SparkR (version 2.1.2)

withColumn: WithColumn

Description

Return a new SparkDataFrame by adding a column or replacing the existing column that has the same name.

Usage

withColumn(x, colName, col)
# S4 method for SparkDataFrame,character
withColumn(x, colName, col)

Arguments

x

a SparkDataFrame.

colName

a column name.

col

a Column expression, or an atomic vector in the length of 1 as literal value.

Value

A SparkDataFrame with the new column added or the existing column replaced.

See Also

rename mutate subset

Other SparkDataFrame functions: SparkDataFrame-class, agg, arrange, as.data.frame, attach, cache, coalesce, collect, colnames, coltypes, createOrReplaceTempView, crossJoin, dapplyCollect, dapply, describe, dim, distinct, dropDuplicates, dropna, drop, dtypes, except, explain, filter, first, gapplyCollect, gapply, getNumPartitions, group_by, head, histogram, insertInto, intersect, isLocal, join, limit, merge, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, take, union, unpersist, with, write.df, write.jdbc, write.json, write.orc, write.parquet, write.text

Examples

# NOT RUN {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
newDF <- withColumn(df, "newCol", df$col1 * 5)
# Replace an existing column
newDF2 <- withColumn(newDF, "newCol", newDF$col1)
newDF3 <- withColumn(newDF, "newCol", 42)
# Use extract operator to set an existing or new column
df[["age"]] <- 23
df[[2]] <- df$col1
df[[2]] <- NULL # drop column
# }