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Commit 429a7291 authored by Chandrasekhar Ramakrishnan's avatar Chandrasekhar Ramakrishnan
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Merge branch 'bootstrapping' into 'master'

bootstrapping

See merge request cramakri/covid-19-dashboard!1
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data/covid-19_jhu-csse/time_series_19-covid-Confirmed.csv filter=lfs diff=lfs merge=lfs -text
data/covid-19_jhu-csse/time_series_19-covid-Deaths.csv filter=lfs diff=lfs merge=lfs -text
data/covid-19_jhu-csse/time_series_19-covid-Recovered.csv filter=lfs diff=lfs merge=lfs -text
data/worldbank/SP.POP.TOTL.zip filter=lfs diff=lfs merge=lfs -text
data/covid-19_rates/** filter=lfs diff=lfs merge=lfs -text
data/covid-19_rates/ts_rates_19-covid-deaths.csv filter=lfs diff=lfs merge=lfs -text
data/covid-19_rates/ts_rates_19-covid-recovered.csv filter=lfs diff=lfs merge=lfs -text
data/covid-19_rates/ts_rates_19-covid-confirmed.csv filter=lfs diff=lfs merge=lfs -text
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updated: '2020-03-11T21:43:12.736000+00:00'
version: '1'
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../../datasets/e9212b58-4994-482b-801c-ed90a960bd04/metadata.yml
\ No newline at end of file
../../datasets/f6726a5b-f973-45d5-b873-30fa0dff772f/metadata.yml
\ No newline at end of file
../../datasets/720e46f5-0f38-48be-86ef-1fcbae258a6f/metadata.yml
\ No newline at end of file
[renku "interactive"] [renku "interactive"]
default_url = /lab default_url = /lab
lfs_auto_fetch = true
arguments: []
baseCommand:
- papermill
class: CommandLineTool
cwlVersion: v1.0
hints: []
inputs:
input_1:
default: ts_folder
inputBinding:
position: 1
prefix: -p
separate: true
shellQuote: true
streamable: false
type: string
input_2:
default:
class: Directory
listing: []
path: ../../data/covid-19_jhu-csse
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separate: true
shellQuote: true
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path: ../../data/worldbank/SP.POP.TOTL.zip
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shellQuote: true
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shellQuote: true
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streamable: false
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class: File
path: ../../notebooks/ToRates.ipynb
inputBinding:
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prefix: --inject-paths
separate: true
shellQuote: true
streamable: false
type: File
input_8:
default: runs/ToRates.run.ipynb
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separate: true
shellQuote: true
streamable: false
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outputs:
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glob: $(inputs.input_8)
streamable: false
type: File
output_1:
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glob: $(inputs.input_6)
streamable: false
type: Directory
permanentFailCodes: []
requirements:
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- class: InitialWorkDirRequirement
listing:
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entryname: runs
writable: true
- entry: '$({"listing": [], "class": "Directory"})'
entryname: data/covid-19_rates
writable: true
- entry: $(inputs.input_2)
entryname: data/covid-19_jhu-csse
writable: false
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entryname: data/worldbank/SP.POP.TOTL.zip
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entryname: notebooks/ToRates.ipynb
writable: false
successCodes: []
temporaryFailCodes: []
arguments: []
baseCommand:
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cwlVersion: v1.0
hints: []
inputs:
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default: ts_folder
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inputBinding:
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separate: true
shellQuote: true
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input_3:
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inputBinding:
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separate: true
shellQuote: true
streamable: false
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input_4:
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listing: []
path: ../../data/covid-19_rates
inputBinding:
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shellQuote: true
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type: File
input_6:
default: runs/Dashboard.run.ipynb
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separate: true
shellQuote: true
streamable: false
type: string
outputs:
output_0:
outputBinding:
glob: $(inputs.input_6)
streamable: false
type: File
permanentFailCodes: []
requirements:
- class: InlineJavascriptRequirement
- class: InitialWorkDirRequirement
listing:
- entry: '$({"listing": [], "class": "Directory"})'
entryname: runs
writable: true
- entry: $(inputs.input_2)
entryname: data/covid-19_jhu-csse
writable: false
- entry: $(inputs.input_4)
entryname: data/covid-19_rates
writable: false
- entry: $(inputs.input_5)
entryname: notebooks/Dashboard.ipynb
writable: false
successCodes: []
temporaryFailCodes: []
# covid-19-dashboard # covid-19-dashboard
This is a Renku project - basically a git repository with some A Renku project for exploring Covid-19 data sources.
bells and whistles. You'll find we have already created some
useful things like `data` and `notebooks` directories and
a `Dockerfile`.
## Working with the project # Data Sources
The simplest way to start your project is right from the Renku ## Covid-19
platform - just click on the `Environments` tab and start a new session.
This will start an interactive environment right in your browser.
To work with the project anywhere outside the Renku platform, - https://github.com/CSSEGISandData/COVID-19
click the `Settings` tab where you will find the - https://github.com/COVID19Tracking/covid-19-crawler
git repo URLs - use `git` to clone the project on whichever machine you want. - https://github.com/openZH/covid_19
### Changing interactive environment dependencies ## General
Initially we install a very minimal set of packages to keep the images small. - https://data.worldbank.org/indicator/SP.POP.TOTL
However, you can add python and conda packages in `requirements.txt` and
`environment.yml` to your heart's content. If you need more fine-grained
control over your environment, please see [the documentation](https://renku.readthedocs.io/en/latest/user/advanced_interfaces.html#dockerfile-modifications).
