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)
```

x

A SparkDataFrame.

cols

A single column name, or a list of names for multiple columns.

probabilities

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.

relativeError

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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