├── rsconnect
    └── shinyapps.io
    │   └── achab94
    │       └── covid-19.dcf
├── google-analytics.html
├── Info.md
├── README.md
├── LICENSE
└── app.R


/rsconnect/shinyapps.io/achab94/covid-19.dcf:
--------------------------------------------------------------------------------
 1 | name: covid-19
 2 | title:
 3 | username:
 4 | account: achab94
 5 | server: shinyapps.io
 6 | hostUrl: https://api.shinyapps.io/v1
 7 | appId: 1935865
 8 | bundleId: 2887166
 9 | url: https://achab94.shinyapps.io/covid-19/
10 | when: 1584377065.26126
11 | 


--------------------------------------------------------------------------------
/google-analytics.html:
--------------------------------------------------------------------------------
 1 | <!-- Global site tag (gtag.js) - Google Analytics -->
 2 | <script async src="https://www.googletagmanager.com/gtag/js?id=UA-160644855-1"></script>
 3 | <script>
 4 |   window.dataLayer = window.dataLayer || [];
 5 |   function gtag(){dataLayer.push(arguments);}
 6 |   gtag('js', new Date());
 7 | 
 8 |   gtag('config', 'UA-160644855-1');
 9 | </script>
10 | 


--------------------------------------------------------------------------------
/Info.md:
--------------------------------------------------------------------------------
 1 | # Credits and informations
 2 | 
 3 | ---
 4 | 
 5 | ##### Author
 6 | 
 7 | This platform was created by Emanuele Degani [(web)](https://achab94.github.io), PhD student at the Department of Statistical Sciences of the University of Padua. Suggestions, criticisms and reports of any bugs are strongly suggested at **degani [at] stat [dot] unipd [dot] it**.
 8 | 
 9 | A big thanks to Emanuele Aliverti, Pietro Belloni, Laura D'Angelo, Nicola Pilia, Stefano Corona, Francesco Mancone for having contributed with valuable suggestions and criticisms.
10 | 
11 | **Update (22nd March 2020)** : Giorgio Sestili, manager of the Facebook page [Coronavirus - Dati e Analisi Scientifiche](https://www.facebook.com/DatiAnalisiCoronavirus), interviewed me and we exchanged some words about this portal, on how it works and how it was born. I suggest you to follow their Facebook page to stay updated on scientifically reliable content on the evolution of the phenomenon. Sadly, that page and the following interview are both in italian.
12 | 
13 | [![](http://img.youtube.com/vi/4lCxc4fH8Ws/0.jpg)](http://www.youtube.com/watch?v=4lCxc4fH8Ws "")
14 | 
15 | **Update (16th March 2020)** : in the last few hours I have recorded a high number of accesses which has compromised the usability of the portal, given the limited computational resources made available by the free rate of the server. I therefore found myself forced to rent more computational resources to the provider (ShinyApps), for a monthly cost of 35 euros. I decided to ask a free offer to anyone wishing to contribute to the expenses, according to their availability, through a [MoneyBox Paypal](https://paypal.me/pools/c/8nqxplRvVc) which would automatically close as soon as the amount necessary would have been achieved (to avoid receiving further unnecessary offers). In less than two hours the total amount was reached: this was possible thanks to the donations of Guido Milano, Gianpaolo Natale, Franco Bocci, Enrico Bonan, Fabio Comini, Stefano De Lazzari. Thank you so much.
16 | 
17 | ---
18 | 
19 | ##### Data source
20 | 
21 | The platform automatically takes the data, whenever a user logs in, from the latest updated version of the database [(link GitHub)](https://github.com/pcm-dpc/COVID-19) made available by the Civil Protection daily around at 18:30pm, immediately after the press conference of the Head of the Department of Civil Protection Angelo Borrelli. It is advisable to consult [this link](https://github.com/pcm-dpc/COVID-19/blob/master/README.md) for any information on the data available and on the data-entry (this is because often some data every day are missing, or arrive late).
22 | 
23 | ---
24 | 
25 | ##### How it was made
26 | 
27 | The platform was created entirely in **R** language, using the features of the Shiny library [(website)](https://shiny.rstudio.com). Other libraries used are `ggplot2` and` ggiraph` (for interactive data-visualization), `dplyr` (for data-processing) and` shinyWidgets` (for some of the widgets included).
28 | The platform lies online on the computing space made available by ShinyApps [(website)](https://www.shinyapps.io).
29 | 
30 | The whole code used to make this portal is available on [this Github repository](https://github.com/Achab94/monitoRing-COVID19).
31 | 
32 | ---
33 | 
34 | ##### License to use
35 | 
36 | ![GitHub license](https://img.shields.io/badge/License-Creative%20Commons%20Attribution%204.0%20International-blue)
37 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # monitoRing-COVID19
 2 | Portale di monitoraggio dell'evoluzione dell'epidemia COVID-19 in Italia.
 3 | 
 4 | ## Descrizione 
 5 | 
 6 | La presente directory fa riferimento al portale [https://achab94.shinyapps.io/covid-19/](https://achab94.shinyapps.io/covid-19/). 
 7 | 
 8 | Il suo scopo è quello di monitorare l'andamento nazionale dell'[epidemia COVID-19](http://www.salute.gov.it/nuovocoronavirus) sul territorio nazionale italiano, elaborando i dati pubblicamente disponibili e quotidianamente aggiornati dalla Protezione Civile. Si prega di consultare il [sito della Protezione Civile](http://www.protezionecivile.it/attivita-rischi/rischio-sanitario/emergenze/coronavirus) per qualunque informazione circa i rischi e le misure di protezione legate all'espanzione dell'epidemia, e la [repository della Protezione Civile](https://github.com/pcm-dpc/COVID-19) per avere maggiori dettagli sui dati a disposizione online.
 9 | 
10 | La realizzazione è stata motivata dalle seguenti ragioni:
11 | - Disporre di uno strumento **facilmente utilizzabile**, **gratuito** ed **usufruibile a tutti** che consentisse di elaborare la grande quantità di dati resa disponibile quotidianamente dalla Protezione Civile;
12 | - Permettere all'utente di elaborare le proprie analisi in modo **interattivo**, a seconda delle proprie esigenze e del proprio interesse;
13 | - Fornire grafici e statistiche sull'andamento temporale di molteplici variabili di interesse, non solo a livello nazionale ma anche sulle dimensioni territoriali **regionali** e **provinciali**. La mia percezione è che queste siano decisamente più interessanti e significative di quelle osservate a livello nazionale, quotidianamente commentate da giornali e fonti di informazioni, che risultano dalla mera aggregazione di evoluzioni locali, con caratteristiche spesso molto eterogenee l'una con l'altrao.
14 | 
15 | ## Caratteristiche e funzionalità
16 | 
17 | Il portale, nella sua versione attuale pubblicamente rilasciata in data 14 Marzo 2020, è strutturato nelle seguenti schede:
18 | - *Evoluzione nazionale*: è la schermata principale, che svolge la funzione di *cruscotto informativo* sull'evoluzione nazionale del fenomeno. 
