| dropna {SparkR} | R Documentation |
dropna, na.omit - Returns a new SparkDataFrame omitting rows with null values.
dropna(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)
na.omit(object, ...)
fillna(x, value, cols = NULL)
## S4 method for signature 'SparkDataFrame'
dropna(x, how = c("any", "all"),
minNonNulls = NULL, cols = NULL)
## S4 method for signature 'SparkDataFrame'
na.omit(object, how = c("any", "all"),
minNonNulls = NULL, cols = NULL)
## S4 method for signature 'SparkDataFrame'
fillna(x, value, cols = NULL)
x |
a SparkDataFrame. |
how |
"any" or "all".
if "any", drop a row if it contains any nulls.
if "all", drop a row only if all its values are null.
if |
minNonNulls |
if specified, drop rows that have less than
|
cols |
optional list of column names to consider. In |
object |
a SparkDataFrame. |
... |
further arguments to be passed to or from other methods. |
value |
value to replace null values with. Should be an integer, numeric, character or named list. If the value is a named list, then cols is ignored and value must be a mapping from column name (character) to replacement value. The replacement value must be an integer, numeric or character. |
A SparkDataFrame.
dropna since 1.4.0
na.omit since 1.5.0
fillna since 1.4.0
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,
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
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D dropna(df)
## End(Not run)
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D fillna(df, 1)
##D fillna(df, list("age" = 20, "name" = "unknown"))
## End(Not run)