Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (2022)

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Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (1)

Visualizing table data is no small task. It requires both data manipulation and data visualization skills from the technical end. It also requires knowledge about your audience. 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.

Thankfully, we have a solution. This article brings you answers on the best R packages for visualizing table data. 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.

Table of contents:

What is Table Data?

Think of table data as something aggregated from tabular data. 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.

Tabular data usually isn’t the best candidate for presenting visually with a table. The reason? It can be huge in dimension, and you’re only interested in a small subset. For example, imagine you had population data for the entire world, and you’re only interested in a single country. You could aggregate the data, so you’re left with a small, presentable subset.

Let’s take a look at one such dataset to drive the point home. You should have the dplyr and gapminder packages installed. Let’s import them both and check how the dataset looks like:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (2)

Image 1 – The GapMinder dataset

And there’s your tabular data. It isn’t directly presentable, as it has over 1700 rows. No one wants to go over that table manually, so let’s aggregate it to something more presentable.

We’ll keep only the records for Poland, tracked through the year and lifeExp columns. You can do the aggregation effortlessly with dplyr:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (3)

Image 2 – Life expectancy in Poland over time

Now that’s something you could present in a table format. 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.

Why Use R Over Excel, Python, or JavaScript?

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. Also, the table won’t be interactive and don’t even get me started on reproducibility.

(Video) Visualize Table Object in Graphic in R (Example) | Draw Barchart | ggplot2 Package | as.data.frame

But 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. Like every tool, these aren’t flawless. Make sure to read our detailed comparisons to R Shiny:

JavaScript is a different story. You can definitely use it to make stunning tables and it probably has the largest number of data visualization libraries. But here’s the thing — JavaScript is not a programming language for data professionals. Nobody uses JavaScript as a go-to language for data science and machine learning. If you’re into web application and dashboard development, it might be a viable option.

For data professionals, it almost always boils down to R or Python. Python is a general-purpose programming language with a strong background in data science and machine learning. R can also do pretty much everything but is tailored more for advanced stats and statistical modeling.

Are you new to R? Here are 6 essential things you do with R as a programmer.

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. At Appsilon, we’ve chosen R as a programming language of choice for data science, machine learning, data visualization, and dashboard development. It’s proven to be better for what we do, but your mileage may vary.

We also have experience with Python and its data science libraries. Read our detailed Pandas vs. dplyr comparison below:

With that out of the way, let’s explore the top R packages for visualizing table data next.

R Packages for Visualizing Table Data

We’ll now go over a collection of R packages for visualizing table data. If you want to follow along, please ensure you have dplyr, gapminder, gt, kableExtra, DT, plotly, and shiny installed.

We’ll use the previously aggregated life expectancy data for Poland as a table data source.

gt

The gt package is designed for making display tables, meaning it doesn’t provide any input to the user. It might be a deal-breaker if you want the ability of filtering data on the fly. Nevertheless, gt makes it so easy to get started:

Yes, that’s it! The table will look plain, but you can always add styles later:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (4)

Image 3 – Unstlyled gt table

Let’s tweak it a bit — we’ll add a title, rename the columns, add a source node, and change the fill color for the first cell:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (5)

Image 4 – Styled gt table

Explore the in’s-and-outs of the gt package from RStudio. The package works well with R markdown and Quarto and can be applied across industry and academia.

The gt package is a good solution if you need minimalistic-looking tables without input controls. With some creative input,gtcan be used to build some contest-winning tables. Let’s explore one similar alternative next.

(Video) Rich Iannone || Making Beautiful Tables with {gt} || RStudio

kableExtra

The kableExtra package lets you create display tables just like gt, but packs rich theme support. It’s also a bit more flexible than the previous package.

Getting started is close to effortless, once again:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (6)

Image 5 – Unstyled kableExtra table

The styling is quite minimalistic once again, but kableExtra has many ready-to-go themes you can use (source). Let’s see how the material theme looks like — we’ll also add a footnote and a stripped look that changes on hover:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (7)

Image 6 – Styled kableExtra table

Just like gt, it’s a good option to go with if you don’t want input controls out of the box. If you do, you’re in luck — the next package has it built-in.

DT

The DT package in R provides an interface to the DataTables library in JavaScript. It can display matrices and data frames as tables, and provides filtering, pagination, and sorting out of the box.