19 | - *Evoluzione regionale*: questa scheramata permette di approfondire l'evoluzione del fenomeno nelle varie regioni d'Italia. Il pannello di sinistra consente l'interazione con l'utente, che può scegliere le regioni che intende studiare/confrontare (fino ad un massimo di 4, per ragioni di resa grafica), quale variabile d'interesse studiare, se visualizzarne i dati cumulati o quelli giornalieri, e su quale data esibire uno dei grafici mostrati a destra. La parte di destra esibisce un confronto delle variazioni relative quotidiane (da un giorno all'altro) su alcune variabili d'interesse, confrontate sulle regioni scelte (con un'interpretazione simile a quella del grafico fornito per l'Evoluzione Nazionale); il grafico con la suddivisione in un determinato giorno dei trattati positivi al COVID-19, confrontato sulle regioni scelte; il grafico con l'evoluzione temporale del fenomeno d'interesse, confrontato sulle regioni scelte.
20 | - *Evoluzione provinciale*: siccome l'unico dato disponibile sulla dimensione provinciale è quello del numero totale dei casi registrati in una determinata provincia, questa è l'unica informazione consultabile. Viene inoltre fornito un confronto con tutte le altre province appartenenti alle regioni alle quali afferiscono le province scelte.
21 | - *Informazioni*: è la pagina che fornisce ulteriori dettagli su chi ha realizzato il sito, chi ha collaborato, come è organizzato e come è stato realizzato.
22 | 
23 | ## Realizzazione
24 | 
25 | Il sito è stato completamente realizzato in linguaggio *R*, utilizzando la libreria [Shiny](https://shiny.rstudio.com), e attualmente risiede su uno spazio messo a disposizione da [ShinyApps](https://www.shinyapps.io). 
26 | 
27 | L'intero codice è consultabile all'interno del file *app.R* della presente repository. Il file è organizzato nella classica struttura di un file Shiny: una sezione sull'UI dell'applicativo (la veste grafica, dell'organizzazione delle sezioni e dei grafici prodotti) ed una seconda sezione relativa alle istruzioni da far compilare al server.
28 | 
29 | ## Crediti
30 | 
31 | Il sito è stato realizzato da Emanuele Degani [(website)](https://achab94.github.io), in condivisione di idee con altri colleghi citati nella sezione Info del portale. Per qualunque critica, suggerimento, dubbio od informazione, siete pregati di contattarmi all'indirizzo mail *emanuele [dot] achab [at] gmail [dot] com* od aprendo una Issues nella repository.
32 | 
33 | 
34 | 


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--------------------------------------------------------------------------------
/app.R:
--------------------------------------------------------------------------------
  1 | library(shiny)
  2 | library(ggplot2)
  3 | library(dplyr)
  4 | library(shinythemes)
  5 | library(scales)
  6 | library(lubridate)
  7 | library(shinyWidgets)
  8 | library(reshape2)
  9 | library(rmarkdown)
 10 | library(ggiraph)
 11 | library(DT)
 12 | 
 13 | # instructions for updating
 14 | # library(rsconnect)
 15 | # rsconnect::deployApp("Dropbox/COVID-19/COVID-19/")
 16 | 
 17 | # Definition of the User Interface ----------------------------------------
 18 | 
 19 | ui <- navbarPage(theme = shinytheme("flatly"),
 20 |                  title = "Monitoring COVID-19 evolution in Italy",
 21 | 
 22 |                  tabPanel("National evolution",
 23 |                           div(style="margin-top:-3.5em",
 24 |                               fluidRow(
 25 |                                   column(width=10,
 26 |                                      htmlOutput("headerCruscotto"),
 27 |                                      htmlOutput("summary"),
 28 |                                      style="padding:15px;")
 29 |                                   )
 30 |                               ),
 31 | 
 32 |                           fluidPage(
 33 |                               tags$head(includeHTML(("google-analytics.html")),
 34 |                                         tags$style(type="text/css", "text {font-family: arial, helvetica}")),
 35 |                               # fluidRow(splitLayout(cellWidths = c("49%", "49%"),
 36 |                               #                      ggiraphOutput("scatterMonitoraggioITA"),
 37 |                               #                      ggiraphOutput("datiGiornalieriITA"))),
 38 |                               # fluidRow(splitLayout(cellWidths = c("49%", "49%"),
 39 |                               #                      ggiraphOutput("barPlotDeltaPercentualiITA"),
 40 |                               #                      ggiraphOutput("scatterRapportoTotalePositiviTamponi"))),
 41 |                               # fluidRow(splitLayout(cellWidths = c("49%", "49%"),
 42 |                               #                      ggiraphOutput("evoluzioneTassi"),
 43 |                               #                      ggiraphOutput("barPlotTrattamento")))
 44 |                               fluidRow(ggiraphOutput("scatterMonitoraggioITA")),
 45 |                               fluidRow(ggiraphOutput("datiGiornalieriITA")),
 46 |                               fluidRow(ggiraphOutput("barPlotDeltaPercentualiITA")),
 47 |                               fluidRow(ggiraphOutput("scatterRapportoTotalePositiviTamponi")),
 48 |                               fluidRow(ggiraphOutput("evoluzioneTassi")),
 49 |                               fluidRow(ggiraphOutput("barPlotTrattamento"))
 50 |                           )
 51 |                  ),
 52 |                  tabPanel("Regional evolution",
 53 |                           sidebarLayout(
 54 |                               sidebarPanel(
 55 |                                   pickerInput(
 56 |                                       inputId = "selezionaRegioni",
 57 |                                       label = "Select the region(s) of interest (max 4)",
 58 |                                       choices = list("Northern Italy" = list("Lombardia", "Liguria", "Piemonte", "Valle d'Aosta",
 59 |                                                                           "Emilia-Romagna", "Friuli Venezia Giulia", "Veneto"),
 60 |                                                      "Central Italy" = list("Lazio", "Marche", "Toscana", "Umbria"),
 61 |                                                      "Sourhern Italy" = list("Abruzzo", "Basilicata", "Calabria",
 62 |                                                                          "Campania", "Molise", "Puglia"),
 63 |                                                      "Insular" = list("Sardegna", "Sicilia")),
 64 |                                       selected = c("Lombardia", "Piemonte"),
 65 |                                       options = list(
 66 |                                           `actions-box` = FALSE,
 67 |                                           header = "Regions",
 68 |                                           `max-options` = 4,
 69 |                                           `max-options-text` = "Select 4 regions at maximum"
 70 |                                       ),
 71 |                                       multiple = TRUE
 72 |                                   ),
 73 | 
 74 |                                   helpText("Trentino Alto Adige does not communicate the regional data, this is why it does not appear among the list."),
 75 | 
 76 |                                   radioButtons("selezionaVariabileRegioni",
 77 |                                                label = "Select the variable of interest",
 78 |                                                choices = list("Total positive cases",
 79 |                                                               "Currently positive patients",
 80 |                                                               "Deceased patients",
 81 |                                                               "Discharged/healed patients",
 82 |                                                               "Hospitalized patients with symptoms",
 83 |                                                               "Intensive care patients",
 84 |                                                               "Home isolation patients",
 85 |                                                               "Administered swabs",
 86 |                                                               "Rate between positives and swabs made"),
 87 |                                                selected = "Total positive cases"
 88 |                                   ),
 89 | 
 90 |                                   materialSwitch(inputId = "giornalieroSiNoRegione",
 91 |                                                  label = "View the daily data",
 92 |                                                  status = "danger"),
 93 | 
 94 |                                   helpText("The graph on the left shows by default the combined count of currently infected patients. If you want to view
 95 |                                            the daily trend (how many new cases have been recorded each day), select the button."),
 96 | 
 97 |                                   uiOutput("selezionaGiornoRegio"),
 98 | 
 99 |                                   helpText("The chart on the right shows by default the current subdivision of the type of treatment for currently infected patients.
