2785 search results for "ggplot"

Circlizing Numbers

November 24, 2014
By
Circlizing Numbers

She makes the sound the sea makes to calm me down (Dissolve Me, Alt-J) Searching how to do draw chord diagrams in the Internet with ggplot2 I found a very-easy-to-use package called circlize which does exactly that. A chord diagram shows relationships between things so the input to draw it is simply a matrix with the intensity … Continue reading...

Read more »

Pipeline to Plot Annual % Change

November 24, 2014
By

Pipes in R make my life incredibly easy, and I think my code easier to read.   Note, there are a couple different flavors of pipes (see magrittr and pipeR).  For now, I choose pipeR.library(quantmod)library(pipeR)library(ggplot2)getSymbols("^GSPC",from="1900-01-01",auto.assign=F) %>>% #get S&P 500 from Yahoo!Finance ( . ) %>>% #get end of year ROC( type="discrete", n=1

Read more »

GTrendsR package to Explore Google trending for Field Dependent Terms

November 24, 2014
By
GTrendsR package to Explore Google trending for Field Dependent Terms

My friend, Steve Simpson, introduced me to Philippe Massicotte and Dirk Eddelbuettel’s GTrendsR GitHub package this week. It’s a pretty nifty wrapper to the Google Trends API that enables one to search phrase trends over time. The trend indices that … Continue reading →

Read more »

When should I change to snow tires in Netherlands

November 23, 2014
By
When should I change to snow tires in Netherlands

The Royal Netherlands Meteorological Institute has weather information by day for a number of Dutch stations. In this post I want to use those data for a practical problem: when should I switch to winter tires? (or is that snow tires? In any case nails...

Read more »

Information Density and Custom Chart Designs

November 21, 2014
By
Information Density and Custom Chart Designs

I’ve been doodling today with a some charts for the Wrangling F1 Data With R living book, trying to see how much information I can start trying to pack into a single chart. The initial impetus came simply from thinking about a count of laps led in a particular race by each drive; this morphed

Read more »

Trading The Odds Volatility Risk Premium: Addressing Data Mining and Curve-Fitting

November 19, 2014
By
Trading The Odds Volatility Risk Premium: Addressing Data Mining and Curve-Fitting

Several readers, upon seeing the risk and return ratio along with other statistics in the previous post stated that the … Continue reading →

Read more »

Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

November 18, 2014
By
Visualizing (generalized) linear mixed effects models, part 2 #rstats #lme4

In the first part on visualizing (generalized) linear mixed effects models, I showed examples of the new functions in the sjPlot package to visualize fixed and random effects (estimates and odds ratios) of (g)lmer results. Meanwhile, I added further features to the functions, which I like to introduce here. This posting is based on the

Read more »

Moving The Earth (well, Alaska & Hawaii) With R

November 16, 2014
By
Moving The Earth (well, Alaska & Hawaii) With R

In a previous post we looked at how to use D3 TopoJSON files with R and make some very D3-esque maps. I mentioned that one thing missing was moving Alaska & Hawaii a bit closer to the continental United States and this post shows you how to do that. The D3 folks have it easy.

Read more »

What size will you be after you lose weight?

November 14, 2014
By
What size will you be after you lose weight?

REDDITORS’ BEFORE AND AFTER MEASUREMENTS ANALYZED Click to enlarge How many pounds do you need to lose in order to reduce your waistline by one inch? How many kilos do you need to lose to reduce your waistline by one centimeter? We wanted to find out. We were having trouble finding published data (though we The post

Read more »

Dynamic occupancy models in Stan

November 14, 2014
By
Dynamic occupancy models in Stan

Occupancy modeling is possible in Stan as shown here, despite the lack of support for integer parameters. In many Bayesian applications of occupancy modeling, the true occupancy states (0 or 1) are directly modeled, but this can be avoided by marginalizing out the true occupancy state. The Stan manual (pg. 96) gives an example of this kind...

Read more »

Sponsors

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)