README.md
In BigelowLab/MeCDC: MeCDC - data from Maine CDC
MeCDC
Simplified access to some data from Maine CDC, in particular COVID-19.
Maine's CDC data is augmented by the COVID19 data hub historical cumulative counts. This data lags Maine's by one or two days.
While this has been developed for Computational Oceanography Lab coding ecosystem and Bigelow Lab it would be easy to adapt to other settings.
Requirements
Installation
remotes::install_github("BigelowLab/MeCDC"")
Usage
Fetch the daily update of cumumaltive cases from Maine CDC.
library(MeCDC)
x <- fetch_covid_cumulative()
x
# Simple feature collection with 16 features and 13 fields
# geometry type: MULTIPOLYGON
# dimension: XY
# bbox: xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# epsg (SRID): 4326
# proj4string: +proj=longlat +datum=WGS84 +no_defs
# # A tibble: 16 x 14
# date geoid County pop density Confirmed Recovered Hospitalizations Deaths dConfirmed dRecovered dHospitalizatio… dDeaths
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2020… 23001 Andro… 107679 230. 36 19 11 1 0.334 0.176 0.102 0.00929
# 2 2020… 23003 Aroos… 67111 10.1 2 1 NA NA 0.0298 0.0149 NA NA
# 3 2020… 23005 Cumbe… 293557 351. 386 212 59 17 1.31 0.722 0.201 0.0579
# 4 2020… 23007 Frank… 29897 17.6 13 5 1 NA 0.435 0.167 0.0334 NA
# 5 2020… 23009 Hanco… 54811 34.5 6 2 1 NA 0.109 0.0365 0.0182 NA
# 6 2020… 23011 Kenne… 122083 141. 97 24 14 4 0.795 0.197 0.115 0.0328
# 7 2020… 23013 Knox 39771 109. 13 8 2 NA 0.327 0.201 0.0503 NA
# 8 2020… 23015 Linco… 34342 75.3 12 8 NA NA 0.349 0.233 NA NA
# 9 2020… 23017 Oxford 57618 27.7 14 10 1 NA 0.243 0.174 0.0174 NA
# 10 2020… 23019 Penob… 151096 44.5 46 32 8 NA 0.304 0.212 0.0529 NA
# 11 2020… 23021 Pisca… 16800 4.24 1 1 NA NA 0.0595 0.0595 NA NA
# 12 2020… 23023 Sagad… 35634 140. 17 9 5 NA 0.477 0.253 0.140 NA
# 13 2020… 23025 Somer… 50592 12.9 16 4 NA NA 0.316 0.0791 NA NA
# 14 2020… 23027 Waldo 39694 54.4 43 5 1 8 1.08 0.126 0.0252 0.202
# 15 2020… 23029 Washi… 31490 12.3 2 2 NA NA 0.0635 0.0635 NA NA
# 16 2020… 23031 York 206229 208. 183 101 36 6 0.887 0.490 0.175 0.0291
# # … with 1 more variable: geometry <MULTIPOLYGON [°]>
Or read an archived version.
x <- read_covid_cumulative("2020-04-21")
Make a graphics that shows cumulative counts and density by county.
plot_cumulative(x)
COVID19 data hub
COVID19 data hub can be read using
datahub <- read_covid19datahub()
tail(datahub)
# # A tibble: 6 x 6
# County Confirmed Recovered Hospitalizations Deaths date
# <chr> <dbl> <dbl> <dbl> <dbl> <date>
# 1 York 156 0 0 4 2020-04-15
# 2 York 164 0 0 6 2020-04-16
# 3 York 170 0 0 6 2020-04-17
# 4 York 175 0 0 6 2020-04-18
# 5 York 177 0 0 6 2020-04-19
# 6 York 181 0 0 6 2020-04-20
You can combine any given day's data with the county geometry.
