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This chapter is from the book

14.3 reshape2

The next most common munging need is either melting data (going from column orientation to row orientation) or casting data (going from row orientation to column orientation). As with most other procedures in R, there are multiple functions available to accomplish these tasks, but we will focus on Hadley Wickham’s reshape2 package. We talk about Wickham a lot, but that is because his products have become so fundamental to the R developer’s toolbox.

14.3.1 melt

Looking at the Aid_00s data.frame, we see that each year is stored in its own column. That is, the dollar amount for a given country and program is found in a different column for each year. This is called a cross table, which while nice for human consumption, is not ideal for graphing with ggplot2 or for some analysis algorithms.

> head(Aid_00s)

  Country.Name                                      Program.Name
1  Afghanistan                         Child Survival and Health
2  Afghanistan         Department of Defense Security Assistance
3  Afghanistan                            Development Assistance
4  Afghanistan Economic Support Fund/Security Support Assistance
5  Afghanistan                                Food For Education
6  Afghanistan                  Global Health and Child Survival
  FY2000  FY2001   FY2002    FY2003     FY2004     FY2005     FY2006
1     NA      NA  2586555  56501189   40215304   39817970   40856382
2     NA      NA  2964313        NA   45635526  151334908  230501318
3     NA 4110478  8762080  54538965  180539337  193598227  212648440
4     NA   61144 31827014 341306822 1025522037 1157530168 1357750249
5     NA      NA       NA   3957312    2610006    3254408     386891
6     NA      NA       NA        NA         NA         NA         NA
      FY2007     FY2008     FY2009
1   72527069   28397435         NA
2  214505892  495539084  552524990
3  173134034  150529862    3675202
4 1266653993 1400237791 1418688520
5         NA         NA         NA
6         NA   63064912    1764252

We want it set up so that each row represents a single country-program-year entry with the dollar amount stored in one column. To achieve this we melt the data using melt from reshape2.

> library(reshape2)
> melt00 <- melt(Aid_00s, id.vars=c("Country.Name", "Program.Name"),
+                variable.name="Year", value.name="Dollars")
> tail(melt00, 10)

      Country.Name
24521     Zimbabwe
24522     Zimbabwe
24523     Zimbabwe
24524     Zimbabwe
24525     Zimbabwe
24526     Zimbabwe
24527     Zimbabwe
24528     Zimbabwe
24529     Zimbabwe
24530     Zimbabwe
                                                Program.Name   Year
24521                       Migration and Refugee Assistance FY2009
24522                                      Narcotics Control FY2009
24523 Nonproliferation, Anti-Terrorism, Demining and Related FY2009
24524                            Other Active Grant Programs FY2009
24525                                Other Food Aid Programs FY2009
24526                                 Other State Assistance FY2009
24527                                 Other USAID Assistance FY2009
24528                                            Peace Corps FY2009
24529                                                Title I FY2009
24530                                               Title II FY2009
        Dollars
24521   3627384
24522        NA
24523        NA
24524   7951032
24525        NA
24526   2193057
24527  41940500
24528        NA
24529        NA
24530 174572685

The id.vars argument specifies which columns uniquely identify a row.

After some manipulation of the Year column and aggregating, this is now prime for plotting, as shown in Figure 14.1. The plot uses faceting, enabling us to quickly see and understand the funding for each program over time.

> library(scales)
> # strip the "FY" out of the year column and convert it to numeric
> melt00$Year <- as.numeric(str_sub(melt00$Year, start=3, 6))
> # aggregate the data so we have yearly numbers by program
> meltAgg <- aggregate(Dollars ~ Program.Name + Year, data=melt00,
+                      sum, na.rm=TRUE)
> # just keep the first 10 characters of program name
> # then it will fit in the plot
> meltAgg$Program.Name <- str_sub(meltAgg$Program.Name, start=1,
+                                 end=10)
>
> ggplot(meltAgg, aes(x=Year, y=Dollars)) +
+     geom_line(aes(group=Program.Name)) +
+     facet_wrap(~ Program.Name) +
+     scale_x_continuous(breaks=seq(from=2000, to=2009, by=2)) +
+     theme(axis.text.x=element_text(angle=90, vjust=1, hjust=0)) +
+     scale_y_continuous(labels=multiple_format(extra=dollar,
+                                               multiple="B"))
Figure 14.1

Figure 14.1 Plot of foreign assistance by year for each of the programs.

14.3.2 dcast

Now that we have the foreign aid data melted, we cast it back into the wide format for illustration purposes. The function for this is dcast, and it has trickier arguments than melt. The first is the data to be used, in our case melt00. The second argument is a formula where the left side specifies the columns that should remain columns and the right side specifies the columns that should become column names. The third argument is the column (as a character) that holds the values to be populated into the new columns, representing the unique values of the right side of the formula argument.

> cast00 <- dcast(melt00, Country.Name + Program.Name ~ Year,
+                 value.var="Dollars")
> head(cast00)

  Country.Name                                      Program.Name 2000
1  Afghanistan                         Child Survival and Health   NA
2  Afghanistan         Department of Defense Security Assistance   NA
3  Afghanistan                            Development Assistance   NA
4  Afghanistan Economic Support Fund/Security Support Assistance   NA
5  Afghanistan                                Food For Education   NA
6  Afghanistan                  Global Health and Child Survival   NA
     2001     2002      2003       2004       2005       2006
1      NA  2586555  56501189   40215304   39817970   40856382
2      NA  2964313        NA   45635526  151334908  230501318
3 4110478  8762080  54538965  180539337  193598227  212648440
4   61144 31827014 341306822 1025522037 1157530168 1357750249
5      NA       NA   3957312    2610006    3254408     386891
6      NA       NA        NA         NA         NA         NA
        2007       2008       2009
1   72527069   28397435         NA
2  214505892  495539084  552524990
3  173134034  150529862    3675202
4 1266653993 1400237791 1418688520
5         NA         NA         NA
6         NA   63064912    1764252
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