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explorer.py
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import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
# ===========================================================================
# GUARDINT color scheme
# ===========================================================================
colors = [
"#600b0c",
"#C01518",
"#ff1c1f",
"#ff5557",
"#ff8e8f",
"#ffc7c7",
"#ffe3e3",
"#efefef",
]
# ===========================================================================
# Functions to be cached
# ===========================================================================
@st.cache
def gen_px_pie(df, values, names, color_discrete_sequence=colors, **kwargs):
fig = px.pie(
df,
values=values,
names=names,
color_discrete_sequence=color_discrete_sequence,
color=kwargs.get("color", None),
color_discrete_map=kwargs.get("color_discrete_map", None),
custom_data=kwargs.get("custom_data", None),
)
# Update what is shown on the slices (on hover)
fig.update_traces(
texttemplate="<b>%{value}</b><br>%{percent}",
hovertemplate="""<b>Answer</b> %{label}
<br><br>given by <b>%{value}</b> respondents or <b>%{percent}</b>
<br>of all who answered the question
<br>given the current filter.
""",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 18, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 12)},
"orientation": kwargs.get("legend_orientation", "h"),
"bgcolor": "#efefef",
"x": -0.2,
"y": 1.1,
},
modebar={"orientation": "v"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.00,
y=0.00,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_go_pie(labels, values, marker_colors=colors, **kwargs):
fig = go.Figure(
data=[
go.Pie(
labels=labels, values=values, marker_colors=marker_colors, sort=False
)
]
)
# Update what is shown on the slices (on hover)
fig.update_traces(
texttemplate="<b>%{value}</b><br>%{percent}",
hovertemplate="""<b>Answer</b> %{label}
<br><br>given by <b>%{value}</b> respondents or <b>%{percent}</b>
<br>of all who answered the question
<br>given the current filter.<extra></extra>
""",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=kwargs.get("height", 450),
margin=dict(l=0, r=0, b=50, t=30),
font={"size": kwargs.get("font_size", 18), "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 12)},
"orientation": "v",
"bgcolor": "#efefef",
"x": kwargs.get("legend_x", -0.2),
"y": kwargs.get("legend_y", 1.1),
},
modebar={"orientation": "v"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.00,
y=0.00,
sizex=kwargs.get("image_sizex", 0.15),
sizey=kwargs.get("image_sizey", 0.15),
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_px_histogram(
df, x, y, nbins, color, labels, color_discrete_map=colors, **kwargs
):
fig = px.histogram(
df,
x=x,
y=y,
nbins=nbins,
color=color,
color_discrete_map=color_discrete_map,
labels=labels,
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": kwargs.get("font_size", 13), "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_px_box(df, x, y, points, color, labels, color_discrete_map=colors, **kwargs):
fig = px.box(
df,
x=x,
y=y,
points=points,
color=color,
labels=labels,
color_discrete_map=color_discrete_map,
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_go_bar_stack(data, **kwargs):
fig = go.Figure(data=data)
# Update layout
fig.update_layout(
barmode="stack",
autosize=False,
width=700,
height=700,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_rank_plt(input_col, options, **kwargs):
input_col_score = pd.Series(index=options)
for i in range(1, 7):
input_col_counts = df[f"{input_col}[{i}]"].value_counts()
scores = input_col_counts.multiply(scoring[i])
input_col_score = input_col_score.add(scores, fill_value=0)
input_col_score = input_col_score.sort_values(ascending=False)
if i == 1:
ranked_first = df[f"{input_col}[1]"].value_counts()
ranked_first_clean = pd.DataFrame(
{
"institution": ranked_first.index,
"No of times<br>ranked first": ranked_first.values,
}
)
input_col_df = pd.DataFrame(
{
"institution": input_col_score.index,
"score": input_col_score.values,
}
)
input_col_df = input_col_df.merge(
ranked_first_clean, on="institution", how="left"
).fillna(0)
input_col_df = input_col_df.sort_values(["score", "No of times<br>ranked first"])
fig = px.bar(
input_col_df.sort_values(by="score"),
y="institution",
x="score",
color="No of times<br>ranked first",
color_continuous_scale=[colors[5], colors[2]],
orientation="h",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_logo.png",