recent <- merge_covid_census(datahub %>% dplyr::filter(date == as.Date("2020-04-20")))
recent
# Simple feature collection with 16 features and 13 fields
# geometry type: MULTIPOLYGON
# dimension: XY
# bbox: xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# CRS: 4326
# # A tibble: 16 x 14
# date geoid County pop density Confirmed Recovered Hospitalizations Deaths dConfirmed dRecovered
# <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2020-04-20 23001 Andro… 107679 230. 35 0 0 1 0.325 0
# 2 2020-04-20 23003 Aroos… 67111 10.1 2 0 0 0 0.0298 0
# 3 2020-04-20 23005 Cumbe… 293557 351. 380 0 0 16 1.29 0
# 4 2020-04-20 23007 Frank… 29897 17.6 13 0 0 0 0.435 0
# 5 2020-04-20 23009 Hanco… 54811 34.5 6 0 0 0 0.109 0
# 6 2020-04-20 23011 Kenne… 122083 141. 97 0 0 4 0.795 0
# 7 2020-04-20 23013 Knox 39771 109. 12 0 0 0 0.302 0
# 8 2020-04-20 23015 Linco… 34342 75.3 12 0 0 0 0.349 0
# 9 2020-04-20 23017 Oxford 57618 27.7 14 0 0 0 0.243 0
# 10 2020-04-20 23019 Penob… 151096 44.5 44 0 0 0 0.291 0
# 11 2020-04-20 23021 Pisca… 16800 4.24 1 0 0 0 0.0595 0
# 12 2020-04-20 23023 Sagad… 35634 140. 16 0 0 0 0.449 0
# 13 2020-04-20 23025 Somer… 50592 12.9 16 0 0 0 0.316 0
# 14 2020-04-20 23027 Waldo 39694 54.4 43 0 0 8 1.08 0
# 15 2020-04-20 23029 Washi… 31490 12.3 2 0 0 0 0.0635 0
# 16 2020-04-20 23031 York 206229 208. 181 0 0 6 0.878 0
# # … with 3 more variables: dHospitalizations <dbl>, dDeaths <dbl>, geometry <MULTIPOLYGON [°]>
BigelowLab/MeCDC documentation built on April 22, 2020, 2:29 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
MeCDC
Simplified access to some data from Maine CDC, in particular COVID-19.
Maine's CDC data is augmented by the COVID19 data hub historical cumulative counts. This data lags Maine's by one or two days.
While this has been developed for Computational Oceanography Lab coding ecosystem and Bigelow Lab it would be easy to adapt to other settings.
Requirements
Installation
remotes::install_github("BigelowLab/MeCDC"")
Usage
Fetch the daily update of cumumaltive cases from Maine CDC.
library(MeCDC)
x <- fetch_covid_cumulative()
x
# Simple feature collection with 16 features and 13 fields
# geometry type: MULTIPOLYGON
# dimension: XY
# bbox: xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# epsg (SRID): 4326
# proj4string: +proj=longlat +datum=WGS84 +no_defs
# # A tibble: 16 x 14
# date geoid County pop density Confirmed Recovered Hospitalizations Deaths dConfirmed dRecovered dHospitalizatio… dDeaths
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2020… 23001 Andro… 107679 230. 36 19 11 1 0.334 0.176 0.102 0.00929
# 2 2020… 23003 Aroos… 67111 10.1 2 1 NA NA 0.0298 0.0149 NA NA
# 3 2020… 23005 Cumbe… 293557 351. 386 212 59 17 1.31 0.722 0.201 0.0579
# 4 2020… 23007 Frank… 29897 17.6 13 5 1 NA 0.435 0.167 0.0334 NA
# 5 2020… 23009 Hanco… 54811 34.5 6 2 1 NA 0.109 0.0365 0.0182 NA
# 6 2020… 23011 Kenne… 122083 141. 97 24 14 4 0.795 0.197 0.115 0.0328
# 7 2020… 23013 Knox 39771 109. 13 8 2 NA 0.327 0.201 0.0503 NA
# 8 2020… 23015 Linco… 34342 75.3 12 8 NA NA 0.349 0.233 NA NA
# 9 2020… 23017 Oxford 57618 27.7 14 10 1 NA 0.243 0.174 0.0174 NA
# 10 2020… 23019 Penob… 151096 44.5 46 32 8 NA 0.304 0.212 0.0529 NA
# 11 2020… 23021 Pisca… 16800 4.24 1 1 NA NA 0.0595 0.0595 NA NA
# 12 2020… 23023 Sagad… 35634 140. 17 9 5 NA 0.477 0.253 0.140 NA
# 13 2020… 23025 Somer… 50592 12.9 16 4 NA NA 0.316 0.0791 NA NA
# 14 2020… 23027 Waldo 39694 54.4 43 5 1 8 1.08 0.126 0.0252 0.202
# 15 2020… 23029 Washi… 31490 12.3 2 2 NA NA 0.0635 0.0635 NA NA
# 16 2020… 23031 York 206229 208. 183 101 36 6 0.887 0.490 0.175 0.0291
# # … with 1 more variable: geometry <MULTIPOLYGON [°]>
Or read an archived version.
