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Story.py
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import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import numpy as np
import pandas as pd
from matplotlib import colormaps
import matplotlib as mpl
import requests
import scipy as sp
import urllib.request
from PIL import Image
# from bokeh.plotting import figure
#from bokeh.transform import linear_cmap, factor_cmap
#from bokeh.palettes import Bokeh
# page urls
url_home = "https://data-vis-101.streamlit.app/"
url_audience = "https://key-data-vis-requirements-audience.streamlit.app/"
url_story = "https://key-data-vis-requirements-story.streamlit.app/"
url_encoding = "https://key-data-vis-requirements-encoding.streamlit.app/"
url_composition = "https://key-data-vis-requirements-composition.streamlit.app/"
url_simplify = "https://key-data-vis-requirements-simplify.streamlit.app/"
# next_page_url
url_next = url_encoding
urls = [url_home, url_audience, url_story, url_encoding, url_composition, url_simplify]
home_title = " Home"
audience_title = "1. Audience"
story_title = "2. Story"
encoding_title = "3. Encoding"
composition_title = "4. Composition"
simplify_title = "5. Simplify"
titles = [home_title, audience_title , story_title, encoding_title, composition_title, simplify_title]
def click_button(url, text):
st.markdown(f'<a style="word-wrap:break-word;color:#ea388f;font-size:20px;font-style:bold; width: fit-content; border-style: none;padding:6px;text-decoration: none;border-width:2px;" href={url} target="_self">{text}</a>', unsafe_allow_html=True)
def pink_button(url, text):
st.markdown(f'<a style="word-wrap:break-word;color:#ea388f;font-size:20px;font-style:bold; width: fit-content; border-style: solid;border-radius:10px;padding:6px;text-decoration: none;border-width:2px;text-align: center;" href={url} target="_self">{text}</a>', unsafe_allow_html=True)
def nav_bar(urls, titles):
with st.popover("Table of contents", use_container_width=True):
click_button(urls[0], titles[0])
click_button(urls[1], titles[1])
click_button(urls[2], titles[2])
click_button(urls[3], titles[3])
click_button(urls[4], titles[4])
click_button(urls[5], titles[5])
def next_page(url_next):
pink_button(url_next, "Next section →")
@st.cache_data
def load_image_from_github(url):
img = np.array(Image.open(requests.get(url, stream=True).raw))
return img
my_pall ={'orange': '#e66101',
'pink': '#CA054D',
'blue': '#1B98E0',
'green':'#A4D4B4',
'purple': '#5e3c99', }
# data analysis
penguins = sns.load_dataset("penguins").dropna()
Penguins = penguins.rename(columns={"species": "Species",
"island": "Island",
"bill_length_mm": "Bill length (mm)",
"bill_depth_mm": "Bill depth (mm)",
"flipper_length_mm": "Flipper length (mm)",
"body_mass_g": "Body mass (g)",
"sex":"Sex"
})
# colour maps
# peng_cmap = factor_cmap(penguins, palette=Bokeh, factors=sorted(penguins.species.unique()))
# building data for ternary plot
locat = ["Locality #01B-32", "Locality #01B-34", "Locality #02C-01"]
local_no = np.random.choice(locat, 200)
local_var = np.zeros(200)
local_var [local_no == "Locality #01B-32"] = 1.3
local_var [local_no == "Locality #01B-34"] = -1.2
Qm_raw = 3.5 * np.random.randn(200) + 5
Qm_raw = Qm_raw + local_var
Qm_raw [Qm_raw <0] = 1.3
F_raw = 1.5 * np.random.randn(200) + 3
F_raw [F_raw <0] = 0.5
Lt_raw = 0.7 * np.random.randn(200) + 2
Lt_raw = Lt_raw - local_var
Lt_raw[Lt_raw <0] = 0.1
total_val = Qm_raw + F_raw + Lt_raw
norm_factor = 100/total_val
Qm = Qm_raw * norm_factor
F = F_raw * norm_factor
Lt = Lt_raw * norm_factor
geo_data = {"Qm":Qm,
"F":F,
"Lt":Lt,
"Locality Number": local_no}
geo_df = pd.DataFrame.from_dict(geo_data)
st.title('5 Concepts for Data Visualisation')
intro_text = """
This website introduces five key concepts to help you build better research data visualisations.
These are not the *only* rules, or the *only* way to approach building a robust and reproducible
graphical research output, but should help you to create visualisations in a methodical way.
"""
st.write(intro_text)
col1, col2 = st.columns(2)
pub_refs = """
# Sources and inspiration
A number of fantastic articles went into this resource. Please read these works: the content shared here is a much-abridged, simplified, shortened version of the content shared in the articles below.
#### Bibliography
Berinato, Scott. 2016. “Visualizations That Really Work.” Harvard Business Review, June 1, 2016. https://hbr.org/2016/06/visualizations-that-really-work.
Franconeri, Steven L., Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, and Jessica Hullman. 2021. “The Science of Visual Data Communication: What Works.” Psychological Science in the Public Interest: A Journal of the American Psychological Society 22 (3): 110–61.
Kelleher, Christa, and Thorsten Wagener. 2011. “Ten Guidelines for Effective Data Visualization in Scientific Publications.” Environmental Modelling & Software: With Environment Data News 26 (6): 822–27.
Midway, Stephen R. 2020. “Principles of Effective Data Visualization.” Patterns (New York, N.Y.) 1 (9): 100141.