It requires even less code to get started than the previous two:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (8)

Image 7 – Unstyled DT table

Yes, the table could definitely benefit from some styling, but offers everything the end-user might need by default. You can extend its functionality further by adding column-level filtering. Here’s the code you’ll need to do that, and also change the column names and add a caption:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (9)

Image 8 – Styled DT table

Here’s what happens if you click on the column-level filters:

(Video) Make your tables look AMAZINGLY beautiful with these two tricks in Power BI

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (10)

Image 9 – Filtering DT tables

It’s astonishing to see so much functionality available by default. Coding that logic manually would take hours for experienced developers, which makes DT an ultimate time-saver.

The DT package might be the best solution for dashboard developers, as it won’t overflow the page if the table has many rows. Also, the search, pagination, and filtering capabilities are implemented for you, so it’s one less thing to worry about.

Plotly

Plotly is a go-to package for interactive data visualizations. It’s available for R, Python, and JavaScript, and you can use it in R Shiny dashboards without any issues. Regular charts (line, bar, scatter…) look spectacular, but how about tables?

Let’s find out. You can use the following snippet to create a basic table from our subset:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (11)

Image 10 – Unstyled Plotly table

The table doesn’t look the best by default and requires a lot more code when compared to the previous options. Styling the table, well, takes time:

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (12)

Image 11 – Styled Plotly table

To summarize, Plotly tables look and feel horrible if you consider it’s one of the go-to packages for data visualization. You can make it work, sure, but is it really worth the trouble?

Tying it All Together

You can use all three of the mentioned R packages for visualizing table data when building web applications with R and R Shiny. To demonstrate, we’ll create a simple Shiny dashboard that:

  • Has a dropdown menu for country selection.
  • Has four separated parts to showcase tables from four different packages.
  • Updates the tables automatically as you change the country.

You can implement all of that in less than 100 lines of code. You’ll understand pretty much everything from the server, as it contains the code for generating tables. The ui will contain placeholders for the charts separated with different headings and UI elements.

Here’s the entire code:

It is somewhat of a lengthy file, but it should feel readable even if you’re not an R Shiny expert.

You should see the following once you run it:

(Video) E-DAB 05: Visualizing Data with Tables, Charts, Conditional Formatting & Dashboards

Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes (13)

Image 12 – R Shiny dashboard for table demonstration

Not too bad, right?

If you’ve developer dashboards with Python’s Dash, then R Shiny will feel a bit different at first. We’ve written a full comparison between the two to get you started:

In a nutshell, both libraries work wonders, and the decision will likely boil down to the ecosystem you’re using. At Appsilon, we’ve used both but mainly stick to R Shiny for enterprise applications.

Are your dashboards slow? Consider Apache Arrow to supercharge your R Shiny dashboards.

RStudio Table Contest

Do you think you have what it takes to produce award-winning data table visualizations? You’re in luck, as RStudio is holding a 2021 Table Contest. It runs from September 30th to November 15th, 2021, so there’s still time to apply.

What are you up against? Browse the phenomenal entries from the 2020 Table Contest.

You should consider a couple of things before applying:

  • Every submission must include all code and data that was used, so the visualization can be reproduced.
  • You can submit an entry as an R Markdown document, a repository, or an RStudio Cloud project.
  • You can use any table-making package available in R.
  • You can submit an entry either as a single table example (an example of a common table popular in a specific field) or as a tutorial (teaching how to craft an excellent table).

Everything looks good? You can submit your entry for the contest by filling in an online form, just make sure to fill it before November 15th, 2021 at midnight Pacific Time.

What’s in it for you? RStudio has excellent prices for the winners:

  • The Grand Prize — A randomized combination of RStudio t-shirts, books, and mugs, plus the prizes below.
  • Runner-Up — Face time with people making table-making packages, and a one-year subscription to the Shinyapps.io Basic plan, plus the prize below.
  • Honorable Mentions — A larger-than-large helping of hexagon-shaped stickers for RStudio packages, plus a side of hex for table-making packages.

You can find more details about the RStudio Table Contest here.

Conclusion

To summarize, we believe R is the most promising option for visualizing table data. We’ve used the mentioned packages countless times when developing enterprise R Shiny dashboards, and we never looked back. With a little ingenuity, you can add creative flair to your tables and better communicate your results.

Looking to participate in RStudio Table Contest? Check our R Shiny Dashboard gallery for inspiration.

If you’re thinking about a career as an R Shiny developer, take a look at our recent guide. It contains many tips and resources for those starting out, and those with years of experience. Plus, you’ll also find out how to get hired at Appsilon — an industry leader building the world’s most advanced R Shiny applications for Fortune 500 companies.

Article Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes comes from Appsilon | End­ to­ End Data Science Solutions.

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(Video) Creating Tables in R

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FAQs

Which R package should you use for data visualization? ›

The ggpubr package is a well-known R package for data visualization. However, many plotting functions of the ggpubr package are one-liner, rather than modular, functions that plot a complete graph.

