/Dados_COVID-19_PT

Portuguese COVID-19 Data - User Friendly Version

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Daily Portuguese COVID-19 Data

Last updated: Thu 17 Nov 2022 (16:20:00 UTC [+0000])

  • Data available from 26 Feb 2020 until 20 Dec 2021 (664 days
    • 0 missing).

Download User Friendly Version

  • Download the user friendly data from: covid19pt_DSSG_Long.csv or use the following direct link in your program:
  • Variables
    • data: Date (Portuguese spelling).
    • origVars: Variable name taken from source data.
    • origType: Original variable count type.
    • other: Other types of origVars.
    • symptoms: Recorded COVID-19 symptoms.
    • sex: Gender (F - Females, M - Males, All - Females & Males).
    • ageGrp: Age groups in years (desconhecidos - unknown).
    • ageGrpLower: Lower limit of age group (useful for sorting).
    • ageGrpUpper: Upper limit of age group.
    • region: Portuguese Regions
    • value: Numeric value.
    • valueUnits: Units for the variable value.

Source

For more information about the data and variables see: https://github.com/dssg-pt/covid19pt-data

The original data were downloaded from an API provide by VOST https://covid19-api.vost.pt/Requests/get_entry/

Summary: Last 10 (available) Days

Date Cases (7 Day Mean) Active Cases Deaths (7 Day Mean)
Sat 11 Dec 2021 5373 (3915.4) 65648 19 (18.7)
Sun 12 Dec 2021 3879 (3928.7) 68117 13 (17.3)
Mon 13 Dec 2021 2314 (3942.7) 68538 15 (17.4)
Tue 14 Dec 2021 3591 (3967.6) 65757 14 (16.4)
Wed 15 Dec 2021 5800 (4041.0) 67960 11 (15.9)
Thu 16 Dec 2021 5137 (4262.3) 69672 19 (15.3)
Fri 17 Dec 2021 4644 (4391.1) 70406 24 (16.4)
Sat 18 Dec 2021 5062 (4346.7) 70440 12 (15.4)
Sun 19 Dec 2021 4266 (4402.0) 72989 25 (17.1)
Mon 20 Dec 2021 2752 (4464.6) 73700 18 (17.6)

Example Usage

Read in the data

Using the data.table package to process the data.

# Load Libraries
library(data.table)
library(here)

# Read in data as a data.frame and data.table object.
CVPT <- fread(here("data", "covid19pt_DSSG_Long.csv"))
# You can use the direct link:
# CV <- fread("https://raw.githubusercontent.com/CEAUL/Dados_COVID-19_PT/master/data/covid19pt_DSSG_Long.csv")

# Looking at the key variables in the original long dataset.
CVPT[, .(data, origVars, origType, sex, ageGrp, region, value, valueUnits)]
##              data   origVars   origType sex ageGrp   region  value valueUnits
##     1: 2020-02-26     ativos     ativos All        Portugal     NA           
##     2: 2020-02-27     ativos     ativos All        Portugal     NA           
##     3: 2020-02-28     ativos     ativos All        Portugal     NA           
##     4: 2020-02-29     ativos     ativos All        Portugal     NA           
##     5: 2020-03-01     ativos     ativos All        Portugal     NA           
##    ---                                                                       
## 60420: 2021-12-16 vigilancia vigilancia All        Portugal  95430      Count
## 60421: 2021-12-17 vigilancia vigilancia All        Portugal  97573      Count
## 60422: 2021-12-18 vigilancia vigilancia All        Portugal  99081      Count
## 60423: 2021-12-19 vigilancia vigilancia All        Portugal 100339      Count
## 60424: 2021-12-20 vigilancia vigilancia All        Portugal 100955      Count

# Order data by original variable name and date.
setkeyv(CVPT, c("origVars", "data"))

# Convert data to a data object in dataset and add a change from previous day variable.
# Added a 7 day rolling average for origVars (except for symptoms). 
# Columns `data` is date in Portuguese.
CV <- CVPT[, data := as.Date(data, format = "%Y-%m-%d")][
  , dailyChange := value - shift(value, n=1, fill=NA, type="lag"), by = origVars][
    grepl("^sintomas", origVars), dailyChange := NA][
  , mean7Day := fifelse(origVars %chin% c("ativos", "confirmados", "obitos", "recuperados"), 
                         frollmean(dailyChange, 7), as.numeric(NA))]

Overall Number of Deaths (daily)

