R help - Dec 2010 - Applying function to a TABLE and also "apply, tapply, sapply etc"

Dear R-help forum members,

Suppose I have a data-frame having two variables and single data for each of
them, as described below.

variable_1           variable_2
        10                          20

I have written a function, say, 'fun' which uses input 10 and 20 and
gives me desired result.

fun = function(X, Y)
         {
         X + Y              #( I am just giving an example of process. Actual
process is
 quite different.) 
         }

result = fun(variable_1[1], variable_2[1])   # Thus, i should be getting answer
30 which I am storing in say 'ans1.csv'

# ____________________________________________________________________

# My problem

Suppose instead of having above dataframe having single data for variable 1 and
variable 2, I have following data as

variable_1           variable_2

       10                         20
        40                         30 
        3                          11

I need to run the function 'fun' for each pair of values taken by
variable_1 and variable_2 separately. Also, the results (= 30, 70 and 14)
obtained for each of above pairs should be stored in different csv files, say
"ans1.csv", "ans2.csv" and "ans3.csv" respectively
which I can use for further analysis. (In reality each of these output files
will consists of 1000 records).

As I had mentioned in my earlier mail, I am new to R and I think I
 should be using apply or sapply or tapply etc., which I have tried but I am not
able to proceed further as I am not able to understand it properly.

It will be a great help to me if I receive the guidance w.r.t 

(a) how do I tackle above problem i.e. how do I apply the function to a table so
that it will generate different csv files pertaining to pair of values "10
and 20", "40 and 30" and "3 and 11";

(b) I am not that sharp to understand the programming aspects of R taht easily,
though I am really keen to learn R, so I will be highly obliged if someone helps
me understand with some simple examples as to how "apply",
"supply", "tapply", "mapply" etc can be used?

I am sure this will go a long way in helping the new learners like me to
undesrtand the proper use of these wonderful commands.

I hope I am able to put forward my problem properly. 
 
Thanking all in advance for the anticipated guidance

Amelia
 Vettori, Auckland





      
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On Wed, Dec 15, 2010 at 4:18 PM, Amelia Vettori
<amelia_vettori at yahoo.co.nz> wrote:
> Dear R-help forum members,
>
> Suppose I have a data-frame having two variables and single data for each
of them, as described below.
>
> variable_1?????????? variable_2
> ??????? 10????????????????????????? 20
>
> I have written a function, say, 'fun' which uses input 10 and 20
and gives me desired result.
>
> fun = function(X, Y)
> ???????? {
> ???????? X + Y????????????? #( I am just giving an example of process.
Actual process is
> ?quite different.)
> ???????? }
>
> result = fun(variable_1[1], variable_2[1])?? # Thus, i should be getting
answer 30 which I am storing in say 'ans1.csv'
>
> # ____________________________________________________________________
>
> # My problem
>
> Suppose instead of having above dataframe having single data for variable 1
and variable 2, I have following data as
>
> variable_1?????????? variable_2
>
> ?????? 10???????????????????????? 20
> ??????? 40 ? ????????????????????? 30
> ??????? 3????????????????????????? 11
>
> I need to run the function 'fun' for each pair of values taken by
variable_1 and variable_2 separately. Also, the results (= 30, 70 and 14)
obtained for each of above pairs should be stored in different csv files, say
"ans1.csv", "ans2.csv" and "ans3.csv" respectively
which I can use for further analysis. (In reality each of these output files
will consists of 1000 records).
>
> As I had mentioned in my earlier mail, I am new to R and I think I
> ?should be using apply or sapply or tapply etc., which I have tried but I
am not able to proceed further as I am not able to understand it properly.
>
> It will be a great help to me if I receive the guidance w.r.t
>
> (a) how do I tackle above problem i.e. how do I apply the function to a
table so that it will generate different csv files pertaining to pair of values
"10 and 20", "40 and 30" and "3 and 11";
>
Say you have the following data frame
> df
  Var1 V2
1   10 20
2   40 30
3    3 11
> str(df)
'data.frame':	3 obs. of  2 variables:
 $ Var1: num  10 40 3
 $ V2  : num  20 30 11

