The idea behind this Shiny app was to make survival analysis visualization accessible to a wider audience, including users with little to no coding experience. Instead of relying on expensive software or learning statistical programming (which may be difficult for some), users can easily generate fully customizable and downloadable survival plots. This app could be useful for researchers, clinicians, and students who may need quick visualizations of their data as they explore interesting trends, or need a quick plot for a presentation or meeting.
The app format was designed to be user-friendly. Users can simply filter for specific groups within their dataset, paste the values into the app, and generate a professional-quality plot to view or download.
https://aliz0124.shinyapps.io/assignment-b4/
Quick Instructions (refer to the "features" heading below for more details)
- Use the slider to select the number of subgroups in your data to plot.
- Input your group name, and paste your time-to-event and censor data directly from your data file. Please note the formatting requirements described in the app.
- Select plot customizations and add a title
- Press the generate plot button, and you're done!
Below are randomly generated sample data for 3 different groups for users to demo the app:
Group 1 time-to-event
21
33
75
72
9
74
38
17
61
31
60
57
17
38
27
44
22
64
54
9
67
16
20
18
46
Group 1 censor
0
0
0
0
1
1
1
0
0
0
0
1
1
0
1
1
0
0
0
1
0
1
0
0
0
Group 2 time-to-event
3
26
74
74
53
3
30
4
75
33
70
35
2
53
70
32
11
49
7
19
6
11
58
29
50
Group 2 censor
1
0
0
0
0
0
0
1
1
0
1
0
0
1
1
1
0
0
1
1
1
0
1
1
1
Group 3 time-to-event
35
65
11
67
10
40
37
73
5
28
71
12
5
67
68
51
40
71
66
68
57
65
Group 3 censor
1
1
1
0
0
1
0
1
1
1
1
1
0
1
1
0
1
1
1
1
0
0
This feature allows users to to define the number of groups they want to analyze using a slider. Based on the input, the app dynamically generates input boxes for time-to-event data, censoring data, and group names, allowing for analysis between subgroups.
The number of groups were kept to a maximum of 5 based on existing Kaplan-Meier survival calculation tools and typical practice, where we only see 2-5 subgroups within a single plot. This maintains clarity and makes the plot easier to interpret.
This feature allows users to fully customize their plot with features like confidence intervals, displaying a p-value using the log rank test if >1 group is inputted, and risk tables.
This feature lets users save their plots as PNG files for sharing results without needing additional formatting. The plot is customized based on their inputs, uses a colorblind-friendly palette and is immediately ready to share with others.
https://aliz0124.shinyapps.io/assignment-b3-aliz0124/
This is an R-based Shiny app for exploring the caffeine content in various drinks. The app allows users to filter by drink type, volume and caffeine levels, or search for individual drinks. Users can also visualize the data through an interactive bubble plot.
The dataset was sourced from Heitor Nunez on Kaggle, who extracted the data from the Caffeine Informer Caffeine Database website, which contains information on the caffeine content of drinks.
This feature enables users to filter the dataset by individual or multiple drink types, making it easier to compare drinks across types or assess caffeine content and other variables within specific groups.
This feature helps users filter the dataset for drinks that meet their specific preferences or needs, such as finding drinks with high caffeine content for a morning pick-me-up or within a specific volume range.
This feature is useful for users who want to save their filtered data to conduct their own analysis outside of the app, or share it with others. It saves the data in CSV format making it easily accessible for analysis in R or Python.
An interactive table feature allows users to further explore their filtered data by sorting individual variables in ascending or descending order, or searching within filtered drinks for individual rows.
This plot provides an accessible overview of how different drinks compare in terms of caffeine amount and volume, allowing users to visually spot trends in their filtered data. Using Plotly's interactive features enables users to hover over individual points to view information, isolate specific drink types, save the plot and more.