The covid19italy R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Italy. The package includes the following three datasets:
italy_total - daily summary of the outbreak on the national levelitaly_region - daily summary of the outbreak on the region levelitaly_province - daily summary of the outbreak on the province levelThe data was pull from Italy Department of Civil Protection
You can install the released version of covid19italy from CRAN with:
Or, install the most recent version from GitHub with:
The covid19italy package dev version is been updated on a daily bases. The update_data function enables a simple refresh of the installed package datasets with the most updated version on Github:
Note: must restart the R session to have the updates available
The italy_total dataset provides an overall summary of the cases in Italy since the beginning of the covid19 outbreak since February 24, 2020. The dataset contains the following fields:
date - timestamp, a Date objecthospitalized_with_symptoms - daily number of patients hospitalized with symptomsintensive_care - daily number of patients on intensive caretotal_hospitalized - daily total number of patients hospitalized (hospitalized_with_symptoms + intensive_care)home_confinement - daily number of people under home confinementcumulative_positive_cases - a daily snapshot of the number of positive casesdaily_positive_cases - daily new positive casesdaily_cases - daily new positive, recovered, and death casesrecovered - total number of recovered cases (cumulative)death - total number of death cases (cumulative)positive_clinical_activity - positive cases emerged from clinical activitypositive_surveys_tests - positive cases emerging from surveys and tests, planned at national or regional levelcumulative_cases - total number of positive cases (cumulative)total_tests - total number of tests performed (cumulative)library(covid19italy)
data(italy_total)
str(italy_total)
#> 'data.frame': 148 obs. of 14 variables:
#> $ date : Date, format: "2020-02-24" "2020-02-25" ...
#> $ hospitalized_with_symptoms: int 101 114 128 248 345 401 639 742 1034 1346 ...
#> $ intensive_care : int 26 35 36 56 64 105 140 166 229 295 ...
#> $ total_hospitalized : int 127 150 164 304 409 506 779 908 1263 1641 ...
#> $ home_confinement : int 94 162 221 284 412 543 798 927 1000 1065 ...
#> $ cumulative_positive_cases : int 221 311 385 588 821 1049 1577 1835 2263 2706 ...
#> $ daily_positive_cases : int 0 90 74 203 233 228 528 258 428 443 ...
#> $ recovered : int 1 1 3 45 46 50 83 149 160 276 ...
#> $ death : int 7 10 12 17 21 29 34 52 79 107 ...
#> $ positive_clinical_activity: int NA NA NA NA NA NA NA NA NA NA ...
#> $ positive_surveys_tests : int NA NA NA NA NA NA NA NA NA NA ...
#> $ cumulative_cases : int 229 322 400 650 888 1128 1694 2036 2502 3089 ...
#> $ total_tests : int 4324 8623 9587 12014 15695 18661 21127 23345 25856 29837 ...
#> $ total_people_tested : int NA NA NA NA NA NA NA NA NA NA ...
head(italy_total)
#> date hospitalized_with_symptoms intensive_care total_hospitalized
#> 1 2020-02-24 101 26 127
#> 2 2020-02-25 114 35 150
#> 3 2020-02-26 128 36 164
#> 4 2020-02-27 248 56 304
#> 5 2020-02-28 345 64 409
#> 6 2020-02-29 401 105 506
#> home_confinement cumulative_positive_cases daily_positive_cases recovered
#> 1 94 221 0 1
#> 2 162 311 90 1
#> 3 221 385 74 3
#> 4 284 588 203 45
#> 5 412 821 233 46
#> 6 543 1049 228 50
#> death positive_clinical_activity positive_surveys_tests cumulative_cases
#> 1 7 NA NA 229
#> 2 10 NA NA 322
#> 3 12 NA NA 400
#> 4 17 NA NA 650
#> 5 21 NA NA 888
#> 6 29 NA NA 1128
#> total_tests total_people_tested
#> 1 4324 NA
#> 2 8623 NA
#> 3 9587 NA
#> 4 12014 NA
#> 5 15695 NA
#> 6 18661 NAThe italy_region dataset provides an overall summary of the cases in Italy’s regions. The dataset contains the following fields:
date - timestamp, a Date objectregion_code - the region coderegion_name - the region namelat - region latitude coordinatelong - region longitude coordinatehospitalized_with_symptoms - daily number of patients hospitalized with symptomsintensive_care - daily number of patients on intensive caretotal_hospitalized - daily total number of patients hospitalized (hospitalized_with_symptoms + intensive_care)home_confinement - daily number of people under home confinementcumulative_positive_cases - a daily snapshot of the number of positive casesdaily_positive_cases - daily new positive casesdaily_cases - daily new positive, recovered, and death casesrecovered - total number of recovered cases (cumulative)death - total number of death cases (cumulative)positive_clinical_activity - positive cases emerged from clinical activitypositive_surveys_tests - positive cases emerging from surveys and tests, planned at national or regional levelcumulative_cases - total number of positive cases, recovered, and death (cumulative)total_tests - total number of tests performed (cumulative)region_spatial - the spatial region names as in the output of the ne_states function from the rnaturalearth packagedata(italy_region)
str(italy_region)
#> 'data.frame': 3108 obs. of 19 variables:
#> $ date : Date, format: "2020-02-24" "2020-02-24" ...
#> $ region_code : int 13 17 18 15 8 6 12 7 3 11 ...
#> $ region_name : chr "Abruzzo" "Basilicata" "Calabria" "Campania" ...
