Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in https://dl.acm.org/doi/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou.
# S4 method for SparkDataFrame,character
freqItems(x, cols, support = 0.01)
A SparkDataFrame.
A vector column names to search frequent items in.
(Optional) The minimum frequency for an item to be considered frequent
.
Should be greater than 1e-4. Default support = 0.01.
a local R data.frame with the frequent items in each column
Other stat functions:
approxQuantile()
,
corr()
,
cov()
,
crosstab()
,
sampleBy()
# NOT RUN {
df <- read.json("/path/to/file.json")
fi = freqItems(df, c("title", "gender"))
# }
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