Read the data for the Italian's Provinces:

CV19_pro_backup<- read.csv("https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-province/dpc-covid19-ita-province.csv")
names(CV19_pro_backup)
##  [1] "data"                    "stato"                  
##  [3] "codice_regione"          "denominazione_regione"  
##  [5] "codice_provincia"        "denominazione_provincia"
##  [7] "sigla_provincia"         "lat"                    
##  [9] "long"                    "totale_casi"            
## [11] "note"

In this data anlysis of the covid19 impact, Italy is divided in two part, the north and the south sides, considering as a centre point a distinctive "geographic center" which also appears on the inevitable road signs, the exact point has been identified, and signalised with a plate on the tourist maps, in "Ponte Cardona", at these exact coordinates: Latitude 42 ° 30 '11 "N (42.503056) - Longitude 12'34'24 "(12.573333).

source:https://www.giuntitvp.it/blog/geoblog/la-palude-del-centro-d-italia/#:~:text=La%20distinzione%20%E2%80%9Ccentro%20geografico%E2%80%9D%20compare,'34'24%22%20E.

For the purpose to identify the difference in the number of cases btween the north and the south of Italy, the data set would be approximately subdivided in two parts, as follow:

CV19_pro_ns<-CV19_pro_backup%>%
  rename(date="data",
         region="denominazione_regione",
         province="denominazione_provincia",
         tot_cases=totale_casi)%>%
  group_by(province)%>%
  mutate(cases=c(0,diff(tot_cases)),case_incidence=round((cases/tot_cases*100),2))%>%
  select(date,region,province,lat,long,tot_cases,cases,case_incidence)%>%
  filter(!province=="In fase di definizione/aggiornamento")%>%
  arrange(desc(date))

head(CV19_pro_ns)
## # A tibble: 6 x 8
## # Groups:   province [6]
##   date       region  province           lat  long tot_cases cases case_incidence
##   <fct>      <fct>   <fct>            <dbl> <dbl>     <int> <dbl>          <dbl>
## 1 2020-12-1… Abruzzo L'Aquila          42.4  13.4     10158    50          0.49 
## 2 2020-12-1… Abruzzo Teramo            42.7  13.7      8471   110          1.3  
## 3 2020-12-1… Abruzzo Pescara           42.5  14.2      6346    37          0.580
## 4 2020-12-1… Abruzzo Chieti            42.4  14.2      6070    65          1.07 
## 5 2020-12-1… Abruzzo Fuori Regione /…  NA    NA         261 -2739      -1049.   
## 6 2020-12-1… Basili… Potenza           40.6  15.8      5787    89          1.54
lat_rg<-range(CV19_pro_ns$lat,na.rm=TRUE)
long_rg<-range(CV19_pro_ns$long,na.rm=TRUE)
require(data.table)
## Loading required package: data.table
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:reshape2':
## 
##     dcast, melt
## The following object is masked from 'package:purrr':
## 
##     transpose
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
data.table(GeoRange=c("min","max"),lat_rg,long_rg)
##    GeoRange   lat_rg   long_rg
## 1:      min 36.92509  7.320149
## 2:      max 46.49933 18.171897
CV19_pro_rm<-CV19_pro_ns%>%
 filter(province=="Roma")

In this section will be considered all the regions with latidude greater than the latidute of the centre point, as well as per the longitude will be considered all the values below the given one. At opposite to estimate the south it will be considered a lat less than the given point and a lon greater.

covid19_north<-CV19_pro_ns%>%filter(lat>=42.503056,long<=12.573333)
head(covid19_north)
## # A tibble: 6 x 8
## # Groups:   province [6]
##   date         region    province       lat  long tot_cases cases case_incidence
##   <fct>        <fct>     <fct>        <dbl> <dbl>     <int> <dbl>          <dbl>
## 1 2020-12-11T… Emilia-R… Piacenza      45.1  9.69     13002    88           0.68
## 2 2020-12-11T… Emilia-R… Parma         44.8 10.3      11173    75           0.67
## 3 2020-12-11T… Emilia-R… Reggio nell…  44.7 10.6      20159   198           0.98
## 4 2020-12-11T… Emilia-R… Modena        44.6 10.9      25241   111           0.44
## 5 2020-12-11T… Emilia-R… Bologna       44.5 11.3      31002   412           1.33
## 6 2020-12-11T… Emilia-R… Ferrara       44.8 11.6       7116   100           1.41
(unique(covid19_north$region))
##  [1] Emilia-Romagna Liguria        Lombardia      P.A. Bolzano   P.A. Trento   
##  [6] Piemonte       Toscana        Umbria         Valle d'Aosta  Veneto        
## 21 Levels: Abruzzo Basilicata Calabria Campania ... Veneto
covid19_south<-CV19_pro_ns%>%filter(lat<42.503056,long>12.573333)%>%
  arrange(desc(date))
head(covid19_south)
## # A tibble: 6 x 8
## # Groups:   province [6]
##   date             region    province   lat  long tot_cases cases case_incidence
##   <fct>            <fct>     <fct>    <dbl> <dbl>     <int> <dbl>          <dbl>
## 1 2020-12-11T17:0… Abruzzo   L'Aquila  42.4  13.4     10158    50          0.49 
## 2 2020-12-11T17:0… Abruzzo   Pescara   42.5  14.2      6346    37          0.580
## 3 2020-12-11T17:0… Abruzzo   Chieti    42.4  14.2      6070    65          1.07 
## 4 2020-12-11T17:0… Basilica… Potenza   40.6  15.8      5787    89          1.54 
## 5 2020-12-11T17:0… Basilica… Matera    40.7  16.6      3196    15          0.47 
## 6 2020-12-11T17:0… Calabria  Cosenza   39.3  16.3      6180    77          1.25
unique(covid19_south$region)
## [1] Abruzzo    Basilicata Calabria   Campania   Lazio      Molise     Puglia    
## [8] Sicilia   
## 21 Levels: Abruzzo Basilicata Calabria Campania ... Veneto
#esquisse::esquisser(covid19_north)

ggplot(covid19_north) +
 aes(x = date, fill = region, weight = cases) +
 geom_bar() +
 scale_fill_hue() +
 theme_minimal()+
  labs(title="Covid19 IT North Geopoints", 
       subtitle="Selected regions as above the centre point",
       caption="Source: Civil Protection")

ggplot(covid19_south) +
 aes(x = date, fill = region, weight = cases) +
 geom_bar() +
 scale_fill_hue() +
 theme_minimal()+
  labs(title="Covid19 IT South Geopoints", 
       subtitle="Selected regions as below the centre point",
       caption="Source: Civil Protection")