#> $ lat : num 42.4 40.6 38.9 40.8 44.5 ...
#> $ long : num 13.4 15.8 16.6 14.3 11.3 ...
#> $ hospitalized_with_symptoms: int 0 0 0 0 10 0 1 0 76 0 ...
#> $ intensive_care : int 0 0 0 0 2 0 1 0 19 0 ...
#> $ total_hospitalized : int 0 0 0 0 12 0 2 0 95 0 ...
#> $ home_confinement : int 0 0 0 0 6 0 0 0 71 0 ...
#> $ cumulative_positive_cases : int 0 0 0 0 18 0 2 0 166 0 ...
#> $ daily_positive_cases : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ recovered : int 0 0 0 0 0 0 1 0 0 0 ...
#> $ death : int 0 0 0 0 0 0 0 0 6 0 ...
#> $ positive_clinical_activity: int NA NA NA NA NA NA NA NA NA NA ...
#> $ positive_surveys_tests : int NA NA NA NA NA NA NA NA NA NA ...
#> $ cumulative_cases : int 0 0 0 0 18 0 3 0 172 0 ...
#> $ total_tests : int 5 0 1 10 148 58 124 1 1463 16 ...
#> $ total_people_tested : int NA NA NA NA NA NA NA NA NA NA ...
#> $ region_spatial : chr "Abruzzo" "Basilicata" "Calabria" "Campania" ...
head(italy_region)
#> date region_code region_name lat long
#> 1 2020-02-24 13 Abruzzo 42.35122 13.39844
#> 2 2020-02-24 17 Basilicata 40.63947 15.80515
#> 3 2020-02-24 18 Calabria 38.90598 16.59440
#> 4 2020-02-24 15 Campania 40.83957 14.25085
#> 5 2020-02-24 8 Emilia-Romagna 44.49437 11.34172
#> 6 2020-02-24 6 Friuli Venezia Giulia 45.64944 13.76814
#> hospitalized_with_symptoms intensive_care total_hospitalized home_confinement
#> 1 0 0 0 0
#> 2 0 0 0 0
#> 3 0 0 0 0
#> 4 0 0 0 0
#> 5 10 2 12 6
#> 6 0 0 0 0
#> cumulative_positive_cases daily_positive_cases recovered death
#> 1 0 0 0 0
#> 2 0 0 0 0
#> 3 0 0 0 0
#> 4 0 0 0 0
#> 5 18 0 0 0
#> 6 0 0 0 0
#> positive_clinical_activity positive_surveys_tests cumulative_cases
#> 1 NA NA 0
#> 2 NA NA 0
#> 3 NA NA 0
#> 4 NA NA 0
#> 5 NA NA 18
#> 6 NA NA 0
#> total_tests total_people_tested region_spatial
#> 1 5 NA Abruzzo
#> 2 0 NA Basilicata
#> 3 1 NA Calabria
#> 4 10 NA Campania
#> 5 148 NA Emilia-Romagna
#> 6 58 NA Friuli-Venezia GiuliaThe italy_region dataset provides an overall summary of the cases in Italy’s regions. The dataset contains the following fields:
date - timestamp, a Date objectregion_code - the region coderegion_name - the region nameprovince_code - the province codeprovince_name - the province nameprovince_abb - the province abbreviationlat - province latitude coordinatelong - province longitude coordinatetotal_cases - total number of positive cases (cumulative)new_tests - daily number of positive casesprovince_spatial - the spatial province names as in the output of the ne_states function from the rnaturalearth packagedata(italy_province)
str(italy_province)
#> 'data.frame': 19490 obs. of 11 variables:
#> $ date : Date, format: "2020-02-24" "2020-02-24" ...
#> $ region_code : int 13 13 13 13 13 17 17 17 18 18 ...
#> $ region_name : chr "Abruzzo" "Abruzzo" "Abruzzo" "Abruzzo" ...
#> $ province_code : int 66 67 68 69 979 76 77 980 78 79 ...
#> $ province_name : chr "L'Aquila" "Teramo" "Pescara" "Chieti" ...
#> $ province_abb : chr "AQ" "TE" "PE" "CH" ...
#> $ lat : num 42.4 42.7 42.5 42.4 NA ...
#> $ long : num 13.4 13.7 14.2 14.2 NA ...
#> $ total_cases : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ new_cases : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ province_spatial: chr "L'Aquila" "Teramo" "Pescara" "Chieti" ...
head(italy_province)
#> date region_code region_name province_code
#> 1 2020-02-24 13 Abruzzo 66
#> 2 2020-02-24 13 Abruzzo 67
#> 3 2020-02-24 13 Abruzzo 68
#> 4 2020-02-24 13 Abruzzo 69
#> 5 2020-02-24 13 Abruzzo 979
#> 6 2020-02-24 17 Basilicata 76
#> province_name province_abb lat long
#> 1 L'Aquila AQ 42.35122 13.39844
#> 2 Teramo TE 42.65892 13.70440
#> 3 Pescara PE 42.46458 14.21365
#> 4 Chieti CH 42.35103 14.16755
#> 5 In fase di definizione/aggiornamento NA NA
#> 6 Potenza PZ 40.63947 15.80515
#> total_cases new_cases province_spatial
#> 1 0 0 L'Aquila
#> 2 0 0 Teramo
#> 3 0 0 Pescara
#> 4 0 0 Chieti
#> 5 0 0 In fase di definizione/aggiornamento
#> 6 0 0 Potenza