We’ll now go over a collection of R packages for visualizing table data. With that out of the way, let’s explore the top R packages for visualizing table data next. R’s dplyr - Which Is The Best Data Analysis Library We also have experience with Python and its data science libraries. It’s proven to be better for what we do, but your mileage may vary. At Appsilon, we’ve chosen R as a programming language of choice for data science, machine learning, data visualization, and dashboard development. You can’t go wrong with R or Python, and the choice will most likely boil down to the personal preference, or the preference of the company you work for. R can also do pretty much everything but is tailored more for advanced stats and statistical modeling.Īre you new to R? Here are 6 essential things you do with R as a programmer. Python is a general-purpose programming language with a strong background in data science and machine learning. If you’re into web application and dashboard development, it might be a viable option.įor data professionals, it almost always boils down to R or Python. Nobody uses JavaScript as a go-to language for data science and machine learning. But here’s the thing - JavaScript is not a programming language for data professionals.
You can definitely use it to make stunning tables and it probably has the largest number of data visualization libraries. R Shiny: Which Excel Alternative Is Right For You? R Shiny: Two Popular Excel Alternatives Compared Make sure to read our detailed comparisons to R Shiny: Also, the table won’t be interactive and don’t even get me started on reproducibility.īut what about a dedicated BI tool, such as Tableau or PowerBI? Honestly, we’ve used them at Appsilon, and they generally have a lot of going for them. Why bother with programming languages if you can style tables however you want with Excel? Well, you’re leaving a lot of customization on the table by sticking with Excel.
Why Use R Over Excel, Python, or JavaScript? How do you do that? There’s a lot of R packages for visualizing table data, but first, let’s discuss why you should use R in the first place. 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?.
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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.
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