Which R packages will this course use to create data visualizations? ›

The focus in this course learning to use ggplot2 to make a variety of visualizations and to polish those visualizations using tools within ggplot as well as vector graphics editing software.

Which of the following is an enhanced data visualization package for R? ›

About: ggvis is a data visualisation package for R that allows to declaratively describe data graphics with a syntax similar in spirit to ggplot2.

Which tool is best for data visualization? ›

  • The Best Data Visualization Software of 2022.
  • Microsoft Power BI.
  • Tableau.
  • Qlik Sense.
  • Klipfolio.
  • Looker.
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  • Domo.
18 Oct 2022

Which data Visualisation is best? ›

Some of the best data visualization tools include Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc. These tools support a variety of visual styles, be simple and easy to use, and be capable of handling a large volume of data.

What R package has data table? ›

Data. table is an extension of data. frame package in R. It is widely used for fast aggregation of large datasets, low latency add/update/remove of columns, quicker ordered joins, and a fast file reader.

What are the best R packages? ›

The 10 Most Important Packages in R for Data Science
  • ggplot2.
  • data.table.
  • dplyr.
  • tidyr.
  • Shiny.
  • plotly.
  • knitr.
  • mlr3.

Is R good for data visualization? ›

Beginners preferably use R for data visualization as it is simple and easy to visualize the data. The popular visualization libraries are ggplot2, plotly, Esquisse, and Shiny.

What are the four types of data visualizations? ›

In each quadrant is one of four types of visualisation: idea illustration, idea generation, visual discovery and everyday dataviz. It quickly became clear that we as market researchers can relate to each of these four types of visualisation as they are all used at different stages of the market research process.

What is the difference between ggplot2 and Plotly? ›

Aesthetically, many users consider ggplot2 to be better looking than Plotly, due to its margins and points. In terms of speed, ggplot2 tends to run much slower than Plotly. With regard to integration, both Plotly and ggplot2 can integrate with a variety of tools, like Python, MATLAB, Jupyter, and React.

Is R shiny free visualization tool? ›

Well, it's built with R Shiny, it's free to access, and has the source code available on GitHub.

Is R shiny free Visualisation tool? ›

Open Source: Building and getting a shiny app online is free of cost, if you wish to deploy your app on the free version of shinyapps.io.

Is R shiny a visualization tool? ›

Shiny is designed for fully interactive visualization, using JavaScript libraries like d3, Leaflet, and Google Charts.

What is the most common methods of data visualization? ›

  1. Pie Chart. Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. ...
  2. Bar Chart. ...
  3. Histogram. ...
  4. Gantt Chart. ...
  5. Heat Map. ...
  6. A Box and Whisker Plot. ...
  7. Waterfall Chart. ...
  8. Area Chart.
17 Sept 2019

What are 4 keys to an effective visualization? ›

Noah Iliinsky discusses the four pillars of effective visualization design, including purpose, content, structure, format, and design types to avoid.

What chart is best for data over time? ›

Line charts are the most effective chart for displaying time-series data. They can handle a ton of data points and multiple data series, and everyone knows how to read them. Just make sure your points are ordered such that time runs from left to right, and use consistent time intervals.

Is data.table faster than data frame? ›

If we observe here, the code for the data. table is less than the code for data. frame and hence, data. table takes less time to compile and gives the output fast so, this makes the data table use widely.

How do I view data in a table in R? ›

How to Use read. table in R (With Examples)
  1. Step 1: View the File. Suppose I have a file called data. ...
  2. Step 2: Use read. table() to Read File into Data Frame. ...
  3. Step 3: View the Data Frame.
7 Dec 2021

Is data.table in tidyverse? ›

But a data. table can be used as input into both base and tidyverse functions. In the examples that follow, data. table tables will be limited to data.

What is R shiny package? ›

Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.

What is dplyr package in R? ›

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges: mutate() adds new variables that are functions of existing variables. select() picks variables based on their names. filter() picks cases based on their values.

How much RAM do I need for R? ›

At least 256 MB of RAM, a mouse, and enough disk space for recovered files, image files, etc. The administrative privileges are required to install and run R‑Studio utilities. A network connection for data recovering over network.

Which is better for data visualization R or Python? ›

While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R's functionality was developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization.

Why is R better than tableau? ›

Learning and using Tableau is a very low time consuming activity, but you could keep playing with the data and nothing might emerge. Whereas, R has a very steep learning curve; any investment you make in R, however, will be returned to you with significant rewards.

