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The source for the original dataset is no longer available so I have completely rebuilt the dataset, and included the whole of GM, from a new verifiable source: NHS England.
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‎pharmacies/README.md‎

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[<img src="thumbnail.png">](trafford_pharmacies_styled.geojson)
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</br>
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<table>
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<tr>
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<td>Dataset name</td>
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<td>Trafford pharmacies</td>
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<td>Pharmacies</td>
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</tr>
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<td>Dataset description</td>
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<td>Dataset containing the location of pharmacies in Trafford.</td>
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<td>Location of pharmacies Trafford and Greater Manchester.</td>
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</tr>
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<tr>
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<td>Source</td>
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<td>Trafford Council</td>
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<td>NHS England</td>
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</tr>
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<td>Publisher</td>
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<td>Trafford Council</td>
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<td>NHS Business Services Authority (NHSBSA) Master Data Replacement (MDR)</td>
2017
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<tr>
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<td>Publisher URL</td>
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<td><a href="http://www.trafford.gov.uk/about-your-council/children-families-and-wellbeing/children-families-and-wellbeing.aspx"></a>http://www.trafford.gov.uk/about-your-council/children-families-and-wellbeing/children-families-and-wellbeing.aspx</td>
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<td><a href="https://opendata.nhsbsa.net/dataset/consolidated-pharmaceutical-list">https://opendata.nhsbsa.net/dataset/consolidated-pharmaceutical-list</a></td>
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<td>Geography</td>
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<td>Points</td>
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</tr>
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<td>Geographic coverage</td>
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<td>Trafford</td>
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<td>Trafford, Greater Manchester</td>
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</tr>
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<td>Temporal coverage</td>
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<td>May 2018</td>
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<td>2024-25 Q4 (2025-03-31)</td>
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</tr>
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<td>Update frequency</td>
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<td>Unknown</td>
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<td>Quarterly</td>
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</tr>
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<td>Licence</td>
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</tr>
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<td>Last updated</td>
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<td>May 2018</td>
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<td>2025-06-11</td>
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</tr>
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<td>Notes</td>

‎pharmacies/gm_pharmacies.csv‎

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‎pharmacies/gm_pharmacies.geojson‎

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‎pharmacies/index.Rmd‎

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---
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pagetitle: "Pharmacies in Trafford and Greater Manchester"
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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("trafford_pharmacies.csv") %>%
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rename_all(. %>% str_to_title() %>% str_replace_all("_", " "))
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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>Pharmacies in Trafford and Greater Manchester</h1>
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*Dataset containing the locations of pharmacies in Trafford and Greater Manchester (based on postcode centroid), including opening times.*
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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>NHS England</td>
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<td><a href="https://opendata.nhsbsa.net/dataset/consolidated-pharmaceutical-list" target="_blank">NHS Business Services Authority (NHSBSA) Master Data Replacement (MDR)</a></td>
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<td><time datetime="2025-03-31">2025-03-31</time></td>
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<td>Electoral ward, 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="2025-06-11">2025-06-11</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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scrollX = TRUE,
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scrollY = 300,
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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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)))
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```
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<div id="tableFooterTitle">Pharmacies within Trafford.</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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|[trafford_pharmacies.csv](trafford_pharmacies.csv) |CSV |
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|[trafford_pharmacies.geojson](trafford_pharmacies.geojson) |GeoJSON |
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|[gm_pharmacies.csv](gm_pharmacies.csv) |CSV |
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|[gm_pharmacies.geojson](gm_pharmacies.geojson) |GeoJSON |
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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>

‎pharmacies/index.html‎

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‎pharmacies/pre-processing.R‎

