Please find in the following some posts about R Programming and visualization of data and models that I bookmarked over the last 2 months and that are definitively worth looking at!

Content:

1. Data Visualization cheatsheet – ggplot2

2. An R Studio update is coming with loads of new features such as this neat update….

3. How to Layout and Design an Infographic or what I see: How to make your conference poster completely in R and so that you can easily adapt it for future conferences

4. Why the Ban on P-Values? And What Now?

5. Going deeper with dplyr: New features in 0.3 and 0.4 (video tutorial)

6. Creating composite figures with ggplot2 for reproducible research

7. A Compendium of Clean Graphs in R

8. An introduction to ggplot with Myfanwy Johnston

9. Analytical Minds – All about data analysis (Part 3b: EDA with ggplot2)

1. Data Visualization cheatsheet – ggplot2

The powerful cheatsheet series by RStudio continues!

Print this out and put it next to your monitor. It will make your PhD easier.

We’ve added a new cheatsheet to our collection. Data Visualization with ggplot2 describes how to build a plot with ggplot2 and the grammar of graphics. You will find helpful reminders of how to use:

• geoms
• stats
• scales
• coordinate systems
• facets
• position adjustments
• legends, and
• themes

The cheatsheet also documents tips on zooming.

Download the cheatsheet here.

By: RStudio

Source: http://blog.rstudio.org/2015/03/30/data-visualization-cheatsheet-plus-spanish-translations/

 2. An R Studio update is coming with loads of new features such as this neat update:

SMARTER COMPLETION ENGINE

Previously RStudio only completed variables that already existed in the global environment, now completion is done based on source code analysis so is provided even for objects that haven’t been fully evaluated:

You can download a preview here: http://www.rstudio.com/products/rstudio/download/preview/

By: RStudio

Source: http://blog.rstudio.org/2015/02/23/rstudio-v0-99-preview-code-completion/

 3. How to Layout and Design an Infographic or what I see: How to make your conference poster completely in R and so that you can easily adapt it for future conferences

Imagine not having to wrestle with layouting problems anymore. You can work towards that with LateX, R Markdown etc or you can take an approach like this with R packages like grid and ggplot2.

Contains the code to make the following:

By: Al-Ahmadgaid Asaad

Source: http://alstatr.blogspot.ca/2015/02/r-how-to-layout-and-design-infographic.html

4. Why the Ban on P-Values? And What Now?

Just recently, the editors of the academic journal Basic and Applied Social Psychology have decided to ban p-values: that’s right, the nexus for inferential decision making… gone! This has created quite a fuss among anyone who relies on significance testing and p-values to do research (especially those, presumably, in social psychology who were hoping to submit a paper to that journal any time soon). The Royal Statistical Society even shared six interesting letters from academics to see how they felt about the decision.

By:  NICOLE RADZIWILL

Source:  http://qualityandinnovation.com/2015/03/07/why-the-ban-on-p-values-and-what-now/

 5. Going deeper with dplyr: New features in 0.3 and 0.4 (video tutorial)

If you saw my last reblog about dplyr, then this is the update for you. Here all the new features are discussed.

Table of contents

This new tutorial runs 37 minutes, but if you only want to watch a particular section, simply click the topic below and it will skip to that point in the video:

• Introduction (starts at 0:00)
• Loading dplyr and the nycflights13 dataset (starts at 1:12)
• Choosing columns: select, rename(starts at 2:28)
• Choosing rows: filter, between, slice, sample_n, top_n, distinct(starts at 5:40)
• Adding new variables: mutate, transmute, add_rownames(starts at 12:38)
• Grouping and counting: summarise, tally, count, group_size, n_groups, ungroup(starts at 15:20)
• Creating data frames: data_frame(starts at 23:01)
• Joining (merging) tables: left_join, right_join, inner_join, full_join, semi_join, anti_join(starts at 25:28)
• Viewing more output: print, View(starts at 31:29)
• Resources (starts at 34:41)

By Kevin Markham

Source: http://www.dataschool.io/dplyr-tutorial-part-2/

 6. Creating composite figures with ggplot2 for reproducible research

Being able to make a figure such as the following might come in handy over time (the blog includes the R code):

By: Michael Kuhn

Source: http://blog.mckuhn.de/2015/03/creating-composite-figures-with-ggplot.html

7. A Compendium of Clean Graphs in R

This is an incredible resource! Loads of clean graphs including R code such as this one:

1 Preface

When done right, graphs can be appealing, informative, and of considerable value to an academic article. Unfortunately, researchers generally suck at making good graphs. I surmise that this is because researchers do not completely master their graphing software, and they are either too lazy or too busy to change this state of affairs. Consequently, the graphs that researchers produce are often no more than a distortion of the ideal Platonian graph that the researcher had in mind.

This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console.

Almost every example in this compendium is driven by the same philosophy: A good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler

…

By: Various Authors

Source: http://shinyapps.org/apps/RGraphCompendium/index.php

8. An introduction to ggplot with Myfanwy Johnston

A video tutorial on ggplot including this neat infographic explaining the syntax. Ggplot seems complex at first but mastering it will enable you to make the best figures!

By: Noam Ross

Source: http://www.noamross.net/blog/2015/3/15/ggplot-tutorial-johnston.html

9. Analytical Minds – All about data analysis

Part 3b: EDA with ggplot2

Another blog on ggplot2 with good explanations and R code. Check also out part 1 (Introduction) and 2 (Data preparation) that were posted earlier!

By: Pedro M.

Source: http://theanalyticalminds.blogspot.pt