R: Complete Data Visualization Solutions

In this lecture, we will demonstrate how to use The Grammar of Graphics to construct our very first ggplot2chart with the superstore sales dataset.

Aesthetics mapping describes how data variables are mapped to the visual property of a plot. In this lecture, we will discuss how to modify aesthetics mapping on geometric objects.

Geometric objects are elements that we mark on the plot. One can use the geometric objects in ggplot2 to create either a line, bar, or box chart. Moreover, one can integrate these simple geometric objects and aesthetic mapping to create a more professional plot. In this lecture, we will introduce how to use geometric objects to create various charts.

Besides mapping particular variables to either the x or y axis, one can first perform statistical transformations on variables, and then remap the transformed variable to a specific position. In this lecture, we will introduce how to perform variable transformations with ggplot2.

Besides setting the aesthetic mapping for each plot or geometric object, one can use scale to control how variables are mapped to the visual property. In this lecture, we will introduce how to adjust the scale of aesthetics in ggplot2.

When performing data exploration, it is essential to compare data across different groups. Faceting is a technique that enables the user to create graphs for subsets of data. In this lecture, we will demonstrate how to use the facet function to create a chart for multiple subsets of data.

To create an overview of a dataset, we may need to combine separate individual plots into one. In this lecture, we will introduce how to combine individual subplots into one plot.

Producing elegant plots in ggplot2 may seem difficult but it is actually quite easy to do. In fact, ggplot2 takes care, by default, of much of the graphical design of the plot, meaning that we can produce beautiful histograms with just a few lines of code. In this lecture, we'll load ggplot2 and then import the dataset. We'll plot a simple histogram, using the default settings. Finally, we'll plot multiple distributions with faceting.

Histograms are useful for certain tasks, but for comparing several variables at once, they are not the best. Box plots can be used instead, since they allow the comparison of the distribution of multiple variables side by side. In this lecture, we'll see what a box plot is and what it represents. We'll create multiple box plots with just two lines of code and then we'll order the plot to achieve better results.

Categorical variables are invariably difficult to visualize in meaningful ways. Bar charts are important for plotting categorical variables and defining their characteristics. In this lecture, we'll talk about bar charts and create simple bar charts in ggplot2. We'll also learn how to automatically order a data.frame and plot ordered bar charts.

In many cases, we are interested in comparing multiple variables at once and checking their correlation. Scatterplots allow us to do just that and are an important tool in a data analyst's toolbox. In this lecture, we'll understand the importance of scatterplots and create simple scatterplots in ggplot2. We'll then create more complex visualizations by tweaking some basic options.

In many cases, the variable time is underestimated. However, time series are extremely useful to determine the temporal pattern of a variable. In this lecture, we'll understand the structure of time-series plots. We'll then plot a simple time-series plot in ggplot2 and customize the plots with color and size.

Many datasets are affected by uncertainty and people not always know how to show this in plots. This lecture will present ways to solve this and take uncertainty into account. In this lecture, we'll see how to handle uncertainty. We'll present simple ways to include uncertainty in bar-charts. Finally, we'll look at scatterplots with double error bars.

By default, ggplot2 creates plots with a grayish background, and without axes lines and white gridlines. This is not the standard look you normally find in scientific manuscripts. In this lecture, we'll understand the graphical elements of the standard theme. We'll then look at changing the default theme and explore the differences between the default theme and other themes.

The default color scale is not always appropriate to spot all the differences in the data we are trying to plot. In many cases, we have to change it so that our plots can become more informative. In this lecture, we'll see how to change the default two colors for plotting continuous variables. Additionally, we'll explore ways to include more colors in the color scale and present a discrete color scale for categorical variables.

ggplot2 uses the names of the columns as labels, meaning that if these are not self-explanatory, the plot will not provide a good framework to understand its meaning. By adding some lines of code, we can customize the plot in order to change the labels and make the plot more informative. In this lecture, we'll see how to add a title for the plot, change the title of the legend, and change the axes labels.

The default plots created by ggplot2 lack several elements that are, in many cases, useful to provide additional information to your audience. However, there are simple functions that can be used to add supplementary elements to the plot. In this lecture, we'll see how to add trend lines to scatterplots. We'll also learn how to add vertical and horizontal lines to plots and how to customize the lines.

In many cases, it is crucial to be able to include textual labels on plots to provide your audience with additional information. This can be done in ggplot2 in both static and dynamic ways. In this lecture, we'll see how to add fixed text labels and dynamic text labels. We'll finish the lecture by learning how to add text outside the plot and change the axis labels.

With the facet_wrap function, it is only possible to create a grid of plots of the same type. However, in some cases, it is necessary to create side-by-side graphs with diverse plots. This can be done using the gridExtra package. In this lecture, we'll review the facet_wrap function. We'll then see how to install the gridExtra package and create multi-plots.

