| group_by {SparkR} | R Documentation |
Groups the SparkDataFrame using the specified columns, so we can run aggregation on them.
group_by(x, ...) groupBy(x, ...) ## S4 method for signature 'SparkDataFrame' groupBy(x, ...) ## S4 method for signature 'SparkDataFrame' group_by(x, ...)
x |
a SparkDataFrame. |
... |
character name(s) or Column(s) to group on. |
A GroupedData.
groupBy since 1.4.0
group_by since 1.4.0
Other SparkDataFrame functions: SparkDataFrame-class,
agg, alias,
arrange, as.data.frame,
attach,SparkDataFrame-method,
broadcast, cache,
checkpoint, coalesce,
collect, colnames,
coltypes,
createOrReplaceTempView,
crossJoin, cube,
dapplyCollect, dapply,
describe, dim,
distinct, dropDuplicates,
dropna, drop,
dtypes, exceptAll,
except, explain,
filter, first,
gapplyCollect, gapply,
getNumPartitions, head,
hint, histogram,
insertInto, intersectAll,
intersect, isLocal,
isStreaming, join,
limit, localCheckpoint,
merge, mutate,
ncol, nrow,
persist, printSchema,
randomSplit, rbind,
rename, repartitionByRange,
repartition, rollup,
sample, saveAsTable,
schema, selectExpr,
select, showDF,
show, storageLevel,
str, subset,
summary, take,
toJSON, unionAll,
unionByName, union,
unpersist, withColumn,
withWatermark, with,
write.df, write.jdbc,
write.json, write.orc,
write.parquet, write.stream,
write.text
## Not run:
##D # Compute the average for all numeric columns grouped by department.
##D avg(groupBy(df, "department"))
##D
##D # Compute the max age and average salary, grouped by department and gender.
##D agg(groupBy(df, "department", "gender"), salary="avg", "age" -> "max")
## End(Not run)