Getting raw data from a frequency table

Question

I searched for some data on naming trends in the USA. I managed to get 1000 items for children born in 2008. The data is generated in this estate:

 male.name n.male female.name n.female
 Jacob 22272 Emma 18587
 Michael 20298 Isabella 18377
 Ethan 20004 Emily 17217
 Joshua 18924 Madison 16853
 Daniel 18717 Ava 16850
 Alexander 18423 Olivia 16845
 Anthony 18158 Sophia 15887
 William 18149 Abigail 14901
 Christopher 17783 Elizabeth 11815
 Matthew 17337 Chloe 11699

I want to get data.framewith two variables: nameand gender. This can be done with a loop, but I find this a rather inefficient way to solve this problem. I believe that some features reshapewill meet my needs.

Suppose that this tab delimited data is stored in data.framewith a name bnames. The cycle can be performed using the function:

 tmp <- character()
  for (i in 1:nrow(bnames)) {
  tmp <- c(tmp, rep(bnames[i,1], bnames[i,2]))
 }

But I want to achieve this with a vector approach. Any suggestions?

Answer 1

( )

# your data:
bnames <- read.table(textConnection(
"male.name n.male female.name n.female
Jacob 22272 Emma 18587
Michael 20298 Isabella 18377
Ethan 20004 Emily 17217
Joshua 18924 Madison 16853
Daniel 18717 Ava 16850
Alexander 18423 Olivia 16845
Anthony 18158 Sophia 15887
William 18149 Abigail 14901
Christopher 17783 Elizabeth 11815
Matthew 17337 Chloe 11699
"), sep=" ", header=TRUE, stringsAsFactors=FALSE)

# how to avoid loop
bnames$male.name[ rep(1:nrow(bnames), times=bnames$n.male) ]

, rep , .

mropa gd047.

:

data_final <- data.frame(
  name = c(
    bnames$male.name[ rep(1:nrow(bnames), times=bnames$n.male) ],
    bnames$female.name[ rep(1:nrow(bnames), times=bnames$n.female) ]
  ),
  gender = rep(
    c("m", "f"),
    times = c(sum(bnames$n.male), sum(bnames$n.female))
  ),
  stringsAsFactors = FALSE
)

[EDIT] :

data_final <- data.frame(
  name = rep(
    c(bnames$male.name, bnames$female.name),
    times = c(bnames$n.male, bnames$n.female)
  ),
  gender = rep(
    c("m", "f"),
    times = c(sum(bnames$n.male), sum(bnames$n.female))
  ),
  stringsAsFactors = FALSE
)

Answer 2

, data.frame rbind() , .

dataNEW <- data.frame(bnames[,1],c("m"), bnames[,c(2,3)], c("f"), bnames[,4])
colnames(dataNEW) <- c("name", "gender", "value", "name", "gender", "value")

:

          name gender value      name gender value
1        Jacob      m 22272      Emma      f 18587
2      Michael      m 20298  Isabella      f 18377
3        Ethan      m 20004     Emily      f 17217
4       Joshua      m 18924   Madison      f 16853
5       Daniel      m 18717       Ava      f 16850
6    Alexander      m 18423    Olivia      f 16845
7      Anthony      m 18158    Sophia      f 15887
8      William      m 18149   Abigail      f 14901
9  Christopher      m 17783 Elizabeth      f 11815
10     Matthew      m 17337     Chloe      f 11699

rbind():

dataNGV <- rbind(dataNEW[1:3],dataNEW[4:6])

:

      name gender value
1        Jacob      m 22272
2      Michael      m 20298
3        Ethan      m 20004
4       Joshua      m 18924
5       Daniel      m 18717
6    Alexander      m 18423
7      Anthony      m 18158
8      William      m 18149
9  Christopher      m 17783
10     Matthew      m 17337
11        Emma      f 18587
12    Isabella      f 18377
13       Emily      f 17217
14     Madison      f 16853
15         Ava      f 16850
16      Olivia      f 16845
17      Sophia      f 15887
18     Abigail      f 14901
19   Elizabeth      f 11815
20       Chloe      f 11699

Answer 3

( ), mropa , ,

library(plyr)
data <- ddply(dataNGV, .(name,gender), 
      function(x) data.frame(name=rep(x[,1],x[,3]),gender=rep(x[,2],x[,3])))

Answer 4

(http://github.com/hadley/data-baby-names.