Rmd file set up

Ensure that you set your .Rmd output to be html_document as per below.

---
title: "Displaying your pretty R tables in GitHub"
author: "Laura Ellis"
date: "11 October, 2020"
output:
  html_document
---

Data set up

Load the necessary libraries and set some color variables which will be used during the table display.

#Load the libraries, if you don't have the library installed, run the following command per library:
#install.packages("dplyr")
library(data.table)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:data.table':
## 
##     between, first, last

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(formattable)
library(DT) 

customGreen0 = "#DeF7E9"
customGreen = "#71CA97"
customRed = "#ff7f7f"

Pull in your data and format it

Download the Austin indicator data set and format it for easy table display.

#Original data set from: https://data.austintexas.gov/City-Government/Imagine-Austin-Indicators/apwj-7zty/data

austinData= fread('https://raw.githubusercontent.com/lgellis/MiscTutorial/master/Austin/Imagine_Austin_Indicators.csv', data.table=FALSE, header = TRUE, stringsAsFactors = FALSE)

#Format the table
i1 <- austinData %>%
  filter(`Indicator Name` %in% 
           c('Prevalence of Obesity', 'Prevalence of Tobacco Use', 
             'Prevalence of Cardiovascular Disease', 'Prevalence of Diabetes')) %>%
  select(c(`Indicator Name`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`)) %>%
  mutate (Average = round(rowMeans(
    cbind(`2011`, `2012`, `2013`, `2014`, `2015`, `2016`), na.rm=T),2), 
    `Improvement` = round((`2011`-`2016`)/`2011`*100,2))

Table Displays

We are now going to call on a few sample libraries which produce beautiful and useful html tables.

Example 1: Beautiful table with the fortmattable package

This example comes from my blog on how to use the formattable package.

#Create a formatter to display the red/green and arrow up/down indicators.
improvement_formatter <- formatter("span", 
                                   style = x ~ style(font.weight = "bold", 
                                                     color = ifelse(x > 0, customGreen, ifelse(x < 0, customRed, "black"))), 
                                   x ~ icontext(ifelse(x>0, "arrow-up", "arrow-down"), x)
                                   )

# Display the table using the formattable package and the custom formatter, "improvement_formatter" from above. 

formattable(i1, align =c("l","c","c","c","c", "c", "c", "c", "r"), list(
  `Indicator Name` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
  `2011`= color_tile(customGreen, customGreen0),
  `2012`= color_tile(customGreen, customGreen0),
  `2013`= color_tile(customGreen, customGreen0),
  `2014`= color_tile(customGreen, customGreen0),
  `2015`= color_tile(customGreen, customGreen0),
  `2016`= color_tile(customGreen, customGreen0),
  `Average` = color_bar(customRed),
  `Improvement` = improvement_formatter
))

Indicator Name	2011	2012	2013	2014	2015	2016	Average	Improvement
Prevalence of Obesity	19.1	23.6	23.3	20.5	24.0	23.2	22.28	-21.47
Prevalence of Tobacco Use	17.4	15.0	15.3	12.2	16.6	16.7	15.53	4.02
Prevalence of Cardiovascular Disease	5.0	4.9	1.5	4.4	4.9	6.2	4.48	-24
Prevalence of Diabetes	8.0	7.2	9.3	7.2	7.5	10.4	8.27	-30

Example 2: Pretty, interactive table with the DT package

Display a beautiful and interactive with the DT package. I love this package for very large tables, that a user may want to interact with explore in a UI for a while before analyzing. I used this package for my very large tables in my pelotonR package

datatable(i1, extensions = "Scroller", width = 1000, options = list(scrollY = 200, scroller = TRUE, scrollX = 200, pageLength = 1))

More pretty R table examples

You can find a great overview of packages to use for beautiful tables in R by David Keyes here.

Export and display in GitHub

Get the HMTL file prepared

Ensure that your file is called index.Rmd
Select “Knit” in the top menu bar within R Studio. This will create your file index.html file

Get your GitHub repo prepared

Create a new GitHub repo
Go to the Code area > click Add file.
Add index.html and index.Rmd file from the above step
Navigate to the repo > Settings > Scroll to GitHub Pages
Select a branch. If you are unsure, just select Main and Save.
Select a theme, I selected Minimal. If you need to save the new README.md file for this, do please do so.
Navigate back to Settings > Scroll to GitHub Pages. Your URL should now be displaying. It will display text similar to: Your site is published at {URL}
Celebrate because your beautiful data tables are now displayed in GitHub!
Optional: Change your README.md file to link to your html page.