| dropna,DataFrame-method {SparkR} | R Documentation |
Returns a new DataFrame omitting rows with null values.
Replace null values.
## S4 method for signature 'DataFrame'
dropna(x, how = c("any", "all"), minNonNulls = NULL,
cols = NULL)
## S4 method for signature 'DataFrame'
fillna(x, value, cols = NULL)
dropna(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)
na.omit(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL)
fillna(x, value, cols = NULL)
x |
A SparkSQL DataFrame. |
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 SparkSQL DataFrame. |
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 DataFrame
A DataFrame
## Not run:
##D sc <- sparkR.init()
##D sqlCtx <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- jsonFile(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 <- jsonFile(sqlCtx, path)
##D fillna(df, 1)
##D fillna(df, list("age" = 20, "name" = "unknown"))
## End(Not run)