SparkR (version 2.3.0)

coltypes: coltypes

Description

Get 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) <- value

Arguments

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

value A character vector with the column types of the given SparkDataFrame

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, except, explain, filter, first, gapplyCollect, gapply, getNumPartitions, group_by, head, hint, histogram, insertInto, intersect, isLocal, isStreaming, join, limit, localCheckpoint, merge, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, rollup, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, summary, take, toJSON, unionByName, union, unpersist, withColumn, withWatermark, with, write.df, write.jdbc, write.json, write.orc, write.parquet, write.stream, write.text

Examples

# NOT RUN {
irisDF <- createDataFrame(iris)
coltypes(irisDF) # get column types
# }
# NOT RUN {
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
# }