Category: Visualization

Sebastian Sauer shows how to display multiple ggplot2 diagrams together using facets as well as a combination of the nest() and map() functions:

One simple way is to plot several facets according to the grouping variable:

d %>% 
  ggplot() +
  aes(x = hp, y = mpg) +
  geom_point() +
  facet_wrap(~ cyl)

Faceting is great, but it’s good to know the other technique as well.

Chisato den Engelsen looks at 465 traditional color palettes used in Japan:

Since each of colours had name, I also was curious if there are some characters that are used more often than other. Colour name was written in two ways in this website. One in Kanji and other in Hiragana.

I love wordcloud2 to visualize the wordcloud, so I can see which characters appears more often the others.

It’s an interesting exercise and all of the R code is included.  Be sure to check out the list of colors with a character representing “rat” or “mouse” in the name.  H/T R-Bloggers

Ellen Talbot shows us how to embrace our inner micromanagers:

Something a little different today for a quick chat about my latest project and why I’m finding the plotly package so helpful!

Are you like me and physically can’t function unless you’ve got a to do list in front of you? Well even if you’re not, imagine my pain while I’m wearing my non – Locke Data hat and trying to plan out the final year of my PhD thesis!

I needed something that updated easily, something visual and something to keep my supervisors in the know. I’ve previously made gantt charts using LaTeX but found it ridiculously clunky to get working and decided there had to be a better way. And if I could include interactivity then all the better, which is how I discovered plotly.

Admittedly, I like gantt charts more than almost any developer I’ve ever met.  They always look so pretty and are wonderful depictions of a world which will never be.

Kristian Larsen demonstrates the corrplot package in R:

First we need to read the packages into the R library. For descriptive statistics of the dataset we use the skimr package and for visualization of correlation matrix we use the corrplot package. We will work with windspeed dataset from the bReeze package:

# Read packages into R library
library(bReeze)
library(corrplot)
library(skimr)

Click through for the demo.

Nina Zumel shows how to use the cdata package to create faceted ggplot2 plots:

First, load the packages and data:

library("ggplot2")
library("cdata")

iris <- data.frame(iris)

Now define the data-shaping transform, or control table. The control table is basically a picture that sketches out the final data shape that I want. I want to specify the x and y columns of the plot (call these the value columns of the data frame) and the column that I am faceting by (call this the key column of the data frame). And I also need to specify how the key and value columns relate to the existing columns of the original data frame.

Read on to see how you can use cdata to tie together different faceted plots.