Rougier, Nicolas P., Michael Droettboom, and Philip E. Bourne. 2014. “Ten Simple Rules for Better Figures.” PLoS Computational Biology 10 (9): e1003833.
#### Other resources
- [Berkeley Library Data Visualisation library guide](https://guides.lib.berkeley.edu/data-visualization/about)
- [Tableau documentation: Visual Best Practices](https://help.tableau.com/current/blueprint/en-us/bp_visual_best_practices.htm)
- [from Data to Viz project](https://www.data-to-viz.com/)
- [Principles of Data Visualization workshop notes](https://ucdavisdatalab.github.io/workshop_data_viz_principles/)
- [What’s visual ‘encoding’ in data viz, and why is it important?](https://medium.com/@sophiewarnes/whats-visual-encoding-in-data-viz-and-why-is-it-important-7406bc88b4b4#:~:text=Encoding%20in%20data%20viz%20basically,trying%20to%20say%20or%20show.)
- [University of Utah visualization design lab](https://vdl.sci.utah.edu/)
"""
with col1:
with st.popover("Publications and references behind this tool", use_container_width=True):
st.markdown(pub_refs)
package_references = """
#### matplotlib
J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.
#### seaborn
Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021.
#### Plotly
Plotly Technologies Inc. Collaborative data science. Montréal, QC, 2015. https://plot.ly.
#### Streamlit
Streamlit documentation, https://docs.streamlit.io/
#### NumPy
Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2.
### pandas
Data structures for statistical computing in python, McKinney, Proceedings of the 9th Python in Science Conference, Volume 445, 2010.
"""
with col2:
with st.popover("Python packages and datasets used", use_container_width=True):
st.header("Python packages")
st.markdown(package_references)
st.header("Datasets")
st.write("We use the `penguins` example dataset from the seaborn library. This can be browsed below:")
st.dataframe(penguins)
st.write("We also generated some random geological data to build a QFL ternary plot. This will regenerate when you reload the page, but will look something similar to this:")
st.dataframe(geo_df)
# nav_bar(urls, titles)
st.title("2. Story")
story_01 = """
A figure can express an idea quickly, succinctly, and straightforwardly. However, it is important to know exactly what the message of the figure is, to avoid creating either pointless plots that don’t really tell much, or overly complex plots that try to tell far too much.
#### Diagram first
Try sketching out some ideas with pen and paper to explore how you might want to present the data. Exploratory data vis (discussed in the previous slide) is also useful for this.
- What's the result you want to showcase? Can you explain it in a single sentence?
- Do you need to show the **broad trends** or the **fine-grained details** to tell the story?
- These should usually be separated; if both are relevant, split them into two separate plots
- What is the **simplest graph possible** that will communicate the meaning?
#### Collaborate and share
Share your plots from an early stage (perhaps even at the sketching-with-pen-and-paper stage) to see if your peers can quickly grasp the message you are hoping to convey.
>You know your data and your research very well; the patterns you are trying to highlight might be very obvious to you when you look at your figure, but completely hidden to someone looking at it for the first time.
#### What sort of plots are even out there?
When trying to figure out what kind of plot wll best help to visualise your message, it's useful to remind yourself of what kind of plots even exist!
"""
plot_inspiration = """
#### Plotting library galleries and showcases
A great place to find out whats available and what you can achieve is to scroll through some of the well-curated example galleries and tutorials published alongside many of the popular Python plotting libraries.
Some notable examples include:
- [Example gallery: seaborn](https://seaborn.pydata.org/examples/index.html)
- [Plotly documentation](https://plotly.com/python/)
- [Bokeh gallery: Topic guide](https://docs.bokeh.org/en/latest/docs/gallery.html)
There are also some larger-scale collected galleries that include multiple libraries:
- [The Python Graph Gallery](https://python-graph-gallery.com/)
- This gallery breaks up plots from a range of different libraries into topics such as distribution, correlation, ranking, evolution, and mapping
#### Articles on data visualisation
While not specifically sharing Python libraries, general articles discussing great data visualisation can be a useful place to discover new kinds of plots and to inspire yourself; these sorts of posts can often be found on [company blogs](https://visme.co/blog/best-data-visualizations/)
#### Research articles
When keeping up-to-date on research in your field, make sure to save any impressive figures that you want to draw inspiration from at a later point!
Some of the high-budget, flashier journals often have impressive graphics in their featured articles (for example, [Nature](https://www.nature.com/subjects/astronomy-and-astrophysics)). Look outside of your research area to discover new ways of plotting.
"""
types_of_plot = """
- [From Data to Viz](https://www.data-to-viz.com/): a flowchart to help you find the most suitable plot for your data
- [The Data Visualisation Catalogue](https://datavizcatalogue.com/): a non-code-based library of different information visualisation types
- [Tableau documentation: Chart choice](https://help.tableau.com/current/blueprint/en-us/bp_visual_best_practices.htm#chart-choice)
"""
st.markdown(story_01)
with st.expander("Basic plot types"):
st.markdown(types_of_plot)
with st.expander("Finding plot inspiration"):
st.markdown(plot_inspiration)
# st.text("")
# st.text("")
# pink_button(url_next, "Next section →")
st.text("")
st.text("")
st.divider()
st.write(r"$\textsf{\scriptsize Authored by Dr Maeve Murphy Quinlan, University of Leeds Research Computing Team © Copyright 2024}$")
st.write("[App home page](https://data-vis-101.streamlit.app/) | [Research Computing Team](https://arc.leeds.ac.uk/about/team/) | [Research Computing Website](https://arc.leeds.ac.uk/)")