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Beginner Tips for Using plyr to Calculate Yearly Changes by Group

I am new to plyr (and R) and have been looking for a little help to get started. Using a baseball dataset as an example, how could I calculate the change of year to year (year) on the fly by the league and team (lg and team)?

library(plyr)
df1 <- aggregate(ab~year+lg+team, FUN=sum, data=baseball)

After a little aggregation, to simplify the glory of the data, the data is as follows:

head(df1)

  year lg team   ab
  1884 UA  ALT  108
  1997 AL  ANA 1703
  1998 AL  ANA 1502
  1999 AL  ANA  660
  2000 AL  ANA   85
  2001 AL  ANA  219

I would like to end up finding something like this

  year lg team   ab yoy
  1997 AL  ANA 1703  NA
  1998 AL  ANA 1502  -201
  1999 AL  ANA  660  -842
  2000 AL  ANA   85  -575
  2001 AL  ANA  219  134

I started by writing the following function, which I think is incorrect:

yoy.func <- function(df) {
  lag <- c(df$ab[-1],0)
  cur <- c(df$ab[1],0)
  df$yoy <- cur -lag
  return(df)
}

Without sucess, I used the following code to try and return the yoy change.

df2 <- ddply(df1, .(lg, team), yoy.func)

Any guidance should be appreciated.

thank

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2 answers

, "plyr" - , , R. -, R , "". , , , !

output <- within(df1, {
  yoy <- ave(ab, team, lg, FUN = function(x) c(NA, diff(x)))
})
head(output)
#   year lg team   ab  yoy
# 1 1884 UA  ALT  108   NA
# 2 1997 AL  ANA 1703   NA
# 3 1998 AL  ANA 1502 -201
# 4 1999 AL  ANA  660 -842
# 5 2000 AL  ANA   85 -575
# 6 2001 AL  ANA  219  134

library(rbenchmark)

benchmark(DDPLY = {
  ddply(df1, .(team, lg), mutate ,
        yoy = c(NA, diff(ab)))
}, WITHIN = {
  within(df1, {
    yoy <- ave(ab, team, lg, FUN = function(x) c(NA, diff(x)))
  })
}, columns = c("test", "replications", "elapsed", 
               "relative", "user.self"))
#     test replications elapsed relative user.self
# 1  DDPLY          100  10.675    4.974    10.609
# 2 WITHIN          100   2.146    1.000     2.128

: data.table

, data.table. . , , .

library(plyr)
df1 <- aggregate(ab~year+lg+team, FUN=sum, data=baseball)
library(data.table)
DT <- data.table(df1)
DT
#       year lg team   ab
#    1: 1884 UA  ALT  108
#    2: 1997 AL  ANA 1703
#    3: 1998 AL  ANA 1502
#    4: 1999 AL  ANA  660
#    5: 2000 AL  ANA   85
#   ---                  
# 2523: 1895 NL  WSN  839
# 2524: 1896 NL  WSN  982
# 2525: 1897 NL  WSN 1426
# 2526: 1898 NL  WSN 1736
# 2527: 1899 NL  WSN  787

:

DT[, yoy := c(NA, diff(ab)), by = "team,lg"]
DT
#       year lg team   ab  yoy
#    1: 1884 UA  ALT  108   NA
#    2: 1997 AL  ANA 1703   NA
#    3: 1998 AL  ANA 1502 -201
#    4: 1999 AL  ANA  660 -842
#    5: 2000 AL  ANA   85 -575
#   ---                       
# 2523: 1895 NL  WSN  839  290
# 2524: 1896 NL  WSN  982  143
# 2525: 1897 NL  WSN 1426  444
# 2526: 1898 NL  WSN 1736  310
# 2527: 1899 NL  WSN  787 -949
+6

diff():

df <- read.table(header = TRUE, text = '  year lg team   ab
  1884 UA  ALT  108
  1997 AL  ANA 1703
  1998 AL  ANA 1502
  1999 AL  ANA  660
  2000 AL  ANA   85
  2001 AL  ANA  219')
require(plyr)
ddply(df, .(team, lg), mutate ,
      yoy = c(NA, diff(ab)))
#   year lg team   ab  yoy
1 1884 UA  ALT  108   NA
2 1997 AL  ANA 1703   NA
3 1998 AL  ANA 1502 -201
4 1999 AL  ANA  660 -842
5 2000 AL  ANA   85 -575
6 2001 AL  ANA  219  134
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