Sample Bias or is this a Classification Problem?

curious white  dog
curious white dog

I am very frustrated with the hiring process. This is going to focus on logic. Given that a diverse workforce is wanted. Take 500 diverse qualified applicants screen to 50 then 5. One screening criteria, phone screens.

Chance is .01 to make it thru the process.
Let us go back to the screening criteria, a cell phone.

A successful phone screen requires a quality cell phone and a quiet place to chat.
Did the diverse workforce even make it to start of the process? Or is the screening process starting earlier making sure that you only get the like you people.

Data Manipulation with R second edition

Jaynal Abedin, Kishor Kumar Das wrote
Data Manipulation with R second edition March 2015 published by Packt Publishing.
I found the first book useful. The second edition continues to be very useful. Chapter 5 covers R and databases. The limits of using R in memory and practical ideas of workarounds to solve the problem. Like ff and filehash packages.

Chapter 4 covers the melt function in the reshape2 package.

A lot of useful information for the hard work of getting data in shape.

Indirect Questioning in Sample Surveys

indirectquestioningIndirect Questioning in Sample Surveys written by Arijit Chaudhuri and Tasos C Christofides. Published by Springer 2013

Some topics are extremely important to have information on, but so sensitive and touchy that it is hard to get people to talk about them.

This is a good book to help with gathering information about sensitive characteristics in surveys.

Lots of math proofs to explain the techniques. Such as Item Count Technique and Three Card Method.

Chapter 4.6.2 is about Crossed Model for the case of two stigmatizing characteristics.

Chapter 7 is is on protection of privacy.

This is a good book. I look forward to digging into it and figuring out how to write computer programs for some of the techniques.

Beginning Data Science with R

begindatascience9783319120652

Beginning Data Science with R written by Manas A. Pathak, published by Springer Publishing 2014.
ISBN 978-3-319-12065-2

Code examples at extras.springer.com

This book is written for coders who already know how to code to learn R for data science.

The book covers how to install and use R, but not an IDE like RStudio.

Chapter 2 includes control structures and functions. That functions in R are treated as first class objects. A fundamental property of functional programming languages.

Chapter 3 is on getting data into R. How do get the data into R is a common question. Years ago I was puzzled about getting data into R. I didn’t want to type it all into an array. You don’t have to type in the data, R will read, pull, connect to all sorts of data sources.

Chapter 4 is a nice over view of data visualization.

The book goes on to cover necessary topics and techniques in Data Science. What I want to point out is Chapter 7.3.1 on nearest neighbors uses a package that I haven’t used before kknn. The package is straight forward to use. The author Pathak has written an easy to grasp explanation of the technique.

This is a good book to get you stated coding in R for data science.

R for Cloud Computing

cloud9781493917013

R for Cloud Computing, An Approach for Data Scientists
A Ohri, published by Springer Science Business Media 2014

This is a useful book on how do cloud computing with R. How to set up your accounts and use OAuth to access services.

Chapter 8.1  page 237 shows how to ensure your R code doesn’t contain your login keys.

The book covers the major services available Amazon AWS, Google Cloud and MicroSoft Azure.

AWS needs a credit card even for the free services.

There are nice data visualization services. Google Vis which has an R package googleVis. And Plot.ly  which has an R library that you install with devtools from git hub, package plotly. Direction are on plot.ly site under r/getting started.

Some things change between when a book is published and when you go to use it. Be flexible and search for what is similar and works.

Google code is gone. There is now a package on cran to interface to Google Analytics called RGoogleAnalytics. Information on Google developer site.

In addition to being a good overview of what is out there are interviews that are fun to read and a nice table of my first 25 R commands.

Practical Tools for Designing and Weighting Survey Samples

survey9781461464488Practical Tools for Designing and Weighting Survey Samples written by: Richard Valliant, Jill Dever and Frauke Kreuter. Published by Springer Science 2013.

This useful book covers code in R and SAS along with EXCELL.

Some of the R packages used are: alabama, doby, quadprog and survey.

Disposition codes Chapter 6.1 page 164 covers several different versions in use. These useful codes help get the most information out of your data, defining outcome rates. Table 6.3 of the concordance between AAPOR, American Association for Public Opinion Research and American Time Use Survey has disposition codes and descriptions.  Codes like 27, unknown eligibility, privacy detector.  Useful for using all of the information you acquire and not throwing a lot of it out.

