coltypes

Get column types of a SparkDataFrame

Set the column types of a SparkDataFrame.

Usage

coltypes(x)

coltypes(x) <- value

# S4 method for class 'SparkDataFrame'
coltypes(x)

# S4 method for class '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

Note

coltypes since 1.6.0

coltypes<- since 1.6.0

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(), dapply(), dapplyCollect(), describe(), dim(), distinct(), drop(), dropDuplicates(), dropna(), dtypes(), except(), exceptAll(), explain(), filter(), first(), gapply(), gapplyCollect(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersect(), intersectAll(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartition(), repartitionByRange(), rollup(), sample(), saveAsTable(), schema(), select(), selectExpr(), show(), showDF(), storageLevel(), str(), subset(), summary(), take(), toJSON(), union(), unionAll(), unionByName(), unpersist(), unpivot(), with(), withColumn(), withWatermark(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

if (FALSE) { # \dontrun{
irisDF <- createDataFrame(iris)
coltypes(irisDF) # get column types
} # }
if (FALSE) { # \dontrun{
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
} # }