Include the Mid 2022 data for small geographies

Author: IGRiosTC

population/mid_year_2022/index.Rmd

@@ -11,7 +11,7 @@ output:
 knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
 library(tidyverse) ; library(htmltools) ; library(DT) 
 
-df <- read_csv("mid-year_2022_population_estimates_local_authority.csv") %>% 
+df <- read_csv("mid-year_2022_population_estimates_ward.csv") %>% 
     rename_all(. %>% str_to_title() %>% str_replace_all("_", " ")) %>% 
   mutate(`Area name` = factor(`Area name`)) %>% 
   rename_all(. %>% str_to_title())
@@ -44,16 +44,18 @@ df <- read_csv("mid-year_2022_population_estimates_local_authority.csv") %>%
     <td>Office for National Statistics</td>
     <td><a href="https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/estimatesofthepopulationforenglandandwales" target="_blank">Office for National Statistics</a></td>
     <td><time datetime="2022-06-30">2022-06-30</time></td>
-    <td>Local authority</td>
+    <td>Local authority, electoral wards, MSOAs, LSOAs and OAs</td>
     <td><a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a></td>
     <td>Contains public sector information licensed under the Open Government Licence v3.0</td>
-    <td><time datetime="2023-11-24">2023-11-24</time></td>
+    <td><time datetime="2024-05-24">2024-05-24</time></td>
   </tr>
 </table>
 
 <h2>Preview</h2>
 Sort, filter and export a selection of the data using the table.
 
+<div id = "renderDT">
+
 ```{r preview}
 datatable(df, 
           class = 'cell-border stripe',
@@ -68,23 +70,27 @@ datatable(df,
               list(extend = "csv", 
                    text = "Download selection")),
             deferRender = TRUE,
-            width = NULL,
             scrollX = TRUE,
-            scrollY = 120,
+            scrollY = 300,
             scroller = TRUE,
             columnDefs = list(
-              list(className = 'dt-left', targets = "_all"),
-               list(visible = TRUE, targets = "_all")
+              list(className = 'dt-left', targets = "_all")
               )))
 ```
-<div id="tableFooterTitle">Mid-2022 population estimates for Trafford</div>
+</div>
+<div id="tableFooterTitle">Mid-2022 population estimates for Trafford's wards</div>
 
 <h2>Data</h2>
 <span id ="downloads">Download the complete dataset using the following links:</span>
 
 |File name |Format |
 |---|---|
+|[mid-year_2022_population_estimates_all_geographies.csv](mid-year_2022_population_estimates_all_geographies.csv) |CSV |
 |[mid-year_2022_population_estimates_local_authority.csv](mid-year_2022_population_estimates_local_authority.csv) |CSV |
+|[mid-year_2022_population_estimates_ward.csv](mid-year_2022_population_estimates_ward.csv) |CSV |
+|[mid-year_2022_population_estimates_msoa.csv](mid-year_2022_population_estimates_msoa.csv) |CSV |
+|[mid-year_2022_population_estimates_lsoa.csv](mid-year_2022_population_estimates_lsoa.csv) |CSV |
+|[mid-year_2022_population_estimates_oa.csv](mid-year_2022_population_estimates_oa.csv) |CSV |
 
 <h2>Pre-processing</h2>
 [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>.

population/mid_year_2022/index.html

@@ -24,12 +24,160 @@ mid2022M <- read_xlsx(tmp, sheet = 9, skip = 7) %>%
   filter(Code %in% c("E08000009")) %>%
   mutate(sex = "Males")
 
