ddply for sum by group in R

I have a sample dataframe "data" as follows:

X            Y  Month   Year    income
2281205 228120  3   2011    1000
2281212 228121  9   2010    1100
2281213 228121  12  2010    900
2281214 228121  3   2011    9000
2281222 228122  6   2010    1111
2281223 228122  9   2010    3000
2281224 228122  12  2010    1889
2281225 228122  3   2011    778
2281243 228124  12  2010    1111
2281244 228124  3   2011    200
2281282 228128  9   2010    7889
2281283 228128  12  2010    2900
2281284 228128  3   2011    3400
2281302 228130  9   2010    1200
2281303 228130  12  2010    2000
2281304 228130  3   2011    1900
2281352 228135  9   2010    2300
2281353 228135  12  2010    1333
2281354 228135  3   2011    2340

I want to use the ddply to compute the income for each Y(not X), if I have four observations for each Y (for example for 2281223 with months 6,9,12 of 2010 and month 3 of 2011). If I have less than four observations (for example for Y =228130), I want to simply ignore it. I use the following commands in R for the above purpose:

require(plyr)
     # the data are in the data csv file
    data<-read.csv("data.csv")
    # convert Y (integers) into factors
    y<-as.factor(y)
    # get the count of each unique Y
    count<-ddply(data,.(Y), summarize, freq=length(Y))
    # get the sum of each unique Y 
    sum<-ddply(data,.(Y),summarize,tot=sum(income))
    # show the sum if number of observations for each Y is less than 4
    colbind<-cbind(count,sum)
    finalsum<-subset(colbind,freq>3)

My output are as follows:

>colbind
       Y freq      Y   tot
1 228120    1 228120  1000
2 228121    3 228121 11000
3 228122    4 228122  6778
4 228124    2 228124  1311
5 228128    3 228128 14189
6 228130    3 228130  5100
7 228135    3 228135  5973
>finalsum
       Y freq    Y.1  tot
3 228122    4 228122 6778

The above code works, but requires many steps. So,I would like to know whether there is a simple way of performing the above task (using the plyr package).

As pointed out in a comment, you can do multiple operations inside the summarize.

This reduces your code to one line of ddply() and one line of subsetting, which is easy enough with the [ operator:

x <- ddply(data, .(Y), summarize, freq=length(Y), tot=sum(income))
x[x$freq > 3, ]

       Y freq  tot
3 228122    4 6778

This is also exceptionally easy with the data.table package:

library(data.table)
data.table(data)[, list(freq=length(income), tot=sum(income)), by=Y][freq > 3]
        Y freq  tot
1: 228122    4 6778

In fact, the operation to calculate the length of a vector has its own shortcut in data.table - use the .N shortcut:

data.table(data)[, list(freq=.N, tot=sum(income)), by=Y][freq > 3]
        Y freq  tot
1: 228122    4 6778

HatMatrix

I think the package dplyr is faster than plyr::ddply and more elegant.

testData <- read.table(file = "clipboard",header = TRUE)
require(dplyr)
testData %>%
  group_by(Y) %>%
  summarise(total = sum(income),freq = n()) %>%
  filter(freq > 3)

来源：https://stackoverflow.com/questions/14035872/ddply-for-sum-by-group-in-r

标签

plyr