# 452 search results for "boxplot"

## Escalating Life Expectancy

July 18, 2016
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I’ve added mortality data to the lifespan package. A result that immediately emerges from these data is that average life expectancy is steadily climbing. The effect is more pronounced for men, rising from around 66.5 in 1994 to 70.0 in 2014. The corresponding values for women are 74.6 and 76.5 respectively. Good news for everyone.

## Birth Month by Gender

July 16, 2016
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Based on some feedback to a previous post I normalised the birth counts by the (average) number of days in each month. As pointed out by a reader, the results indicate a gradual increase in the number of conceptions during (northern hemisphere) Autumn and Winter, roughly up to the end of December. Normalising the data

## Most Probable Birth Month

July 14, 2016
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In a previous post I showed that the data from www.baseball-reference.com support Malcolm Gladwell’s contention that more professional baseball players are born in August than any other month. Although this might be explained by the 31 July cutoff for admission to baseball leagues, it was suggested that it could also be linked to a larger

## useR! 2016 Tutorials: Part 2

July 7, 2016
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by Joseph Rickert Last week, I mentioned a few of the useR tutorials that I had the opportunity to attend. Here are the links to the slides and code for all but two of the tutorials: Regression Modeling Strategies and the rms Package - Frank Harrell Using Git and GitHub with R, RStudio, and R Markdown - Jennifer Bryan...

## The ggthemr package – Theme and colour your ggplot figures

July 4, 2016
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Want better colours for ggplot2? "Ggthemr" is an R package that provides new colour themes and also the specification of your own colour palettes. Change the look and feel of your ggplot2 plots in R with two quick commands! Beautiful figures await!

## R for Publication by Page Piccinini: Lesson 6, Part 1 – Linear Mixed Effects Models

June 26, 2016
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In today’s lesson we’ll learn about linear mixed effects models (LMEM), which give us the power to account for multiple types of effects in a single model. This is Part 1 of a two part lesson. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that Lesson 6, Part...

## R for Publication by Page Piccinini: Lesson 5 – Analysis of Variance (ANOVA)

June 20, 2016
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In today’s lesson we’ll take care of the baseline issue we had in the last lesson when we have a linear model with an interaction. To do that we’ll be learning about analysis of variance or ANOVA. We’ll also be going over how to make barplots with error bars, but not without hearing my reasons Lesson 5: Analysis...

## Venn Diagram Comparison of Boruta, FSelectorRcpp and GLMnet Algorithms

June 18, 2016
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Feature selection is a process of extracting valuable features that have significant influence on dependent variable. This is still an active field of research and machine wandering. In this post I compare few feature selection algorithms: traditional GLM with regularization, computationally demanding Boruta and entropy based filter from FSelectorRcpp (free of Java/Weka) package....

## R for Publication by Page Piccinini: Lesson 4 – Multiple Regression

June 13, 2016
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Introduction Today we’ll see what happens when you have not one, but two variables in your model. We will also continue to use some old and new dplyr calls, as well as another parameter for our ggplot2 figure. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done Lesson 4: Multiple...

## Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

June 9, 2016
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Welcome to Introduction to R for Data Science Session 7: Multiple Regression + Dummy Coding, Partial and Part Correlations [Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the...