-df <- bind_rows(mid2022P, mid2022F, mid2022M) %>%
+df_la <- bind_rows(mid2022P, mid2022F, mid2022M) %>%
   mutate(geography = "Local authority",
          period = "2022-06-30") %>%
   mutate(aged_0_to_15 = rowSums(select(., `0`:`15`)),
          aged_16_to_64 = rowSums(select(., `16`:`64`)),
          aged_65_and_over = rowSums(select(., `65`:`90+`))) %>% 
-  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) 
-  
-write_csv(df, "mid-year_2022_population_estimates_local_authority.csv")
+  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)
+
+write_csv(df_la, "mid-year_2022_population_estimates_local_authority.csv")
+
+
+#Wards
+
+# Source: ONS
+# URL: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates
+# Licence: Open Government Licence 3.0
+
+tmp <- tempfile(fileext = ".xlsx")
+
+GET(url = "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/wardlevelmidyearpopulationestimatesexperimental/mid2021andmid2022/sapewardstablefinal.xlsx",
+    write_disk(tmp))
+
+df_ward <- read_xlsx(tmp, sheet = 8, skip = 3) %>%
+  filter(`LAD 2023 Code` == "E08000009") %>%
+  rename(area_code = `Ward 2023 Code`, area_name = `Ward 2023 Name`) %>%
+  pivot_longer(Total:M90, names_to = "age", values_to = "value") %>%
+  mutate(period = "2022-06-30") %>%
+  filter(age != "Total") %>%
+  separate(age, into = c("sex", "age"), sep = 1) %>%
+  mutate(sex = case_match(sex,"F"~"Females","M"~"Males")) %>%
+  pivot_wider(names_from = "sex", values_from = value) %>%
+  mutate(Persons = Females + Males) %>%
+  pivot_longer(Females:Persons, names_to = "sex", values_to = "value") %>%
+  pivot_wider(values_from = "value", names_from = "age") %>%
+  select(-`LAD 2023 Code`, -`LAD 2023 Name`) %>%
+  rename(`90+` = `90`) %>%
+  mutate(geography = "Ward") %>%
+  mutate(all_ages = rowSums(select(., `0`:`90+`)),
+           aged_0_to_15 = rowSums(select(., `0`:`15`)),
+         aged_16_to_64 = rowSums(select(., `16`:`64`)),
+         aged_65_and_over = rowSums(select(., `65`:`90+`))) %>% 
+  select(period, area_code, area_name, geography, sex, all_ages, aged_0_to_15, aged_16_to_64, aged_65_and_over, everything()) 
+
+write_csv(df_ward, "mid-year_2022_population_estimates_ward.csv")
+
+#MSOAs
+
+#House of Commons Library MSOA Names
+# URL: https://visual.parliament.uk/msoanames
+
+lookup <- read_csv("https://houseofcommonslibrary.github.io/msoanames/MSOA-Names-Latest.csv") %>%
+  filter(Laname=="Trafford") %>%
+  select(area_code = msoa11cd, area_name = msoa11hclnm)
+
+# Source: ONS
+# URL: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates
+# Licence: Open Government Licence 3.0
+
+tmp <- tempfile(fileext = ".xlsx")
+
+GET(url = "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/middlesuperoutputareamidyearpopulationestimates/mid2021andmid2022/sapemsoasyoatablefinal.xlsx",
+    write_disk(tmp))
+
+df_msoa <- read_xlsx(tmp, sheet = 6, skip = 3) %>%
+  filter(`LAD 2021 Code` == "E08000009") %>%
+  rename(area_code = `MSOA 2021 Code`) %>%
+  pivot_longer(Total:M90, names_to = "age", values_to = "value") %>%
+  mutate(period = "2022-06-30") %>%
+  filter(age != "Total") %>%
+  separate(age, into = c("sex", "age"), sep = 1) %>%
+  mutate(sex = case_match(sex,"F"~"Females","M"~"Males")) %>%
+  pivot_wider(names_from = "sex", values_from = value) %>%
+  mutate(Persons = Females + Males) %>%
+  pivot_longer(Females:Persons, names_to = "sex", values_to = "value") %>%
+  pivot_wider(values_from = "value", names_from = "age") %>%
+  select(-`LAD 2021 Code`, -`LAD 2021 Name`) %>%
+  rename(`90+` = `90`) %>%
+  mutate(geography = "MSOA") %>%
+  mutate(all_ages = rowSums(select(., `0`:`90+`)),
+         aged_0_to_15 = rowSums(select(., `0`:`15`)),
