dropna, na.omit - Returns a new SparkDataFrame omitting rows with null values.
fillna - Replace null values.
dropna(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)na.omit(object, ...)
fillna(x, value, cols = NULL)
# S4 method for SparkDataFrame
dropna(x, how = c("any", "all"),
minNonNulls = NULL, cols = NULL)
# S4 method for SparkDataFrame
na.omit(object, how = c("any", "all"),
minNonNulls = NULL, cols = NULL)
# S4 method for SparkDataFrame
fillna(x, value, cols = NULL)
a SparkDataFrame.
"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
is specified, how is ignored.
if specified, drop rows that have less than
minNonNulls
non-null values.
This overwrites the how parameter.
optional list of column names to consider. In fillna
,
columns specified in cols that do not have matching data
type are ignored. For example, if value is a character, and
subset contains a non-character column, then the non-character
column is simply ignored.
a SparkDataFrame.
further arguments to be passed to or from other methods.
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.
Other SparkDataFrame functions: SparkDataFrame-class
,
agg
, arrange
,
as.data.frame
, attach
,
cache
, coalesce
,
collect
, colnames
,
coltypes
,
createOrReplaceTempView
,
crossJoin
, dapplyCollect
,
dapply
, describe
,
dim
, distinct
,
dropDuplicates
, 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
,
withColumn
, with
,
write.df
, write.jdbc
,
write.json
, write.orc
,
write.parquet
, write.text
# NOT RUN {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
dropna(df)
# }
# NOT RUN {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
fillna(df, 1)
fillna(df, list("age" = 20, "name" = "unknown"))
# }
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