coltypes
coltypes.RdGet column types of a SparkDataFrame
Set the column types of a SparkDataFrame.
Usage
coltypes(x)
coltypes(x) <- value
# S4 method for SparkDataFrame
coltypes(x)
# S4 method for SparkDataFrame,character
coltypes(x) <- valueArguments
- x
A SparkDataFrame
- value
A character vector with the target column types for the given SparkDataFrame. Column types can be one of integer, numeric/double, character, logical, or NA to keep that column as-is.
See also
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
as.data.frame(),
attach,SparkDataFrame-method,
broadcast(),
cache(),
checkpoint(),
coalesce(),
collect(),
colnames(),
createOrReplaceTempView(),
crossJoin(),
cube(),
dapplyCollect(),
dapply(),
describe(),
dim(),
distinct(),
dropDuplicates(),
dropna(),
drop(),
dtypes(),
exceptAll(),
except(),
explain(),
filter(),
first(),
gapplyCollect(),
gapply(),
getNumPartitions(),
group_by(),
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(),
unpivot(),
withColumn(),
withWatermark(),
with(),
write.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
Examples
if (FALSE) {
irisDF <- createDataFrame(iris)
coltypes(irisDF) # get column types
}
if (FALSE) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
coltypes(df) <- c("character", "integer") # set column types
coltypes(df) <- c(NA, "numeric") # set column types
}