+         aged_16_to_64 = rowSums(select(., `16`:`64`)),
+         aged_65_and_over = rowSums(select(., `65`:`90+`))) %>%
+  left_join(lookup, by = "area_code") %>%
+  select(period, area_code, area_name, geography, sex, all_ages, aged_0_to_15, aged_16_to_64, aged_65_and_over, everything(),-`MSOA 2021 Name`) 
+
+write_csv(df_msoa, "mid-year_2022_population_estimates_msoa.csv")
+
+#LSOA
+
+# Source: ONS
+# URL: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/lowersuperoutputareamidyearpopulationestimates
+# Licence: Open Government Licence 3.0
+
+tmp <- tempfile(fileext = ".xlsx")
+
+GET(url = "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/lowersuperoutputareamidyearpopulationestimates/mid2021andmid2022/sapelsoasyoatablefinal.xlsx",
+    write_disk(tmp))
+
+df_lsoa <- read_xlsx(tmp, sheet = 6, skip = 3) %>%
+  filter(`LAD 2021 Code` == "E08000009") %>%
+  rename(area_code = `LSOA 2021 Code`, area_name = `LSOA 2021 Name`) %>%
+  pivot_longer(Total:M90, names_to = "age", values_to = "value") %>%
+  mutate(period = "2022-06-30") %>%
+  filter(age != "Total") %>%
+  separate(age, into = c("sex", "age"), sep = 1) %>%
+  mutate(sex = case_match(sex,"F"~"Females","M"~"Males")) %>%
+  pivot_wider(names_from = "sex", values_from = value) %>%
+  mutate(Persons = Females + Males) %>%
+  pivot_longer(Females:Persons, names_to = "sex", values_to = "value") %>%
+  pivot_wider(values_from = "value", names_from = "age") %>%
+  select(-`LAD 2021 Code`, -`LAD 2021 Name`) %>%
+  rename(`90+` = `90`) %>%
+  mutate(geography = "LSOA") %>%
+  mutate(all_ages = rowSums(select(., `0`:`90+`)),
+         aged_0_to_15 = rowSums(select(., `0`:`15`)),
+         aged_16_to_64 = rowSums(select(., `16`:`64`)),
+         aged_65_and_over = rowSums(select(., `65`:`90+`))) %>% 
+  select(period, area_code, area_name, geography, sex, all_ages, aged_0_to_15, aged_16_to_64, aged_65_and_over, everything()) 
+
+write_csv(df_lsoa, "mid-year_2022_population_estimates_lsoa.csv")
+
+
+#OA
+
+# Source: ONS
+# URL: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/censusoutputareapopulationestimatessupportinginformation
+# Licence: Open Government Licence 3.0
+
+tmp <- tempfile(fileext = ".xlsx")
+
+GET(url = "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/censusoutputareapopulationestimatessupportinginformation/mid2022/sapeoatablefinal2022v2.xlsx",
+    write_disk(tmp))
+
+df_oa <- read_xlsx(tmp, sheet = 5, skip = 3) %>%
+  filter(`LAD 2021 Code` == "E08000009") %>%
+  rename(area_code = `OA 2021 Code`) %>%
+  pivot_longer(Total:M90, names_to = "age", values_to = "value") %>%
+  mutate(period = "2022-06-30") %>%
+  filter(age != "Total") %>%
+  separate(age, into = c("sex", "age"), sep = 1) %>%
+  mutate(sex = case_match(sex,"F"~"Females","M"~"Males")) %>%
+  pivot_wider(names_from = "sex", values_from = value) %>%
+  mutate(Persons = Females + Males) %>%
+  pivot_longer(Females:Persons, names_to = "sex", values_to = "value") %>%
+  pivot_wider(values_from = "value", names_from = "age") %>%
+  select(-`LAD 2021 Code`, -`LAD 2021 Name`) %>%
+  rename(`90+` = `90`) %>%
+  mutate(geography = "OA", area_name = area_code) %>%
+  mutate(all_ages = rowSums(select(., `0`:`90+`)),
+         aged_0_to_15 = rowSums(select(., `0`:`15`)),
+         aged_16_to_64 = rowSums(select(., `16`:`64`)),
+         aged_65_and_over = rowSums(select(., `65`:`90+`))) %>% 
+  select(period, area_code, area_name, geography, sex, all_ages, aged_0_to_15, aged_16_to_64, aged_65_and_over, everything()) 
+
+write_csv(df_oa, "mid-year_2022_population_estimates_oa.csv")
+
+write_csv(bind_rows(df_la,df_ward,df_lsoa,df_msoa,df_oa), "mid-year_2022_population_estimates_all_geographies.csv")