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Simplify.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_home
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_2(urls, titles)
# # st.markdown("", unsafe_allow_html=False)
# nav_bar(urls, titles)
st.title("5. Simplify")
simplify_01 = """
The simpler your plot the better.
### Use the simplest and most basic plot possible to tell your story
A lot of visual clutter and plot complexity comes from not having a solid idea of the message of the plot before you start plotting, and attempting to fit too much in.
### Remove clutter whenever possible
**If your plot is confusing, try to clarify it by removing something, not adding extra labels and annotations.** Beware of chartjunk, like filled backgrounds and uneccessary grids.
"""
st.markdown(simplify_01)
with sns.axes_style("darkgrid"):
sns.set_context("notebook")
sns.set_palette("Set2")
fig_02 = sns.lmplot(data=Penguins, x="Body mass (g)", y="Flipper length (mm)", hue="Island", markers=["o", "x", "*"])
# sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1),)
st.pyplot(fig_02, use_container_width=True,)
st.divider()
st.write("Sometimes this can be as simple as removing grid lines.")
with sns.axes_style("ticks"):
sns.set_context("notebook")
sns.set_palette("Set2")
fig_02 = sns.lmplot(data=Penguins, x="Body mass (g)", y="Flipper length (mm)", hue="Island", markers=["o", "x", "*"])
# sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1),)
st.pyplot(fig_02, use_container_width=True,)
st.divider()
st.write("Sometimes, it means splitting your plot into multiple panels and making each panel more simple. Don't pick a complicated and unusual statistical plot just because it looks interesting: does it actually serve the dataset and the message or result you want to convey? Might it confuse or mislead the reader?",
"Similar questions should be asked before building interactive or 3D visualisations. Does this actually help the reader to understand the message?")
def annotate(data, **kws):
r, p = sp.stats.pearsonr(data['Body mass (g)'], data['Flipper length (mm)'])
ax = plt.gca()
ax.text(.05, .8, 'r={:.2f}, p={:.2g}'.format(r, p),
transform=ax.transAxes)
with sns.axes_style("ticks"):
sns.set_context("talk")
sns.set_palette("Set2")
fig_03 = sns.lmplot(data=Penguins, x="Body mass (g)", y="Flipper length (mm)", col="Island", )
# sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1),)
fig_03.map_dataframe(annotate)
st.pyplot(fig_03, use_container_width=True,)
# st.text("")
# st.text("")
# pink_button(url_next, "Back to home →")
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/)")