The document provides an overview of the 'plyr' package in R, which simplifies working with large datasets using a split-apply-combine strategy. It details how plyr can replace traditional loops and outlines its key functions and use cases, along with rules for splitting input data types. Additionally, it includes examples of data frame structures and references for further information.

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Ragini Jain
MSc CA 1st
Year (2015 - 2017)
plyr

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'for-loop' is the traditional approach
large data set
counter
condition
[ ]
element access
useful for overlapping data

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What is plyr
plyr is a R package that makes it easy to
work with large data sets.
large data set
apply function ( … )
split
… to each chunk of data in parallel
combine

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plyr
● Plyr supports functionality to
– Breakup a big problem into manageable pieces (split)
– Operate on each piece independently (apply)
– Put all the pieces together to get the final result (combine)
● Split-apply-combine (SAC) is similar to map-reduce (MR)
● Designed for parallel operation
● Similar to SQL group by operator
● Programmatically in R, 'plyr'
– Provides replacement for loops
– Abstracts away details of underlying data structure

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plyr
● Use it when
– Each piece of data will be
processed only once
– Each piece is processed
independently of all other
pieces.
● Don't use it when
– Each iteration requires
overlapping data (eg.
running mean)
– Each iteration depends on
the previous iteration (eg.
dynamic simulation)

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plyr in R environment
> installed.packages()
> install.packages("plyr")
> library('plyr')

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plyr 12 key functions
a = array
d = data frame
l = list
_ means the output is discarded

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plyr 12 key functions
a*ply( .data, .margins, .fun, …, .progress=”none”)
d*ply( .data, .variables, .fun, …, .progress=”none”)
l*ply( .data, .fun, …, .progress=”none”)
data which will be split up, processed and combined.
describe how to split up the input into pieces.
processing function
which is applied to each piece

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Rules for splitting input data
● a*ply
– Arrays are sliced by dimension in to lower-d pieces
– Must respond to dim() and accept multi-dimensional indexing
● d*ply
– Data frames are subsetted by combination of variables
– Must work with split() and coercible to a list.
● l*ply
– Each element in a list is a piece
– Must work with length() and [[

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Data frame structure visualization
A data frame is used for storing data variables.
It is a list of vectors of equal length.
> n = c(2, 3, 5)
> s = c("aa", "bb", "cc")
> b = c(TRUE, FALSE, TRUE)
> df = data.frame(n, s, b)
> df
n s b
1 2 aa TRUE
2 3 bb FALSE
3 5 cc TRUE
>

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Data frame and ddply()
> df <-data.frame(matrix(rnorm(216),72,3),
c(rep("A",24),
rep("B",24),
rep("C",24)),
c(rep("J",36),
rep("K",36))
> colnames(df) <- c("v1", "v2", "v3", "dim1", "dim2")
> ddply(df,
c("dim1", "dim2"),
function(df)mean(df$v1))

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References
● Hadley Wickham, Journal Of Statistical Software, 40(1), 2011,
http://www.jstatsoft.org/article/view/v040i01
● Github 'plyr' repo
https://github.com/hadley/plyr
● CRAN
https://cran.r-project.org/web/packages/plyr/index.html
●

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Thank you.
● Questions
● Clarifications
● Suggestions
● Feedback
Ragini Jain
15030142023@sicsr.ac.in