xref="paper",
yref="paper",
x=1.0,
y=0.0,
sizex=0.25,
sizey=0.25,
xanchor="right",
yanchor="bottom",
)
)
return fig
chart_config = {
"displaylogo": False,
"modeBarButtonsToRemove": ["hoverClosestPie"],
"toImageButtonOptions": {
"width": 700,
"height": 450,
"scale": (210 / 25.4) / (700 / 300),
},
}
def print_total(number):
st.write(f"**{number}** respondents answered the question with the current filter")
def answered_by(group):
if group == "cso":
text = "CSO professionals"
else:
text = "media professionals"
st.caption(f"This question was only answered by {text}")
# ===========================================================================
# Import data from stored pickle
# ===========================================================================
df = pd.read_pickle("data/merged.pkl")
# ===========================================================================
# General configuration
# ===========================================================================
st.set_page_config(
page_title="IOI Survey Data Explorer",
page_icon="https://raw.githubusercontent.com/snv-berlin/ioi/master/guardint_favicon.png",
)
def callback():
st.experimental_set_query_params(section=st.session_state.section)
sections = [
"Overview",
"Resources",
"Protection",
"Constraints",
"Attitudes",
]
try:
query_params = st.experimental_get_query_params()
query_section = query_params["section"][0]
if "section" not in st.session_state:
st.session_state.section = query_section
except KeyError:
st.experimental_set_query_params(section=sections[0])
query_params = st.experimental_get_query_params()
query_section = query_params["section"][0]
if "section" not in st.session_state:
st.session_state.section = query_section
selected_section = st.sidebar.radio(
"Choose section",
sections,
index=sections.index(query_section),
key="section",
on_change=callback,
)
st.caption(
"__" + selected_section + "__ | Civil Society Organisation and Media professionals"
)
filters = {
"country": st.sidebar.selectbox(
"Country", ["All", "United Kingdom", "Germany", "France"]
),
"field": st.sidebar.selectbox(
"Field", ["All", "CSO Professionals", "Media Professionals"]
),
}
filter = np.full(len(df.index), True)
for column_name, selectbox in filters.items():
if selectbox == "All":
continue
else:
filter = filter & (df[column_name] == selectbox)
###############################################################################
# Custom JS/CSS
###############################################################################
# This causes the page to scroll to top when section is changed
components.html(
f"""
<!--{st.session_state.section}-->
<script>
window.parent.document.querySelector('section.main').scrollTo(0, 0);
</script>
""",
height=0,
)
# Here, a custom font is loaded from the GitHub repo
st.markdown(
""" <style>
@font-face {
font-family: 'Roboto Mono';
font-style: normal;
font-weight: 400;
font-display: swap;
src: url(https://raw.githubusercontent.com/snv-berlin/ioi/master/roboto_mono.woff2) format('woff2');
unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
}
html, body, [class*="css"] {
font-family: 'Roboto Mono';
}
.st-bx {
font-family: 'Roboto Mono' !important;
}
.st-ae {
font-family: 'Roboto Mono' !important;
}
.css-1rh8hwn {
font-size: 0.8rem;
}
button {
border-width: 3px 3px 3px 3px !important;
border-radius: 0 !important;
}
h3 {line-height: 1.3}
footer {visibility: hidden;}
.e8zbici2 {visibility: hidden;}
.custom-footer {
display: block;
padding-top: 150px;
margin-bottom: -400px;
color: rgba(38, 39, 48, 0.4);
flex: 0 1 0%;
font-size: 0.8rem !important;
max-width: 730px;
width: 100%;
}
strong {
font-style: bold;
font-weight: 700;
color: #000;
}
code {
color: #ff1c1f;
}
a {
color: #ff1c1f !important;
}
a:hover {
color: #ff5557 !important;
}
a:visited {
color: #600b0c !important;
}
</style>
""",
unsafe_allow_html=True,
)
# ===========================================================================
# Overview
# ===========================================================================
if selected_section == "Overview":
st.write("# Overview")
# TODO Add correct links
st.write(
"""
This is the _merged_ data containing the responses to the questions
from both the Civil Society Scrutiny questionnaire and the Media
Scrutiny questionnaire. Since only some questions were asked in both
questionnaires, this _merged_ data represents a subset of all the
questions that were asked in both surveys individually.
