442 search results for "boxplot"

Using knitr and R to make instructor/student handout versions

November 29, 2015
By

I teach some of my lab sections using R, and so I need to create lab handouts that include nicely formatted R commands and R output as an example for the students. These handouts will also include exercises where the students will be writing their own R code, or interpreting the results, or generating figures.

Read more »

What the candidates say, analyzing republican debates using R

November 26, 2015
By
What the candidates say, analyzing republican debates using R

As most people realize, this is probably one of the most data-rich primary campaigns in history, with hundreds of professional pollsters poring over every data-point trying to understand voter’s intention. So here is another data-rich post to that end. I was glad to discover the University of California at Santa Barbara’s webpage with tons of high-quality data related to the...

Read more »

Modeling gene expression with broom: a case study in tidy analysis

November 25, 2015
By
Modeling gene expression with broom: a case study in tidy analysis

Previously in this series Cleaning and visualizing genomic data: a case study in tidy analysis In the last post, we examined an available genomic dataset from Brauer et al 2008 about yeast gene expression under nutrient starvation. We learned to tidy it with the dplyr and tidyr packages, and saw how useful this tidied form is for visualizing...

Read more »

How to Search for Census Data from R

November 16, 2015
By
How to Search for Census Data from R

In my course Learn to Map Census Data in R I provide people with a handful of interesting demographics to analyze. This is convenient for teaching, but people often want to search for other demographic statistics. To address that, today I will work through an example of starting with a simple demographic question and using R The post

Read more »

Free Webinar: Learn to Map Unemployment Data in R

November 10, 2015
By
Free Webinar: Learn to Map Unemployment Data in R

Last month I ran my first webinar (“Make a Census Explorer with Shiny”). About 100 people showed up, and feedback from the participants was great. I also had a lot of fun myself. Because of this, I’ve decided to do one more webinar before my free trial with the webinar service ends. Here are the The post

Read more »

Factor codings for models in R

Factor codings for models in R

I am holding an exercise on generalised models these days. Preparing a task on factor coding in generalised linear models, I realised that the help on the internet on that is not so easy to understand. At least what I found. So in order to help people ...

Read more »

The 5th Tribe, Support Vector Machines and caret

October 15, 2015
By
The 5th Tribe, Support Vector Machines and caret

by Joseph Rickert In his new book, The Master Algorithm, Pedro Domingos takes on the heroic task of explaining machine learning to a wide audience and classifies machine learning practitioners into 5 tribes*, each with its own fundamental approach to learning problems. To the 5th tribe, the analogizers, Pedro ascribes the Support Vector Machine (SVM) as it's master algorithm....

Read more »

Colouring a plot using a continuous variable in R

September 29, 2015
By
Colouring a plot using a continuous variable in R

I often find myself coming back to this answer I gave on Stack Overflow in 2014. It shows how to colour a plot based on an independent continuous variable using the base graphics… Continue reading →

Read more »

Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

September 24, 2015
By
Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

This post will demonstrate a stop-loss rule inspired by Andrew Lo’s paper “when do stop-loss rules stop losses”? Furthermore, it … Continue reading →

Read more »

Convergence and Asymptotic Results

September 24, 2015
By
Convergence and Asymptotic Results

Last week, in our mathematical statistics course, we’ve seen the law of large numbers (that was proven in the probability course), claiming that given a collection  of i.i.d. random variables, with To visualize that convergence, we can use > m=100 > mean_samples=function(n=10){ + X=matrix(rnorm(n*m),nrow=m,ncol=n) + return(apply(X,1,mean)) + } > B=matrix(NA,100,20) > for(i in 1:20){ + B=mean_samples(i*10) + } > colnames(B)=as.character(seq(10,200,by=10)) > boxplot(B) It is...

Read more »

Sponsors

Mango solutions



plotly webpage

dominolab webpage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training

datasociety

http://www.eoda.de





ODSC

ODSC

CRC R books series





Six Sigma Online Training









Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)