Map themes – This allows a change in the style for the map and once can choose from location only, heatmaps or clustering (the last two are only available for point layers, that is when you select Points in the Location Type). Through the clustering option, one could group individual location points into larger circular clusters that fall within a cluster radius – giving a high level view and then the ability to drill down into each region. If heatmaps are chosen any values in the Size or Color will be ignored and the tooltips will not be available.
Read the whole thing.
First we need to read the packages into the R library. For descriptive statistics of the dataset we use the
skimrpackage and for visualization of correlation matrix we use the
corrplotpackage. We will work with windspeed dataset from the
bReezepackage:# Read packages into R library library(bReeze) library(corrplot) library(skimr)
Click through for the demo.
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
ycolumns 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.
Did you know that you regularly read type set in size 8, or even smaller? In printed materials, captions and less important information (think: photograph credits, newsletter headline subtext, magazine staff listings) are usually reduced to something between 7.5 to 9 points. We generally read that size type without much issue, like glasses. The reason why we can comfortably read those small sizes is because the designers chose an effective font that keeps its clarity and legibility when shrunk.
Designers don’t make the font that tiny to give you a headache. They do it to establish a font hierarchy. Our brains interpret the biggest size as the most important and the littlest size as the least important. So we can create a hierarchy of font sizes to structure our work and communicate even more clearly.
The font hierarchy is important, but so is picking a font which is clear at the sizes you want to use.
Now we can run a single pipeline that combines data processing steps and
data.frame(x = 1:20) %.>% mutate(., y = cos(3*x)) %.>% ggplot(., aes(x = x, y = y)) %.>% geom_point() %.>% geom_line() %.>% ggtitle("piped ggplot2")
Check it out.
Another value generating visualisation package in R is
dygraphs. This package focuses on creating interactive visualisations with elegant interactive coding modules. Furthermore, the package specialises in creating visualisations for machine learning methods. The below coding generates different visualisation graphs with
ggplot2is customizeable. Frankly, one can change a heap of details – not everything probably, but a lot. Of course, one can add a theme to the ggplot call, in order to change the theme. However, a more catch-it-all approach would be to change the standard theme of ggplot itself. In this post, we’ll investigate this option.
To date, I’ve only used themes others have created, but if you need to customize a theme, there’s a lot you can do here.
The overall idea of these functions is to visualize your stocks and portfolio’s performance with a just a few lines of simple code. I’ve created individual functions for each of the calculations and plots, and some other functions that gather all of them into a single list of objects for further use.
On the other hand, the
larespackage is “my personal library used to automate and speed my everyday work on Analysis and Machine Learning tasks”. I am more than happy to share it with you for your personal use. Feel free to install, use, and comment on any of its code and functionalities and I’ll happy to help you with it. I have previously shared other uses of the library in other posts which might also interest you: Visualizing ML Results (binary), Visualizing ML Results (continuous)and AutoML to understand datasets.
NOTE 1: The following post was written by a non-economist or professional investor. I am open to your comments and technical corrections if needed. Glad to learn as always!
NOTE 2: I will be using the less customizable functions in this post so we can focus more on the outputs than in the coding part; but once again, feel free to use the functions and dive into the library to understand or change them!
NOTE 3: All currency units are USD ($).
It does seem to be easy to use for this scenario.
Tyler describes the rayshader package in a gorgeous blog post: his goal was to generate 3-D representations of landscape data that “looked like a paper weight”. (Incidentally, you can use this package to produce actual paper weights with 3-D printing.) To this end, he went beyond simply visualizing a 3-D surface in
rgland added a rectangular “base” to the surface as well as shadows cast by the geographic features. He also added support for detecting (or specifying) a water level: useful for representing lakes or oceans (like the map of the Monterey submarine canyon shown below) and for visualizing the effect of changing water levels like this animation of draining Lake Mead.
It looks great.
We are going to narrow down the data set to focus on 4 key health metrics. Specifically the prevalence of obesity, tobacco use, cardiovascular disease and obesity. We are then going to select only the indicator name and yearly KPI value columns. Finally we are going to make extra columns to display the 2011 to 2016 yearly average and the 2011 to 2016 metric improvements.
Tables are an area of data visualization that we tend to forget at our own peril.