Read this book before you design your next survey, questionnaire to get the results that you are looking for, with appropriate  sample size and power.

color in R

There are 101 shades of gray in R.  Along with lightgray, lightslategray, slategray, darkgray, darkslategray. Way more shades of gray than I will ever use.  I think I will try lavenderblush4 and chocolate4

colors()
[1] “white” “aliceblue” “antiquewhite” “antiquewhite1”
[5] “antiquewhite2” “antiquewhite3” “antiquewhite4” “aquamarine”
[9] “aquamarine1” “aquamarine2” “aquamarine3” “aquamarine4”
[13] “azure” “azure1” “azure2” “azure3”
[17] “azure4” “beige” “bisque” “bisque1”
[21] “bisque2” “bisque3” “bisque4” “black”
[25] “blanchedalmond” “blue” “blue1” “blue2”
[29] “blue3” “blue4” “blueviolet” “brown”
[33] “brown1” “brown2” “brown3” “brown4”
[37] “burlywood” “burlywood1” “burlywood2” “burlywood3”
[41] “burlywood4” “cadetblue” “cadetblue1” “cadetblue2”
[45] “cadetblue3” “cadetblue4” “chartreuse” “chartreuse1”
[49] “chartreuse2” “chartreuse3” “chartreuse4” “chocolate”
[53] “chocolate1” “chocolate2” “chocolate3” “chocolate4”
[57] “coral” “coral1” “coral2” “coral3”
[61] “coral4” “cornflowerblue” “cornsilk” “cornsilk1”
[65] “cornsilk2” “cornsilk3” “cornsilk4” “cyan”
[69] “cyan1” “cyan2” “cyan3” “cyan4”
[73] “darkblue” “darkcyan” “darkgoldenrod” “darkgoldenrod1”
[77] “darkgoldenrod2” “darkgoldenrod3” “darkgoldenrod4” “darkgray”
[81] “darkgreen” “darkgrey” “darkkhaki” “darkmagenta”
[85] “darkolivegreen” “darkolivegreen1” “darkolivegreen2” “darkolivegreen3”
[89] “darkolivegreen4” “darkorange” “darkorange1” “darkorange2”
[93] “darkorange3” “darkorange4” “darkorchid” “darkorchid1”
[97] “darkorchid2” “darkorchid3” “darkorchid4” “darkred”
[101] “darksalmon” “darkseagreen” “darkseagreen1” “darkseagreen2”
[105] “darkseagreen3” “darkseagreen4” “darkslateblue” “darkslategray”
[109] “darkslategray1” “darkslategray2” “darkslategray3” “darkslategray4”
[113] “darkslategrey” “darkturquoise” “darkviolet” “deeppink”
[117] “deeppink1” “deeppink2” “deeppink3” “deeppink4”
[121] “deepskyblue” “deepskyblue1” “deepskyblue2” “deepskyblue3”
[125] “deepskyblue4” “dimgray” “dimgrey” “dodgerblue”
[129] “dodgerblue1” “dodgerblue2” “dodgerblue3” “dodgerblue4”
[133] “firebrick” “firebrick1” “firebrick2” “firebrick3”
[137] “firebrick4” “floralwhite” “forestgreen” “gainsboro”
[141] “ghostwhite” “gold” “gold1” “gold2”
[145] “gold3” “gold4” “goldenrod” “goldenrod1”
[149] “goldenrod2” “goldenrod3” “goldenrod4” “gray”
[153] “gray0” “gray1” “gray2” “gray3”
[157] “gray4” “gray5” “gray6” “gray7”
[161] “gray8” “gray9” “gray10” “gray11”
[165] “gray12” “gray13” “gray14” “gray15”
[169] “gray16” “gray17” “gray18” “gray19”
[173] “gray20” “gray21” “gray22” “gray23”
[177] “gray24” “gray25” “gray26” “gray27”
[181] “gray28” “gray29” “gray30” “gray31”
[185] “gray32” “gray33” “gray34” “gray35”
[189] “gray36” “gray37” “gray38” “gray39”
[193] “gray40” “gray41” “gray42” “gray43”
[197] “gray44” “gray45” “gray46” “gray47”
[201] “gray48” “gray49” “gray50” “gray51”
[205] “gray52” “gray53” “gray54” “gray55”
[209] “gray56” “gray57” “gray58” “gray59”
[213] “gray60” “gray61” “gray62” “gray63”
[217] “gray64” “gray65” “gray66” “gray67”
[221] “gray68” “gray69” “gray70” “gray71”
[225] “gray72” “gray73” “gray74” “gray75”
[229] “gray76” “gray77” “gray78” “gray79”
[233] “gray80” “gray81” “gray82” “gray83”
[237] “gray84” “gray85” “gray86” “gray87”
[241] “gray88” “gray89” “gray90” “gray91”
[245] “gray92” “gray93” “gray94” “gray95”
[249] “gray96” “gray97” “gray98” “gray99”
[253] “gray100” “green” “green1” “green2”
[257] “green3” “green4” “greenyellow” “grey”
[261] “grey0” “grey1” “grey2” “grey3”
[265] “grey4” “grey5” “grey6” “grey7”
[269] “grey8” “grey9” “grey10” “grey11”
[273] “grey12” “grey13” “grey14” “grey15”
[277] “grey16” “grey17” “grey18” “grey19”
[281] “grey20” “grey21” “grey22” “grey23”
[285] “grey24” “grey25” “grey26” “grey27”
[289] “grey28” “grey29” “grey30” “grey31”
[293] “grey32” “grey33” “grey34” “grey35”
[297] “grey36” “grey37” “grey38” “grey39”
[301] “grey40” “grey41” “grey42” “grey43”
[305] “grey44” “grey45” “grey46” “grey47”
[309] “grey48” “grey49” “grey50” “grey51”
[313] “grey52” “grey53” “grey54” “grey55”
[317] “grey56” “grey57” “grey58” “grey59”
[321] “grey60” “grey61” “grey62” “grey63”
[325] “grey64” “grey65” “grey66” “grey67”
[329] “grey68” “grey69” “grey70” “grey71”
[333] “grey72” “grey73” “grey74” “grey75”
