| coltypes {SparkR} | R Documentation |
Get column types of a SparkDataFrame
Set the column types of a SparkDataFrame.
coltypes(x) coltypes(x) <- value ## S4 method for signature 'SparkDataFrame' coltypes(x) ## S4 replacement method for signature 'SparkDataFrame,character' coltypes(x) <- value
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. |
value A character vector with the column types of the given SparkDataFrame
coltypes since 1.6.0
coltypes<- since 1.6.0
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, withColumn,
withWatermark, with,
write.df, write.jdbc,
write.json, write.orc,
write.parquet, write.stream,
write.text
## Not run:
##D irisDF <- createDataFrame(iris)
##D coltypes(irisDF) # get column types
## End(Not run)
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D coltypes(df) <- c("character", "integer") # set column types
##D coltypes(df) <- c(NA, "numeric") # set column types
## End(Not run)