Static plots are the standard for publishing in traditional media, such as journal papers. However, the world is moving towards an Internet-based presentation of results and even scientific journals are quickly adapting it. Many now offer the possibility of including interactive plots. In R, we can create plots for the Web with the rCharts package, which is a bit more difficult to install than ggplot2. In this lecture, we'll discuss the rCharts package. We'll install devtools first and then install rCharts from GitHub.

rCharts features a syntax more similar to standard plotting in R than what we saw with ggplot2. However, it is easy to pick it up by by understanding simple examples and then including additional details. In this lecture, we'll explain the syntax used in rCharts. We'll then include more details in our code and then add JavaScript functions for more flexibility.

Even though we know nothing about HTML and CSS, we can still obtain beautiful bar charts using templates created by other users. In this lecture, we'll see how to plot basic interactive bar charts and then add axis labels to it. We'll then use a template for an elegant finish.

If too many data points are present in our dataset, scatterplot visualization may become very confusing in static plots. However, in interactive plots, this limitation no longer applies, since we can select to visualize only a part of the dataset. In this lecture, we'll create basic interactive scatterplots. We'll understand the interactivity and then add elements and controls.

Time-series plots are a great way to visualize the temporal pattern of a variable. However, sometimes, we cannot fully understand the exact date of each point based only on the values on the x axis. Interactive visualization can solve this problem by adding tooltips in which we can take a look at the raw data. In this lecture, we'll see how to set the data in the correct format. We'll then plot a basic time-series plot and then add interactive elements to it.

Being able to plot spatial data on web maps is certainly helpful and a crucial skill to have, but it can be difficult since it requires knowledge of different technologies. R makes this process very easy with dedicated functions that allow us to plot on web GIS services very easily. In this lecture, we will understand what web mapping is. We will cover the available mapping platforms and the required packages.

Plotting data with the plotGoogleMaps function is not as easy as using the plot function. With a simple step-by-step guide, we can achieve good command of the function enabling us to plot whatever data we choose. In this lecture, we'll first install plotGoogleMaps and then create our first map. We'll then look at customizing the plotting window.

An interactive map with just one layer is hardly useful for our purpose. Many a times, we are faced with the challenge of plotting several data at once. This requires some additional work and understanding, but it is definitely not hard in R. In this lecture, we'll understand the layer system. Then, we'll add layers with the right options and then check the results.

Plotting raster data on Google Maps can be tricky. The plotGoogleMaps function does not handle rasters very well and if not done correctly, the visualization will fail. This lecture will show users how to plot rasters successfully. In this lecture, we'll first download the seismic risk map. We'll then understand the limitations of plotting rasters on Google Maps and then see how to plot rasters successfully.

Plotting on Google Maps is easy but Google Maps is a commercial product. If we want to use them on a commercial website, we would need to pay for it. OpenStreetMaps is free to use, knowing how to use it is certainly an advantage. In this lecture, we'll first install LeafletR. We'll then see how LeafletR works with geoJSON data. Finally, we'll plot and customize our map.

Using open data for our analysis requires a deep knowledge of the data provider and the actual data we are using. Without this knowledge, we may end up with erroneous results. In this lecture, we'll try to understand the World Bank data. We'll see what data is available from them and then look at the R package to download this data.

Downloading data from the World Bank can be difficult since it requires users to know the acronyms used to refer to the different datasets. However, with some help, this process becomes very easy. In this lecture, we'll understand the import process. We'll search the correct indicator and then download the data.

To create a spatial map of the World Bank data, we just have to download the data and then transform it into spatial data. However, in the dataset, there are no coordinates or any other information that will help us do that. The solution is to use the geocoding information from another dataset for this purpose. In this lecture, we'll use the natural earth data to transform our data into spatial objects. We'll understand the transformation process and then plot the results in a map.

Using the World Bank data just to plot a static spatial map is very limitative. There are tons of other functionalities that you can perform with this data and this lecture serves to provide some guidance into these additional avenues of research. In this lecture, we'll see how to download more than one dataset. We'll then see how to perform correlation analysis and end this lecture with how to turn our map interactive.

RStudio has an R Markdown workflow built in; we can use its GUI to create markdown reports in HTML, PDF, slides, or the Microsoft Word format. In this lecture, we will introduce how to build an R Markdown report with RStudio.

In an R Markdown report, one can embed R code chunks into the report with the knitr syntax. In this lecture, we will introduce how to create and control the output with different code chunk configurations.

You need to have created and opened a new R Markdown (.rmd) file in RStudio to proceed with the remaining lectures.

In order to interact with the reports' figures, one can create interactive graphics with ggvis. In this lecture, we will demonstrate how to build our first interactive plot from the real estate dataset.

The ggvis package uses similar grammar and syntax to ggplot2, and we can use this basic syntax to create figures. In this lecture, we will cover how to use the ggvis syntax and grammar to build advanced plots.

Besides making different plots with various layer types, one can control the axes and legends of a ggvis plot. We can also rescale the mapping of the data and determine how it should be displayed on the plot with the scale function. In this lecture, we will demonstrate how to set the appearance properties of both axes and legends and how to scale data in ggvis.

One of the most attractive features of ggvis is that it can be used to create an interactive web form. This allows the user to subset the data, or even change the visual properties of the plot, through interacting with the web form. In this lecture, we will introduce how to add interactivity to a ggvis object.