# Change the ggplot theme.
theme_set(theme_bw())
# Data error prevents by sex plot.
# obMF <- CV[origType=="obitos" & sex %chin% c("M", "F") & ageGrp=="" & region == "Portugal"]
obAll <- CV[origType=="obitos" & sex %chin% c("All") & ageGrp=="" & region == "Portugal"][ 
  , sex := NA]

obAll %>% 
  ggplot(aes(x = data, y = dailyChange)) +
  geom_bar(stat = "identity", fill = "grey75") +
  geom_line(data = obAll, aes(x = data, y = mean7Day), group=1, colour = "brown") +
  scale_x_date(date_breaks = "2 months",
               date_labels = "%b-%y",
               limits = c(min(cvwd$data2, na.rm = TRUE), NA)) +
  scale_y_continuous(breaks = seq(0, max(obAll[, dailyChange], na.rm = TRUE) + 50, 50)) +
  theme(legend.position = "bottom") +
  labs(
    title = "COVID-19 Portugal: Number Daily Deaths with 7 Day Rolling Mean",
    x = "",
    y = "Number of Deaths",
    colour = "",
    fill = "",
    caption = paste0("Updated on: ", format(Sys.time(), "%a %d %b %Y (%H:%M:%S %Z [%z])"))
    )
## Warning: Removed 1 rows containing missing values (`position_stack()`).
## Warning: Removed 7 rows containing missing values (`geom_line()`).

Recorded Number of Confirmed COVID-19 Cases by Age Group

CV[origType=="confirmados" & !(ageGrp %chin% c("", "desconhecidos"))][
  , .(valueFM = sum(value)), .(data, ageGrp)] %>%
  ggplot(., aes(x=data, y=valueFM, colour = ageGrp)) +
  geom_line() +
  scale_x_date(date_breaks = "2 months",
               date_labels = "%b-%y",
               limits = c(min(cvwd$data2, na.rm = TRUE), NA)) +
  scale_y_continuous(labels = scales::number_format(big.mark = ",")) +
  theme(legend.position = "bottom") +
  labs(
    title = "COVID-19 Portugal: Number of Confirmed Cases by Age Group",
    x = "",
    y = "Number of Confirmed Cases",
    caption = paste0("Updated on: ", format(Sys.time(), "%a %d %b %Y (%H:%M:%S %Z [%z])")),
    colour = "Age Group")
## Warning: Removed 54 rows containing missing values (`geom_line()`).

Recorded Number of Confirmed COVID-19 Cases by Region

CV[origType=="confirmados" & ageGrp=="" & region!="Portugal"] %>%
  ggplot(., aes(x=data, y=value, colour=region)) +
  geom_line() +
  scale_x_date(date_breaks = "2 months",
               date_labels = "%b-%y",
               limits = c(min(cvwd$data2, na.rm = TRUE), NA)) +
  scale_y_log10(labels = scales::number_format(big.mark = ",")) +
  theme(legend.position = "bottom") +
  labs(
    title = "COVID-19 Portugal: Number of Confirmed Cases by Region",
    x = "",
    y = "Number of Confirmed Cases",
    caption = paste0("Updated on: ", format(Sys.time(), "%a %d %b %Y (%H:%M:%S %Z [%z])")),
    colour = "Region")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 648 rows containing missing values (`geom_line()`).

Issues & Notes

Use and interpret with care.

The data are provided as is. Any quality issues or errors in the source data will be reflected in the user friend data.

Please create an issue to discuss any errors, issues, requests or improvements.

Calculated change between days can be negative (dailyChange).

CV[dailyChange<0 & !(origType %in% c("vigilancia", "internados"))][
  , .(data, origType, origVars, value, dailyChange)]
##             data    origType              origVars    value dailyChange
##    1: 2020-05-12      ativos                ativos 23737.00     -249.00
##    2: 2020-05-16      ativos                ativos 23785.00     -280.00
##    3: 2020-05-17      ativos                ativos 23182.00     -603.00
##    4: 2020-05-18      ativos                ativos 21548.00    -1634.00
##    5: 2020-05-22      ativos                ativos 21321.00     -862.00
##   ---                                                                  
## 1025: 2021-11-01          rt           rt_nacional     1.05       -0.03
## 1026: 2021-11-03          rt           rt_nacional     1.03       -0.02
## 1027: 2021-11-29          rt           rt_nacional     1.17       -0.02
## 1028: 2021-12-17          rt           rt_nacional     1.07       -0.01
## 1029: 2020-05-21 transmissao transmissao_importada   767.00       -3.00