| pivot {SparkR} | R Documentation |
Pivot a column of the GroupedData and perform the specified aggregation. There are two versions of pivot function: one that requires the caller to specify the list of distinct values to pivot on, and one that does not. The latter is more concise but less efficient, because Spark needs to first compute the list of distinct values internally.
## S4 method for signature 'GroupedData,character' pivot(x, colname, values = list())
x |
a GroupedData object |
colname |
A column name |
values |
A value or a list/vector of distinct values for the output columns. |
GroupedData object
pivot since 2.0.0
## Not run:
##D df <- createDataFrame(data.frame(
##D earnings = c(10000, 10000, 11000, 15000, 12000, 20000, 21000, 22000),
##D course = c("R", "Python", "R", "Python", "R", "Python", "R", "Python"),
##D period = c("1H", "1H", "2H", "2H", "1H", "1H", "2H", "2H"),
##D year = c(2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016)
##D ))
##D group_sum <- sum(pivot(groupBy(df, "year"), "course"), "earnings")
##D group_min <- min(pivot(groupBy(df, "year"), "course", "R"), "earnings")
##D group_max <- max(pivot(groupBy(df, "year"), "course", c("Python", "R")), "earnings")
##D group_mean <- mean(pivot(groupBy(df, "year"), "course", list("Python", "R")), "earnings")
## End(Not run)