x <- read_covid_cumulative("2020-04-21")
Make a graphics that shows cumulative counts and density by county.
plot_cumulative(x)
COVID19 data hub
COVID19 data hub can be read using
datahub <- read_covid19datahub()
tail(datahub)
# # A tibble: 6 x 6
# County Confirmed Recovered Hospitalizations Deaths date
# <chr> <dbl> <dbl> <dbl> <dbl> <date>
# 1 York 156 0 0 4 2020-04-15
# 2 York 164 0 0 6 2020-04-16
# 3 York 170 0 0 6 2020-04-17
# 4 York 175 0 0 6 2020-04-18
# 5 York 177 0 0 6 2020-04-19
# 6 York 181 0 0 6 2020-04-20
You can combine any given day's data with the county geometry.
recent <- merge_covid_census(datahub %>% dplyr::filter(date == as.Date("2020-04-20")))
recent
# Simple feature collection with 16 features and 13 fields
# geometry type: MULTIPOLYGON
# dimension: XY
# bbox: xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# CRS: 4326
# # A tibble: 16 x 14
# date geoid County pop density Confirmed Recovered Hospitalizations Deaths dConfirmed dRecovered
# <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2020-04-20 23001 Andro… 107679 230. 35 0 0 1 0.325 0
# 2 2020-04-20 23003 Aroos… 67111 10.1 2 0 0 0 0.0298 0
# 3 2020-04-20 23005 Cumbe… 293557 351. 380 0 0 16 1.29 0
# 4 2020-04-20 23007 Frank… 29897 17.6 13 0 0 0 0.435 0
# 5 2020-04-20 23009 Hanco… 54811 34.5 6 0 0 0 0.109 0
# 6 2020-04-20 23011 Kenne… 122083 141. 97 0 0 4 0.795 0
# 7 2020-04-20 23013 Knox 39771 109. 12 0 0 0 0.302 0
# 8 2020-04-20 23015 Linco… 34342 75.3 12 0 0 0 0.349 0
# 9 2020-04-20 23017 Oxford 57618 27.7 14 0 0 0 0.243 0
# 10 2020-04-20 23019 Penob… 151096 44.5 44 0 0 0 0.291 0
# 11 2020-04-20 23021 Pisca… 16800 4.24 1 0 0 0 0.0595 0
# 12 2020-04-20 23023 Sagad… 35634 140. 16 0 0 0 0.449 0
# 13 2020-04-20 23025 Somer… 50592 12.9 16 0 0 0 0.316 0
# 14 2020-04-20 23027 Waldo 39694 54.4 43 0 0 8 1.08 0
# 15 2020-04-20 23029 Washi… 31490 12.3 2 0 0 0 0.0635 0
# 16 2020-04-20 23031 York 206229 208. 181 0 0 6 0.878 0
# # … with 3 more variables: dHospitalizations <dbl>, dDeaths <dbl>, geometry <MULTIPOLYGON [°]>
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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