## Posts

### Finding a Suitable Linear Model for Ozone Prediction

### Interpreting Linear Prediction Models

### Getting Your Point Across with Infographics

### Box Plot Alternatives: Beeswarm and Violin Plots

### Visualizing Time-Series Data with Line Plots

The line plot is the go-to plot for visualizing time-series data (i.e. measurements for several points in time) as it allows for showing trends along time. Here, we’ll use stock market data to show how line plots can be created using native R, the MTS package, and ggplot.

### Staticman: An Alternative to Disqus for Comments on Static Sites

Comments are an important aspect of many websites, particularly blogs, whose success depends on their ability to create communities. However, including comments is inherently more difficult for static websites than for dynamic websites (e.g. managed through Wordpress). With Hugo, comments can be easily integrated via Disqus. The disadvantage, however, is that foreign JavaScript code needs to be executed and that the comments are not part of the page itself. Here, I will explain how comments can be integrated into a web page using Staticman.

### Bar Plots and Error Bars

Bar plots display quantities according to the height of bars. Since standard bar plots do not indicate the level of variation in the data, they are most appropriate for showing individual values (e.g. count data) rather than aggregates of several values (e.g. arithmetic means). Although variation can be shown through error bars, this is only appropriate if the data are normally distributed.

### Comparing Medians and Inter-Quartile Ranges Using the Box Plot

The box plot is useful for comparing the quartiles of quantitative variables. More specifically, lower and upper ends of a box (the hinges) are defined by the first (Q1) and third quartile (Q3). The median (Q2) is shown as a horizontal line within the box. Additionally, outliers are indicated by the whiskers of the boxes whose definition is implementation-dependent. For example, in `geom_boxplot`

of ggplot2, whiskers are defined by the inter-quartile range (IQR = Q3 - Q1), extending no further than 1.5 * IQR.

### Using probability distributions in R: dnorm, pnorm, qnorm, and rnorm

R is a great tool for working with distributions. However, one has to know which specific function is the right wrong. Here, I’ll discuss which functions are available for dealing with the normal distribution: dnorm, pnorm, qnorm, and rnorm.