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:
install.packages("covid19italy")
Or, install the most recent version from GitHub with:
# install.packages("devtools") devtools::install_github("RamiKrispin/covid19Italy")
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) #> Classes 'tbl_df', 'tbl' and 'data.frame': 520 obs. of 19 variables: #> $ date : Date, format: "2020-02-24" "2020-02-25" ... #> $ hospitalized_with_symptoms : num 101 114 128 248 345 ... #> $ intensive_care : num 26 35 36 56 64 105 140 166 229 295 ... #> $ total_hospitalized : num 127 150 164 304 409 ... #> $ home_confinement : num 94 162 221 284 412 ... #> $ cumulative_positive_cases : num 221 311 385 588 821 ... #> $ daily_positive_cases : num 0 90 74 203 233 228 528 258 428 443 ... #> $ recovered : num 1 1 3 45 46 50 83 149 160 276 ... #> $ death : num 7 10 12 17 21 29 34 52 79 107 ... #> $ positive_clinical_activity : num NA NA NA NA NA NA NA NA NA NA ... #> $ positive_surveys_tests : num NA NA NA NA NA NA NA NA NA NA ... #> $ cumulative_cases : num 229 322 400 650 888 ... #> $ total_tests : num 4324 8623 9587 12014 15695 ... #> $ total_people_tested : num NA NA NA NA NA NA NA NA NA NA ... #> $ new_intensive_care : num NA NA NA NA NA NA NA NA NA NA ... #> $ total_positive_molecular_test : num NA NA NA NA NA NA NA NA NA NA ... #> $ total_positive_rapid_antigen_test: num NA NA NA NA NA NA NA NA NA NA ... #> $ molecular_test : num NA NA NA NA NA NA NA NA NA NA ... #> $ rapid_antigen_test : num 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 new_intensive_care #> 1 4324 NA NA #> 2 8623 NA NA #> 3 9587 NA NA #> 4 12014 NA NA #> 5 15695 NA NA #> 6 18661 NA NA #> total_positive_molecular_test total_positive_rapid_antigen_test #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA #> molecular_test rapid_antigen_test #> 1 NA NA #> 2 NA NA #> 3 NA NA #> 4 NA NA #> 5 NA NA #> 6 NA NA
The 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': 10920 obs. of 26 variables: #> $ date : Date, format: "2020-02-24" "2020-02-24" ... #> $ region_code : chr "13" "17" "18" "15" ... #> $ 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: num 0 0 0 0 10 0 1 0 76 0 ... #> $ intensive_care : num 0 0 0 0 2 0 1 0 19 0 ... #> $ total_hospitalized : num 0 0 0 0 12 0 2 0 95 0 ... #> $ home_confinement : num 0 0 0 0 6 0 0 0 71 0 ... #> $ cumulative_positive_cases : num 0 0 0 0 18 0 2 0 166 0 ... #> $ daily_positive_cases : num 0 0 0 0 0 0 0 0 0 0 ... #> $ recovered : num 0 0 0 0 0 0 1 0 0 0 ... #> $ death : num 0 0 0 0 0 0 0 0 6 0 ... #> $ positive_clinical_activity: chr "" "" "" "" ... #> $ positive_surveys_tests : chr "" "" "" "" ... #> $ cumulative_cases : num 0 0 0 0 18 0 3 0 172 0 ... #> $ total_tests : num 5 0 1 10 148 ... #> $ total_people_tested : chr "" "" "" "" ... #> $ new_intensive_care : chr "" "" "" "" ... #> $ total_positive_tests : chr "" "" "" "" ... #> $ total_fast_tests : chr "" "" "" "" ... #> $ daily_positive_tests : chr "" "" "" "" ... #> $ daily_fast_tests : chr "" "" "" "" ... #> $ nuts_code_1 : chr "" "" "" "" ... #> $ nuts_code_2 : chr "" "" "" "" ... #> $ 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 08 Emilia-Romagna 44.49437 11.34172 #> 6 2020-02-24 06 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 0 #> 2 0 #> 3 0 #> 4 0 #> 5 18 #> 6 0 #> total_tests total_people_tested new_intensive_care total_positive_tests #> 1 5 #> 2 0 #> 3 1 #> 4 10 #> 5 148 #> 6 58 #> total_fast_tests daily_positive_tests daily_fast_tests nuts_code_1 #> 1 #> 2 #> 3 #> 4 #> 5 #> 6 #> nuts_code_2 region_spatial #> 1 Abruzzo #> 2 Basilicata #> 3 Calabria #> 4 Campania #> 5 Emilia-Romagna #> 6 Friuli-Venezia Giulia
The 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) #> Classes 'tbl_df', 'tbl' and 'data.frame': 74918 obs. of 14 variables: #> $ date : Date, format: "2020-02-24" "2020-02-24" ... #> $ region_name : chr "Abruzzo" "Abruzzo" "Abruzzo" "Abruzzo" ... #> $ region_code : chr "13" "13" "13" "13" ... #> $ province_name : chr "L'Aquila" "Teramo" "Pescara" "Chieti" ... #> $ province_spatial: chr "L'Aquila" "Teramo" "Pescara" "Chieti" ... #> $ province_abb : chr "AQ" "TE" "PE" "CH" ... #> $ province_code : chr "066" "067" "068" "069" ... #> $ lat : num 42.4 42.7 42.5 42.4 NA ... #> $ long : num 13.4 13.7 14.2 14.2 NA ... #> $ new_cases : num 0 0 0 0 0 0 0 0 0 0 ... #> $ total_cases : num 0 0 0 0 0 0 0 0 0 0 ... #> $ nuts_code_1 : chr "" "" "" "" ... #> $ nuts_code_2 : chr "" "" "" "" ... #> $ nuts_code_3 : chr "" "" "" "" ... head(italy_province) #> date region_name region_code province_name #> 1 2020-02-24 Abruzzo 13 L'Aquila #> 2 2020-02-24 Abruzzo 13 Teramo #> 3 2020-02-24 Abruzzo 13 Pescara #> 4 2020-02-24 Abruzzo 13 Chieti #> 5 2020-02-24 Abruzzo 13 In fase di definizione/aggiornamento #> 6 2020-02-24 Basilicata 17 Potenza #> province_spatial province_abb province_code lat #> 1 L'Aquila AQ 066 42.35122 #> 2 Teramo TE 067 42.65892 #> 3 Pescara PE 068 42.46458 #> 4 Chieti CH 069 42.35103 #> 5 In fase di definizione/aggiornamento 979 NA #> 6 Potenza PZ 076 40.63947 #> long new_cases total_cases nuts_code_1 nuts_code_2 nuts_code_3 #> 1 13.39844 0 0 #> 2 13.70440 0 0 #> 3 14.21365 0 0 #> 4 14.16755 0 0 #> 5 NA 0 0 #> 6 15.80515 0 0