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added hr{3,4}ms to explorer
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@linozen
linozen committed Dec 7, 2021
1 parent d578301 commit 5992112
Showing 1 changed file with 112 additions and 33 deletions.
145 changes: 112 additions & 33 deletions explorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,7 +324,7 @@ def print_total(number):
st.write(f"**{number}** respondents answered the question with the current filter")


def print_answered_by(group):
def answered_by(group):
if group == "cso":
text = "CSO professionals"
else:
Expand Down Expand Up @@ -472,6 +472,10 @@ def callback():
color: #000;
}
code {
color: #ff1c1f;
}
a {
color: #ff1c1f !important;
}
Expand Down Expand Up @@ -569,7 +573,7 @@ def callback():
)

country_counts = df[filter]["country"].value_counts()
st.write("### Country")
st.write("### Country `[country]`")
print_total(country_counts.sum())
st.plotly_chart(
gen_px_pie(
Expand All @@ -587,7 +591,7 @@ def callback():
config=chart_config,
)

st.write("### Field")
st.write("### Field [`field`]")
field_counts = df[filter]["field"].value_counts()
print_total(field_counts.sum())
st.plotly_chart(
Expand All @@ -600,7 +604,7 @@ def callback():
config=chart_config,
)

st.write("### Gender")
st.write("### Gender `[gender]`")
gender_counts = df[filter]["gender"].value_counts()
print_total(gender_counts.sum())
st.plotly_chart(
Expand Down Expand Up @@ -632,7 +636,7 @@ def callback():

st.write("## Human Resources")

st.write("### What is your employment status?")
st.write("### What is your employment status? `[hr1]`")
hr1_counts = df[filter]["hr1"].value_counts()
print_total(hr1_counts.sum())
st.plotly_chart(
Expand All @@ -654,7 +658,7 @@ def callback():
)

st.write(
"### How many days per month do you work on surveillance by intelligence agencies?"
"### 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())
Expand Down Expand Up @@ -710,10 +714,85 @@ def callback():
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?"
"### 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())
Expand Down Expand Up @@ -754,7 +833,7 @@ def callback():
)

st.write(
"### How do you assess your level of expertise concerning the **legal** aspects of surveillance by intelligence agencies?"
"### 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())
Expand All @@ -768,7 +847,7 @@ def callback():
)

st.write(
"### How do you assess your level of expertise concerning the **political** aspects of surveillance by intelligence agencies?"
"### 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())
Expand All @@ -782,7 +861,7 @@ def callback():
)

st.write(
"### How do you assess your level of expertise concerning the **technical** aspects of surveillance by intelligence agencies?"
"### 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())
Expand All @@ -798,7 +877,7 @@ def callback():
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?"
"### 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())
Expand All @@ -812,11 +891,11 @@ def callback():
)

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)"
"### 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.caption("This question was only answered by media professionals")
st.plotly_chart(
gen_px_pie(
finance2ms_counts,
Expand All @@ -835,7 +914,7 @@ def callback():
)

