| dropna {SparkR} | R Documentation |
Returns a new SparkDataFrame omitting rows with null values.
Replace 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 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. |
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. |
x |
A SparkDataFrame. |
cols |
optional list of column names to consider. 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
Other SparkDataFrame functions: SparkDataFrame-class,
[[, agg,
arrange, as.data.frame,
attach, cache,
collect, colnames,
coltypes, columns,
count, dapply,
describe, dim,
distinct, dropDuplicates,
drop, dtypes,
except, explain,
filter, first,
group_by, head,
histogram, insertInto,
intersect, isLocal,
join, limit,
merge, mutate,
ncol, persist,
printSchema,
registerTempTable, rename,
repartition, sample,
saveAsTable, selectExpr,
select, showDF,
show, str,
take, unionAll,
unpersist, withColumn,
write.df, write.jdbc,
write.json, write.parquet,
write.text
## Not run:
##D sc <- sparkR.init()
##D sqlCtx <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- read.json(sqlCtx, path)
##D dropna(df)
## End(Not run)
## Not run:
##D sc <- sparkR.init()
##D sqlCtx <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- read.json(sqlCtx, path)
##D fillna(df, 1)
##D fillna(df, list("age" = 20, "name" = "unknown"))
## End(Not run)