- Plotting distributions (ggplot2)
- How to make any plot in ggplot2?
- Visualizing Data in R with ggplot2 (Part 3)
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- Graphics with ggplot2
Plotting distributions (ggplot2)How should investors assess risk in the stocks that they buy or sell? Analysts use it often when they want to determine a stock's risk profile. However, while beta does say something about price risk, it has its limits for investors looking to determine fundamental risk factors. Beta is a measure of a stock's volatility in relation to the overall market. A stock that swings more than the market over time has a beta above 1. If a stock moves less than the market, the stock's beta is less than 1. High-beta stocks are supposed to be riskier but provide higher return potential; low-beta stocks pose less risk but also lower returns. Beta is a component of the capital asset pricing model CAPMwhich is used to calculate the cost of equity funding. The CAPM formula uses the total average market return and the beta value of the stock to determine the rate of return that shareholders might reasonably expect based on perceived investment risk. In this way, beta can impact a stock's expected rate of return and share valuation. To followers of CAPM, beta is useful. A stock's price variability is important to consider when assessing risk. If you think about risk as the possibility of a stock losing its value, beta has appeal as a proxy for risk. Intuitively, it makes plenty of sense. Think of an early-stage technology stock with a price that bounces up and down more than the market. It's hard not to think that stock will be riskier than, say, a safe-haven utility industry stock with a low beta. Sure, there are variations on beta depending on things such as the market index used and the time period measured. But broadly speaking, the notion of beta is fairly straightforward. It's a convenient measure that can be used to calculate the costs of equity used in a valuation method. For starters, beta doesn't incorporate new information. Consider a utility company: let's call it Company X. Company X has been considered a defensive stock with a low beta. When it entered the merchant energy business and assumed more debt, X's historic beta no longer captured the substantial risks the company took on. At the same time, many technology stocks are relatively new to the market and thus have insufficient price history to establish a reliable beta. Another troubling factor is that past price movement is a poor predictor of the future. Betas are merely rear-view mirrors, reflecting very little of what lies ahead.
How to make any plot in ggplot2?
First things first, Marioly, thanks for being a loving wife and an exceptional mother. If you want to plot these curves without animating them, the code is extremely easy with ggplot If you want animate the plot, you can use gganimate. This package allows you to add an aesthetic component related to a frame time variable and it creates the animation by looping through each value of the frame variable and joins the plot with ImageMagick. All other lines of code are required for the static version of the plot. The plot below has 4 dimensions: x GPD per capitay Life expectancycolor continentsize population. With an animation, you can add a 5 th dimension time and see the change through time. This animation is very similar conceptually to the ones created by the late, great Hans Rosling. For example, the animation below shows the position of the players of each team and the ball for a basketball game. It also shows the convex hull of the positions of the players. Subscribe to the site to get notified when new posts come up! To explain concepts easier to understand with animations Relationship between trigonometric functions and x and y axis lengths in a unit circle Source Spatio-Temporal data visualization For example, the animation below shows the position of the players of each team and the ball for a basketball game. About US In this webpage you will see lots of resources to master data analysis skills. Specifically we focus on Excel, Minitab, and the R language.
Visualizing Data in R with ggplot2 (Part 3)
R in Action 2nd ed significantly expands upon this material. The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. Its popularity in the R community has exploded in recent years. Origianlly based on Leland Wilkinson's The Grammar of Graphicsggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Grouping can be represented by color, symbol, size, and transparency. The creation of trellis plots i. Mastering the ggplot2 language can be challenging see the Going Further section below for helpful resources. There is a helper function called qplot for quick plot that can hide much of this complexity when creating standard graphs. The qplot function can be used to create the most common graph types. While it does not expose ggplot 's full power, it can create a very wide range of useful plots. The format is:. Here are some examples using automotive data car mileage, weight, number of gears, number of cylinders, etc. Unlike base R graphs, the ggplot2 graphs are not effected by many of the options set in the par function. They can be modified using the theme function, and by adding graphic parameters within the qplot function. For greater control, use ggplot and other functions provided by the package. We have only scratched the surface here. To learn more, see the ggplot reference siteand Winston Chang's excellent Cookbook for R site. Though slightly out of date, ggplot2: Elegant Graphics for Data Anaysis is still the definative book on this subject. Try the free first chapter of this interactive tutorial on ggplot2. Kabacoff, Ph. Graphics with ggplot2 The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. For line plots, color associates levels of a variable with line color. For density and box plots, fill associates fill colors with a variable. Legends are drawn automatically. The geom option is expressed as a character vector with one or more entries. When the number of observations is greater than 1, a more efficient smoothing algorithm is employed. Methods include "lm" for regression, "gam" for generalized additive models, and "rlm" for robust regression.
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