[337] “grey76” “grey77” “grey78” “grey79”
[341] “grey80” “grey81” “grey82” “grey83”
[345] “grey84” “grey85” “grey86” “grey87”
[349] “grey88” “grey89” “grey90” “grey91”
[353] “grey92” “grey93” “grey94” “grey95”
[357] “grey96” “grey97” “grey98” “grey99”
[361] “grey100” “honeydew” “honeydew1” “honeydew2”
[365] “honeydew3” “honeydew4” “hotpink” “hotpink1”
[369] “hotpink2” “hotpink3” “hotpink4” “indianred”
[373] “indianred1” “indianred2” “indianred3” “indianred4”
[377] “ivory” “ivory1” “ivory2” “ivory3”
[381] “ivory4” “khaki” “khaki1” “khaki2”
[385] “khaki3” “khaki4” “lavender” “lavenderblush”
[389] “lavenderblush1” “lavenderblush2” “lavenderblush3” “lavenderblush4”
[393] “lawngreen” “lemonchiffon” “lemonchiffon1” “lemonchiffon2”
[397] “lemonchiffon3” “lemonchiffon4” “lightblue” “lightblue1”
[401] “lightblue2” “lightblue3” “lightblue4” “lightcoral”
[405] “lightcyan” “lightcyan1” “lightcyan2” “lightcyan3”
[409] “lightcyan4” “lightgoldenrod” “lightgoldenrod1” “lightgoldenrod2”
[413] “lightgoldenrod3” “lightgoldenrod4” “lightgoldenrodyellow” “lightgray”
[417] “lightgreen” “lightgrey” “lightpink” “lightpink1”
[421] “lightpink2” “lightpink3” “lightpink4” “lightsalmon”
[425] “lightsalmon1” “lightsalmon2” “lightsalmon3” “lightsalmon4”
[429] “lightseagreen” “lightskyblue” “lightskyblue1” “lightskyblue2”
[433] “lightskyblue3” “lightskyblue4” “lightslateblue” “lightslategray”
[437] “lightslategrey” “lightsteelblue” “lightsteelblue1” “lightsteelblue2”
[441] “lightsteelblue3” “lightsteelblue4” “lightyellow” “lightyellow1”
[445] “lightyellow2” “lightyellow3” “lightyellow4” “limegreen”
[449] “linen” “magenta” “magenta1” “magenta2”
[453] “magenta3” “magenta4” “maroon” “maroon1”
[457] “maroon2” “maroon3” “maroon4” “mediumaquamarine”
[461] “mediumblue” “mediumorchid” “mediumorchid1” “mediumorchid2”
[465] “mediumorchid3” “mediumorchid4” “mediumpurple” “mediumpurple1”
[469] “mediumpurple2” “mediumpurple3” “mediumpurple4” “mediumseagreen”
[473] “mediumslateblue” “mediumspringgreen” “mediumturquoise” “mediumvioletred”
[477] “midnightblue” “mintcream” “mistyrose” “mistyrose1”
[481] “mistyrose2” “mistyrose3” “mistyrose4” “moccasin”
[485] “navajowhite” “navajowhite1” “navajowhite2” “navajowhite3”
[489] “navajowhite4” “navy” “navyblue” “oldlace”
[493] “olivedrab” “olivedrab1” “olivedrab2” “olivedrab3”
[497] “olivedrab4” “orange” “orange1” “orange2”
[501] “orange3” “orange4” “orangered” “orangered1”
[505] “orangered2” “orangered3” “orangered4” “orchid”
[509] “orchid1” “orchid2” “orchid3” “orchid4”
[513] “palegoldenrod” “palegreen” “palegreen1” “palegreen2”
[517] “palegreen3” “palegreen4” “paleturquoise” “paleturquoise1”
[521] “paleturquoise2” “paleturquoise3” “paleturquoise4” “palevioletred”
[525] “palevioletred1” “palevioletred2” “palevioletred3” “palevioletred4”
[529] “papayawhip” “peachpuff” “peachpuff1” “peachpuff2”
[533] “peachpuff3” “peachpuff4” “peru” “pink”
[537] “pink1” “pink2” “pink3” “pink4”
[541] “plum” “plum1” “plum2” “plum3”
[545] “plum4” “powderblue” “purple” “purple1”
[549] “purple2” “purple3” “purple4” “red”
[553] “red1” “red2” “red3” “red4”
[557] “rosybrown” “rosybrown1” “rosybrown2” “rosybrown3”
[561] “rosybrown4” “royalblue” “royalblue1” “royalblue2”
[565] “royalblue3” “royalblue4” “saddlebrown” “salmon”
[569] “salmon1” “salmon2” “salmon3” “salmon4”
[573] “sandybrown” “seagreen” “seagreen1” “seagreen2”
[577] “seagreen3” “seagreen4” “seashell” “seashell1”
[581] “seashell2” “seashell3” “seashell4” “sienna”
[585] “sienna1” “sienna2” “sienna3” “sienna4”
[589] “skyblue” “skyblue1” “skyblue2” “skyblue3”
[593] “skyblue4” “slateblue” “slateblue1” “slateblue2”
[597] “slateblue3” “slateblue4” “slategray” “slategray1”
[601] “slategray2” “slategray3” “slategray4” “slategrey”
[605] “snow” “snow1” “snow2” “snow3”
[609] “snow4” “springgreen” “springgreen1” “springgreen2”
[613] “springgreen3” “springgreen4” “steelblue” “steelblue1”
[617] “steelblue2” “steelblue3” “steelblue4” “tan”
[621] “tan1” “tan2” “tan3” “tan4”
[625] “thistle” “thistle1” “thistle2” “thistle3”
[629] “thistle4” “tomato” “tomato1” “tomato2”
[633] “tomato3” “tomato4” “turquoise” “turquoise1”
[637] “turquoise2” “turquoise3” “turquoise4” “violet”
[641] “violetred” “violetred1” “violetred2” “violetred3”
[645] “violetred4” “wheat” “wheat1” “wheat2”
[649] “wheat3” “wheat4” “whitesmoke” “yellow”
[653] “yellow1” “yellow2” “yellow3” “yellow4”
[657] “yellowgreen”