100 |                                            If you want to view the evolution of the day-to-day breakdown, drag the button.")
101 | 
102 |                               ),
103 | 
104 |                               mainPanel(tags$style(type="text/css",
105 |                                                    ".shiny-output-error { visibility: hidden; }",
106 |                                                    ".shiny-output-error:before { visibility: hidden; }"
107 |                                                    ),
108 |                                         verticalLayout(
109 |                                             # fluidRow(
110 |                                             #     splitLayout(cellWidths = c("49%", "49%"), ggiraphOutput("scatterRegionale"), ggiraphOutput("barPlotDeltaPercentualiRegionale"))
111 |                                             # ),
112 |                                             # fluidRow(
113 |                                             #     splitLayout(cellWidths = c("49%", "49%"), ggiraphOutput("evoluzioneTassiRegionale"), ggiraphOutput("barPlotRegionale"))
114 |                                             # )
115 |                                             fluidRow(ggiraphOutput("scatterRegionale")),
116 |                                             fluidRow(ggiraphOutput("barPlotDeltaPercentualiRegionale")),
117 |                                             fluidRow(ggiraphOutput("evoluzioneTassiRegionale")),
118 |                                             fluidRow(ggiraphOutput("barPlotRegionale"))
119 |                                         )
120 |                               )
121 |                           )
122 |                  ),
123 |                  tabPanel("Provincial evolution",
124 |                           sidebarLayout(
125 |                               sidebarPanel(
126 |                                   pickerInput(
127 |                                       inputId = "selezionaProvincie",
128 |                                       label = "Select the province(s) of interest (max 4)",
129 |                                       choices = list("Abruzzo" = list("Chieti", "L'Aquila", "Pescara", "Teramo"),
130 |                                                      "Basilicata" = list("Matera", "Potenza"),
131 |                                                      "Calabria" = list("Catanzaro", "Cosenza", "Crotone", "Reggio di Calabria", "Vibo Valentia"),
132 |                                                      "Campania" = list("Avellino", "Benevento", "Caserta", "Napoli", "Salerno"),
133 |                                                      "Emilia-Romagna" = list("Bologna", "Ferrara", "Forlì-Cesena", "Modena", "Parma", "Piacenza", "Ravenna", "Reggio nell'Emilia", "Rimini"),
134 |                                                      "Friuli Venezia Giulia" = list("Gorizia", "Pordenone", "Trieste", "Udine"),
135 |                                                      "Lazio" = list("Frosinone", "Latina", "Rieti", "Roma", "Viterbo"),
136 |                                                      "Liguria" = list("Genova", "Imperia", "La Spezia", "Savona"),
137 |                                                      "Lombardia" = list("Bergamo", "Brescia", "Como", "Cremona", "Lecco", "Lodi", "Mantova", "Milano", "Monza e della Brianza", "Pavia", "Sondrio", "Varese"),
138 |                                                      "Marche" = list("Ancona", "Ascoli Piceno", "Fermo", "Macerata", "Pesaro e Urbino"),
139 |                                                      "Molise" = list("Campobasso", "Isernia"),
140 |                                                      "Piemonte" = list("Alessandria", "Asti", "Biella", "Cuneo", "Novara", "Torino", "Verbano-Cusio-Ossola", "Vercelli"),
141 |                                                      "Puglia" = list("Bari", "Barletta-Andria-Trani", "Brindisi", "Foggia", "Lecce", "Taranto"),
142 |                                                      "Sardegna" = list("Cagliari", "Nuoro", "Oristano", "Sassari", "Sud Sardegna"),
143 |                                                      "Sicilia" = list("Agrigento", "Caltanissetta", "Catania", "Enna", "Messina", "Palermo", "Ragusa", "Siracusa", "Trapani"),
144 |                                                      "Toscana" = list( "Arezzo", "Firenze", "Grosseto", "Livorno", "Lucca", "Massa Carrara", "Pisa", "Pistoia", "Prato", "Siena"),
145 |                                                      "Trentino-Alto Adige" = list("Bolzano", "Trento"),
146 |                                                      "Umbria" = list("Perugia", "Terni"),
147 |                                                      "Valle d'Aosta" = list("Aosta"),
148 |                                                      "Veneto" = list("Belluno", "Padova", "Rovigo", "Treviso", "Venezia", "Verona", "Vicenza")),
149 |                                       selected = c("Bergamo", "Brescia", "Milano"),
150 |                                       options = list(
151 |                                           `actions-box` = FALSE,
152 |                                           header = "Province",
153 |                                           `max-options` = 5,
154 |                                           `max-options-text` = "Select 5 regions at maximum"
155 |                                       ),
156 |                                       multiple = TRUE
157 |                                   ),
158 | 
159 |                                   materialSwitch(inputId = "giornalieroSiNoProvincia",
160 |                                                  label = "View the daily data",
161 |                                                  status = "danger"),
162 | 
163 |                                   helpText("The graph on the left shows by default the combined count of the total of positives. If you want to view
164 |                                            the daily trend (how many new positive cases have been recorded each day), select the button."),
165 | 
166 |                                   uiOutput("selezionaGiornoProv"),
167 | 
168 |                                   helpText("The chart on the right shows by default today's regional comparison of the number of total cases recorded.
169 |                                            If you want to view the evolution day by day, drag the button.")
