Table of Contents
Pedagogical Material
As part of the Hacking Health Covid-19, the SKEMA Global Lab in AI provided to SKEMA’ students a fully developped data science environment to realize their project. See [here].
For this specific module, this team used these following courses:
- Module 1: Data Visualizations
- Module 2: Data Warehouse
- Module 11: Covid-19 and Public Policies
Project Presentation
As we all know, Italy was one of the most affected countries at the beginning of the Covid-19 epidemic. Through covid19italy R package, we analyzed the distribution of cases in Italy on a daily basis and per region.
Technical Process
Dashboard 1: Evolution of Italy Covid-19
---
title: "Covid 19 daily "
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
output: html_document
---
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
<div class="layout-chunk" data-layout="l-body">
```r
plot_ly(data = italy_total,
x = ~ date,
y = ~home_confinement,
name = 'Home Confinement',
fillcolor = '#FDBBBC',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ hospitalized_with_symptoms,
name = "Hospitalized with Symptoms",
fillcolor = '#E41317') %>%
add_trace(y = ~intensive_care,
name = 'Intensive Care',
fillcolor = '#9E0003') %>%
layout(title = "Italy - Distribution of Active Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
</div>
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
<div class="layout-chunk" data-layout="l-body">
```r
plot_ly(data = italy_total,
x = ~ date,
y = ~ cumulative_cases,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ death,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Italy - Distribution of Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
</div>
### Chart C
<div class="layout-chunk" data-layout="l-body">
```r
italy_region %>%
filter(date == max(date)) %>%
plot_ly(labels = ~region_name, values = ~ cumulative_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Italy - Cases Distribution by Region")
```
</div>
\newpage
Row
-----------------------------------------------------------------------
### Total Deaths
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(max(italy_total$death),
icon = "fa-skull")
```
</div>
### Cumulative Cases
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(max(italy_total$cumulative_cases),
icon = "fa-virus-slash")
```
</div>
### Total Tests
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(max(italy_total$total_tests),
icon = "fa-briefcase-medical")
```
</div>
Dashboard 2: Italy Covid-19 Daily
---
title: "Covid 19 Italy"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
<div class="layout-chunk" data-layout="l-body">
```r
plot_ly(data = italy_total2,
x = ~ date,
y = ~ log(diff_total_test),
name = 'Daily Tests',
color = '#1f77b4',
type = 'scatter',
mode = '‘lines') %>%
add_trace( y = ~ log(diff_death),
name = "Daily Deaths",
color = '#E41317') %>%
add_trace(y = ~ log(diff_cumulative_positive_cases),
name = 'Daily Positive Cases',
color = 'forestgreen') %>%
layout(title = "Italy - Distribution of Covid19 Cases daily",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases per day on a log scale"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
</div>
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
<div class="layout-chunk" data-layout="l-body">
```r
plot_ly(data = italy_total2,
x = ~ date,
y = ~ diff_cumulative_cases,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ diff_death,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~ diff_recovered,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Italy - Distribution of Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
</div>
### Chart C
<div class="layout-chunk" data-layout="l-body">
```r
italy_region %>%
filter(date == max(date)) %>%
plot_ly(labels = ~region_name, values = ~daily_positive_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "New Cases Distribution by Region")
```
</div>
\newpage
Row
-----------------------------------------------------------------------
### New Deaths
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(tail(italy_total2$diff_death, n = 1),
icon = "fa-skull")
```
</div>
### New Cases
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(tail(italy_total2$diff_cumulative_cases, n = 1),
icon = "fa-virus-slash")
```
</div>
### New Tests
<div class="layout-chunk" data-layout="l-body">
```r
valueBox(tail(italy_total2$diff_total_test, n = 1),
icon = "fa-briefcase-medical")
```
</div>
Row
-----------------------------------------------------------------------
### New Deaths
<div class="layout-chunk" data-layout="l-body">
```r
gauge(tail(italy_total2$diff_death, n = 1), min = 0, max = max(italy_total2$diff_death),
symbol = ' ', gaugeSectors( success = c(0, 300), warning = c(301, 600),
danger = c(601, max(italy_total2$diff_death))
))
```
</div>
### New Cases
<div class="layout-chunk" data-layout="l-body">
```r
gauge(tail(italy_total2$diff_cumulative_cases, n = 1), min = 0,
max = max(italy_total2$diff_cumulative_cases), symbol = ' ', gaugeSectors( success = c(0, 1000),
warning = c(1001, 4000), danger = c(4001, max(italy_total2$diff_cumulative_positive_cases))
))
```
</div>
### New Tests
<div class="layout-chunk" data-layout="l-body">
```r
gauge(tail(italy_total2$diff_total_test, n = 1), min = 0,
max = max(italy_total2$diff_total_test), symbol = ' ', gaugeSectors(success = c(40001,
max(italy_total2$diff_total_test) ), warning = c(20001, 40000), danger = c(0, 20000))
)
```
</div>
Dashboard 3: Region Distribution of Covid 19 Cases in Italy
---
title: "Region Distribution of Covid 19 Cases Italy"
output: flexdashboard::flex_dashboard
---
Column
-------------------------------------
### Lombardia
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Lombardia") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Lombardia - Cases Distribution by Province")
```
</div>
Column {.tabset}
-------------------------------------
### Emilia-Romagna
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Emilia-Romagna") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Emilia-Romagna - Cases Distribution by Province")
```
</div>
### Piemonte
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Piemonte") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Piemonte - Cases Distribution by Province")
```
</div>
### Abruzzo
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Abruzzo") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Abruzzo - Cases Distribution by Province")
```
</div>
### Basilicata
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Basilicata") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Basilicata - Cases Distribution by Province")
```
</div>
### Calabria
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Calabria") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Calabria - Cases Distribution by Province")
```
</div>
### Campania
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Campania") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Campania - Cases Distribution by Province")
```
</div>
### Friuli Venezia Giulia
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Friuli Venezia Giulia") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Friuli Venezia Giulia - Cases Distribution by Province")
```
</div>
### Lazio
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Lazio") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Lazio - Cases Distribution by Province")
```
</div>
### Liguria
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Liguria") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Liguria - Cases Distribution by Province")
```
</div>
### Marche
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Marche") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Marche - Cases Distribution by Province")
```
</div>
### Molise
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Molise") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Molise - Cases Distribution by Province")
```
</div>
### Puglia
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Puglia") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Puglia - Cases Distribution by Province")
```
</div>
### Sardegna
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Sardegna") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Sardegna - Cases Distribution by Province")
```
</div>
### Sicilia
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Sicilia") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Sicilia - Cases Distribution by Province")
```
</div>
### Toscana
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Toscana") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Toscana - Cases Distribution by Province")
```
</div>
### Umbria
<div class="layout-chunk" data-layout="l-body">
```r
italy_province %>%
filter(date == max(date), region_name == "Umbria") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Umbria - Cases Distribution by Province")
```
</div>
To go further with our pedagogical platform
- Our Coding School
- Our Potential Modules
- Module 1: Data Visualizations
- Module 2: Data Warehouse
- Module 3: News Collection and Analysis
- Module 4: Predictive Modelling
- Module 5: Social Media Collection and Analysis
- Module 6: Mapping
- Module 7: Bibliometrics
- Module 8: Topic Modelling
- Module 9: Covid-19 and International Flows
- Module 10: Covid-19 and Finance
- Module 11: Covid-19 and Public Policies
- Module 12: Covid-19 and Ethics
- Our Databases and APIs