Compute histogram statistics for given column
histogram.RdThis function computes a histogram for a given SparkR Column.
Arguments
- df
the SparkDataFrame containing the Column to build the histogram from.
- col
the column as Character string or a Column to build the histogram from.
- nbins
the number of bins (optional). Default value is 10.
See also
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(),
group_by(),
head(),
hint(),
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(),
unpivot(),
withColumn(),
withWatermark(),
with(),
write.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
Examples
if (FALSE) {
# Create a SparkDataFrame from the Iris dataset
irisDF <- createDataFrame(iris)
# Compute histogram statistics
histStats <- histogram(irisDF, irisDF$Sepal_Length, nbins = 12)
# Once SparkR has computed the histogram statistics, the histogram can be
# rendered using the ggplot2 library:
require(ggplot2)
plot <- ggplot(histStats, aes(x = centroids, y = counts)) +
geom_bar(stat = "identity") +
xlab("Sepal_Length") + ylab("Frequency")
}