170 | 
171 |                               ),
172 | 
173 |                               mainPanel(tags$style(type="text/css",
174 |                                                    ".shiny-output-error { visibility: hidden; }",
175 |                                                    ".shiny-output-error:before { visibility: hidden; }"
176 |                               ),
177 |                               verticalLayout(
178 |                                   fluidRow(
179 |                                       splitLayout(cellWidths = c("49%", "49%"), ggiraphOutput("scatterProvinciale"), ggiraphOutput("barPlotProvinciale"))
180 |                                   ),
181 |                                   h3("Relative increase (%) in the number of positive cases between yesterday and today"),
182 |                                   fluidRow(
183 |                                       DTOutput("dfProvinceIncrementoMaggiore")
184 |                                   )
185 |                               )
186 |                               )
187 |                           )
188 |                  ),
189 |                  tabPanel("Credits and informations", div(style="margin-top:-2.5em", includeMarkdown("Info.md")))
190 | )
191 | 
192 | 
193 | server <- function(input, output) {
194 |     dataProvinceInput <- reactive({
195 |         storicoProvince <- read.csv(file = "https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province.csv", stringsAsFactors = FALSE)
196 |         storicoProvince <- subset(storicoProvince, denominazione_provincia!="In fase di definizione/aggiornamento")
197 |         storicoProvince$denominazione_regione[storicoProvince$denominazione_regione %in% list("P.A. Trento", "P.A. Bolzano")] <- "Trentino-Alto Adige"
198 | 
199 |         myProvince <- data.frame(data=as.Date(storicoProvince$data),
200 |                                  Provincia = storicoProvince$denominazione_provincia,
201 |                                  Regione = storicoProvince$denominazione_regione,
202 |                                  totale_casi = storicoProvince$totale_casi,
203 |                                  x = (as.numeric(as.Date(storicoProvince$data)) - min(as.numeric(as.Date(storicoProvince$data))) + 1))
204 |         myProvince <- myProvince %>%
205 |             group_by(Provincia) %>%
206 |             mutate(diff_totale_casi = diff(c(0, totale_casi)))
207 |         myProvince
208 |     })
209 | 
210 |     dataRegioniInput <- reactive({
211 |         storicoRegioni <- read.csv(file = "https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv", stringsAsFactors = FALSE)
212 | 
213 |         myRegioni <- data.frame(data=as.Date(storicoRegioni$data),
214 |                                 Regione = storicoRegioni$denominazione_regione,
215 |                                 totale_attualmente_positivi = storicoRegioni$totale_positivi,
216 |                                 nuovi_attualmente_positivi = storicoRegioni$variazione_totale_positivi,
217 |                                 deceduti = storicoRegioni$deceduti,
218 |                                 dimessi_guariti = storicoRegioni$dimessi_guariti,
219 |                                 totale_casi = storicoRegioni$totale_casi,
220 |                                 ricoverati_con_sintomi = storicoRegioni$ricoverati_con_sintomi,
221 |                                 terapia_intensiva = storicoRegioni$terapia_intensiva,
222 |                                 isolamento_domiciliare = storicoRegioni$isolamento_domiciliare,
223 |                                 tamponi = storicoRegioni$tamponi,
224 |                                 x = (as.numeric(as.Date(storicoRegioni$data)) - min(as.numeric(as.Date(storicoRegioni$data))) + 1))
225 |         myRegioni <- myRegioni %>%
226 |             group_by(Regione) %>%
227 |             mutate(diff_deceduti = diff(c(0, deceduti)),
228 |                    diff_dimessi_guariti = diff(c(0, dimessi_guariti)),
229 |                    diff_ricoverati_con_sintomi = diff(c(0, ricoverati_con_sintomi)),
230 |                    diff_terapia_intensiva = diff(c(0, terapia_intensiva)),
231 |                    diff_isolamento_domiciliare = diff(c(0, isolamento_domiciliare)),
232 |                    diff_totale_casi = diff(c(0, totale_casi)),
233 |                    diff_tamponi = diff(c(0, tamponi)),
234 |                    totalratetamponi = round(totale_casi/tamponi, 4),
235 |                    dailyratetamponi = round(diff_totale_casi/diff_tamponi, 4))
236 |         myRegioni
237 |     })
238 | 
239 |     dataItaliaInput <- reactive({
240 |         storicoItalia <- read.csv(file = "https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-andamento-nazionale/dpc-covid19-ita-andamento-nazionale.csv", stringsAsFactors = FALSE)
241 | 
242 |         myItalia <- data.frame(data=as.Date(storicoItalia$data),
243 |                                totale_attualmente_positivi = storicoItalia$totale_positivi,
244 |                                nuovi_attualmente_positivi = storicoItalia$variazione_totale_positivi,
245 |                                deceduti = storicoItalia$deceduti,
246 |                                diff_deceduti = diff(c(0, storicoItalia$deceduti)),
247 |                                dimessi_guariti = storicoItalia$dimessi_guariti,
248 |                                diff_dimessi_guariti = diff(c(0, storicoItalia$dimessi_guariti)),
249 |                                totale_casi = storicoItalia$totale_casi,
250 |                                diff_totale_casi = diff(c(0, storicoItalia$totale_casi)),
251 |                                ricoverati_con_sintomi = storicoItalia$ricoverati_con_sintomi,
252 |                                diff_ricoverati_con_sintomi = diff(c(0, storicoItalia$ricoverati_con_sintomi)),
253 |                                terapia_intensiva = storicoItalia$terapia_intensiva,
254 |                                diff_terapia_intensiva = diff(c(0, storicoItalia$terapia_intensiva)),
255 |                                isolamento_domiciliare = storicoItalia$isolamento_domiciliare,
256 |                                diff_isolamento_domiciliare = diff(c(0, storicoItalia$isolamento_domiciliare)),
257 |                                tamponi = storicoItalia$tamponi,
258 |                                diff_tamponi = diff(c(0, storicoItalia$tamponi)),
259 |                                casi_testati = storicoItalia$casi_testati,
260 |                                diff_casi_testati = diff(c(0,storicoItalia$casi_testati)),
261 |                                x = (as.numeric(as.Date(storicoItalia$data)) - min(as.numeric(as.Date(storicoItalia$data))) + 1))
262 |         myItalia
263 |     })
264 | 
265 |     output$selezionaGiornoRegio <- renderUI({
266 |         sliderTextInput(
267 |             inputId = "selezionaGiornoRegio",
268 |             label = "Select the day of interest",
269 |             grid = FALSE,
270 |             force_edges = TRUE,
271 |             width = 500,
272 |             selected = as.Date(dataRegioniInput()$data[length(dataRegioniInput()$data)]),
273 |             choices = seq(as.Date("2020-02-24"), as.Date(dataRegioniInput()$data[length(dataRegioniInput()$data)]), 1)
274 |         )
275 |     })
276 | 
277 |     output$selezionaGiornoProv <- renderUI({
278 |         sliderTextInput(
279 |             inputId = "selezionaGiornoProv",
280 |             label = "Select the day of interest",
281 |             grid = FALSE,
282 |             force_edges = TRUE,
283 |             width = 500,
284 |             selected = as.Date(dataProvinceInput()$data[length(dataProvinceInput()$data)]),