How do I master data visualization? ›

Nine Considerations for Your Next Data Visualization
  1. Establish the goal of your visualization. ...
  2. Clean up and understand your dataset. ...
  3. Know your audience. ...
  4. Choose a type of chart. ...
  5. Don't try to pack too much into one chart. ...
  6. Map the data to visual variables. ...
  7. Text is “totally underrated.” Use It.

What are the 5 steps in data visualization explain? ›

Data Visualization
  1. Develop your research question.
  2. Get or create your data.
  3. Clean your data.
  4. Choose a chart type.
  5. Choose your tool.
  6. Prepare data.
  7. Create chart.
31 Oct 2022

What are the 5 common charts that are used for good visualization? ›

Data Visualization: How to Choose the Right Chart and Graph for Your Data
  • Bar Graph. Bar charts are among the most frequently used chart types. ...
  • Pie Chart. A pie chart is a circular graph divided into slices. ...
  • Doughnut Chart. ...
  • Line Graph. ...
  • Area Chart. ...
  • Treemap Chart. ...
  • Waterfall Chart. ...
  • Scatter Plot.
12 Jul 2018

What are the two basic types of data visualization? ›

In general, there are two different types of data visualization: exploration, which helps find a story the data is telling you, and an explanation, which tells a story to an audience. Both types of data visualization must take into account the audience's expectations.

Why is ggplot so good? ›

The answer is because ggplot2 is declaratively and efficiently to create data visualization based on The Grammar of Graphics. The layered grammar makes developing charts structural and effusive.

Is Seaborn better than ggplot? ›

In general, ggplot2 plot graphics are visually sharper than that of seaborn.

Which is better Matplotlib or ggplot2? ›

The ggplot2 library is used in the R statistical programming language while Matplotlib is used in Python. Although both libraries allow you to create highly customized data visualizations, ggplot2 generally allows you to do so in fewer lines of code compared to Matplotlib.

How hard is it to learn R Shiny? ›

Along with Shiny elements, you can use HTML elements to stylize your content in your application. In my opinion, R Shiny is very easy to learn despite how powerful the tool is. If you're working on a side project or looking to add something to your portfolio, I highly recommend trying it out.

Why is R Shiny so slow? ›

The most common reason is the Shiny app code has not been optimized. You can use the profvis package to help you understand how R spends its time. You also might want to make sure your server is large enough to host your apps.

Is R Shiny better than tableau? ›

While both the products offer good speed and efficient data processing, R Shiny is easier when it comes to repeatability and scalability. In Tableau, when a dashboard is deleted or decommissioned, it has to be created from scratch.

Is Shiny app free? ›

It's free, open source, and available from GitHub. Shiny Server is a server program that Linux servers can run to host a Shiny app as a web page.

What are the most popular R packages? ›

The 10 Most Important Packages in R for Data Science
  • ggplot2.
  • data.table.
  • dplyr.
  • tidyr.
  • Shiny.
  • plotly.
  • knitr.
  • mlr3.

Which library is the most used visualization? ›

1. Matplotlib. Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community.

Which is the popular IDE for R? ›

StatET for R

StatET is an eclipse based IDE for R programming. It provides a set of unmatched tools for R code writing and package building. Features include integrated R console, Object browser, and R help, and its support for multiple local and remote installations.

What is Ggplot package in R? ›

ggplot2 package in R Programming Language also termed as Grammar of Graphics is a free, open-source, and easy-to-use visualization package widely used in R. It is the most powerful visualization package written by Hadley Wickham.

What is the easiest data visualization tool to use? ›

The best data visualization tools include Google Charts, Tableau, Grafana, Chartist. js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3. js. The best tools offer a variety of visualization styles, are easy to use, and can handle large data sets.

What are the 4 levels of visualization? ›

These stages are exploration, analysis, synthesis, and presentation.

Is R better for data visualization? ›

Prefer R for data analysis projects, due to its advanced data visualization capabilities. Python is most suitable for machine learning and is also an excellent tool for data science pipelines.

Is there a GUI for R? ›

Perhaps the most stable, full-blown GUI is R Commander, which can also run under Windows, Linux, and MacOS (see the documentation for technical requirements). Both of these programs can make R a lot easier to use.

Why is ggplot the best? ›

The answer is because ggplot2 is declaratively and efficiently to create data visualization based on The Grammar of Graphics. The layered grammar makes developing charts structural and effusive.

Which is better ggplot or Matplotlib? ›

The ggplot2 library is used in the R statistical programming language while Matplotlib is used in Python. Although both libraries allow you to create highly customized data visualizations, ggplot2 generally allows you to do so in fewer lines of code compared to Matplotlib.

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