| dropna {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'
na.omit(object, 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(object, ...)
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
Other DataFrame functions: $,
$<-, select,
select,
select,DataFrame,Column-method,
select,DataFrame,list-method,
selectExpr; DataFrame-class,
dataFrame, groupedData;
[, [, [[,
subset; agg,
agg,
count,GroupedData-method,
summarize, summarize;
arrange, arrange,
arrange, orderBy,
orderBy; as.data.frame,
as.data.frame,DataFrame-method;
attach,
attach,DataFrame-method;
cache; collect;
colnames, colnames,
colnames<-, colnames<-,
columns, names,
names<-; coltypes,
coltypes, coltypes<-,
coltypes<-; columns,
dtypes, printSchema,
schema, schema;
count, nrow;
describe, describe,
describe, summary,
summary,
summary,PipelineModel-method;
dim; distinct,
unique; dtypes;
except, except;
explain, explain;
filter, filter,
where, where;
first, first;
groupBy, groupBy,
group_by, group_by;
head; insertInto,
insertInto; intersect,
intersect; isLocal,
isLocal; join;
limit, limit;
merge, merge;
mutate, mutate,
transform; ncol;
persist; printSchema;
rbind, rbind,
unionAll, unionAll;
registerTempTable,
registerTempTable; rename,
rename, withColumnRenamed,
withColumnRenamed;
repartition; sample,
sample, sample_frac,
sample_frac;
saveAsParquetFile,
saveAsParquetFile,
write.parquet, write.parquet;
saveAsTable, saveAsTable;
saveDF, saveDF,
write.df, write.df;
selectExpr; showDF,
showDF; show,
show,
show,GroupedData-method;
take; transform,
withColumn, withColumn;
unpersist; write.json,
write.json
## 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)