Basic statistics utilities in Clojure.
- First, add the dependency to Leiningen from Clojars:
[erdos.stats "0.1.0-SNAPSHOT"] - Fetch the dependencies with
lein deps - You need to require the namespace:
(require '[erdos.stats :as stats])
The following functions are implemented. For usage, see the examples and the source code.
- sampling:
sample-with-probs,rand-samples - quantiles:
quantiles,tertiles,quintiles,sextiles, etc. - exploration:
hist,report,hist-print-ascii - operations:
sum,prod - stats:
mean,median,median-low,median-high,mode,variance,sd(aka.standard-deviation),skewness,kurtosis
(mean [1 2 2 3 4]) ; => 2.5
(mode [1 2 2 3 4]) ; => 2
(median [1 2 2 3 4]) ; => 2You can get a general idea about the distribution of values in a collection using the hist function. Given a collection it returns a vector of buckets that represents a histogram.
(stats/hist (repeatedly 1000 stats/normal))
;; => [1 9 25 54 74 134 157 174 150 90 67 43 13 5 3 1]When given two arguments, the first argument is the number of buckets and the second argument is the collection.
(stats/hist 3 (range 99))
;; => [33 33 33]You can print basic statistics to the standard output.
In this example, we create a series of random numbers with Gaussian distribution.
(def xs (repeatedly 10000 stats/normal))Calling (stats/report xs) results something like the following:
--------------
Count: 10000
Min: -4.15101725230182
Mean: -0.008607583618416693
Max: 3.875746881304692
Median: -0.0038671443172906385
Mode: -0.5172982636402658
Variance: 0.997574255434155
Deviation: 0.9987863912940319
Skewness: -0.020849505804486506
Kurtosis: 3.0074920441970137
Histogram:
┌────────────────────────────────────────────────┐
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│ ▗██████████████ │
│ ▟███████████████▙▄ │
│ ▟▟██████████████████▙▖ │
│ ▗▗▄████████████████████████▄▄ │
└────────────────────────────────────────────────┘
Copyright © 2016 Janos Erdos
Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.