Bar Chart Race Animation Application

Author

Elegant Starmie
Chloe Chu (cac466), Aditya Manthri (anm97),
Tia Yi (ty379), Jai Kishore Kumar Chandnani (jc3556)

Published

December 4, 2023

Bar chart race; what, why, how?

  • Growing amount of time-series data available for analysis

  • Yet few tools support creation of animated charts that use time dimension for displaying changes in data dynamically

  • Recognizing the growing demand for this compelling format, our project focuses on developing a Shiny application in R, designed to simplify the creation of bar chart races

  • Our application addresses the challenge of making visually striking and informative animations accessible to users who may not have coding expertise

Bar chart race tool

The application allows users to upload datasets that contain temporal elements. For the initial demo of this project, we pre-configured World Bank Data, where the process includes:

  • Data preprocessing
  • Data manipulation
  • Build static plots
  • Stitching plots together with transition_states()
  • Rendering with gganimate, extending ggplot2 to include animation
  • Integration with Shiny app for user interface

Highlights from EDA

After data wrangling, we facet each year as the base plot for each frame.

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ purrr     1.0.2
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Attaching package: 'scales'


The following object is masked from 'package:purrr':

    discard


The following object is masked from 'package:readr':

    col_factor


New names:
Rows: 392882 Columns: 68
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (4): Country Name, Country Code, Indicator Name, Indicator Code
dbl (63): 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, ...
lgl  (1): ...68

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Warning: 1 parsing failure.
row col expected actual
 64  -- a number  ...68
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

User-Driven Exploration and Analysis

  • Data-Driven Narratives:

    • Powerful storytelling tool, users observe trends in their data.

  • Technical Aspects:

    • Data manipulation and gganimate for creating visually appealing transitions.

  • Interactive Features:

    • Users can customize features of their visualization

Animation Example

Generated using our Shiny Application

Conclusions + Future Work

  • Our App facilitates transformation of static data -> appealing & interactive narrative.

  • User-Friendly Web Interface: Shiny App.

  • Seamless user experience, future work - optimizing the animation speed (multi-threading).

  • Enhance the processing efficiency.

  • Create a community around the website- users can share visualizations, insights, + collaborate on projects.

  • Future Work Concept