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A set of SparkDataFrame functions working with NA values — dropna • SparkR

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 class 'SparkDataFrame'
dropna(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)

# S4 method for class 'SparkDataFrame'
na.omit(object, how = c("any", "all"), minNonNulls = NULL, cols = NULL)

# S4 method for class '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 is specified, how is ignored.

minNonNulls

if specified, drop rows that have less than minNonNulls non-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.

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(), unpivot(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

if (FALSE) { # \dontrun{
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
dropna(df)
} # }
if (FALSE) { # \dontrun{
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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