380 search results for "boxplot"

Cluster Analysis of the NFL’s Top Wide Receivers

December 29, 2014
By
Cluster Analysis of the NFL’s Top Wide Receivers

“The time has come to get deeply into football. It is the only thing we have left that ain't fixed.”Hunter S. Thompson, Hey Rube Column, November 9, 2004I have to confess that I haven’t been following the NFL this year as much as planned or hoped.  On only 3 or 4 occasions this year have I been able to...

Read more »

My Commonly Done ggplot2 graphs: Part 2

December 18, 2014
By
My Commonly Done ggplot2 graphs: Part 2

In my last post I described some of my commonly done ggplot2 graphs. It seems as though some people are interested in these, so I was going to follow this up with other plots I make frequently. Scatterplot colored by continuous variable The setup of the data for the scatterplots will be the same as

Read more »

Sequence of shopping carts in-depth analysis with R

December 4, 2014
By
Sequence of shopping carts in-depth analysis with R

Although the sankey diagram from the previous post provided us with a very descriptive tool, we can consider it a rather exploratory analisys. As I mentioned, sequence mining can give us the opportunity to recommend this or that product based on previous purchases, but we should find the right moment and patterns in purchasing behavior.... Read More »

Read more »

Study of a plot

December 3, 2014
By
Study of a plot

I began to think on a nice way of plotting campaign expenditures in a paper I'm working on. I thought this would be something like the following--simple but meaningful even when there are outliers in both tails. Though I like the seniors Tukey's boxplot and scatter plots, I had already used them the last time … Read More...

Read more »

Visualizing Historical & Most-likely First Snowfall Dates for U.S. Regions

November 26, 2014
By
Visualizing Historical & Most-likely First Snowfall Dates for U.S. Regions

UPDATE: You can now run this as a local Shiny app by entering shiny::runGist("95ec24c1b0cb433a76a5", launch.browser=TRUE) at an R prompt (provided all the dependent libraries (below) are installed) or use it interactively over at Shiny Apps. The impending arrival of the first real snowfall of the year in my part of Maine got me curious about

Read more »

Visualization of probabilistic forecasts

November 21, 2014
By
Visualization of probabilistic forecasts

This week my research group discussed Adrian Raftery’s recent paper on “Use and Communication of Probabilistic Forecasts” which provides a fascinating but brief survey of some of his work on modelling and communicating uncertain futures. Coincidentally, today I was also sent a copy of David Spiegelhalter’s paper on “Visualizing Uncertainty About the Future”. Both are

Read more »

Bioinformatics journals: time from submission to acceptance, revisited

October 13, 2014
By
Bioinformatics journals: time from submission to acceptance, revisited

Before we start: yes, we’ve been here before. There was the Biostars question “Calculating Time From Submission To Publication / Degree Of Burden In Submitting A Paper.” That gave rise to Pierre’s excellent blog post and code + data on Figshare. So why are we here again? 1. It’s been a couple of years. 2.

Read more »

ggvis 0.4

October 13, 2014
By
ggvis 0.4

ggvis 0.4 is now available on CRAN. You can install it with: install.packages("ggvis") The major features of this release are: Boxplots, with layer_boxplots() chickwts %>% ggvis(~feed, ~weight) %>% layer_boxplots() Better stability when errors occur. Better handling of empty data and malformed data. More consistent handling of data in compute pipeline functions. Because of these changes,

Read more »

An Intro to Models and Generalized Linear Models in R

October 13, 2014
By
An Intro to Models and Generalized Linear Models in R

By Chris Campbell – Senior Consultant, UK. For many types of data, we have made a measurement of some variable that looks normally distributed. The middle value is the most likely, most values are similar to the middle value, and a … Continue reading →

Read more »

Multiple Tests, an Introduction

September 24, 2014
By
Multiple Tests, an Introduction

Last week, a student asked me about multiple tests. More precisely, she ran an experience over – say – 20 weeks, with the same cohort of – say – 100 patients. An we observe some size=100 nb=20 set.seed(1) X=matrix(rnorm(size*nb),size,nb) (here, I just generate some fake data). I can visualize some trajectories, over the 20 weeks, library(RColorBrewer) cl1=brewer.pal(12,"Set3") cl2=brewer.pal(8,"Set2") cl=c(cl1,cl2)...

Read more »