R Tutorial: Case Studies: Building Web Applications with Shiny in R | Inputs and outputs
Want to learn more? Take the full course at https://learn.datacamp.com/courses/case-studies-building-web-applications-with-shiny-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
---
So far, you've reviewed how to create a Shiny app and add text to it. You might be thinking that this isn't very useful as a web application—it's more of a text document.
In order to be useful, Shiny apps need inputs and outputs.
Inputs are what gives users a way to interact with a Shiny app. An input is anything that the user can interact with, using the mouse or keyboard, to modify its value. For example, this is a text input, where users can enter text,
and this is a numeric input, which lets users select a number.
In fact, Shiny provides many different input functions, some of which, are shown here.
Inputs are created by calling an input function inside the UI. Here is the code that creates the two inputs that you saw earlier. You may notice that the code for the two inputs has some similarities.
Most input functions have the word "Input" in their name and have the same first two arguments: inputId and label. The inputId has to be unique, because later on you'll use this ID to retrieve the value of the input. The label argument is the descriptive text that appears above the input. Some inputs can also have other arguments specific to that input type, which we'll see later on.
Outputs can be any object that R creates and that we want to display in our app - such as a plot, a table, or even just text.
Creating an output, for example, a plot, is a two-step process. First you need to tell Shiny where to place the plot by adding an output placeholder in the UI. Similar to input functions, output placeholder functions also have an outputId parameter that must be unique. The second step is to actually write the R code for the plot. This is done in the server.
So far we only wrote code in the UI - creating inputs and
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from DataCamp · DataCamp · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
SQL Server Tutorial: Date manipulation
DataCamp
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
R Tutorial: Adding aesthetics to represent a variable
DataCamp
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
Python Tutorial: Preparation for modeling
DataCamp
Python Tutorial: Machine Learning modeling steps
DataCamp
R Tutorial: The prior model
DataCamp
R Tutorial: Data & the likelihood
DataCamp
R Tutorial: The posterior model
DataCamp
R Tutorial: An Introduction to plotly
DataCamp
R Tutorial: Plotting a single variable
DataCamp
R Tutorial: Bivariate graphics
DataCamp
Python Tutorial: Customer Segmentation in Python
DataCamp
Python Tutorial: Time cohorts
DataCamp
Python Tutorial: Calculate cohort metrics
DataCamp
Python Tutorial: Cohort analysis visualization
DataCamp
R Tutorial: Building Dashboards with flexdashboard
DataCamp
R Tutorial: Anatomy of a flexdashboard
DataCamp
R Tutorial: Layout basics
DataCamp
R Tutorial: Advanced layouts
DataCamp
Python Tutorial: Time Series Analysis in Python
DataCamp
Python Tutorial: Correlation of Two Time Series
DataCamp
Python Tutorial: Simple Linear Regressions
DataCamp
Python Tutorial: Autocorrelation
DataCamp
R Tutorial: The gapminder dataset
DataCamp
R Tutorial: The filter verb
DataCamp
R Tutorial: The arrange verb
DataCamp
R Tutorial: The mutate verb
DataCamp
R Tutorial: What is cluster analysis?
DataCamp
R Tutorial: Distance between two observations
DataCamp
R Tutorial: The importance of scale
DataCamp
R Tutorial: Measuring distance for categorical data
DataCamp
Python Tutorial: Plotting multiple graphs
DataCamp
Python Tutorial: Customizing axes
DataCamp
Python Tutorial: Legends, annotations, & styles
DataCamp
Python Tutorial: Introduction to iterators
DataCamp
Python Tutorial: Playing with iterators
DataCamp
Python Tutorial: Using iterators to load large files into memory
DataCamp
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
SQL Tutorial: Tables: At the core of every database
DataCamp
SQL Tutorial: Update your database as the structure changes
DataCamp
Python Tutorial: Classification-Tree Learning
DataCamp
Python Tutorial: Decision-Tree for Classification
DataCamp
Python Tutorial: Decision-Tree for Regression
DataCamp
Python Tutorial: Census Subject Tables
DataCamp
Python Tutorial: Census Geography
DataCamp
Python Tutorial: Using the Census API
DataCamp
R Tutorial: A/B Testing in R
DataCamp
R Tutorial: Baseline Conversion Rates
DataCamp
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
R Tutorial: Introduction to qualitative data
DataCamp
R Tutorial: Understanding your qualitative variables
DataCamp
R Tutorial: Making Better Plots
DataCamp
SQL Tutorial: OLTP and OLAP
DataCamp
SQL Tutorial: Storing data
DataCamp
SQL Tutorial: Database design
DataCamp
Python Tutorial: Introduction to spaCy
DataCamp
Python Tutorial: Statistical Models
DataCamp
Python Tutorial: Rule-based Matching
DataCamp
Related AI Lessons
⚡
⚡
⚡
⚡
Building AI Presence: When Generic Tools Hit Their Limits
Dev.to AI
I Stopped Losing Freelance Writing Clients the Moment I Started Using These Claude Prompts
Medium · AI
Revolutionizing Trading: Top 5 AI-Powered Finance Tools for 2026
Dev.to AI
No-Code AI Automation Tools: An Honest Comparison
Dev.to · AdamVibe
🎓
Tutor Explanation
DeepCamp AI