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summary

summary.Rd

Computes specified statistics for numeric and string columns. Available statistics are:

  • count

  • mean

  • stddev

  • min

  • max

  • arbitrary approximate percentiles specified as a percentage (e.g., "75%")

If no statistics are given, this function computes count, mean, stddev, min, approximate quartiles (percentiles at 25%, 50%, and 75%), and max. This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting Dataset. If you want to programmatically compute summary statistics, use the agg function instead.

Usage

summary(object, ...)

# S4 method for SparkDataFrame
summary(object, ...)

Arguments

object

a SparkDataFrame to be summarized.

...

(optional) statistics to be computed for all columns.

Value

A SparkDataFrame.

Note

summary(SparkDataFrame) since 1.5.0

The statistics provided by summary were change in 2.3.0 use describe for previous defaults.

See also

describe

Other SparkDataFrame functions: SparkDataFrame-class, agg(), alias(), arrange(), as.data.frame(), attach,SparkDataFrame-method, broadcast(), cache(), checkpoint(), coalesce(), collect(), colnames(), coltypes(), createOrReplaceTempView(), crossJoin(), cube(), dapply(), dapplyCollect(), describe(), dim(), distinct(), drop(), dropDuplicates(), dropna(), dtypes(), except(), exceptAll(), explain(), filter(), first(), gapply(), gapplyCollect(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersect(), intersectAll(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartition(), repartitionByRange(), rollup(), sample(), saveAsTable(), schema(), select(), selectExpr(), show(), showDF(), storageLevel(), str(), subset(), take(), toJSON(), union(), unionAll(), unionByName(), unpersist(), unpivot(), with(), withColumn(), withWatermark(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

if (FALSE) {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
summary(df)
summary(df, "min", "25%", "75%", "max")
summary(select(df, "age", "height"))
}