Then
> apply(df, 1, sum)  ##compute sum() for each row
 1  2  3
30 70 14
> apply(df, 2, sum)  ##compute sum() for each column
Var1   V2
  53   61

> (b) I am not that sharp to understand the programming aspects of R taht
easily, though I am really keen to learn R, so I will be highly obliged if
someone helps me understand with some simple examples as to how
"apply", "supply", "tapply", "mapply"
etc can be used?
>
Only some examples that I understand well.
##apply function to each element of a list (data frames are lists)
##compute sum() for each column
> lapply(df, sum)
$Var1
[1] 53

$V2
[1] 61

##sapply() is a variation of lapply(); see the docs
> sapply(df, sum)
Var1   V2
  53   61

##using the 'iris' data frame, for each Species level compute mean()
of the Sepal.Length column
> with(iris, tapply(Sepal.Length, Species, mean))
    setosa versicolor  virginica
     5.006      5.936      6.588

##a friendlier interface is provided by by()
> with(iris, by(Sepal.Length, Species, mean))
Species: setosa
[1] 5.006
------------------------------------------------------------
Species: versicolor
[1] 5.936
------------------------------------------------------------
Species: virginica
[1] 6.588

##the same, now for four variables at the same time
> by(iris[1:4], iris$Species, mean)
iris$Species: setosa
Sepal.Length  Sepal.Width Petal.Length  Petal.Width
       5.006        3.428        1.462        0.246
------------------------------------------------------------
iris$Species: versicolor
Sepal.Length  Sepal.Width Petal.Length  Petal.Width
       5.936        2.770        4.260        1.326
------------------------------------------------------------
iris$Species: virginica
Sepal.Length  Sepal.Width Petal.Length  Petal.Width
       6.588        2.974        5.552        2.026


For an example of mapply see this recent post:
http://r.789695.n4.nabble.com/calculating-mean-of-list-components-tp3088986p3089057.html

For more on vectorization, see sections 3 and 4 of the 'R inferno'
[1]. Also check 'Some Hints for the R Beginner' [2].
[1] http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
[2] http://www.burns-stat.com/pages/Tutor/hints_R_begin.html

Regards
Liviu

> I am sure this will go a long way in helping the new learners like me to
undesrtand the proper use of these wonderful commands.
>
> I hope I am able to put forward my problem properly.
>
> Thanking all in advance for the anticipated guidance
>
> Amelia
> ?Vettori, Auckland
>
>
>
>
>
>
> ? ? ? ?[[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>


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Amelia of Aukland writes:
> Suppose instead of having above dataframe having single data for 
> variable 1 and variable 2, I have following data as
> 
> variable_1           variable_2
> 
>        10                         20
>         40                         30 
>         3                          11
And so you do:
foo <- data.frame(variable_1=c(10,40,3), variable_2=c(20,30,11))
> I need to run the function 'fun' for each pair of values taken by 
> variable_1 and variable_2 separately. Also, the results (= 30, 70 
> and 14) obtained for each of above pairs should be stored in 
> different csv files, say "ans1.csv", "ans2.csv" and
"ans3.csv"
> respectively which I can use for further analysis. (In reality each 
> of these output files will consists of 1000 records).
No function is necessary, though apply() would work as has been pointed 
out.
foo$answer <- foo$variable_1 + foo$variable_2

Writing to a csv is another matter--
write.csv(foo, 'foo.csv')


-- 
Curt Seeliger, Data Ranger
Raytheon Information Services - Contractor to ORD
seeliger.curt@epa.gov
541/754-4638



	[[alternative HTML version deleted]]