To see all answers given by members of civil society organisations,
click [here](https://ioi.civsoc.sehn.dev). To see all answers given
by members of media organisations, click
[here](https://ioi.media.sehn.dev). Use the sidebar on the left to
choose the section of the survey that is relevant for you and use
the drop-down menus to filter by country or survey type.
"""
)
col1, col2 = st.columns(2)
col1.metric("Respondents", len(df[filter].index))
col2.metric(
"Cumulative years spent working on SBIA",
int(df[filter]["expertise1"].sum()),
)
col1, col2 = st.columns(2)
col1.metric(
"Avg. years spent working on SBIA†",
"%.1f" % df[filter]["expertise1"].mean(),
)
col2.metric(
"Avg. No. of FOI requests in the past 5 years",
int(df[filter]["foi2"].mean()),
)
col1, col2 = st.columns(2)
col1.metric(
"Media professionals",
len(df[filter & (df.field == "Media Professionals")].index),
)
col2.metric(
"Civil Society Organisation professionals",
len(df[filter & (df.field == "CSO Professionals")].index),
)
st.caption(
"""†For the calculation of the mean, only valid numerical answers were
counted. This is why the number might differ from the number one gets
when simply dividing e.g. the cumulative years spent working on SBIA
by the overall number of respondents (including those who haven't
specified their experience in years).
"""
)
st.write("## Open Data")
st.write(
"""We cleaned and anonymised the data so you can take look at it yourself.
Using the buttons below, you can download the entire dataset. If you want
to find out how this page was created, you can take a look at the code on
[Github](https://github.com/snv-berlin/ioi).
"""
)
with open("data/merged.csv", "rb") as file:
st.download_button(
label="Download data as CSV",
data=file,
file_name="GUARDINT_survey_data_merged.csv",
)
country_counts = df[filter]["country"].value_counts()
st.write("### Country `[country]`")
print_total(country_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=country_counts,
names=country_counts.index,
color=country_counts.index,
color_discrete_map={
"Germany": colors[0],
"United Kingdom": colors[2],
"France": colors[5],
},
),
use_container_width=True,
config=chart_config,
)
st.write("### Field [`field`]")
field_counts = df[filter]["field"].value_counts()
print_total(field_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=field_counts,
names=field_counts.index,
),
use_container_width=True,
config=chart_config,
)
st.write("### Gender `[gender]`")
gender_counts = df[filter]["gender"].value_counts()
print_total(gender_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=gender_counts,
names=gender_counts.index,
color=gender_counts.index,
color_discrete_map={
"Not specified": colors[0],
"Male": colors[1],
"Female": colors[2],
"Other": colors[3],
},
),
use_container_width=True,
config=chart_config,
)
# TODO Privacy notice
# ===========================================================================
# Resources
# ===========================================================================
if selected_section == "Resources":
st.write("# Resources")
st.write("## Human Resources")
st.write("### What is your employment status? `[hr1]`")
hr1_counts = df[filter]["hr1"].value_counts()
print_total(hr1_counts.sum())
st.plotly_chart(
gen_px_pie(
hr1_counts,
values=hr1_counts,
names=hr1_counts.index,
color=hr1_counts.index,
color_discrete_map={
"Full-time": colors[0],
"Part-time (>50%)": colors[1],
"Part-time (<50%)": colors[2],
"Freelance": colors[3],
"Other": colors[4],
},
),
use_container_width=True,
config=chart_config,
)
st.write(
"### How many days per month do you work on surveillance by intelligence agencies? `[hr2]`"
)
hr2_counts = df[filter]["hr2"].value_counts()
print_total(hr2_counts.sum())
st.plotly_chart(
gen_px_histogram(
df=df[filter],
x="hr2",
y=None,
nbins=None,
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"hr2": "days per month"},
