How To Make Nice Tables

How To Make Nice Tables – It doesn’t matter how good your data analysis is if you don’t communicate your results effectively. In most reports, communication of results occurs through data visualization and tabulation. with narrative texts; These include content sets used to communicate results.

Plain writing is a topic for another post. Data visualization in R is a huge topic (masterfully covered in Kieran Healy’s Data Visualization: A Practical Introduction and Claus Wilke’s Fundamentals of Data Visualization). But what about the tables? Fortunately for R users, there are many ways to create beautiful charts that effectively communicate your results.

How To Make Nice Tables

If you use RMarkdown (and if you don’t you should really consider it) any dataframe you call in a code change will be displayed using the dataframe’s print method in your YAML. As Yihui Xie said, “Printing objects with small pieces of R code basically imitates the R console.” This default output leaves a lot to be desired. “Table” output with fixed width fonts like this never motivated anyone to take action.

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Improving this result is quite easy. The default data frame printing method in YAML can be changed to one of the following options.

Creating paginated tables works especially well when you have long tables and want to give the user the ability to see the output:

There are also many packages that provide functions to produce nicely formatted tables. Here are some packages I’ve used that look good and communicate results effectively.

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Gt is a new(ish) package. It’s “built to be simple yet powerful… [with] an emphasis on simple functions for everyday chart viewing needs.” The package includes functions designed to do things like:

The gt package provides a general philosophy of tables (similar to the grammar of graph theory).

Package. This package from Thomas Mock offers some beautiful layouts modeled after the tables of the New York Times.

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The CableExtra package builds on the cable output of the Knitr package. Author Hao Zhu said:

The purpose of Cable Extra is to help build common complex charts and manage chart formats. Import the pipe symbol %>% from Magritand; All actions can be verbalized; So basically you can basically add “layers” to a string output in a similar way to ggplot2.

Sharla Gelfand used the CableExtra package in a report she did for the Ontario College of Nursing and loved it.

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Kun Ren’s Format Table package is “designed to use formatting within data frames to make data presentation easier, richer, more flexible and more informative.”

One of the most striking features is the colors, shadows of the background; Other features include the ability to move bars and charts beyond text blocks.

If you’re looking for an example of the Formattable package in use, Laura Ellis has a really nice setup.

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The main attraction of the DT package is filtering in HTML tables; Ability to add pagination and sorting. Users can cut, move, and rearrange maps in ways that help them make sense of your results.

I hadn’t heard of this package until Mara Averick tweeted about it recently. I haven’t used it, but the documentation is incredible. As in the DT package, It has the ability to sort and filter data. It is much more attractive than the DT package. One of the examples (below) looks like the beautiful tables made by FiveThirtyEight.

Working with packages can be a bit more difficult. To simplify the process of creating tables

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This package simplifies and improves the appearance and format of tables built with rpackage. The package offers several conditional formatters that are highly customizable and easy to use.

Package to add colors to tables; Add interactive flames; To use custom themes; It makes it easier to insert images into charts and save charts in PNG and HTML format.

The above packages are designed to generate HTML tables. If you combine with Word, For tables, your best bet is the Flextable package (although the tables produced by this package can be included in the HTML and PDF outputs). The flextable package, designed by David Gohel, provides a simple syntax to shape the content and format of your tables.

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The packages mentioned above are the ones I have used and had good success with. When I put out a call on Twitter for other packages to make tables with R, I got some promising responses.

LaTeX and HTML tables with an easy and modern interface. Features include text formatting; number format; background color frames, Includes patches and adjustments. Cells can span multiple rows and/or columns. Tables can be manipulated using standard R subset or dplr functions.

As with the DT package, The rhandsontable package allows users to manipulate data in tables. It includes “powerful features such as data validation, sorting, grouping, data fitting, formula support or column ordering.” Working with charts feels like viewing data in Excel, which is useful for users who work with Microsoft’s spreadsheet program everywhere. Users can even make changes to the data in the tables created with this package.

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A package called Pixedust, surprisingly, is designed to link with the Broom package to produce a sort of model output table. Using pixedust is a three-step process.

Functions remove extra decimal places; It allows you to do things like not use scientific notation and p-values.

To ensure that users read and understand the results of your complex statistical analysis; It looks like a great package.

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There are definitely charts packages that I missed (while finishing this post, I found this list of RStudio RMarkdown tutorials); But it should give you a good place to start making your tables more beautiful and efficient.

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It doesn’t matter how good your data analysis is if you don’t communicate your results effectively. In most reports, communication of results occurs through data visualization and tabulation. with narrative texts; These include content sets used to communicate results.

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If you use RMarkdown (and if you don’t, you should really consider it); Any data frame in a piece of code that you call will be displayed using the print data frame method in your YAML. As Yihui Xie said, “Printing objects with small pieces of R code basically imitates the R console.” This default output leaves a lot to be desired. “Table” output with fixed width fonts like this never motivated anyone to take action.

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Kun Ren’s Format Table package is “designed to use formatting within data frames to make data presentation easier, richer, more flexible and more informative.”

If you’re looking for an example of the Formattable package in use, Laura Ellis has a really nice setup.

I hadn’t heard of this package until Mara Averick tweeted about it recently. I haven’t used it, but the documentation is incredible. As in the DT package, It has the ability to sort and filter data. It is more visually appealing than the DT package. One of the examples (below) looks like the beautiful tables made by FiveThirtyEight.

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The packages mentioned above are the ones I have used and had good success with. When I put out a call on Twitter for other packages to make tables with R, I got some promising responses.

As with the DT package, The rhandsontable package allows users to manipulate data in tables. It includes “powerful features such as data validation, sorting, grouping, data fitting, formula support or column ordering.” Working with charts feels a lot like displaying data in Excel, which will be useful for users who are used to working with Microsoft’s ubiquitous charting program. Users can even make changes to the data in the tables created with this package.

There are definitely charts packages I missed (while finishing this post, I found this list of RStudio RMarkdown tutorials); But it should give you a good place to start making your tables more beautiful and efficient. While I was working on my motorcycle (modification) earlier this year I had what looked like a blind spot. I had a pretty good feel for the look I wanted. I clicked on the blue velvet sofa, but got stuck trying to figure out what to do with the dining table top. It feels like an afterthought in the greatness of the design… because it is

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