SparkR (version 3.1.2)

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(), 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()

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
# }

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