Calculates the approximate quantiles of numerical columns of a SparkDataFrame. The result of this algorithm has the following deterministic bound: If the SparkDataFrame has N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from the SparkDataFrame so that the *exact* rank of x is close to (p * N). More precisely, floor((p - err) * N) <= rank(x) <= ceil((p + err) * N). This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in [[https://doi.org/10.1145/375663.375670 Space-efficient Online Computation of Quantile Summaries]] by Greenwald and Khanna. Note that NA values will be ignored in numerical columns before calculation. For columns only containing NA values, an empty list is returned.
# S4 method for SparkDataFrame,character,numeric,numeric
approxQuantile(x, cols, probabilities, relativeError)A SparkDataFrame.
A single column name, or a list of names for multiple columns.
A list of quantile probabilities. Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
The relative target precision to achieve (>= 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.
The approximate quantiles at the given probabilities. If the input is a single column name, the output is a list of approximate quantiles in that column; If the input is multiple column names, the output should be a list, and each element in it is a list of numeric values which represents the approximate quantiles in corresponding column.
Other stat functions:
corr(),
cov(),
crosstab(),
freqItems(),
sampleBy()
# NOT RUN {
df <- read.json("/path/to/file.json")
quantiles <- approxQuantile(df, "key", c(0.5, 0.8), 0.0)
# }
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