## Project configuration
Project options can be found in `.renku/renku.ini`. In this
project there is currently only one option, which specifies
the default type of environment to open, in this case `/lab` for
JupyterLab. You may also choose `/tree` to get to the "classic" Jupyter
interface.
## Moving forward
Once you feel at home with your project, we recommend that you replace
this README file with your own project documentation! Happy data wrangling!
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File added
%% Cell type:code id: tags:
``` python
import pandas as pd
import os
from IPython.display import display, HTML, Markdown
```
%% Cell type:code id: tags:parameters
``` python
ts_folder = "../data/covid-19_jhu-csse/"
rates_folder = "../data/covid-19_rates/"
out_folder = None
PAPERMILL_OUTPUT_PATH = None
```
%% Cell type:code id: tags:
``` python
# Read in the data
```
%% Cell type:code id: tags:
``` python
def read_jhu_covid_df(name):
filename = os.path.join(ts_folder, f"time_series_19-covid-{name}.csv")
df = pd.read_csv(filename)
df = df.set_index(['Province/State', 'Country/Region', 'Lat', 'Long'])
df.columns = pd.to_datetime(df.columns)
return df
jhu_frames_map = {
"confirmed": read_jhu_covid_df("Confirmed"),
"deaths": read_jhu_covid_df("Deaths"),
"recovered": read_jhu_covid_df("Recovered")
}
```
%% Cell type:code id: tags:
``` python
def read_rates_covid_df(name):
filename = os.path.join(rates_folder, f"ts_rates_19-covid-{name}.csv")
df = pd.read_csv(filename).drop("Unnamed: 0", axis=1)
df = df.set_index(['Country/Region'])
df.columns = pd.to_datetime(df.columns)
return df
rates_frames_map = {
"confirmed": read_rates_covid_df("confirmed"),
"deaths": read_rates_covid_df("deaths"),
"recovered": read_rates_covid_df("recovered")
}
```
%% Cell type:code id: tags:
``` python
# Compile data needed for the visualizations
```
%% Cell type:code id: tags:
``` python
# Compute geospatial coordinates
country_coords_df = jhu_frames_map['confirmed'].reset_index([2,3])[['Lat', 'Long']]
country_coords_df = country_coords_df.groupby(level='Country/Region').mean()
```
%% Cell type:code id: tags:
``` python
# Identify countries with 100 or more cases
case_count_ser = jhu_frames_map['confirmed'].iloc[:,-1].groupby(level='Country/Region').sum()
countries_over_thresh = case_count_ser[case_count_ser > 99].index
```
%% Cell type:markdown id: tags:
# Questions About COVID-19 and Its Spread
These plots should be taken with a large grain of salt. I am not an epidemiologist, so the analyses shown here are completely naive. There are large discrepencies in the data from different countries for a variety of reasons (rates of testing, demographics, etc.) so that make direct comparisons inaccurate. Nonetheless, I think there is a lot of interesting information in this data.
%% Cell type:code id: tags:
``` python
data_ts = jhu_frames_map['confirmed'].iloc[:,-1].name.strftime("%b %d %Y")
display(HTML(f"<em>Data up to {data_ts}</em>"))
```
%% Cell type:markdown id: tags:
## How are cases per 100,000 distributed geographically?
%% Cell type:code id: tags:
``` python
import altair as alt
from vega_datasets import data
```
%% Cell type:code id: tags:
``` python
# Compile the basic df
map_df = pd.concat([
rates_frames_map['confirmed'].iloc[:,-1],
rates_frames_map['deaths'].iloc[:,-1],
rates_frames_map['recovered'].iloc[:,-1],
country_coords_df], axis=1)
# Restrict to countries with 100 or more cases
map_df = map_df.loc[countries_over_thresh].dropna()
map_df = map_df.reset_index()
map_df.columns = ['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Lat', 'Long']
```
%% Cell type:code id: tags:
``` python
def map_of_variable(map_df, variable):
# Data generators for the background
sphere = alt.sphere()
graticule = alt.graticule()
# Source of land data
source = alt.topo_feature(data.world_110m.url, 'countries')
# Layering and configuring the components
p = alt.layer(
alt.Chart(sphere).mark_geoshape(fill='#cae6ef'),
alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
alt.Chart(source).mark_geoshape(fill='#dddddd', stroke='#aaaaaa'),
alt.Chart(map_df).mark_circle(opacity=0.6).encode(
longitude='Long:Q',
latitude='Lat:Q',
size=alt.Size(f'{variable}:Q', title="Cases"),
color=alt.value('steelblue'),
tooltip=["Country/Region:N", "Confirmed:Q", "Deaths:Q", "Recovered:Q"]
)
).project(
'naturalEarth1'
).properties(width=600, height=400, title=f"{variable} cases per 100,000"
).configure_view(stroke=None)
return p
```
%% Cell type:code id: tags:
``` python
display(map_of_variable(map_df, 'Confirmed'))
display(HTML('''
<p style="font-size: smaller">Data Source:
<a href="https://github.com/CSSEGISandData/COVID-19">JHU CSSE</a> and
<a href="https://data.worldbank.org/indicator/SP.POP.TOTL">World Bank</a>
</p>'''))
```
%% Cell type:code id: tags:
``` python
bars = alt.Chart(map_df).mark_bar().encode(
x='Confirmed:Q',
y=alt.Y("Country/Region:N", sort='-x')
)
text = bars.mark_text(
align='left',
baseline='middle',
dx=3 # Nudges text to right so it doesn't appear on top of the bar
).encode(
text=alt.Text('Confirmed:Q', format=".3")
)
(bars + text).properties(height=900)
```
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