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reshape function

The arguments to this function are described in terms of longitudinal data, as that is the application motivating the functions. A ‘wide’ longitudinal dataset will have one record for each individual with some time-constant variables that occupy single columns and some time-varying variables that occupy a column for each time point. In ‘long’ format there will be multiple records for each individual, with some variables being constant across these records and others varying across the records. A ‘long’ format dataset also needs a ‘time’ variable identifying which time point each record comes from and an ‘id’ variable showing which records refer to the same person.

If the data frame resulted from a previous reshape then the operation can be reversed simply by reshape(a) . The direction argument is optional and the other arguments are stored as attributes on the data frame.

If direction = “wide” and no varying or v.names arguments are supplied it is assumed that all variables except idvar and timevar are time-varying. They are all expanded into multiple variables in wide format.

If direction = “long” the varying argument can be a vector of column names (or a corresponding index). The function will attempt to guess the v.names and times from these names. The default is variable names like x.1 , x.2 , where sep = “.” specifies to split at the dot and drop it from the name. To have alphabetic followed by numeric times use sep = “” .

Variable name splitting as described above is only attempted in the case where varying is an atomic vector, if it is a list or a matrix, v.names and times will generally need to be specified, although they will default to, respectively, the first variable name in each set, and sequential times.

Also, guessing is not attempted if v.names is given explicitly. Notice that the order of variables in varying is like x.1 , y.1 , x.2 , y.2 .

Quick-R: Reshape

R in Action (2nd ed) significantly expands upon this material. Use promo code ria38 for a 38% discount.

Reshaping Data

R provides a variety of methods for reshaping data prior to analysis.

Transpose

Use the t() function to transpose a matrix or a data frame. In the later case, rownames become variable (column) names.

# example using built-in dataset

mtcars

t(mtcars)

The Reshape Package

Hadley Wickham has created a comprehensive package called reshape to massage data. Both an introduction and article are available. There is even a video!

Basically, you “melt” data so that each row is a unique id-variable combination. Then you “cast” the melted data into any shape you would like. Here is a very simple example.

mydata

id time x1 x2 1 1 5 6 1 2 3 5 2 1 6 1 2 2 2 4

# example of melt function

library(reshape)

mdata <- melt(mydata, id=c("id","time")) newdata id time variable value 1 1 x1 5 1 2 x1 3 2 1 x1 6 2 2 x1 2 1 1 x2 6 1 2 x2 5 2 1 x2 1 2 2 x2 4 # cast the melted data # cast(data, formula, function) subjmeans <- cast(mdata, id~variable, mean) timemeans <- cast(mdata, time~variable, mean) subjmeans id x1 x2 1 4 5.5 2 4 2.5 timemeans time x1 x2 1 5.5 3.5 2 2.5 4.5 There is much more that you can do with the melt( ) and cast( ) functions. See the documentation for more details. Going Further To practice massaging data, try this course in cleaning data in R.

How can I reshape my data in R? | R FAQ

When there are multiple measurements of the same subject, across time or using different tools, the data is often described as being in “wide” format if there is one observation row per subject with each measurement present as a different variable and “long” format if there is one observation row per measurement (thus, multiple rows per subject). Different functions require different formats, and so the need to reshape a dataset may arise.

Below, we start with a dataset in wide format. Students have been measured using five metrics: read, write, math, science, and socst.

hsb2 id female race ses schtyp prog read write math science socst 1 70 0 4 1 1 1 57 52 41 47 57 2 121 1 4 2 1 3 68 59 53 63 61 3 86 0 4 3 1 1 44 33 54 58 31 4 141 0 4 3 1 3 63 44 47 53 56 5 172 0 4 2 1 2 47 52 57 53 61 6 113 0 4 2 1 2 44 52 51 63 61 7 50 0 3 2 1 1 50 59 42 53 61 8 11 0 1 2 1 2 34 46 45 39 36 9 84 0 4 2 1 1 63 57 54 58 51 10 48 0 3 2 1 2 57 55 52 50 51

To reformat this dataset into long form, we will use the reshape function. The arguments we provide include a list of variable names that define the different times or metrics (varying), the name we wish to give the variable containing these values in our long dataset (v.names), the name we wish to give the variable describing the different times or metrics (timevar), the values this variable will have (times), and the end format for the data (direction). Additionally, we have provided new row names.

## Wide to long l id female race ses schtyp prog subj score 99 1 1 1 1 1 3 read 34 299 1 1 1 1 1 3 write 44 499 1 1 1 1 1 3 math 40 699 1 1 1 1 1 3 science 39 899 1 1 1 1 1 3 socst 41 139 2 1 1 2 1 3 read 39 339 2 1 1 2 1 3 write 41 539 2 1 1 2 1 3 math 33 739 2 1 1 2 1 3 science 42 939 2 1 1 2 1 3 socst 41

After sorting by id, we can see that we have five rows per student and their five scores appear in the score variable with the subj variable indicating which test on which the score was measured. For each of the five rows per id, the female, race, ses, schtyp, and prog variables are unchanging.

We can similarly go from this long form back to our original wide form again using reshape with different arguments (most importantly, direction = “wide”). With timevar, we indicate the variable that will define the multiple measurements per subject. With idvar, we list the variables that should do not vary within subject.

## Long to wide w id female race ses schtyp prog score.read score.write score.math score.science score.socst 99 1 1 1 1 1 3 34 44 40 39 41 139 2 1 1 2 1 3 39 41 33 42 41 84 3 0 1 1 1 2 63 65 48 63 56 112 4 1 1 1 1 2 44 50 41 39 51 76 5 0 1 1 1 2 47 40 43 45 31 149 6 1 1 1 1 2 47 41 46 40 41 50 7 0 1 2 1 2 57 54 59 47 51 94 8 1 1 1 1 2 39 44 52 44 48 60 9 0 1 2 1 3 48 49 52 44 51 154 10 1 1 2 1 1 47 54 49 53 61

Once again, we have one observation per student. We can compare the dimensions of our wide dataset w to our original dataset:

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