A set of SparkDataFrame functions working with NA values
nafunctions.Rddropna, na.omit - Returns a new SparkDataFrame omitting rows with null values.
Usage
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)Arguments
- 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
minNonNullsis specified, how is ignored.- minNonNulls
if specified, drop rows that have less than
minNonNullsnon-null values. This overwrites the how parameter.- cols
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.- 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.
See also
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
as.data.frame(),
attach,SparkDataFrame-method,
broadcast(),
cache(),
checkpoint(),
coalesce(),
collect(),
colnames(),
coltypes(),
createOrReplaceTempView(),
crossJoin(),
cube(),
dapply(),
dapplyCollect(),
describe(),
dim(),
distinct(),
drop(),
dropDuplicates(),
dtypes(),
except(),
exceptAll(),
explain(),
filter(),
first(),
gapply(),
gapplyCollect(),
getNumPartitions(),
group_by(),
head(),
hint(),
histogram(),
insertInto(),
intersect(),
intersectAll(),
isLocal(),
isStreaming(),
join(),
limit(),
localCheckpoint(),
merge(),
mutate(),
ncol(),
nrow(),
persist(),
printSchema(),
randomSplit(),
rbind(),
rename(),
repartition(),
repartitionByRange(),
rollup(),
sample(),
saveAsTable(),
schema(),
select(),
selectExpr(),
show(),
showDF(),
storageLevel(),
str(),
subset(),
summary(),
take(),
toJSON(),
union(),
unionAll(),
unionByName(),
unpersist(),
unpivot(),
with(),
withColumn(),
withWatermark(),
write.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
Examples
if (FALSE) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
dropna(df)
}
if (FALSE) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
fillna(df, 1)
fillna(df, list("age" = 20, "name" = "unknown"))
}