285 |             choices = seq(as.Date("2020-02-24"), as.Date(dataProvinceInput()$data[length(dataProvinceInput()$data)]), 1)
286 |         )
287 |     })
288 | 
289 |     output$headerCruscotto <- reactive({
290 |         ultimaData <- dataItaliaInput()$data[length(dataItaliaInput()$data)]
291 |         paste0(h1("The current* situation of the CoVid-19 epidemic in Italy:"), h5("* (updated to the latest available data, dating back to 18pm on", day(ultimaData), "August 2020)"))
292 |     })
293 | 
294 |     output$summary <- reactive({
295 |         attualmente_positivi_ultimo <- dataItaliaInput()$totale_attualmente_positivi[length(dataItaliaInput()$totale_attualmente_positivi)]
296 |         attualmente_positivi_penultimo <- dataItaliaInput()$totale_attualmente_positivi[length(dataItaliaInput()$totale_attualmente_positivi) - 1]
297 |         delta_attualmente_positivi <- diff(c(attualmente_positivi_penultimo, attualmente_positivi_ultimo))
298 | 
299 |         deceduti_ultimo <- dataItaliaInput()$deceduti[length(dataItaliaInput()$deceduti)]
300 |         deceduti_penultimo <- dataItaliaInput()$deceduti[length(dataItaliaInput()$deceduti) - 1]
301 |         delta_deceduti <- diff(c(deceduti_penultimo, deceduti_ultimo))
302 | 
303 |         dimessi_guariti_ultimo <- dataItaliaInput()$dimessi_guariti[length(dataItaliaInput()$dimessi_guariti)]
304 |         dimessi_guariti_penultimo <- dataItaliaInput()$dimessi_guariti[length(dataItaliaInput()$dimessi_guariti) - 1]
305 |         delta_dimessi_guariti <- diff(c(dimessi_guariti_penultimo, dimessi_guariti_ultimo))
306 | 
307 |         totale_casi_ultimo <- dataItaliaInput()$totale_casi[length(dataItaliaInput()$totale_casi)]
308 |         totale_casi_penultimo <- dataItaliaInput()$totale_casi[length(dataItaliaInput()$totale_casi) - 1]
309 |         delta_totale_casi <- diff(c(totale_casi_penultimo, totale_casi_ultimo))
310 | 
311 |         casi_testati_ultimo <- dataItaliaInput()$casi_testati[length(dataItaliaInput()$casi_testati)]
312 |         casi_testati_penultimo <- dataItaliaInput()$casi_testati[length(dataItaliaInput()$casi_testati) - 1]
313 |         delta_casi_testati <- diff(c(casi_testati_penultimo, casi_testati_ultimo))
314 | 
315 |         delta_dimessiguariti_attualmentepositivi <- diff(c(dimessi_guariti_penultimo/attualmente_positivi_penultimo, dimessi_guariti_ultimo/attualmente_positivi_ultimo))
316 |         paste0("<ul>",
317 |               "<li><b>Currently positive patients: </b>", attualmente_positivi_ultimo, " (",
318 |               if(delta_attualmente_positivi > 0) "+" else "-", round(abs(delta_attualmente_positivi/attualmente_positivi_penultimo) * 100, 2), "% over the previous day, ", round(attualmente_positivi_ultimo/totale_casi_ultimo * 100, 2) , "% of the total positive cases)",
319 |               "</li>",
320 |               "<li><b>Deceased patients: </b>", deceduti_ultimo, " (",
321 |               if(delta_deceduti > 0) "+" else "-", round(abs(delta_deceduti/deceduti_penultimo) * 100, 2), "% over the previous day, ", round(deceduti_ultimo/totale_casi_ultimo * 100, 2), "% of the total positive cases)",
322 |               "</li>",
323 |               "<li><b>Discharged/healed patients: </b>", dimessi_guariti_ultimo, " (",
324 |               if(delta_dimessi_guariti > 0) "+" else "-", round(abs(delta_dimessi_guariti/dimessi_guariti_penultimo) * 100, 2), "% over the previous day, ", round(dimessi_guariti_ultimo/totale_casi_ultimo * 100, 2), "% of the total positive cases)",
325 |               "</li>",
326 |               "<li><b>Total positive cases: </b>", totale_casi_ultimo, " (",
327 |               if(delta_totale_casi > 0) "+" else "-", round(abs(delta_totale_casi/totale_casi_penultimo) * 100, 2), "% over the previous day, ", round(totale_casi_ultimo/casi_testati_ultimo * 100, 2), "% of the total tested cases)",
328 |               "</li>",
329 |               "<li><b>Total tested cases: </b>", casi_testati_ultimo, " (",
330 |               if(delta_casi_testati > 0) "+" else "-", round(abs(delta_casi_testati/casi_testati_penultimo) * 100, 2), "% over the previous day. Notice this number is different from the total number of administered swabs)",
331 |               "</li>",
332 |               "</ul>
333 |               <i>For an optimal consultation of this portal, which allows <b> interaction with the graphics </b>, it is advisable to access from desktop instead of mobile or tablet.</i>
334 |               "
335 |         )
336 |     })
337 | 
338 |     output$scatterMonitoraggioITA <- renderggiraph({
339 |         myItaliaReshaped <- melt(dataItaliaInput(), id.vars = "data",
340 |                                  measure.vars = c("totale_attualmente_positivi",
341 |                                                   "deceduti",
342 |                                                   "dimessi_guariti",
343 |                                                   "totale_casi",
344 |                                                   "terapia_intensiva"))
345 | 
346 |         gg_point <- ggplot(myItaliaReshaped) +
347 |             geom_point_interactive(aes(x = data, y = value, color = variable, tooltip = value), size=1.5) +
348 |             geom_line(aes(x=data, y=value, color=variable), size=0.5) +
349 |             scale_color_manual(values = c("totale_attualmente_positivi" = "red3",
350 |                                           "dimessi_guariti" = "dodgerblue3",
351 |                                           "deceduti" = "green4",
352 |                                           "totale_casi" = "black",
353 |                                           "terapia_intensiva" = "pink4"),
354 |                                labels = c("totale_attualmente_positivi" = "Currently positive",
355 |                                           "dimessi_guariti" = "Discharged or healed",
356 |                                           "deceduti" = "Deceased",
357 |                                           "totale_casi" = "Total positive cases",
358 |                                           "terapia_intensiva" = "Intensive care treated")) +
359 |             theme_bw() + scale_y_continuous(trans = log10_trans(),
360 |                                             breaks = trans_breaks("log10", function(x) 10^x),
361 |                                             labels = trans_format("log10", math_format(10^.x))) +
362 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
363 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
364 |             labs(x="", y="", title="Overall monitoring of COVID-19", caption = "total data (Log10 scale)") +
365 |             theme(legend.position="bottom",
366 |                   legend.title=element_blank(),
367 |                   axis.title.x=element_blank(),
368 |                   panel.grid.minor.x = element_blank(),
369 |                   legend.margin=margin(t=0, r=0, b=0, l=0, unit="cm"))
370 |         girafe_options(girafe(ggobj = gg_point, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
371 |     })
372 | 
373 |     output$barPlotDeltaPercentualiITA <- renderggiraph({
374 |         myItalia <- dataItaliaInput()
375 |         delayDays <- 14
376 |         myItaliaRelDiffs <- data.frame(data=as.Date(tail(myItalia$data, delayDays)),
377 |                                        TotalePositivi = tail(diff(myItalia$totale_casi)/myItalia$totale_casi[-length(myItalia$totale_casi)], delayDays),
378 |                                        Deceduti = tail(diff(myItalia$deceduti)/myItalia$deceduti[-length(myItalia$deceduti)], delayDays),
379 |                                        DimessiGuariti = tail(diff(myItalia$dimessi_guariti)/myItalia$dimessi_guariti[-length(myItalia$dimessi_guariti)], delayDays),
