[R] How to let R repeat computations over a number of variables

[David Winsemius]

Uli Kleinwechter <ulikleinwechter at yahoo.com.mx> wrote in
news:47DADC55.2070803 at yahoo.com.mx: 

> Hello,
> 
> I have written a small script to read a dataset, compute some basic 
> descriptives and write them to a file (see below). The variable 
> "maizeseedcash" of which the statistics are calculated is contained
> in the data frame agr_inputs. My question is whether there is a way
> to make R compute the statistics not only for maizeseedcash but also
> for other variables in the dataset. I thought about a thing like a
> loop which repeats the computations according to a set of variables
> which I would like to be able to specify before. Is something like
> that possible and if so, how would it look like?
> 
> All hints are appreciated.

My hint would be to first look at ?summary or the describe function in 
Hmisc package. My second hint would be to start referring to your R 
objects by their correct names, in this case  use "dataframe" instead 
of dataset.

If summary and describe do not satisfy, then you could wrap your work 
into a function, say func.summ and feed column arguments to it with:

 apply(agr_inputs, 2, func.summ)

There are several areas where the code could be more compact. If you 
let "probs" be a vector, you can get all of your quantiles at once:

> quantile(runif(100), probs=c(0.25, 0.5, 0.75))
      25%       50%       75% 
0.2240003 0.4919313 0.7359661 

The names get carried forward when appended in a vector. See:

> test <- c(1,2, quantile(runif(100), probs=c(0.25, 0.5, 0.75)), 4,5)
> test
                          25%       50%       75%                     
1.0000000 2.0000000 0.2228890 0.4978050 0.8440893 4.0000000 5.0000000 

And you can reference named elements by name with named indexing:

> test["25%"]
      25% 
0.2228890 

Or use summary:

> summary(runif(100))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.003962 0.215400 0.441800 0.474600 0.735100 0.997600 

>  summary(runif(100))["Mean"]
  Mean 
0.4973 

Best of luck;
David Winsemius


> 
> ********
> sink("agr_inputs.txt", append=FALSE, type="output")
> 
> agr_inputs<-read.csv2("agric_inputs.csv")
> 
> attach(agr_inputs)
> 
> min<-min(maizeseedcash)
> 
> q25<-quantile(maizeseedcash, probs=.25)
> 
> median<-quantile(maizeseedcash, probs=.50)
> 
> mean<-mean(maizeseedcash)
> 
> q75<-quantile(maizeseedcash, probs=.75)
> 
> max<-max(maizeseedcash)
> 
> var<-var(maizeseedcash)
>              
> sd<-sd(maizeseedcash)
> 
> varcoeff<-sd/mean*100
> 
> Measure<-c("Min","25%", "Median", "Mean", "75%", "Max", "Var", "SD",
> "VarCoeff")
> 
> maizeseedcas<-c( min, q25, median, mean, q75, max, var, sd,
> varcoeff) 
> 
> solution<-data.frame(Measure, maizeseedcas)
> 
> print (solution)
> 
> detach(agr_inputs)
> 
> sink()
> 
> ******
> 
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