On 12/15/2010 7:18 AM, Amelia Vettori wrote:
> Dear R-help forum members,
>
> Suppose I have a data-frame having two variables and single data for
>  each of them, as described below.
>
> variable_1           variable_2 10                          20
>
> I have written a function, say, 'fun' which uses input 10 and 20
and
>  gives me desired result.
>
> fun = function(X, Y) { X + Y              #( I am just giving an
> example of process. Actual process is quite different.) }
>
> result = fun(variable_1[1], variable_2[1])   # Thus, i should be
> getting answer 30 which I am storing in say 'ans1.csv'
>
> #
> ____________________________________________________________________
>
> # My problem
>
> Suppose instead of having above dataframe having single data for
> variable 1 and variable 2, I have following data as
>
> variable_1           variable_2
>
> 10                         20 40                         30 3 11
>
> I need to run the function 'fun' for each pair of values taken by
> variable_1 and variable_2 separately. Also, the results (= 30, 70 and
> 14) obtained for each of above pairs should be stored in different
> csv files, say "ans1.csv", "ans2.csv" and
"ans3.csv" respectively
> which I can use for further analysis. (In reality each of these
> output files will consists of 1000 records).
>
> As I had mentioned in my earlier mail, I am new to R and I think I
> should be using apply or sapply or tapply etc., which I have tried
> but I am not able to proceed further as I am not able to understand
> it properly.
>
> It will be a great help to me if I receive the guidance w.r.t
>
> (a) how do I tackle above problem i.e. how do I apply the function to
> a table so that it will generate different csv files pertaining to
> pair of values "10 and 20", "40 and 30" and "3 and
11";
>
> (b) I am not that sharp to understand the programming aspects of R
> taht easily, though I am really keen to learn R, so I will be highly
>  obliged if someone helps me understand with some simple examples as
>  to how "apply", "supply", "tapply",
"mapply" etc can be used?
>
> I am sure this will go a long way in helping the new learners like me
> to undesrtand the proper use of these wonderful commands.
>
> I hope I am able to put forward my problem properly.
>
> Thanking all in advance for the anticipated guidance
>
> Amelia Vettori, Auckland
# a slightly more complicated demonstration function, which
# gives a result that make sense for writing to a CSV file.
fun <- function(X, Y) {
	data.frame(result=X + Y)
}

foo <- data.frame(variable_1=c(10,40,3), variable_2=c(20,30,11))

# using apply
# This only really works if the columns in foo are the same
# type because it will be transformed into a matrix (which
# is of one type). Also, since the column names of the data.frame
# don't match the arugments of fun, the unname is needed.
# do.call is a somewhat advanced function that lets you call
# a function with arguments that are stored in some other
# list.
apply(foo, 1, function(x) do.call("fun", as.list(unname(x))))

# version using apply, where foo has been transformed into
# something more like what apply would expect.
foo.m <- as.matrix(foo)
colnames(foo.m) <- c("X","Y")
apply(foo.m, 1, function(x){do.call("fun", as.list(x))})

# using lapply
# lapply takes a list, which for this looping purpose would have
# to be the row indexes of foo. This version does not reqire
# the different arguements to be the same type.
lapply(1:nrow(foo), function(i) {fun(foo[i,1],foo[i,2])})

# using mapply
# This one is more designed for when multiple arguments to a
# function are changing.
mapply(fun, foo[,1], foo[,2])

# using Vectorize
# A different approach, where instead of creating the looping
# structure, create a new function which is vectorized over its
# arguements.
fun.v <- Vectorize(fun)
fun.v(foo[,1], foo[,2])

# storing the results to disk
results <- mapply(fun, foo[,1], foo[,2])
# results is a list, each element of which is one of the returned
# sets of results corresponding to a row in the original data.frame
lapply(1:length(results), function(r) {write.csv(results[r],
file=paste("ans",r,".csv",sep=""))})

# if you didn't need different file names (the name of which depends on
# the position of the result in the list, not anything in the result
# itself), it could be simpler.
lapply(results, summary)


-- 
Brian S. Diggs, PhD
Senior Research Associate, Department of Surgery
Oregon Health & Science University