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## Pharmacies in Trafford ##
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## Pharmacies in Trafford and Greater Manchester ##
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# Source: NHS Business Services Authority (NHSBSA) Master Data Replacement (MDR)
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# Publisher URL: https://opendata.nhsbsa.net/dataset/consolidated-pharmaceutical-list
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# - Consolidated Pharmaceutical List - 2024-25 Quarter 4
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# Source: Trafford Council
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# Publisher URL: http://www.trafford.gov.uk/about-your-council/children-families-and-wellbeing/children-families-and-wellbeing.aspx
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# Licence: Open Government Licence
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# load libraries
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# Load required packages ---------------------------
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library(tidyverse) ; library(sf)
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# load data ---------------------------
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df <- read_csv("trafford_pharmacies.csv")
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# load local authorities ---------------------------
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la <- st_read("https://github.com/traffordDataLab/spatial_data/raw/master/local_authority/2016/gm_local_authority_full_resolution.geojson") %>%
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select(area_code, area_name)
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# Load raw data and clean ---------------------------
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df_raw <- read_csv("https://opendata.nhsbsa.net/dataset/240d142d-df82-4e97-b051-12371519e4e1/resource/545609ba-8f93-4adf-8f5a-7a34a1a31881/download/consol_pharmacy_list_202425q4.csv")
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# match with ONS postcodes ---------------------------
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postcodes <- read_csv("https://www.traffordDataLab.io/spatial_data/postcodes/GM_postcodes_2018-02.csv")
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df_postcodes <- left_join(df, postcodes, by = "postcode")
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df <- df_raw %>%
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filter(HEALTH_AND_WELLBEING_BOARD %in% c("BOLTON","BURY","MANCHESTER","OLDHAM","ROCHDALE","SALFORD","STOCKPORT","TAMESIDE","TRAFFORD","WIGAN")) %>%
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mutate(address = str_to_title(str_replace_all(paste0(ADDRESS_FIELD_1, ", ", ADDRESS_FIELD_2, ", ", ADDRESS_FIELD_3, ", ", ADDRESS_FIELD_4), ", NA", "")),
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HEALTH_AND_WELLBEING_BOARD = str_to_title(HEALTH_AND_WELLBEING_BOARD),
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PHARMACY_TRADING_NAME = str_to_title(PHARMACY_TRADING_NAME),
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ORGANISATION_NAME = str_to_title(ORGANISATION_NAME)) %>%
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arrange(HEALTH_AND_WELLBEING_BOARD, PHARMACY_TRADING_NAME) %>%
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select(pharmacy_name = PHARMACY_TRADING_NAME,
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address,
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postcode = POST_CODE,
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opening_hours_monday = PHARMACY_OPENING_HOURS_MONDAY,
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opening_hours_tuesday = PHARMACY_OPENING_HOURS_TUESDAY,
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opening_hours_wednesday = PHARMACY_OPENING_HOURS_WEDNESDAY,
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opening_hours_thursday = PHARMACY_OPENING_HOURS_THURSDAY,
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opening_hours_friday = PHARMACY_OPENING_HOURS_FRIDAY,
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opening_hours_saturday = PHARMACY_OPENING_HOURS_SATURDAY,
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opening_hours_sunday = PHARMACY_OPENING_HOURS_SUNDAY,
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organisation_data_service_code = `PHARMACY_ODS_CODE_(F-CODE)`,
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health_and_wellbeing_board = HEALTH_AND_WELLBEING_BOARD)
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# convert to spatial object and creat spatial join ---------------------------
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sf <- df_postcodes %>%
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filter(!is.na(lat)) %>%
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st_as_sf(coords = c("long", "lat")) %>%
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st_set_crs(4326) %>%
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st_join(la, join = st_within, left = FALSE)
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# write data ---------------------------
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write_csv(st_set_geometry(sf, value = NULL), "trafford_pharmacies.csv")
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st_write(sf, "trafford_pharmacies.geojson")
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# Match pharmacy data with ONS postcodes to obtain spatial coordinates, ward and la information ---------------------------
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postcodes <- read_csv("https://www.trafforddatalab.io/spatial_data/postcodes/gm_postcodes.csv") %>%
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select(postcode, ward_code, ward_name, la_code, la_name, lon, lat)
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df_with_coords <- left_join(df, postcodes, by = "postcode")
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# Convert to spatial object ---------------------------
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sf <- df_with_coords %>%
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filter(!is.na(lat)) %>%
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st_as_sf(coords = c("lon", "lat")) %>%
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st_set_crs(4326)
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# Write data ---------------------------
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file.remove(c("gm_pharmacies.geojson",
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"trafford_pharmacies.geojson"))
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# NOTE: There are a few pharmacies where the value of health_and_wellbeing_board differs from la_name, therefore I'm using the former as the filter.
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write_sf(sf, "gm_pharmacies.geojson", driver = "GeoJSON")
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write_sf(sf %>% filter(health_and_wellbeing_board == "Trafford"), "trafford_pharmacies.geojson", driver = "GeoJSON")
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write_csv(st_set_geometry(sf, value = NULL), "gm_pharmacies.csv")
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write_csv(st_set_geometry(sf, value = NULL) %>% filter(health_and_wellbeing_board == "Trafford"), "trafford_pharmacies.csv")

‎pharmacies/thumbnail.png‎

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