split a data item by values in a column. — replyr

Partitions from by values in grouping column, and returns list. Only advised for a moderate number of groups and better if grouping column is an index. This plus lapply and replyr::bind_rows is powerful enough to implement "The Split-Apply-Combine Strategy for Data Analysis" https://www.jstatsoft.org/article/view/v040i01

replyr_split(
  df,
  gcolumn,
  ...,
  ocolumn = NULL,
  decreasing = FALSE,
  partitionMethod = "extract",
  maxgroups = 100,
  eagerCompute = FALSE
)

Arguments

df	remote dplyr data item
gcolumn	grouping column
...	force later values to be bound by name
ocolumn	ordering column (optional)
decreasing	if TRUE sort in decreasing order by ocolumn
partitionMethod	method to partition the data, one of 'split' (only works over local data frames), or 'extract'
maxgroups	maximum number of groups to work over
eagerCompute	if TRUE call compute on split results

Value

list of data items

Examples


d <- data.frame(group=c(1,1,2,2,2),
                order=c(.1,.2,.3,.4,.5),
                values=c(10,20,2,4,8))
dSplit <- replyr_split(d, 'group', partitionMethod='extract')
dApp <- lapply(dSplit, function(di) data.frame(as.list(colMeans(di))))
replyr_bind_rows(dApp)
#>   group order    values
#> 1     1  0.15 15.000000
#> 2     2  0.40  4.666667