| mutate {SparkR} | R Documentation |
Return a new SparkDataFrame with the specified columns added or replaced.
mutate(.data, ...) transform(`_data`, ...) ## S4 method for signature 'SparkDataFrame' mutate(.data, ...) ## S4 method for signature 'SparkDataFrame' transform(`_data`, ...)
.data |
a SparkDataFrame. |
... |
additional column argument(s) each in the form name = col. |
_data |
a SparkDataFrame. |
A new SparkDataFrame with the new columns added or replaced.
mutate since 1.4.0
transform since 1.5.0
Other SparkDataFrame functions: SparkDataFrame-class,
agg, alias,
arrange, as.data.frame,
attach,SparkDataFrame-method,
broadcast, cache,
checkpoint, coalesce,
collect, colnames,
coltypes,
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,
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
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D newDF <- mutate(df, newCol = df$col1 * 5, newCol2 = df$col1 * 2)
##D names(newDF) # Will contain newCol, newCol2
##D newDF2 <- transform(df, newCol = df$col1 / 5, newCol2 = df$col1 * 2)
##D
##D df <- createDataFrame(list(list("Andy", 30L), list("Justin", 19L)), c("name", "age"))
##D # Replace the "age" column
##D df1 <- mutate(df, age = df$age + 1L)
## End(Not run)