st.write(
"### How important are the following funding categories for your organisation's work on intelligence-related issues?"
"### How important are the following funding categories for your organisation's work on intelligence-related issues? `[finance2cs]`"
)
finance2cs_options = [
"private_foundations",
Expand Down Expand Up @@ -867,9 +946,9 @@ def callback():
"Not important at all",
"I prefer not to say",
]:
for label in finance2cs_options:
for option in finance2cs_options:
try:
count = df[filter][f"finance2cs[{label}]"].value_counts()[importance]
count = df[filter][f"finance2cs[{option}]"].value_counts()[importance]
except KeyError:
count = 0
if importance == "Very important":
Expand All @@ -890,8 +969,8 @@ def callback():
df[filter][f"finance2cs[{option}]"].value_counts().sum()
for option in finance2cs_options
]
answered_by("cso")
print_total(max(totals))
print_answered_by("cso")
st.plotly_chart(
gen_go_bar_stack(
data=[
Expand Down Expand Up @@ -1028,7 +1107,7 @@ def callback():
)
foi5_df = pd.DataFrame(columns=("option", "count", "country"))
# TODO Map proper labels
for label in [
for option in [
"not_aware",
"not_covered",
"too_expensive",
Expand All @@ -1039,10 +1118,10 @@ def callback():
"dont_know",
"prefer_not_to_say",
]:
foi5_data = df[filter]["country"][df[f"foi5[{label}]"] == 1].tolist()
foi5_data = df[filter]["country"][df[f"foi5[{option}]"] == 1].tolist()
for i in foi5_data:
foi5_df = foi5_df.append(
{"option": label, "count": foi5_data.count(i), "country": i},
{"option": option, "count": foi5_data.count(i), "country": i},
ignore_index=True,
)
foi5_df = foi5_df.drop_duplicates()
Expand Down Expand Up @@ -1105,9 +1184,9 @@ def callback():
"I don't know",
"I prefer not to say",
]:
for label in ["sectraining", "e2e"]:
for option in ["sectraining", "e2e"]:
try:
count = df[filter][f"protectops1[{label}]"].value_counts()[answer]
count = df[filter][f"protectops1[{option}]"].value_counts()[answer]
except KeyError:
count = 0
if answer == "Yes":
Expand Down Expand Up @@ -1204,7 +1283,7 @@ def callback():
"Slightly important",
"Not important at all",
]:
for label in [
for option in [
"encrypted_email",
"vpn",
"tor",
Expand All @@ -1214,7 +1293,7 @@ def callback():
"other",
]:
try:
count = df[filter][f"protectops3[{label}]"].value_counts()[importance]
count = df[filter][f"protectops3[{option}]"].value_counts()[importance]
except KeyError:
count = 0
if importance == "Very important":
Expand Down Expand Up @@ -1349,9 +1428,9 @@ def callback():
protectleg3_dont_know = []
protectleg3_prefer_not_to_say = []
for answer in ["Yes", "No", "I don't know", "I prefer not to say"]:
for label in ["free_counsel", "cost_insurance", "other"]:
for option in ["free_counsel", "cost_insurance", "other"]:
try:
count = df[filter][f"protectleg3[{label}]"].value_counts()[answer]
count = df[filter][f"protectleg3[{option}]"].value_counts()[answer]
except KeyError:
count = 0
if answer == "Yes":
Expand Down Expand Up @@ -1569,9 +1648,9 @@ def callback():
constraintinter5_dont_know = []
constraintinter5_prefer_not_to_say = []
for answer in ["Yes", "No", "I don't know", "I prefer not to say"]:
for label in constraintinter5_options:
for option in constraintinter5_options:
try:
count = df[filter][f"constraintinter5[{label}]"].value_counts()[answer]
count = df[filter][f"constraintinter5[{option}]"].value_counts()[answer]
except KeyError:
count = 0
if answer == "Yes":
Expand Down Expand Up @@ -1647,9 +1726,9 @@ def callback():
constraintinter6_dont_know = []
constraintinter6_prefer_not_to_say = []
for answer in ["Yes", "No", "I don't know", "I prefer not to say"]:
for label in constraintinter6_options:
for option in constraintinter6_options:
try:
count = df[filter][f"constraintinter6[{label}]"].value_counts()[answer]
count = df[filter][f"constraintinter6[{option}]"].value_counts()[answer]
except KeyError:
count = 0
if answer == "Yes":
Expand Down Expand Up @@ -1768,11 +1847,11 @@ def callback():
"prefer_not_to_say",
]
attitude3_df = pd.DataFrame(columns=("option", "count", "country"))
for label in attitude3_options:
attitude3_data = df[filter]["country"][df[f"attitude3[{label}]"] == 1].tolist()
for option in attitude3_options:
attitude3_data = df[filter]["country"][df[f"attitude3[{option}]"] == 1].tolist()
for i in attitude3_data:
attitude3_df = attitude3_df.append(
{"option": label, "count": attitude3_data.count(i), "country": i},
{"option": option, "count": attitude3_data.count(i), "country": i},
ignore_index=True,
)
attitude3_df = attitude3_df.drop_duplicates()
Expand Down

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