),
use_container_width=True,
config=chart_config,
)
st.plotly_chart(
gen_px_box(
df=df[filter],
points="all",
x="country",
y="hr2",
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"hr2": "days per month"},
),
use_container_width=True,
config=chart_config,
)
df["hr2_more_than_five"] = np.where(df["hr2"] > 5, True, False)
df["hr2_more_than_five"] = df["hr2_more_than_five"].replace(
{True: "more than 5 days", False: "5 days or less"}
)
hr2_more_than_five_counts = df[filter]["hr2_more_than_five"].value_counts()
st.plotly_chart(
gen_px_pie(
hr2_more_than_five_counts,
values=hr2_more_than_five_counts,
names=hr2_more_than_five_counts.index,
color=hr2_more_than_five_counts.index,
),
use_container_width=True,
config=chart_config,
)
st.write("### Which type of medium do you work for? `[hr3ms]`")
hr3ms_df = pd.DataFrame(columns=("option", "count", "country"))
answered_by("media")
hr3ms_options = [
"daily_newspaper",
"weekly_newspaper",
"magazine",
"tv",
"radio",
"news_agency",
"online_stand_alone",
"online_of_offline",
]
hr3ms_options_clean = [
"Daily newspaper",
"Weekly newspaper",
"Magazine",
"TV",
"Radio",
"News agency",
"Online outlet<br>(standalone)",
"Online outlet<br>(of an offline publication)",
]
for option, option_clean in zip(hr3ms_options, hr3ms_options_clean):
hr3ms_data = df[filter]["country"][df[f"hr3ms[{option}]"] == 1].tolist()
for i in hr3ms_data:
hr3ms_df = hr3ms_df.append(
{"option": option_clean, "count": hr3ms_data.count(i), "country": i},
ignore_index=True,
)
hr3ms_df = hr3ms_df.drop_duplicates()
if filters["field"] == "CSO Professionals":
print_total(0)
else:
# If one respondent chose at least one medium it counts towards the total
hr3ms_col_list = [col for col in df[filter].columns if col.startswith("hr3ms")]
hr3ms_df_total = df[filter][hr3ms_col_list]
hr3ms_df_total["answered"] = [
"Y" if x > 0 else "N" for x in np.sum(hr3ms_df_total.values == True, 1)
]
print_total(hr3ms_df_total["answered"].value_counts().sort_index()[1])
st.plotly_chart(
gen_px_histogram(
hr3ms_df,
x="option",
y="count",
nbins=None,
color="country",
color_discrete_map={
"Germany": colors[0],
"United Kingdom": colors[2],
"France": colors[4],
},
labels={"count": "people who work<br>for this medium"},
),
use_container_width=True,
config=chart_config,
)
st.write(
"### Within the past year, did you have enough time to cover surveillance by intelligence agencies? `[hr4ms]`"
)
answered_by("media")
hr4ms_counts = df[filter]["hr4ms"].value_counts().sort_index()
st.plotly_chart(
gen_go_pie(
labels=hr4ms_counts.sort_index().index,
values=hr4ms_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
st.write("## Expertise")
st.write(
"### How many years have you spent working on surveillance by intelligence agencies? `[expertise1]`"
)
expertise1_counts = df[filter]["expertise1"].value_counts()
print_total(expertise1_counts.sum())
st.plotly_chart(
gen_px_histogram(
df[filter],
x="expertise1",
y=None,
nbins=20,
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"expertise1": "years"},
),
use_container_width=True,
config=chart_config,
)
st.plotly_chart(
gen_px_box(
df=df[filter],
points="all",
x="country",
y="expertise1",
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"expertise1": "years"},
),
use_container_width=True,
config=chart_config,
)
st.write(
"### How do you assess your level of expertise concerning the **legal** aspects of surveillance by intelligence agencies? `[expertise2]`"
)
expertise2_counts = df[filter]["expertise2"].value_counts().sort_index()
print_total(expertise2_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=expertise2_counts.sort_index().index,
values=expertise2_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
st.write(
"### How do you assess your level of expertise concerning the **political** aspects of surveillance by intelligence agencies `[expertise3]`?"