380 |                                        Tamponi = tail(diff(myItalia$tamponi)/myItalia$tamponi[-length(myItalia$tamponi)], delayDays),
381 |                                        TerapiaIntensiva = tail(diff(myItalia$terapia_intensiva)/myItalia$terapia_intensiva[-length(myItalia$terapia_intensiva)], delayDays))
382 | 
383 |         myItaliaRelDiffsReshaped <- melt(myItaliaRelDiffs, id.vars = "data", measure.vars = c("TotalePositivi",
384 |                                                                                               "Deceduti",
385 |                                                                                               "DimessiGuariti",
386 |                                                                                               "Tamponi",
387 |                                                                                               "TerapiaIntensiva"), variable.name = "Dimensione")
388 |         gg_bar <- ggplot(myItaliaRelDiffsReshaped, aes(x=data, y=value)) +
389 |             geom_bar_interactive(aes(tooltip=percent(value, 0.01)), stat="identity", position="dodge", width=0.5) +
390 |             scale_y_continuous(labels = scales::percent) +
391 |             scale_x_date(date_labels = "%d/%m", date_breaks = "1 day", minor_breaks = "1 day") +
392 |             theme_bw() +
393 |             facet_wrap(~Dimensione, ncol=1, scales="free_y",
394 |                        labeller = as_labeller(c(
395 |                            `TotalePositivi` = "Total positive cases",
396 |                            `Deceduti` = "Deceased",
397 |                            `DimessiGuariti` = "Discharged or healed",
398 |                            `Tamponi` = "Swabs",
399 |                            `TerapiaIntensiva` = "Intensive care treated"
400 |                        ))) +
401 |             labs(x="", y="", title=paste("Relative variation from the previous day"), caption=paste("daily data (last", delayDays, "days)")) +
402 |             theme(legend.position="none",
403 |                   axis.title.x=element_blank(),
404 |                   panel.grid.minor.x = element_blank())
405 |         girafe_options(girafe(ggobj = gg_bar, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
406 |     })
407 | 
408 |     output$datiGiornalieriITA <- renderggiraph({
409 |         myItalia <- dataItaliaInput()
410 | 
411 |         myItaliaDiffs <- data.frame(data = myItalia$data,
412 |                                     diff_totale_casi = myItalia$diff_totale_casi,
413 |                                     diff_deceduti = myItalia$diff_deceduti,
414 |                                     diff_dimessi_guariti = myItalia$diff_dimessi_guariti,
415 |                                     diff_tamponi = myItalia$diff_tamponi,
416 |                                     diff_terapia_intensiva = myItalia$diff_terapia_intensiva)
417 | 
418 |         myItaliaDiffsReshaped <- melt(myItaliaDiffs, id.vars = "data", variable.name = "Variabile")
419 | 
420 |         gg_point <- ggplot(myItaliaDiffsReshaped, aes(x=data, y=value)) +
421 |             geom_line(size=0.75) + geom_smooth(size=0.5) + geom_point_interactive(aes(tooltip=round(value, 2)), size=1.5) +
422 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
423 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
424 |             facet_wrap(~Variabile, ncol=1, scales="free_y",
425 |                        labeller = as_labeller(c(
426 |                            `diff_totale_casi` = "Total positive cases",
427 |                            `diff_deceduti` = "Deceased",
428 |                            `diff_dimessi_guariti` = "Discharged or healed",
429 |                            `diff_tamponi` = "Swabs",
430 |                            `diff_terapia_intensiva` = "Intensive care treated"
431 |                        ))) +
432 |             theme_bw() +
433 |             theme(axis.title.x=element_blank(),
434 |                   panel.grid.minor.x = element_blank()) +
435 |             labs(x="", y="", title=paste("Overall monitoring of COVID-19"), caption="daily data")
436 | 
437 |         girafe_options(girafe(ggobj = gg_point, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
438 |     })
439 | 
440 |     output$evoluzioneTassi <- renderggiraph({
441 |         myItalia <- dataItaliaInput()
442 | 
443 |         myItaliaDiffs <- data.frame(data = myItalia$data,
444 |                                     tasso_deceduti = myItalia$deceduti/myItalia$totale_casi,
445 |                                     tasso_dimessi_guariti = myItalia$dimessi_guariti/myItalia$totale_casi)
446 | 
447 |         myItaliaDiffsReshaped <- melt(myItaliaDiffs, id.vars = "data", variable.name = "Variabile")
448 | 
449 |         gg_point <- ggplot(myItaliaDiffsReshaped, aes(x=data, y=value, color=Variabile)) +
450 |             geom_line(size=0.75) + geom_smooth(size=0.5, show.legend = F) +
451 |             geom_point_interactive(aes(tooltip=percent(value, 0.01)), size=1.5) +
452 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
453 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
454 |             scale_y_continuous(labels=percent) +
455 |             labs(x="", y="", title="Monitoring of healing and lethality rates", caption="total data") +
456 |             theme_bw() +
457 |             scale_color_brewer(breaks=c("tasso_dimessi_guariti", "tasso_deceduti"),
458 |                               labels=c("% discharged/healed out of positives", "% deceased out of positives"),
459 |                               palette="Set1") +
460 |             theme(legend.position="bottom",
461 |                   legend.title=element_blank(),
462 |                   axis.title.x=element_blank(),
463 |                   panel.grid.minor.x = element_blank(),
464 |                   legend.margin=margin(t=0, r=0, b=0, l=-1, unit="cm"))
465 | 
466 |         girafe_options(girafe(ggobj = gg_point, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
467 |     })
468 | 
469 |     output$scatterRapportoTotalePositiviTamponi <- renderggiraph({
470 |         gg_point <- ggplot(dataItaliaInput(), aes(data, y=diff_totale_casi/diff_casi_testati)) +
471 |             geom_smooth(method = 'loess', se=T) +
472 |             geom_point_interactive(aes(tooltip=percent(diff_totale_casi/diff_casi_testati, 0.01)), size=2) +
473 |             geom_line(size=0.5) +
474 |             theme_bw() +
475 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "3 days", minor_breaks = "1 day",
476 |             #              limits = c(as.Date("2020-04-20"), as.Date(today()))) +
477 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days", limits = c(as.Date("2020-04-21"), as.Date(today()))) +
478 |             scale_y_continuous(labels=percent, limits = c(0, 0.15)) +
479 |             labs(x="", y="", title="Daily rate between new positives and person tested", caption="daily data") +
480 |             theme(axis.title.x=element_blank(),
481 |                   panel.grid.minor.x = element_blank())
482 | 
483 |         girafe_options(girafe(ggobj = gg_point, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
484 |     })
485 | 
486 |     output$barPlotTrattamento <- renderggiraph({
487 |         myItaliaDiffs <- data.frame(data = dataItaliaInput()$data,
488 |                                     ricoverati_con_sintomi = dataItaliaInput()$ricoverati_con_sintomi,
489 |                                     terapia_intensiva = dataItaliaInput()$terapia_intensiva,
490 |                                     isolamento_domiciliare = dataItaliaInput()$isolamento_domiciliare)
491 | 
492 |         myItaliaDiffsReshaped <- melt(myItaliaDiffs, id.vars = "data", variable.name = "Variabile")
