| coltypes {SparkR} | R Documentation |
Get column types of a DataFrame
Set the column types of a DataFrame.
## S4 method for signature 'DataFrame' coltypes(x) ## S4 replacement method for signature 'DataFrame,character' coltypes(x) <- value coltypes(x) coltypes(x) <- value
x |
A SparkSQL DataFrame |
value |
A character vector with the target column types for the given DataFrame. Column types can be one of integer, numeric/double, character, logical, or NA to keep that column as-is. |
x |
A SparkSQL DataFrame |
value A character vector with the column types of the given 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<-; columns,
dtypes, printSchema,
schema, schema;
count, nrow;
describe, describe,
describe, summary,
summary,
summary,PipelineModel-method;
dim; distinct,
unique; dropna,
dropna, fillna,
fillna, na.omit,
na.omit; 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, 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,
write.df; selectExpr;
showDF, showDF;
show, show,
show,GroupedData-method; str;
take; unpersist;
withColumn, withColumn;
write.json, write.json;
write.text, write.text
## Not run:
##D irisDF <- createDataFrame(sqlContext, iris)
##D coltypes(irisDF)
## End(Not run)
## Not run:
##D sc <- sparkR.init()
##D sqlContext <- sparkRSQL.init(sc)
##D path <- "path/to/file.json"
##D df <- read.json(sqlContext, path)
##D coltypes(df) <- c("character", "integer")
##D coltypes(df) <- c(NA, "numeric")
## End(Not run)