1. plyer
for
Split-­‐Apply-­‐Combine
Automa4ng
one
pa6ern
of
data
munging
and
analysis
Eric
Klusman
2013-­‐11-­‐14

2. What
is
plyr?
• A
library
of
func4ons
for
R
for
doing
analysis
in
a
split-­‐apply-­‐combine
pa6ern
– Split
the
data
into
subgroups
– Apply
some
func4on
to
summarize,
model,
or
plot
each
subgroup
– Combine
the
results
of
the
subgroups
back
together
• Automate
the
loops
and
avoid
the
bookkeeping
code
• Assump4on:
data
can
be
processed
piecewise
2

3. Example:
Baby
Names
•
•
•
•
From
Hadley
Wickham,
h6p://plyr.had.co.nz/09-­‐user/
Top
1000
U.S.
boy
and
girl
baby
names
from
1880
to
2008
Derived
from
Social
Security
Administra4on
dataset
1000
*
2
*
129
=
258000
obs
on
4
vars
>
head(bnames)
>
tail(bnames)
year
name
percent
sex
1
1880
John
0.081541
boy
2
1880
William
0.080511
boy
3
1880
James
0.050057
boy
4
1880
Charles
0.045167
boy
5
1880
George
0.043292
boy
6
1880
Frank
0.027380
boy
year
name
percent
sex
257995
2008
Diya
0.000128
girl
257996
2008
Carleigh
0.000128
girl
257997
2008
Iyana
0.000128
girl
257998
2008
Kenley
0.000127
girl
257999
2008
Sloane
0.000127
girl
258000
2008
Elianna
0.000127
girl
3

4. Groupwise
summaries
• What
if
we
want
to
compute
the
rank
of
a
name
within
a
sex
and
year?
• Easy
for
a
single
year
and
sex;
hard
in
general.
#
Split
pieces
<-­‐
split(bnames,
list(bnames$sex,
bnames$year))
#
Apply
results
<=
vector(“list”,
length(pieces))
for(i
in
seq_along(pieces))
{
piece
<-­‐
pieces[[i]]
piece
<-­‐
transform(piece,
rank
=
rank(-­‐percent,
ties.method=“first”))
results[[i]]
<-­‐
piece
}
#
Combine
result
<-­‐
do.call(“rbind”,
results)
4

5. Using
plyr
bnames
<-­‐
ddply(bnames,
c(“sex”,
“year”),
transform,
rank
=
rank(-­‐percent,
ties.method=“first”))
5

6. One-­‐line
summaries
ddply(bnames,
c(“name”),
summarize,
tot
=
sum(percent))
ddply(bnames,
c(“length”),
summarize,
tot
=
sum(percent))
ddply(bnames,
c(“year”,
“sex”),
summarize,
tot
=
sum(percent))
fl
<-­‐
ddply(bnames,
c(“year”,
“sex”,
“first”),
summarize,
tot
=
sum(percent))
library(ggplot2)
qplot(year,
tot,
data
=
fl,
geom
=
“line”,
color
=
“sex”,
facets
=
~
first)
6

7. 7

8. plyr
func4ons
are
named
by
their
input
and
output
types
ioply
where
i
is
the
input
type
and
o
is
the
output
type
Func%on
Input
data
type
Output
data
type
ddply
Data
frame
Data
frame
aaply
Array
Array
daply
Dataframe
Array
d_ply
Dataframe
None;
used
for
plo`ng
or
prin4ng
ldply
List
Dataframe
alply
Array
List
8

9. Base
R
vs.
plyr
Base
func-on
aggregate
d
d
ddply
+
colwise
apply
a
a/l
aaply
/
alply
by
l
l
dlply
lapply
l
l
llply
mapply
a
a/l
maply
/
mlply
replicate
r
a/l
raply
/
rlply
sapply
l
a
laply
sweep
a
a
-­‐
tapply
•
Input
Output
plyr
func-on
a
a
-­‐
Input
and
output
types
are
indicated
by
first
le6er:
array,
data
frame,
list,
replica4on
9