493 | 
494 |         gg_bar <- ggplot(myItaliaDiffsReshaped, aes(x=data, y=value, fill=Variabile)) +
495 |             geom_bar_interactive(aes(tooltip=value), stat="identity", position = "stack") +
496 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
497 |             labs(x="", y="", title="Subdivision of the health treatment for the currently positive patients", caption="total data") +
498 |             theme_bw() +
499 |             scale_fill_brewer(palette="Set2", labels=c("Hospitalized with symptoms", "Intensive care", "Home isolation")) +
500 |             theme(legend.position="bottom",
501 |                   legend.title=element_blank(),
502 |                   axis.title.x=element_blank(),
503 |                   panel.grid.minor.x = element_blank(),
504 |                   legend.margin=margin(t=0, r=0, b=0, l=-1, unit="cm"))
505 | 
506 |         girafe_options(girafe(ggobj = gg_bar, width_svg = 14, height_svg = 13), opts_toolbar(saveaspng=FALSE))
507 |     })
508 | 
509 | 
510 |     output$scatterRegionale <- renderggiraph({
511 |         myDataRegion <- subset(dataRegioniInput(), Regione %in% input$selezionaRegioni)
512 | 
513 |         if(input$giornalieroSiNoRegione){
514 |             ### daily visualization
515 |             if(input$selezionaVariabileRegioni == "Total positive cases") plotVar <- "diff_totale_casi"
516 |             if(input$selezionaVariabileRegioni == "Currently positive patients") plotVar <- "nuovi_attualmente_positivi"
517 |             if(input$selezionaVariabileRegioni == "Deceased patients") plotVar <- "diff_deceduti"
518 |             if(input$selezionaVariabileRegioni == "Discharged/healed patients") plotVar <- "diff_dimessi_guariti"
519 |             if(input$selezionaVariabileRegioni == "Hospitalized patients with symptoms") plotVar <- "diff_ricoverati_con_sintomi"
520 |             if(input$selezionaVariabileRegioni == "Intensive care patients") plotVar <- "diff_terapia_intensiva"
521 |             if(input$selezionaVariabileRegioni == "Home isolation patients") plotVar <- "diff_isolamento_domiciliare"
522 |             if(input$selezionaVariabileRegioni == "Administered swabs") plotVar <- "diff_tamponi"
523 |             if(input$selezionaVariabileRegioni == "Rate between positives and swabs made") plotVar <- "dailyratetamponi"
524 |         } else {
525 |             ### global visualization
526 |             if(input$selezionaVariabileRegioni == "Total positive cases") plotVar <- "totale_casi"
527 |             if(input$selezionaVariabileRegioni == "Currently positive patients") plotVar <- "totale_attualmente_positivi"
528 |             if(input$selezionaVariabileRegioni == "Deceased patients") plotVar <- "deceduti"
529 |             if(input$selezionaVariabileRegioni == "Discharged/healed patients") plotVar <- "dimessi_guariti"
530 |             if(input$selezionaVariabileRegioni == "Hospitalized patients with symptoms") plotVar <- "ricoverati_con_sintomi"
531 |             if(input$selezionaVariabileRegioni == "Intensive care patients") plotVar <- "terapia_intensiva"
532 |             if(input$selezionaVariabileRegioni == "Home isolation patients") plotVar <- "isolamento_domiciliare"
533 |             if(input$selezionaVariabileRegioni == "Administered swabs") plotVar <- "tamponi"
534 |             if(input$selezionaVariabileRegioni == "Rate between positives and swabs made") plotVar <- "totalratetamponi"
535 |         }
536 | 
537 |         myDataRegionVariable <- myDataRegion[, c("data", "Regione", plotVar)]
538 |         colnames(myDataRegionVariable) <- c("data", "Region", "plotVar")
539 | 
540 |         gg_point <- ggplot(myDataRegionVariable, aes(x = data, y = plotVar, color = Region)) +
541 |             geom_point_interactive(aes(tooltip=plotVar), size=1.25) +
542 |             geom_line(size=0.75) +
543 |             theme_bw() +
544 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
545 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
546 |             labs(x="", y="", title=input$selezionaVariabileRegioni, caption = paste0(if(input$giornalieroSiNoRegione) "daily data" else "total data")) +
547 |             theme(legend.position = "bottom",
548 |                   legend.title=element_blank(),
549 |                   legend.text = element_text(size=13),
550 |                   axis.title.x=element_blank(),
551 |                   panel.grid.minor.x = element_blank(),
552 |                   legend.margin=margin(t=-0.25, r=0, b=0, l=0, unit="cm"))
553 | 
554 |         girafe_options(girafe(ggobj = gg_point, width_svg = 12, height_svg = 11), opts_toolbar(saveaspng=FALSE))
555 |     })
556 | 
557 |     output$barPlotRegionale <- renderggiraph({
558 |         myDataRegion <- subset(dataRegioniInput(), Regione %in% input$selezionaRegioni)
559 |         myDataRegionMelted <- melt(myDataRegion, id.vars=c("data", "Regione"),
560 |                                    measure.vars=c("ricoverati_con_sintomi","terapia_intensiva","isolamento_domiciliare"),
561 |                                    variable.name="Trattamento")
562 |         gg_bar <- ggplot(subset(myDataRegionMelted, data==as.character(input$selezionaGiornoRegio)), aes(fill=Trattamento, x=value, y=Regione)) +
563 |             geom_bar_interactive(aes(tooltip=value), position="stack", stat="identity") +
564 |             theme_bw() +
565 |             labs(x="", y="", title="Subdivision of the health treatment for the infected", caption=paste0("daily data (", format(as.Date(input$selezionaGiornoRegio), "%d/%m/%Y"), ")")) +
566 |             scale_fill_brewer(breaks=c("ricoverati_con_sintomi", "terapia_intensiva", "isolamento_domiciliare"),
567 |                               labels=c("Hospitalized with symptoms", "Intensive care", "Home isolation"),
568 |                               palette="Set2") +
569 |             theme(legend.position = "top",
570 |                   legend.text=element_text(size=11),
571 |                   legend.title=element_blank(),
572 |                   axis.title.x=element_blank(),
573 |                   panel.grid.minor.x = element_blank(),
574 |                   legend.margin=margin(t=0, r=0, b=0, l=-1.25, unit="cm"))
575 |         girafe_options(girafe(ggobj = gg_bar, width_svg = 12, height_svg = 11), opts_toolbar(saveaspng=FALSE))
576 |     })
577 | 
578 |     output$barPlotDeltaPercentualiRegionale <- renderggiraph({
579 |         myDataRegion <- subset(dataRegioniInput(), Regione %in% input$selezionaRegioni)
580 | 
581 |         delayDays <- 7
582 |         myRegioniRelDiffs <- myDataRegion %>%
583 |             group_by(Regione) %>%
584 |             mutate(CasiPositiviTotali = diff(c(0, totale_casi))/c(1, totale_casi[-n()]),
585 |                    Deceduti = diff(c(0, deceduti))/c(1, deceduti[-n()]),
586 |                    DimessiGuariti = diff(c(0, dimessi_guariti))/c(1, dimessi_guariti[-n()]),
587 |                    TerapiaIntensiva = diff(c(0, terapia_intensiva))/c(1, terapia_intensiva[-n()]))
588 | 
589 |         myRegioniRelDiffs <- myRegioniRelDiffs %>%
590 |             group_by(Regione) %>%
591 |             slice(tail(row_number(), delayDays))
592 | 
593 |         myRegioniRelDiffsReshaped <- melt(myRegioniRelDiffs, id.vars = c("data", "Regione"), measure.vars = c("CasiPositiviTotali",
594 |                                                                                                               "Deceduti",
595 |                                                                                                               "DimessiGuariti",
596 |                                                                                                               "TerapiaIntensiva"), variable.name = "Dimensione")