CellularAutomation R Package

CellularAutomation R Package for doing cellular automation.  The package looks like it has all the functions needed to do cellular automata. The documentation is  light on how and what to do. It is going to take more time and research to come up with an explanation for general audiences. Until then enjoy this picture and example code.

ca = CellularAutomaton(n = 110, t = 100, seed = c(0, 0, 1, 0, 0, 0), bg = -1)
ca$plot(col = c(“white”, “purple”))

automaa celluar

 

Cellular Automation in Image Processing and Geometry

9783319064307cellautoCellular Automation in Image Processing and Geometry

Edited by Paul Rosin, Andrew Adamatzky and Xianfang Sun

Published by Springer March 2014

This morning I went looking for a book to explain the topic of Cellular Automata.  Last night at Ruby Brightnight which is a code challenge group, I found that I couldn’t adequately explain cellular automata. Our challenge was to code the game of life in ruby.

This book looks helpful. Especially chapter 13 Interactive Cellular Automata Systems for Creative Projects written by Angus Graeme Forbes.  The chapter discusses the game of life. Then goes into Fluid Automata. A very pretty algorithm with pseudo code.

This is an interesting book worth digging into.

Applied Predictive Modeling

apppredictlearnMax Kuhn and Kjell Johnson; Applied Predictive Modeling published by Springer 2013

This is such a good book it has taken me awhile to work through the book.  All the while finding examples of why people should read the book.

The summary in 2.3 does a good job of explaining why this subject is so important. Easy to pick a model, hard to get it correct with reliable, trustworthy results.

I was asked what models were in the book. All the commonly used ones  like K-Nearest Neighbors, plus models like Multivariate Adaptive Regression Spines and Cubist Regression Trees for Regression Models.

Classification Models including Nearest Shrunken Centroid and Nonlinear Classification Models.

Well thought out examples with the R packages and example code.

Take your time and work through this book.