)
expertise3_counts = df[filter]["expertise3"].value_counts().sort_index()
print_total(expertise3_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=expertise3_counts.sort_index().index,
values=expertise3_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
st.write(
"### How do you assess your level of expertise concerning the **technical** aspects of surveillance by intelligence agencies? `[expertise4]`"
)
expertise4_counts = df[filter]["expertise4"].value_counts().sort_index()
print_total(expertise4_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=expertise4_counts.sort_index().index,
values=expertise4_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
st.write("## Financial Resources")
st.write(
"### How do you assess the financial resources that have been available for your work on intelligence over the past 5 years? `[finance1]`"
)
finance1_counts = df[filter]["finance1"].value_counts().sort_index()
print_total(finance1_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=finance1_counts.sort_index().index,
values=finance1_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
st.write(
"### If you wanted to conduct investigative research into surveillance by intelligence agencies, could you access extra funding for this research? (For example, a special budget or a stipend) `[finance2ms]`"
)
finance2ms_counts = df[filter]["finance2ms"].value_counts()
answered_by("media")
print_total(finance2ms_counts.sum())
st.plotly_chart(
gen_px_pie(
finance2ms_counts,
values=finance2ms_counts,
names=finance2ms_counts.index,
color=finance2ms_counts.index,
color_discrete_map={
"No": colors[0],
"Yes": colors[2],
"I don't know": colors[4],
"I prefer not to say": colors[5],
},
),
use_container_width=True,
config=chart_config,
)
st.write(
"### How important are the following funding categories for your organisation's work on intelligence-related issues? `[finance2cs]`"
)
finance2cs_options = [
"private_foundations",
"donations",
"national_public_funds",
"corporate_sponsorship",
"international_public_funds",
"other",
]
finance2cs_options_clean = [
"Private foundations",
"Donations",
"National public funds",
"Corporate sponsorships",
"International public funds",
"Other",
]
finance2cs_very_important = []
finance2cs_somewhat_important = []
finance2cs_important = []
finance2cs_slightly_important = []
finance2cs_not_important = []
finance2cs_prefer_not_to_say = []
for importance in [
"Very important",
"Somewhat important",
"Important",
"Slightly important",
"Not important at all",
"I prefer not to say",
]:
for option in finance2cs_options:
try:
count = df[filter][f"finance2cs[{option}]"].value_counts()[importance]
except KeyError:
count = 0
if importance == "Very important":
finance2cs_very_important.append(count)
elif importance == "Somewhat important":
finance2cs_somewhat_important.append(count)
elif importance == "Important":
finance2cs_important.append(count)
elif importance == "Slightly important":
finance2cs_slightly_important.append(count)
elif importance == "Not important at all":
finance2cs_not_important.append(count)
elif importance == "I prefer not to say":
finance2cs_prefer_not_to_say.append(count)
else:
continue
totals = [
df[filter][f"finance2cs[{option}]"].value_counts().sum()
for option in finance2cs_options
]
answered_by("cso")
print_total(max(totals))
st.plotly_chart(
gen_go_bar_stack(
data=[
go.Bar(
name="Very important",
x=finance2cs_options_clean,
y=finance2cs_very_important,
marker_color=colors[0],
),
go.Bar(
name="Somewhat important",
x=finance2cs_options_clean,
y=finance2cs_somewhat_important,
marker_color=colors[1],
),
go.Bar(
name="Important",
x=finance2cs_options_clean,
y=finance2cs_important,
marker_color=colors[2],
),
go.Bar(
name="Slightly important",
x=finance2cs_options_clean,
y=finance2cs_slightly_important,
marker_color=colors[3],
),