597 |         gg_bar <- ggplot(myRegioniRelDiffsReshaped, aes(x=data, y=value, fill=Regione)) +
598 |             geom_bar_interactive(aes(tooltip=percent(value, 0.01)), stat="identity", position="dodge", width=0.5) +
599 |             scale_y_continuous(labels = scales::percent) +
600 |             scale_x_date(date_labels = "%d/%m", date_breaks = "1 day", minor_breaks = "1 day") +
601 |             theme_bw() +
602 |             theme(legend.position = "bottom",
603 |                   legend.title=element_blank(),
604 |                   legend.text = element_text(size=11),
605 |                   axis.title.x=element_blank(),
606 |                   panel.grid.minor.x = element_blank(),
607 |                   legend.margin=margin(t=-0.25, r=0, b=0, l=0, unit="cm")) +
608 |             facet_wrap(~Dimensione, ncol=1, scales="free_y",
609 |                        labeller = as_labeller(c(
610 |                            `CasiPositiviTotali` = "Total positive cases",
611 |                            `Deceduti` = "Deceased",
612 |                            `DimessiGuariti` = "Discharged or healed",
613 |                            `TerapiaIntensiva` = "Intensive care treated"
614 |                        ))) +
615 |             labs(x="", y="", title=paste("Relative variation from the previous day"), caption=paste("daily data (last", delayDays, "days)"))
616 | 
617 |         girafe_options(girafe(ggobj = gg_bar, width_svg = 12, height_svg = 11), opts_toolbar(saveaspng=FALSE))
618 |     })
619 | 
620 |     output$evoluzioneTassiRegionale <- renderggiraph({
621 |         myDataRegion <- subset(dataRegioniInput(), Regione %in% input$selezionaRegioni)
622 | 
623 |         myRegioniRates <- myDataRegion %>%
624 |             group_by(Regione) %>%
625 |             mutate(tasso_deceduti = deceduti/totale_casi,
626 |                    tasso_dimessi_guariti = dimessi_guariti/totale_casi)
627 | 
628 |         myRegioniRatesReshaped <- melt(myRegioniRates, id.vars = list("data", "Regione"), measure.vars = list("tasso_deceduti", "tasso_dimessi_guariti"), variable.name = "Tasso")
629 | 
630 |         gg_point <- ggplot(myRegioniRatesReshaped, aes(x=data, y=value, color=Tasso)) +
631 |             geom_line(size=0.75) + geom_smooth(size=0.75, show.legend = F) +
632 |             geom_point_interactive(aes(tooltip=percent(value, 0.01)), size=1.5) +
633 |             # scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
634 |             scale_x_date(date_labels = "%d/%m", date_breaks = "15 days") +
635 |             scale_y_continuous(labels=percent) +
636 |             labs(x="", y="", title="Monitoring of healing and lethality rates", caption="total data") +
637 |             theme_bw() +
638 |             scale_color_brewer(breaks=c("tasso_dimessi_guariti", "tasso_deceduti"),
639 |                               labels=c("% discharged/healed out of positives", "% deceased out of positives"),
640 |                               palette="Set1") +
641 |             facet_wrap(~Regione, ncol=1, scales="free_y") +
642 |             theme(legend.position="top",
643 |                   legend.title=element_blank(),
644 |                   legend.text=element_text(size=10),
645 |                   axis.title.x=element_blank(),
646 |                   panel.grid.minor.x = element_blank(),
647 |                   legend.margin=margin(t=0, r=0, b=0, l=-1, unit="cm"))
648 | 
649 |         girafe_options(girafe(ggobj = gg_point, width_svg = 12, height_svg = 11), opts_toolbar(saveaspng=FALSE))
650 |     })
651 | 
652 |     output$scatterProvinciale <- renderggiraph({
653 |         myDataProvince <- subset(dataProvinceInput(), Provincia %in% input$selezionaProvincie)
654 | 
655 |         if(input$giornalieroSiNoProvincia){
656 |             ### daily visualization
657 |             plotVar <- "diff_totale_casi"
658 |         } else {
659 |             ### global visualization
660 |             plotVar <- "totale_casi"
661 |         }
662 | 
663 |         myDataProvinceVariable <- myDataProvince[, c("data", "Provincia", plotVar)]
664 |         colnames(myDataProvinceVariable) <- c("data", "Provincia", "plotVar")
665 | 
666 |         gg_point <- ggplot(myDataProvinceVariable, aes(x = data, y = plotVar, color = Provincia)) +
667 |             geom_point_interactive(aes(tooltip=plotVar), size=1.5) +
668 |             geom_line(size=0.75) +
669 |             theme_bw() +
670 |             scale_x_date(date_labels = "%d/%m", date_breaks = "7 days", minor_breaks = "1 day") +
671 |             labs(x="", y="", title="Total positive cases", caption=paste0(if(input$giornalieroSiNoProvincia) "daily data" else "total data")) +
672 |             theme(legend.position = "bottom",
673 |                   legend.title=element_blank(),
674 |                   legend.text = element_text(size=13),
675 |                   axis.title.x=element_blank(),
676 |                   panel.grid.minor.x = element_blank(),
677 |                   legend.margin=margin(t=-0.25, r=0, b=0, l=0, unit="cm"))
678 |         girafe(ggobj = gg_point)
679 |     })
680 | 
681 |     output$barPlotProvinciale <- renderggiraph({
682 |         myDataProvinceRegioni <- subset(dataProvinceInput(), Regione %in% as.character(unique(dataProvinceInput()$Regione[dataProvinceInput()$Provincia %in% input$selezionaProvincie])))
683 | 
684 |         gg_bar <- ggplot(subset(myDataProvinceRegioni, data==as.character(input$selezionaGiornoProv)), aes(x=totale_casi, y=Provincia)) +
685 |             geom_bar_interactive(aes(tooltip=totale_casi), position="dodge", stat="identity", width = 0.75) +
686 |             theme_bw() +
687 |             labs(x="Total positive cases", y="", title=paste0("Regional comparison of the selected provinces"),
688 |                  caption=paste0("total data (till ", format(as.Date(input$selezionaGiornoProv), "%d/%m/%Y"), ")")) +
689 |             scale_fill_brewer(palette="Set2") +
690 |             theme(legend.position = "none",
691 |                   axis.title.x=element_blank(),
692 |                   panel.grid.minor.x = element_blank()) +
693 |             facet_grid(rows=vars(Regione), scales="free_y")
694 | 
695 |         girafe(ggobj = gg_bar)
696 |     })
697 | 
698 |     output$dfProvinceIncrementoMaggiore <- renderDT({
699 |         myProvince <- dataProvinceInput() %>%
700 |             group_by(Provincia) %>%
701 |             mutate(relincr_totale_casi = diff(c(0, totale_casi))/c(1, totale_casi[-n()]))
702 | 
703 |         lastDataProvince <- tail(myProvince, 107)
704 | 
705 |         df_output <- data.frame(Provincia=as.character(lastDataProvince$Provincia),
706 |                                 Regione=as.character(lastDataProvince$Regione),
707 |                                 IncrementoGiornaliero=lastDataProvince$relincr_totale_casi,
708 |                                 CasiIeri=(lastDataProvince$totale_casi - lastDataProvince$diff_totale_casi),
709 |                                 CasiOggi=lastDataProvince$totale_casi)
710 |         df_output <- df_output[order(df_output$IncrementoGiornaliero, decreasing = T),]
711 |         colnames(df_output) <- c("Province", "Region", "Daily increase", "Total positives (yesterday)", "Total positives (today)")
712 |         datatable(df_output, filter = 'bottom', rownames = FALSE,
713 |                   options = list(
714 |                       searching = T,
715 |                       pageLength = 10)) %>% formatPercentage(columns = "Daily increase", digits = 2)
716 |     })
717 | }
718 | 
719 | # Run the application
720 | shinyApp(ui = ui, server = server)
721 | 


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