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Mid Year 2022 for Trafford local authority level
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‎population/mid_year_2022/index.Rmd‎

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---
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pagetitle: "Mid-2022 population estimates"
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lang: "en-GB"
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output:
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html_document:
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css: ../../guidance/template/styles.css
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self_contained: TRUE
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
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library(tidyverse) ; library(htmltools) ; library(DT)
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df <- read_csv("mid-year_2022_population_estimates_local_authority.csv") %>%
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rename_all(. %>% str_to_title() %>% str_replace_all("_", " ")) %>%
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mutate(`Area name` = factor(`Area name`)) %>%
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rename_all(. %>% str_to_title())
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```
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<header>
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<span id="a11yNav"><a href="#downloads" class="themeBlockDark">Skip to downloads</a></span>
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<a href="https://www.trafforddatalab.io/data.html"><img src="https://www.trafforddatalab.io/assets/logo/trafforddatalab_logo.svg" alt="Trafford Data Lab" aria-label="Return to Trafford Data Lab data page" width="100" class="traffordDataLabLogo"/></a>
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<h1>Mid-2022 population estimates</h1>
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*Estimates of the usual resident population in Trafford during mid-2022.*
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</header>
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<main>
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<h2>Metadata</h2>
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<table id="metadata" class="table table-condensed">
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<thead>
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<tr class="header">
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<th>Source</th>
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<th>Publisher</th>
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<th>Time period</th>
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<th>Geography</th>
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<th>Licence</th>
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<th>Attribution</th>
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<th>Last updated</th>
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</tr>
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</thead>
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<tr>
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<td>Office for National Statistics</td>
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<td><a href="https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/estimatesofthepopulationforenglandandwales" target="_blank">Office for National Statistics</a></td>
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<td><time datetime="2022-06-30">2022-06-30</time></td>
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<td>Local authority</td>
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<td><a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a></td>
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<td>Contains public sector information licensed under the Open Government Licence v3.0</td>
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<td><time datetime="2023-11-24">2023-11-24</time></td>
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</tr>
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</table>
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<h2>Preview</h2>
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Sort, filter and export a selection of the data using the table.
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```{r preview}
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datatable(df,
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class = 'cell-border stripe',
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rownames = FALSE,
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extensions = c('Scroller', 'Buttons', 'KeyTable'),
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options = list(
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keys = TRUE,
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search = list(regex = TRUE),
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searchHighlight = TRUE,
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dom = 'ftB',
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buttons = list(
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list(extend = "csv",
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text = "Download selection")),
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deferRender = TRUE,
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width = NULL,
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scrollX = TRUE,
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scrollY = 120,
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scroller = TRUE,
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columnDefs = list(
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list(className = 'dt-left', targets = "_all"),
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list(visible = TRUE, targets = "_all")
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)))
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```
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<div id="tableFooterTitle">Mid-2022 population estimates for Trafford's wards</div>
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<h2>Data</h2>
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<span id ="downloads">Download the complete dataset using the following links:</span>
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|File name |Format |
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|---|---|
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|[mid-year_2022_population_estimates_local_authority.csv](mid-year_population_estimates_local_authority.csv) |CSV |
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<h2>Pre-processing</h2>
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[The R script used to process the data](pre-processing.R) can be adapted to other areas. For information about R visit the <a href="https://cran.r-project.org" target="_blank">CRAN website</a>.
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</main>
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<script>
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// Fixes to dynamic elements created by DataTables to improve accessibility
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var removeElFn = setInterval(removeElement, 1000);
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function removeElement() {
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try {
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var el = document.getElementsByClassName("dataTables_scrollBody")[0].childNodes[0];
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el.removeChild(el.childNodes[0]); // Remove unwanted hidden text element from table - causing accessibility alert as it has no label
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el.parentNode.setAttribute('tabindex', '0'); // add attribute allowing keyboard users to access scrollable area
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clearInterval(removeElFn); // cancel any further calls to this function as we have completed all actions
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}
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catch(e) {
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// form element 'el' doesn't exist yet so setInterval will call the function again until it does
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}
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}
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</script>

‎population/mid_year_2022/index.html‎

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‎population/mid_year_2022/mid-year_2022_population_estimates_local_authority.csv‎

