For the CHerPP project it will be important to understand the demographics of the area around the dock redevelopment project and beyond. This is important to ensure that we can gain as close to a representative sample as possible.
Given the nature of the project ethnicity will be an important demographic factor. ## Preamble We are using the NOMIS API to access this data so will need to load the nomisr package. Also load the other standard packages as well.
# load libraries
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(nomisr)First we have a look at all data available through NOMIS the includes population.
a <- nomis_search(name = '*population*')
tibble::glimpse(a)Rows: 110
Columns: 14
$ agencyid <chr> "NOMIS", "NOMIS", "NOMIS", "NOMIS…
$ id <chr> "NM_17_1", "NM_17_5", "NM_31_1", …
$ uri <chr> "Nm-17d1", "Nm-17d5", "Nm-31d1", …
$ version <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ annotations.annotation <list> [<data.frame[9 x 2]>], [<data.fr…
$ components.attribute <list> [<data.frame[7 x 4]>], [<data.fr…
$ components.dimension <list> [<data.frame[4 x 3]>], [<data.fr…
$ components.primarymeasure.conceptref <chr> "OBS_VALUE", "OBS_VALUE", "OBS_VA…
$ components.timedimension.codelist <chr> "CL_17_1_TIME", "CL_17_5_TIME", "…
$ components.timedimension.conceptref <chr> "TIME", "TIME", "TIME", "TIME", "…
$ description.value <chr> "A residence based labour market …
$ description.lang <chr> "en", "en", "en", "en", "en", NA,…
$ name.value <chr> "annual population survey", "annu…
$ name.lang <chr> "en", "en", "en", "en", "en", "en…