Deaths registered weekly in England and Wales, provisional
Source:R/ons_mortality.R
ons_mortality.RdProvisional counts of the number of deaths registered in England and Wales, by age, sex and region, from week commencing 8th January 2010 to 3rd April 2020.
Usage
data(ons_mortality)Format
Data frame with five columns
- category_1
character, containing the names of the groups for counts, for example "Total deaths", "all ages".
- category_2
character, subcategory of names of groups where necessary, for example details of region: "East", details of age bands "15-44".
- counts
numeric, numbers of deaths in whole numbers and average numbers with decimal points. To retain the integrity of the format this column data is left as character.
- date
date, format is yyyy-mm-dd; all dates are a Friday.
- week_no
integer, each week in a year is numbered sequentially.
Source
Collected by Zoë Turner, Apr-2020 from https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales
Details
Source and licence acknowledgement
This data has been made available through Office of National Statistics under the Open Government Licence https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
Examples
data(ons_mortality)
library(dplyr)
library(tidyr)
# create a dataset that is "wide" with each date as a column
ons_mortality |>
select(-week_no) |>
pivot_wider(
names_from = date,
values_from = counts
)
#> # A tibble: 97 × 537
#> category_1 category_2 `2010-01-08` `2010-01-15` `2010-01-22` `2010-01-29`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Total deaths all ages 12968 12541 11762 11056
#> 2 Total deaths average o… 12050 12600 11692. 11069.
#> 3 All respirato… v 2001 2345 2293 1955 1698
#> 4 Persons Under 1 y… 61 69 66 68
#> 5 Persons 01-14 24 29 18 21
#> 6 Persons 15-44 299 331 347 336
#> 7 Persons 45-64 1570 1465 1392 1300
#> 8 Persons 65-74 2049 1860 1781 1666
#> 9 Persons 75-84 3952 3883 3501 3359
#> 10 Persons 85+ 5012 4902 4654 4305
#> # ℹ 87 more rows
#> # ℹ 531 more variables: `2010-02-05` <dbl>, `2010-02-12` <dbl>,
#> # `2010-02-19` <dbl>, `2010-02-26` <dbl>, `2010-03-05` <dbl>,
#> # `2010-03-12` <dbl>, `2010-03-19` <dbl>, `2010-03-26` <dbl>,
#> # `2010-04-02` <dbl>, `2010-04-09` <dbl>, `2010-04-16` <dbl>,
#> # `2010-04-23` <dbl>, `2010-04-30` <dbl>, `2010-05-07` <dbl>,
#> # `2010-05-14` <dbl>, `2010-05-21` <dbl>, `2010-05-28` <dbl>, …