R Shiny Flex Dashboard Interactive Data Visualization

How does R Shiny work? In this lecture we'll go over a high level overview of what R Shiny does and how R Shiny works. 

R Flex dashboards can be used standalone or in combination with R Shiny. R Flex dashboards are hands down the fastest way to build R Shiny applications. 

You can publish your work in many more ways than just a R Shiny Flex dashboard using R Markdown. 

Learn how to control the layout of a R flex dashboard with a few simple settings. 

By using R evaluation techniques such as tidy evaluation we can enhance our R Shiny applications making them more dynamic. 

R Tidyverse power functions available in dplyr help you get your work done more efficiently. 

R Tidyverse handles both standard evaluation and non standard evaluation (aka Tidy evaluation). Tidy evaluation is the latest recommended standard to use with the r tidyverse. 

Add R Shiny to our R flex dashboard so that we can add controls. 

Spotting errors in your R Tidyverse code

R programming data visualisation options

ggplot is the tidyverse  r programming library for creating static visualisations. It's very versatile, works well with plotly interactive visualisations and is a good way for understanding other libraries which use a similar methodology such as high charter. 

Tidyverse R programming ggplot geoms allow you to specif chart types

Tidyverse r programming ggplot layering multiple charts and text

Tidyverse r programming ggplot layering multiple charts with Facets

Tidyverse r programming upgrading your ggplots to interactive HTML widgets using Plotly

Tidyverse r programming HighCharter is a wrapper library for high charts which is a beautifully implemented interactive visualisation library.

Tidyverse r programming DataTables JS is a wrapper to a popular java script control for displaying and working with interactive tables suitable for HTML dashboards

TidyQuant is a great new library that enhances the popular r library quantmod while following the r Tidyverse methodologies. Aside from using R Tidyverse standards it also offers a number of other enhancements that make it much faster and easier to use. 

Learn to create functions for our R Shiny Flex Dashboard using R Tidyverse code. 

Add our R Tidyverse Functions to a standalone flex dashboard which can easily be distributed independent of a Shiny server. 

Enhance our flex dashboard with Shiny.

Get finer control over our R Shiny Flex Dashboard with the Shiny function Event Reactive. 

In this series of videos we're going to implement a R Shiny Flexdashboard that will generate a random network graph displayed as a graph visualization and a table. To learn more about graphs see my lectures on the benefits and use cases of graphs. Graphs could potentailly revolutionise the way you work with data so taking a little bit of time to understand what they're actually good for will be well worth your effort. To cover off how to implement different graph solutions is really a full course in itself so this course will introduce a very basic example of how to use R iGraph and R visNetwork together as an example of how to integrate different types of visualisations into a R Shiny Flexdahsboard. 

Pay careful attention to how the overall solution is structured as that's really half of the lesson.  The key concepts you will learn are.

Data Prep and Visualization
In this section we will start creating some data with R iGraph and then visualizing it with R visNetwork. We will do this independent of a R Shiny application or dashboard. This will make it much easier to develop and debug your code as there will be much less code to look at any one point in time.  

Convert Scripts to Functions
By converting our R sripts to functions your code will be much better organised. You can easily reuse your code across multiple applications or swap out your code to make your application more flexible. You can parameterize your functions so that they can be reutilised in more circumstances. 

Standalone Flexdashboard
In this section we will customise a R flex dashboard layout and drop in our visualisations starting without Shiny which means the dashboard can easily be shared even without a R Shiny server. 

Shiny Interactivity
In this section we will add additional interactivity to the dashboard through Shiny controls such as input boxes which are not available to standalone flexdashboards. Controls such as input boxes can be used to trigger R code on a R Shiny server which can be used to update your visualizations using the appropriate render function. 

Shiny Concepts
In this section we will go over what is required to output a single dataset to multiple visualisations such as a graph network and a data table. We will discuss the common mistakes that are easy to make and the use of reactive functions in R Shiny.  

