How can you use R to create custom visualizations for BI presentations?

2 Step 2: Load and prepare your data

Before you can create your visuals, you need to load and prepare your data in R. You can use the readr package to import data from various sources, such as CSV, Excel, or JSON files. You can also use the dplyr package to manipulate and transform your data, such as filtering, grouping, summarizing, or joining. You can also use the tidyr package to tidy your data, which means arranging it in a consistent and logical format. Tidy data makes it easier to create and interpret your visuals.

3 Step 3: Choose your visualization type

Once you have your data ready, you can choose the type of visualization that best conveys your message and insights. R offers a wide range of visualization types, such as bar charts, line charts, scatter plots, histograms, box plots, heat maps, and more. You can also create custom visualization types by combining or modifying existing ones. You can use the ggplot2 package to create most of these visualization types using a common syntax. You can also use the plotly package to create interactive versions of these visualization types using the ggplotly function.

4 Step 4: Customize your visualization

One of the advantages of using R to create your visuals is that you can customize them to fit your style and preferences. You can use the ggplot2 package to modify various aspects of your visuals, such as colors, shapes, sizes, scales, axes, legends, titles, labels, and more. You can also use the ggthemes package to apply predefined themes to your visuals, or create your own themes using the theme function. You can also use the plotly package to add interactivity and animation to your visuals, such as tooltips, zooming, panning, filtering, and more.

5 Step 5: Arrange and annotate your visualization

Another way to enhance your visuals is to arrange and annotate them in a meaningful and coherent way. You can use the cowplot package to arrange multiple visuals in a grid or a nested layout. You can also use the patchwork package to combine and align multiple visuals using operators. You can also use the ggplot2 package to annotate your visuals with text, lines, arrows, or shapes. You can also use the plotly package to add annotations to your interactive visuals using the layout function.

6 Step 6: Export and share your visualization

The final step is to export and share your visuals with your audience. You can use the ggsave function to save your visuals as static images in various formats, such as PNG, JPEG, PDF, or SVG. You can also use the htmlwidgets package to save your interactive visuals as HTML files that can be opened in any browser. You can also use the rmarkdown package to create reports or slides that include your visuals and your narrative. You can also use the shiny package to deploy your interactive web applications to the web or to a local server.