Added description of app

examples/dash/plotly-large-dataset/README.md

@@ -1,23 +1,40 @@
 # Plotting large datasets in Dash
 
-Interactive Dash applications that display a figure denoting flight date and time (24h) vs flight delay time (minute). You can select the date range you want to visualize in `resampler` and `combined`.
-
-The applications plot large datasets using one of:
+Interactive Dash applications that plot large datasets using one of:
 - [**WebGL**](https://plotly.com/python/webgl-vs-svg/) (in `webgl` folder): a powerful technology that uses GPU to accelerate computation, helping you render figures more effectively. This method is generally ideal for figures with up to 100,000-200,000 markers (terminology for data points in charts), depending on the power of your GPU. For figures larger than that, it's often optimal to aggregate the data points first
 
+- [**`plotly-resampler`**](https://github.com/predict-idlab/plotly-resampler) (in `resampler` folder): an external library that dynamically aggregates time-series data respective to the current graph view. This approach helps you downsample your dataset at the cost of losing some details.
+
+- Combined approach (in `combined` folder).
+
+We will be using a commercial flight dataset that documents information such as flight delays in the first half (1/1-6/30) of 2006. You can find it [here](https://github.com/vega/falcon/blob/master/data/flights-3m.csv). For the purpose of this project, we will focus on plotting departure delays.
+
+Once you download the dataset, run `python csv-clean.py flights-3m.csv` to obtain the cleaned csv file `flights-3m-cleaned.csv`. Move the cleaned file to the `data` folder in any of the project folders (`webgl`, `resample` or  `combined`) you want to test. 
+
+## Description
+
+On its home page, the apps will display a scatter plot figure denoting departure delay time (minute) of around 3 million flights, captured below. You can select the date range you want to visualize in `resampler` and `combined`.
+
+- `webgl`
+
 ![](static/app_webgl.png)
 
-- [**`plotly-resampler`**](https://github.com/predict-idlab/plotly-resampler) (in `resampler` folder): an external library that dynamically aggregates time-series data respective to the current graph view. This approach helps you downsample your dataset at the cost of losing some details.
+- `resampler`
 
 ![](static/app_resampler.png)
 
-- Combined approach (in `combined` folder).
+- `combined`
 
 ![](static/app_combined.png)
 
-We will be using a commercial flight dataset that documents information such as flight departure date/time and delays. You can find it [here](https://github.com/vega/falcon/blob/master/data/flights-3m.csv).
+You can also click on the graph and drag your cursor around to zoom into any part of the graph you want. 
+
+![](static/zoom_in.gif)
+
+To revert the figure to its original state, click on the `Reset axes` button at the upper right corner of the figure.
+
+![](static/zoom_out.gif)
 
-Once you download the dataset, run `python csv-clean.py flights-3m.csv` to obtain the cleaned csv file `flights-3m-cleaned.csv`. Move the cleaned file to the `data` folder in any of the project folders (`webgl`, `resample` or  `combined`) you want to test. 
 
 ## Local testing