Subsetting in a function to calculate a row total

I have a data frame with results for certain instruments, and I want to create a new column which contains the totals of each row. Because I have different numbers of instruments each time I run an analysis on new data, I need a function to dynamically calculate the new column with the Row Total.

To simply my problem, here’s what my data frame looks like:

    Type    Value
1   A   10
2   A   15
3   A   20
4   A   25
5   B   30
6   B   40
7   B   50
8   B   60
9   B   70
10  B   80
11  B   90

My goal is to achieve the following:

    A   B   Total
1   10  30  40
2   15  40  55
3   20  50  70
4   25  60  85
5       70  70
6       80  80
7       90  90

I’ve tried various method, but this way holds the most promise:

myList <- list(a = c(10, 15, 20, 25), b = c(30, 40, 50, 60, 70, 80, 90))
tmpDF <- data.frame(sapply(myList, '[', 1:max(sapply(myList, length))))
> tmpDF
   a  b
1 10 30
2 15 40
3 20 50
4 25 60
5 NA 70
6 NA 80
7 NA 90
totalSum <- rowSums(tmpDF)
totalSum <- data.frame(totalSum)
tmpDF <- cbind(tmpDF, totalSum)
> tmpDF
   a  b totalSum
1 10 30       40
2 15 40       55
3 20 50       70
4 25 60       85
5 NA 70       NA
6 NA 80       NA
7 NA 90       NA

Even though this way did succeeded in combining two data frames of different lengths, the ‘rowSums’ function gives the wrong values in this example. Besides that, my original data isn't in a list format, so I can't apply such a 'solution'.

I think I’m overcomplicating this problem, so I was wondering how can I …

• Subset data from a data frame on the basis of ‘Type’,
• Insert these individual subsets of different lengths into a new data frame,
• Add an ‘Total’ column to this data frame which is the correct sum of the individual subsets.

An added complication to this problem is that this needs to be done in an function or in an otherwise dynamic way, so that I don’t need to manually subset the dozens of ‘Types’ (A, B, C, and so on) in my data frame.

Here’s what I have so far, which doesn’t work, but illustrates the lines I’m thinking along:

TotalDf <- function(x){
    tmpNumberOfTypes <- c(levels(x$Type))
    for( i in tmpNumberOfTypes){
        subSetofData <- subset(x, Type = i, select = Value)
        if( i == 1) {
        totalDf <- subSetOfData }
        else{
        totalDf <- cbind(totalDf, subSetofData)}
    }
    return(totalDf)
}

Thanks in advance for any thoughts or ideas on this,

Regards,

EDIT:

Thanks to the comment of Joris (see below) I got an end in the right direction, however, when trying to translate his solution to my data frame, I run into additional problems. His proposed answer works, and gives me the following (corr开发者_运维知识库ect) sum of the values of A and B:

> tmp78 <- tapply(DF$value,DF$id,sum)
> tmp78
 1  2  3  4  5  6 
 6  8 10 12  9 10 
> data.frame(tmp78)
  tmp78
1     6
2     8
3    10
4    12
5     9
6    10

However, when I try this solution on my data frame, it doesn’t work:

> subSetOfData <- copyOfTradesList[c(1:3,11:13),c(1,10)]
> subSetOfData
   Instrument AccountValue
1         JPM         6997
2         JPM         7261
3         JPM         7545
11        KFT         6992
12        KFT         6944
13        KFT         7069
> unlist(sapply(rle(subSetOfData$Instrument)$lengths,function(x) 1:x))
Error in rle(subSetOfData$Instrument) : 'x' must be an atomic vector
> subSetOfData$InstrumentNumeric <- as.numeric(subSetOfData$Instrument)
> unlist(sapply(rle(subSetOfData$InstrumentNumeric)$lengths,function(x) 1:x))
     [,1] [,2]
[1,]    1    1
[2,]    2    2
[3,]    3    3
> subSetOfData$id <- unlist(sapply(rle(subSetOfData$InstrumentNumeric)$lengths,function(x) 1:x))
Error in `$<-.data.frame`(`*tmp*`, "id", value = c(1L, 2L, 3L, 1L, 2L,  : 
  replacement has 3 rows, data has 6

I have the disturbing idea that I’m going around in circles…

Two thoughts :

1) you could use na.rm=T in rowSums

2) How do you know which one has to go with which? You might add some indexing.

eg :

DF <- data.frame(
  type=c(rep("A",4),rep("B",6)),
  value = 1:10,
  stringsAsFactors=F
)


DF$id <- unlist(lapply(rle(DF$type)$lengths,function(x) 1:x))

Now this allows you to easily tapply the sum on the original dataframe

tapply(DF$value,DF$id,sum)

And, more importantly, get your dataframe in the correct form :

> DF
   type value id
1     A     1  1
2     A     2  2
3     A     3  3
4     A     4  4
5     B     5  1
6     B     6  2
7     B     7  3
8     B     8  4
9     B     9  5
10    B    10  6

> library(reshape)
> cast(DF,id~type)
  id  A  B
1  1  1  5
2  2  2  6
3  3  3  7
4  4  4  8
5  5 NA  9
6  6 NA 10

TV <- data.frame(Type = c("A","A","A","A","B","B","B","B","B","B","B")
             , Value = c(10,15,20,25,30,40,50,60,70,80,90)
             , stringsAsFactors = FALSE)

# Added Type C for testing
# TV <- data.frame(Type = c("A","A","A","A","B","B","B","B","B","B","B", "C", "C", "C")
#                  , Value = c(10,15,20,25,30,40,50,60,70,80,90, 100, 150, 130)
#                  , stringsAsFactors = FALSE)

lnType <- with(TV, tapply(Value, Type, length))
lnType <- as.integer(lnType)
lnType

id <- unlist(mapply(FUN = rep_len, length.out = lnType, x = list(1:max(lnType))))
(TV <- cbind(id, TV))

require(reshape2)
tvWide <- dcast(TV, id ~ Type)

# Alternatively
# tvWide <- reshape(data = TV,  direction = "wide", timevar = "Type",  ids = c(id, Type))

tvWide <- subset(tvWide, select = -id)

# If you want something neat without the <NA>
# for(i in 1:ncol(tvWide)){
#
#     if (is.na(tvWide[j,i])){
#       tvWide[j,i] = 0
#     }
#     
#   }
# }

tvWide
transform(tvWide, rowSum=rowSums(tvWide, na.rm = TRUE))