This document provides an overview of statistical functions and graphs available in R. It lists common distributions that can be evaluated or simulated in R, including beta, binomial, Cauchy, chi-squared, exponential, F, gamma, geometric, hypergeometric, log-normal, logistic, negative binomial, normal, Poisson, Student's t, uniform, and Weibull. It also discusses functions for calculating median, mean, variance, standard deviation, five number summary, and generating stem-and-leaf plots, histograms, density plots, bar plots, pie charts in R. Categorical data can be grouped using the cut function and summarized using table.

1. StatisticsThe Dataminingtools.net Team

2. StatisticsR can be used to provide a comprehensive set of statistical tables. Functions are provided to evaluate the cumulative distribution function P(X ≤ x), the probability density function and the quantile function (given q, the smallest x such that P(X ≤ x) > q), and to simulate from the distribution.

3. DistributionsDistribution R name additional argumentsbeta beta shape1, shape2, ncpbinomial binom size, probCauchy cauchy location, scalechi-squared chisqdf, ncpexponential exp rateF f df1, df2, ncpgamma gamma shape, scalegeometric geomprob

4. DistributionsDistribution R name additional argumentshypergeometric hyper m, n, klog-normal lnormmeanlog, sdloglogistic logis location, scalenegative binomial nbinomsize, probnormal norm mean, sdPoisson pois lambdaStudent’s t tdf, ncpuniform unif min, maxWeibullweibull shape, scaleWilcoxonwilcox m, n

5. Statistics

6. Barplots

7. Barplots

8. Pie Charts

9. Pie Charts

10. functionsUsing the functions median(), mean(), var(), sd(), fivenum(), summary()

11. Stem-and-leaf plotsstem produces a stem-and-leaf plot of the values in x. The parameter scale can be used to expand the scale of the plot. A value of scale=2 will cause the plot to be roughly twice as long as the default.

12. Stem and Leaf charts> table(age)age 4 6 7 11 12 23 32 33 34 43 44 45 54 56 65 2 1 1 2 5 6 2 1 2 1 1 1 2 2 2 > stem(age) The decimal point is 1 digit(s) to the right of the |0 | 4467 1 | 1122222 2 | 333333 3 | 22344 4 | 345 5 | 4466 6 | 55age=scan()1: 23 4 12 23 43 237: 23 44 56 54 32 1213: 11 32 65 34 23 1219: 65 12 33 12 54 425: 7 6 56 45 34 11 2332: Read 31 items

13. Categorization of Data> salary <- c(23,2,34,1,32,22,45,3,4,5,12)> bins <- cut(salary, breaks=c(0,10,20,30,40,max(salary)))> bins [1] (20,30] (0,10] (30,40] (0,10] (30,40] (20,30] [7] (40,45] (0,10] (0,10] (0,10] (10,20]Levels: (0,10] (10,20] (20,30] (30,40] (40,45]> table(bins)bins (0,10] (10,20] (20,30] (30,40] (40,45] 5 1 2 2 1 > pie(table(bins))

14. Categorization of Data

15. Histograms> data [1] 2 3 1 3 2 4 1 2 4 1 3 4 1 2 1 2 3 2 5 4 1[22] 2 3 4 3 2 4 3> hist(data)> rug(jitter(data))

16. Density> lines(density(eruptions,bw="SJ"),col="red")> data(faithful)> hist(eruptions,15,prob=T)> lines(density(eruptions))> lines(density(eruptions,bw="SJ"),col="red")

17. Density