Infer an R package for tidyverse friendly statistical inference is now on CRAN. Happy to see this package made onto CRAN. It is a different way to look at inference. Great addition to the tool box.

# SPSS and RStudio

What if you have a lot of old data files in SPSS form and you do not have SPSS software. You are in luck if you have Rstudio. Current versions of Rstudio load SPSS data files without needing a special library for the task. There are libraries for this task.

To load SPSS data:

Open RStudio, on menu bar choose File, scroll to Import Data set, choose from SPSS.

A window opens to load the data.

Enter file name or url into file/url box.

Preview data in preview window. If it looks okay push the import button. And the data is loaded into Rstudio ready for use.

This is a very useful feature of RStudio.

# A Crash Course in Statistics

Written by Ryan J. Winter, A Crash Course in Statistics published by Sage Publications, 2018 is an easy to read, short, concise book. It is just the right book when you want a quick overview of what you have forgotten about Statistics. The code in the book is SPSS available at the book’s web-site at study.sagepub.com/winter .

Covers descriptive Statistics, Chi-Square, t-Test, and ANOVA.

The book can be used as a text-book as it has quizzes at the end of each chapter.

If you do not have SPSS Rstudio will load the SPSS data files ready to use.

# February Already

It is February again. I have lots of back log to complete. Book reviews and interesting topics.

# Statistical Analysis with Measurement Error or Misclassification

Written by Grace Y. Yi , Statistical Analysis with Measurement Error or Misclassification, published by Springer Science Business Media LLC 2017.

Is a treasure of a book to go with a coding book. It gives the what, why and how of Missing data , Measurement error and Misclassification.

Chapter 2 covers Measurement error, incorrect readings of precise measurement. For example reading a three as an eight.

Systemic error, Sampling error, statistical bias, each type of error has it own way of handling it. And often the data contains more than on type of error.

Naive estimators incur larger bias than than estimators obtained from valid metrics but the later ones entail more variation than the naive estimators.

Lots to think about, Chapter 9 asks a lot of good questions.

Use the most plausible method to handle missing, mis classified and error prone data. The methods are well covered in the book.

This is a Stat’s book the key to the symbols is the beginning of the book.

It is know that ignoring measurement error can cause misleading results.

# Hidden Inequalities in the Workplace

Publisher Springer International Publishing, 2018

Hidden Inequalities in the Workplace

Editors: Valerie Caven and Stefanos Nachmias

I have been commissioned by AONW to do a study on Ageism in the Workplace. I am glad that I found this book while doing research. It is a timely book on difficult topics.

They make a business case for diversity: The real benefit assigned to diversity management is gaining competitive advantage and enhance performance thru human capital.

The Quality of Work Among Older Workers

Chapter 5 page 91

written by Christopher Lawton and Daniel Wheatly

This chapter sheds light into this under explored area of the labor market. Concluding with that working into later life can bring benefits to society including; higher national output, lower unemployment, lower welfare costs and reduced health speeding.

Cognitive Biases in Recruitment, Selection and Promotion: The Risk of Subconscious Discrimination

written by Zara Whysall

This chapter states that despite documented benefits of workplace diversity, progress in achieving this has been slow.

This book has given me a lot to think about and a lot more to explore.

# Tables with R

Cirque du Soleil Kurios show has an act where they mirror a table. It is amazing to see people upside down mirroring a table.

R programming language has several packages for doing tables with R. Basic has a function called table. Which is good enough. Sometimes you want more. At a meeting last night someone said pander was the best package. Someone else said that they liked htmltable better. Also there is xtable and tables. tables was written by someone to be like SAS PROC TABULATE. Many choices, pick out the one that you understand the directions and meets your publishing needs. Better depends on your point of view.

table

tables

xtable

htmltable

pander

# Functions in R

Wish I was at the coast.

R does a lot with functions. Let’s start with a simple function statement. The base R has a function called function.

In the following code:

f is the objects name

x is the varible

*the function x +1 goes between { }*

Pretty simple

f <- function(x) {x + 1}

Take

f(4)

results

[1] 5

Write your code try other functions. It is easier to write a function in R than other languages.

# Data Visualisation with R

Data Visualisation with R

written by Thomas Rahlf

published by Springer International Publishing 2017

Originally published as Datendesign mit R, 2014

www.datavisualisation-r.com

This is a well written book for designers. Part one of the book basics and techniques covers more than the basics. Fig 2.1 is of Elements of a figure. R has the commands to put all these things on a graph.

Typefaces, fonts and symbols again more information than I usually see in an R book.

Part two is the examples. 100 examples are on their web site. The examples talk about good design layout and readability.

One of my favorites is figure 6.3.7 Tree Map. Tree Map is a good way to see proportions. How much is each part of the budget. Small important items do not disappear.

Enjoy this book. I am having fun getting the code to work on other data.

# Data the World’s Most Valuable Resource

I just read an Article in the May 6th 2017 The Economist. Briefing The data economy, Fuel of he future.

An interesting thought that data is the oil of this century.

“Data are to this century what oil was to the last one: a driver of growth and change.” page 19

This idea gives you a lot to think about.

One thing to think about is the lack of fungibility for data.

And who owns the data?

Lot’s to think about.