Each section of this course will have the code availble for the previous sections so you can jump in at any point you like. 

One thing which will massively accelerate your learning is not just following the examples but modifying them and experimenting to see what happens when you swap out pieces and adjust the parameters. So jump in and get your hands dirty. If something doesn't work, scale it back and simplify it until it does. Make use of the help files and post questions when you get stuck. 

Data Prep and Visualization

In this section we will start creating some data with iGraph and then visualizing it with visNetwork. We will do this independent of a R Shiny application or dashboard. This will make it much easier to develop and debug your R code as there will be much less R code to look at any one point in time.  

Convert R Scripts to R Functions

By converting our R scripts to R functions your R code will be much better organised. You can easily reuse your R code across multiple applications or swap out your code to make your application more flexible. You can parameterize your functions so that they can be reutilised in more circumstances.

Standalone R Flexdashboard

In this section we will customize a R flex dashboard layout and drop in our visualizations starting without R Shiny which means the dashboard can easily be shared even without a R Shiny server.

R Shiny Interactivity

In this section we will add additional interactivity to the dashboard through R Shiny controls such as input boxes which are not available to standalone R flexdashboards. Controls such as input boxes can be used to trigger R code on a R Shiny server which can be used to update your visualizations using the appropriate render function. 

R Shiny Concepts

In this section we will go over what is required to output a single dataset to multiple visualisations such as a graph network and a data table. We will discuss the common mistakes that are easy to make and the use of reactive functions in R Shiny.  

Applications of graph networks. Why you want to learn more about graph networks in R

Applications of graph networks specifically for customer insights. 

R & Python are the top 2 programming languages for data analytics and data science right now. There is a lot of debate over which one is better and the information tends to get a bit skewed. R & Python are each stronger at different things. This video covers my opinion on the strengths of each language so that people can make a more informed decision about which one to use for their circumstances.

Using the zoo timeseries functions to fill down missing values.

Want to efficiently deal with NA values and bulk column operations?

Data cleaning and prep is one of the most time-consuming tasks in data science. Learn some powerful tips to streamline those processes here.

Want to learn how to access and analyse email with R?

How about no code data visualisation with R?

In this video I'm going to go over a number of questions from one of students. You'll learn how you can integrate existing R scripts into dashboards, how to clean up your code, functional programming, converting from wide to long data formats to make ggplot visualisation code more efficient and more.

Q&A R Script to R Shiny Flexdashboard, ggplot, reproducibility, functional programming, rmarkdown

Why is it taking so long to render my dashboards after I've created them?

After watching this video this student was able to cut a long load time by over 50% which is a great result.

In this video we will look at how we can use PURRR Map or more specifically MAP_DF from the PURRR library along with read_csv from the readr library to load 1000 csv files into a dataframe within 3 seconds.

PURRR and READR are both part of the R Tidyverse core library and ecosystem.

Map is a powerful alternative to for loop and lapply. Map is more concise and functionally focused when compared to for loops.

Map when compared to lapply allows for us to return values as explicit data types other than lists. For example map_df will return your results in a dataframe / tibble eliminating the need to do a row_bind after calling lapply. Map_dbl or map_int will return numeric vectors which can easily be passed into addtional numeric functions. Map_chr will return a character vector which you can pass into stringr operations such as str_c, paste or paste0.

Demonstrates how you can use R Tidyverse to combine Excel files

In this video I show you how to build in an export function into your R Shiny Flexdashboards.

Learn how to filter and present tabular data through the dashboard and make it more interactive and dynamic.

Learn how and why you might want to implement a dynamic column selector for your interactive dashboards.

A lot of the time when you're doing data analysis you end up with many more columns than you can see on the screen.

Now you could basically subset the columns so you're just seeing less columns but the problem with that is that maybe you just want to see a different column at different points in time or you have different clients with different requirements.

So instead of hard-coding these values in what we can do is we can create a dynamic selection instead so that anybody can have whatever they want or whatever you let them have whenever they want to actually see it.