![]() ![]() ![]() Now that’s something you could present in a table format. Image 2 – Life expectancy in Poland over time Let’s import them both and check how the dataset looks like: You should have the dplyr and gapminder packages installed. Let’s take a look at one such dataset to drive the point home. You could aggregate the data, so you’re left with a small, presentable subset. ![]() For example, imagine you had population data for the entire world, and you’re only interested in a single country. The reason? It can be huge in dimension, and you’re only interested in a small subset. Tabular data usually isn’t the best candidate for presenting visually with a table. Tabular data is made of rows and columns, and a place where they intersect gives you specific information about a single record - for example, the number of people living in Poland in 2021. Think of table data as something aggregated from tabular data. Best R Packages for Visualizing Table Data.Why Use R Over Excel, Python, or JavaScript?.We’ll go over four of them today, and we’ll also show you how to tie them together in an interactive R Shiny application. This article brings you answers on the best R packages for visualizing table data. Ask yourself - For whom are you visualizing the data? Do you need interactivity? Will you include the table in a web application? The list of questions goes on and on. It also requires knowledge about your audience. It requires both data manipulation and data visualization skills from the technical end. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |