MonkmanMH

Categorical data analysis in R - a resource list

R packages

vcd & vcdExtra http://cran.r-project.org/web/packages/vcd/ http://cran.r-project.org/web/packages/vcdExtra/

MonkmanMH

# CALCULATING PERCENTILES IN R 
#
# a basic percentile function using "ecdf" [Empirical Cumulative Distribution Function]
# using a data file "percentiledata" with variable VALUE
percentileFUN <- ecdf(percentiledata$VALUE)
percentileFUN
percentileFUN(percentiledata$VALUE)
# write the percentile values to the source file
percentiledata$pctl <- percentilefunction(percentiledata$VALUE)
#

MonkmanMH

Random numbers in R

The creation of random numbers, or the random selection of elements in a set (or population), is an important part of statistics and data science. From simulating coin tosses to selecting potential respondents for a survey, we have a heavy reliance on random number generation.

R offers us a variety of solutions for random number generation; here's a quick overview of some of the options.

runif, rbinom, rnorm

One simple solution is to use the runif function, which generates a stated number of values between two end points (but not the end points themselves!) The function uses the continuous uniform distribution, meaning that every value between the two end points has an equal probability of being sampled.

MonkmanMH

# BINOMIAL CONFIDENCE INTERVAL CALCULATOR
#
# the binomial distribuion approximates the Normal distribution
# http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
#
# read the data file
bin_data <- read.csv("bin_data.csv")
#
#
# the binomial confidence calculator function

MonkmanMH

# PRINT THE INTEGERS 1 THROUGH 10
#
# VERSION 1 -- using while()
# make the initial assignment of variable count_1 to 0 (not necessary)
count_1 <- 0
# the while loop - conditional statement in the first parenthesis, 
# then the repeated steps within the {}
while (count_1 < 10) 
{  count_1 <- count_1 + 1
   print(count_1)

MonkmanMH

Using Google Maps API and R

[source: http://www.r-bloggers.com/using-google-maps-api-and-r/] [address modifications added by MonkmanMH]

This script uses RCurl and RJSONIO to download data from Google's API to get the latitude, longitude, location type, and formatted address

library(RCurl)

MonkmanMH

num,date,day,day2,day.night,vs,attend,cloud,sun,temp.c,temp.f,wind,note
1,6/5/2013,Wed,1,1,Kelowna,3026,mainly sunny,4,21,70,,Opening Night
2,6/6/2013,Thu,1,1,Kelowna,1082,mainly sunny,4,18,64,,
3,6/7/2013,Fri,3,1,Kelowna,1542,mainly sunny,4,19,66,windy,
4,6/11/2013,Tue,1,1,Medford,1014,mostly cloudy,2,17,63,,
5,6/12/2013,Wed,1,1,Medford,1003,mostly cloudy,2,16,60,,
6,6/13/2013,Thu,1,1,Medford,1015,partly cloudy,3,19,66,,
7,6/21/2013,Fri,3,1,Bend,1248,sunny,5,18,64,,
8,6/22/2013,Sat,3,1,Bend,1640,sunny,5,21,70,,
9,6/23/2013,Sun,2,0,Bend,1246,cloudy,1,18,64,,

MonkmanMH

# ######################
#
# Blog with output and discussion:
# "Fair weather fans? (An R scatter plot matrix)" 2013-07-18
# http://bayesball.blogspot.ca/2013/07/fair-weather-fans-r-scatter-plot-matrix.html
#
# data: pulled from www.harbourcats.com
# saved on Google Drive:
# https://docs.google.com/spreadsheet/ccc?key=0Art4wpcrwqkBdHZvTUFzOUo5U3BzMHFveXdYOTdTWUE&usp=sharing
# File / Download as > Comma Separated Values (CSV)

MonkmanMH

#
# for details see
# http://bayesball.blogspot.ca/2013/06/annotating-select-points-on-x-y-plot.html
#
# load the ggplot2 and grid packages
library(ggplot2)
library(grid)
# read data (note csv files are renamed)
tbl1 = read.csv("FanGraphs_Leaderboard_h.csv")
tbl2 = read.csv("FanGraphs_Leaderboard_d.csv")

MonkmanMH

# load the package and data set "Teams"
install.packages("Lahman")
library("Lahman")
data(Teams)
#
#
# CREATE LEAGUE SUMMARY TABLES
# ============================
#
# select a sub-set of teams from 1901 [the establishment of the American League] forward to 2012