In R, tabular data is stored in data frames. This topic covers the various ways of transforming a single table.
The most flexible base R function for reshaping data is
?reshape for its syntax.
# create unbalanced longitudinal (panel) data set set.seed(1234) df <- data.frame(identifier=rep(1:5, each=3), location=rep(c("up", "down", "left", "up", "center"), each=3), period=rep(1:3, 5), counts=sample(35, 15, replace=TRUE), values=runif(15, 5, 10))[-c(4,8,11),] df identifier location period counts values 1 1 up 1 4 9.186478 2 1 up 2 22 6.431116 3 1 up 3 22 6.334104 5 2 down 2 31 6.161130 6 2 down 3 23 6.583062 7 3 left 1 1 6.513467 9 3 left 3 24 5.199980 10 4 up 1 18 6.093998 12 4 up 3 20 7.628488 13 5 center 1 10 9.573291 14 5 center 2 33 9.156725 15 5 center 3 11 5.228851
Note that the data.frame is unbalanced, that is, unit 2 is missing an observation in the first period, while units 3 and 4 are missing observations in the second period. Also, note that there are two variables that vary over the periods: counts and values, and two that do not vary: identifier and location.
To reshape the data.frame to wide format,
# reshape wide on time variable df.wide <- reshape(df, idvar="identifier", timevar="period", v.names=c("values", "counts"), direction="wide") df.wide identifier location values.1 counts.1 values.2 counts.2 values.3 counts.3 1 1 up 9.186478 4 6.431116 22 6.334104 22 5 2 down NA NA 6.161130 31 6.583062 23 7 3 left 6.513467 1 NA NA 5.199980 24 10 4 up 6.093998 18 NA NA 7.628488 20 13 5 center 9.573291 10 9.156725 33 5.228851 11
Notice that the missing time periods are filled in with NAs.
In reshaping wide, the "v.names" argument specifies the columns that vary over time. If the location variable is not necessary, it can be dropped prior to reshaping with the "drop" argument. In dropping the only non-varying / non-id column from the data.frame, the v.names argument becomes unnecessary.
reshape(df, idvar="identifier", timevar="period", direction="wide", drop="location")
To reshape long with the current df.wide, a minimal syntax is
However, this is typically trickier:
# remove "." separator in df.wide names for counts and values names(df.wide)[grep("\\.", names(df.wide))] <- gsub("\\.", "", names(df.wide)[grep("\\.", names(df.wide))])
Now the simple syntax will produce an error about undefined columns.
With column names that are more difficult for the
reshape function to automatically parse, it is sometimes necessary to add the "varying" argument which tells
reshape to group particular variables in wide format for the transformation into long format. This argument takes a list of vectors of variable names or indices.
reshape(df.wide, idvar="identifier", varying=list(c(3,5,7), c(4,6,8)), direction="long")
In reshaping long, the "v.names" argument can be provided to rename the resulting varying variables.
Sometimes the specification of "varying" can be avoided by use of the "sep" argument which tells
reshape what part of the variable name specifies the value argument and which specifies the time argument.
Often data comes in tables. Generally one can divide this tabular data in wide and long formats. In a wide format, each variable has its own column.
|Person||Height [cm]||Age [yr]|
However, sometimes it is more convenient to have a long format, in which all variables are in one column and the values are in a second column.
Base R, as well as third party packages can be used to simplify this process. For each of the options, the
mtcars dataset will be used. By default, this dataset is in a long format. In order for the packages to work, we will insert the row names as the first column.
mtcars # shows the dataset data <- data.frame(observation=row.names(mtcars),mtcars)
There are two functions in base R that can be used to convert between wide and long format:
long <- stack(data) long # this shows the long format wide <- unstack(long) wide # this shows the wide format
However, these functions can become very complex for more advanced use cases. Luckily, there are other options using third party packages.
This package uses
gather() to convert from wide to long and
spread() to convert from long to wide.
library(tidyr) long <- gather(data, variable, value, 2:12) # where variable is the name of the # variable column, value indicates the name of the value column and 2:12 refers to # the columns to be converted. long # shows the long result wide <- spread(long,variable,value) wide # shows the wide result (~data)
The data.table package extends the
reshape2 functions and uses the function
melt() to go from wide to long and
dcast() to go from long to wide.
library(data.table) long <- melt(data,'observation',2:12,'variable', 'value') long # shows the long result wide <- dcast(long, observation ~ variable) wide # shows the wide result (~data)