SparkR (version 2.1.2)

mutate: Mutate

Description

Return a new SparkDataFrame with the specified columns added or replaced.

Usage

mutate(.data, ...)
transform(`_data`, ...)
# S4 method for SparkDataFrame
mutate(.data, ...)
# S4 method for SparkDataFrame
transform(`_data`, ...)

Arguments

.data

a SparkDataFrame.

...

additional column argument(s) each in the form name = col.

_data

a SparkDataFrame.

Value

A new SparkDataFrame with the new columns added or replaced.

See Also

rename withColumn

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, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, take, union, unpersist, withColumn, 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 <- mutate(df, newCol = df$col1 * 5, newCol2 = df$col1 * 2)
names(newDF) # Will contain newCol, newCol2
newDF2 <- transform(df, newCol = df$col1 / 5, newCol2 = df$col1 * 2)

df <- createDataFrame(list(list("Andy", 30L), list("Justin", 19L)), c("name", "age"))
# Replace the "age" column
df1 <- mutate(df, age = df$age + 1L)
# }