Last updated: Wed 09 Feb 2022 (03:11:45 UTC [+0000])
- Data available from 26 Feb 2020 until 20 Dec 2021 (664 days
- 0 missing).
- 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 oforigVars.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 Regionsvalue: Numeric value.valueUnits: Units for the variablevalue.
- Download the original unprocessed data (json to CSV) from: covid19pt_DSSG_Orig.csv
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/
| 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) |
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))]# 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$data2, na.rm = TRUE), NA)) +
scale_y_continuous(breaks = seq(0, max(obAll[, dailyChange], na.rm = TRUE) + 50, 50)) +
theme(legend.position = "bottom") +
labs(
title = "COVID-19 Portugal: Number Daily Deaths with 7 Day Rolling Mean",
x = "",
y = "Number of Deaths",
colour = "",
fill = "",
caption = paste0("Updated on: ", format(Sys.time(), "%a %d %b %Y (%H:%M:%S %Z [%z])"))
)
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 7 row(s) containing missing values (geom_path).
CV[origType=="confirmados" & !(ageGrp %chin% c("", "desconhecidos"))][
, .(valueFM = sum(value)), .(data, ageGrp)] %>%
ggplot(., aes(x=data, y=valueFM, colour = ageGrp)) +
geom_line() +
scale_x_date(date_breaks = "2 months",
date_labels = "%b-%y",
limits = c(min(cvwd$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 row(s) containing missing values (geom_path).
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 row(s) containing missing values (geom_path).
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.
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