurements. They're formatted as above in a single data file, with sta…

Accepted Answer

This is totally begging for a dataframe, then it's just this one-liner with power-tools like `ddply` (amazingly powerful):

```
tot_mamj <- ddply(rain[rain$month %in% 3:6,-2], 'year', colwise(sum))
```

giving your aggregate of total for M/A/M/J, by year:

```
   year station_1 station_2 station_3 station_4 station_5 ...
1  1972  8.618960  5.697739 10.083192  9.264512 11.152378 ...
2  1973 18.571748 18.903280 11.832462 18.262272 10.509621 ...
3  1974 22.415201 22.670821 32.850745 31.634717 20.523778 ...
4  1975 16.773286 17.683704 18.259066 14.996550 19.007762 ...
...
```

Below is perfectly working code. We create a dataframe whose col.names are 'station_n'; also extra columns for year and month (factor, or else integer if you're lazy, see the footnote). Now you can do arbitrary analysis by month or year (using plyr's split-apply-combine paradigm):

```
require(plyr) # for d*ply, summarise
#require(reshape) # for melt

# Parameterize everything here, it's crucial for testing/debugging
all_years <- c(1970:2011)
nYears <- length(all_years)  
nStations <- 101
# We want station names as vector of chr (as opposed to simple indices)
station_names <- paste ('station_', 1:nStations, sep='')

rain <- data.frame(cbind(
  year=rep(c(1970:2011),12),
  month=1:12
))
# Fill in NAs for all data
rain[,station_names] <- as.numeric(NA)
# Make 'month' a factor, to prevent any numerical funny stuff e.g accidentally 'aggregating' it
rain$month <- factor(rain$month)

# For convenience, store the row indices for all years, M/A/M/J
I.mamj <- which(rain$month %in% 3:6)

# Insert made-up seasonal data for M/A/M/J for testing... leave everything else NA intentionally
rain[I.mamj,station_names] <- c(3,5,9,6) * runif(4*nYears*nStations)

# Get our aggregate of MAMJ totals, by year
# The '-2' column index means: "exclude month, to prevent it also getting 'aggregated'"
excludeMonthCol = -2
tot_mamj <- ddply(rain[rain$month %in% 3:6, excludeMonthCol], 'year', colwise(sum))

# voila!!
#    year station_1 station_2 station_3 station_4 station_5
# 1  1972  8.618960  5.697739 10.083192  9.264512 11.152378
# 2  1973 18.571748 18.903280 11.832462 18.262272 10.509621
# 3  1974 22.415201 22.670821 32.850745 31.634717 20.523778
# 4  1975 16.773286 17.683704 18.259066 14.996550 19.007762
```

As a footnote, before I converted month from numeric to factor, it was getting silently 'aggregated' (until I put in the '-2': exclude column reference). However, better still is when you make it a factor, it will refuse point-blank to be aggregate'd, and throw an error (which is desirable for debugging):

```
 ddply(rain[rain$month %in% 3:6, ], 'year', colwise(sum))
Error in Summary.factor(c(3L, 3L, 3L, 3L, 3L, 3L), na.rm = FALSE) : 
  sum not meaningful for factors
```

Referring to objects using variable strings in R

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