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period,area_code,area_name,geography,sex,all_ages,aged_0_to_15,aged_16_to_64,aged_65_and_over,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90+
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2022-06-30,E08000009,Trafford,Local authority,Persons,236301,49323,145158,41820,2283,2426,2507,2888,2872,3049,3242,3110,3365,3282,3517,3684,3307,3343,3298,3150,3030,2981,2726,1728,1804,1713,2192,2486,2443,2547,2375,2431,2451,2563,2753,2849,2884,3087,3086,3163,3439,3509,3564,3497,3526,3801,3632,3598,3377,3195,3176,3276,3156,3211,3442,3401,3372,3225,3211,3183,3205,3210,3212,3075,2879,2764,2671,2581,2478,2355,2261,2187,2102,2009,1998,2030,2050,2018,2166,2192,1631,1667,1663,1538,1240,1169,1183,1225,1079,989,902,757,660,615,2134
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2022-06-30,E08000009,Trafford,Local authority,Females,121052,24013,74021,23018,1115,1146,1197,1446,1394,1533,1605,1509,1627,1572,1684,1806,1597,1618,1629,1535,1488,1453,1325,779,864,838,1114,1231,1200,1225,1268,1186,1191,1267,1374,1529,1533,1663,1622,1720,1783,1840,1891,1821,1878,1987,1878,1889,1751,1635,1660,1617,1600,1626,1759,1725,1728,1599,1612,1627,1649,1599,1649,1565,1449,1448,1339,1298,1249,1184,1108,1138,1067,1065,1041,1066,1115,1049,1129,1183,907,944,944,875,699,674,715,696,616,586,536,445,399,373,1464
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2022-06-30,E08000009,Trafford,Local authority,Males,115249,25310,71137,18802,1168,1280,1310,1442,1478,1516,1637,1601,1738,1710,1833,1878,1710,1725,1669,1615,1542,1528,1401,949,940,875,1078,1255,1243,1322,1107,1245,1260,1296,1379,1320,1351,1424,1464,1443,1656,1669,1673,1676,1648,1814,1754,1709,1626,1560,1516,1659,1556,1585,1683,1676,1644,1626,1599,1556,1556,1611,1563,1510,1430,1316,1332,1283,1229,1171,1153,1049,1035,944,957,964,935,969,1037,1009,724,723,719,663,541,495,468,529,463,403,366,312,261,242,670

‎population/mid_year_2022/pre_processing.R‎

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#Mid-year estimates at Local authority level
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# Source: ONS
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# URL: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/estimatesofthepopulationforenglandandwales
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# Licence: Open Government Licence 3.0
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library(httr) ; library(readxl) ; library(tidyverse)
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tmp <- tempfile(fileext = ".xlsx")
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GET(url = "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/estimatesofthepopulationforenglandandwales/mid20222023localauthorityboundaires/mye22tablesew2023geogs.xlsx",
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write_disk(tmp))
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mid2022P <- read_xlsx(tmp, sheet = 7, skip = 7) %>%
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filter(Code %in% c("E08000009")) %>%
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mutate(sex = "Persons")
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mid2022F <- read_xlsx(tmp, sheet = 8, skip = 7) %>%
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filter(Code %in% c("E08000009")) %>%
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mutate(sex = "Females")
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mid2022M <- read_xlsx(tmp, sheet = 9, skip = 7) %>%
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filter(Code %in% c("E08000009")) %>%
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mutate(sex = "Males")
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df <- bind_rows(mid2022P, mid2022F, mid2022M) %>%
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mutate(geography = "Local authority",
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period = "2022-06-30") %>%
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mutate(aged_0_to_15 = rowSums(select(., `0`:`15`)),
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aged_16_to_64 = rowSums(select(., `16`:`64`)),
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aged_65_and_over = rowSums(select(., `65`:`90+`))) %>%
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select(period, area_code = Code, area_name = Name, geography, sex, all_ages = `All ages`, aged_0_to_15, aged_16_to_64, aged_65_and_over, everything(), -Geography)
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write_csv(df, "mid-year_2022_population_estimates_local_authority.csv")

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