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+    "markdown": "---\ntitle: Maps in R using ggplot2 and OSM packages\nsubtitle: \"NHSE R drop in session.\"\nauthor: \"Pablo Leon\"\ndate: \"2023-10-06\"\ncategories: [NHS England]\n---\n\n::: {.cell}\n\n```{.r .cell-code}\n# List of required packages\nrequired_packages <- c(\"here\", \"sf\", \"ggplot2\", \"readxl\", \"dplyr\", \"janitor\")\n\n# Check if required packages are not installed, then install and load them\nfor (package in required_packages) {\n  if (!requireNamespace(package, quietly = TRUE)) {\n    install.packages(package)\n  }\n}\n\n# Load the required packages\nlibrary(here)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nhere() starts at /Users/craigshenton/Documents/GitHub/nhs-r-reporting\n```\n:::\n\n```{.r .cell-code}\nlibrary(sf)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nLinking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE\n```\n:::\n\n```{.r .cell-code}\nlibrary(ggplot2)\nlibrary(readxl)\nlibrary(dplyr)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n\nAttaching package: 'dplyr'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:stats':\n\n    filter, lag\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:base':\n\n    intersect, setdiff, setequal, union\n```\n:::\n\n```{.r .cell-code}\nlibrary(janitor)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n\nAttaching package: 'janitor'\n```\n:::\n\n::: {.cell-output .cell-output-stderr}\n```\nThe following objects are masked from 'package:stats':\n\n    chisq.test, fisher.test\n```\n:::\n:::\n\n\n## Intro {background=\"#43464B\"}\n\nThese slides were presented in the NHSE-R drop in session on Friday 26th May 2023.\n\nIn R we can plot geospatial data using several methods, today I will focus on static maps using `ggplot2` and `osm` packages. Drawing maps usually imply these steps:\n\n-   Getting shapefiles to draw a map\n-   Obtaining metadata to plot on the map \n-   In R we load the multypoligon objects using `geom_sf()` and `coord_sf()`\n-   Then we can take advantage of ggplot2 framework to overlay one map on top of another\n\nAn introduction to plotting maps in R using ggplot2 can be found on this online book: <https://ggplot2-book.org/maps.html>.\n\n\n\n## 1. NHS Health boundaries{auto-animate=\"true\" background=\"#43464B\"}\n\nThe Office for National Statistics provides a free and open access to several geographic products. There is a specific section for `Health boundaries` on their `Open Geography Portal` website: <https://geoportal.statistics.gov.uk/>. \n\nFrom `Clinical Commissioning Groups` section download  `2021 Boundaries` **shapefile** zipped file.\n\n::: columns\n::: {.column width=\"40%\"}\n\n![Health Boundaries](Figures_maps_slides/02 Health boundaries.png)\n:::\n\n::: {.column width=\"60%\"}\n\n![Health Boundaries details](Figures_maps_slides/05 Unzip Shapefile.png)\n:::\n:::\n\n\n## 2. Unzip CCG boundaries into R {background=\"#43464B\"}\n\nLoad unzipped files into R using `Open Geography Portal` \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Define a function to download and cache the CCG data\ndownload_CCG_data <- function() {\n  data_dir <- here::here(\"data\") # Use here::here() for consistent path resolution\n  \n  # Check if the data directory exists, create it if not\n  if (!dir.exists(data_dir)) {\n    dir.create(data_dir, recursive = TRUE)\n  }\n  \n  zip_file <- file.path(data_dir, \"CCGoutcomes.zip\")\n  \n  # Check if the file already exists, if not, download and unzip it\n  if (!file.exists(zip_file)) {\n    download.file(\n      url = \"https://files.digital.nhs.uk/48/4DB2CA/CCG_OIS_MAR_2022_Excels_Files.zip\",\n      destfile = zip_file\n    )\n    unzip(zip_file, exdir = data_dir, junkpaths = TRUE)\n  }\n}\n\n# Call the function to download and cache the CCG data\ndownload_CCG_data()\n```\n:::\n\n\n## 3. Check shapefiles content {background=\"#43464B\"}\n\nCheck unzipped file contents. We shuold obtain a collection of boundaries files including the .shp file for CCG map.\n\n\n::: columns\n::: {.column width=\"40%\"}\n\n![Health Boundaries](Figures_maps_slides/06 Unzip Shapefile.png)\n:::\n\n::: {.column width=\"60%\"}\n\n:::\n:::\n\n## 4.Load CCG Shapefile and check map {background=\"#43464B\"}\n\nNow we can plot using  `ggplot()` and `geom_sf()`function the CCG map for England\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Function to load CCG boundaries data\nload_CCG_boundaries <- function() {\n  # Load CCG boundaries shapefile\n  shapefile_path <- \"data/Clinical_Commissioning_Groups_April_2021/CCG_APR_2021_EN_BGC.shp\"\n  \n  # Read the shapefile using sf::st_read()\n  CCG_boundaries <- sf::st_read(shapefile_path)\n  return(CCG_boundaries)\n}\n\n# Function to create CCG map\ncreate_CCG_map <- function(CCG_boundaries) {\n  # Create ggplot map\n  CCG_map <- ggplot2::ggplot(data = CCG_boundaries) +\n    ggplot2::geom_sf(size = 0.5, color = \"black\", fill = \"coral\") +\n    ggplot2::ggtitle(\"CCG Boundaries plot. April 2021\") +\n    ggplot2::coord_sf()\n  \n  return(CCG_map)\n}\n\nCCG_boundaries <- load_CCG_boundaries()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nReading layer `CCG_APR_2021_EN_BGC' from data source \n  `/Users/craigshenton/Documents/GitHub/nhs-r-reporting/docs/tutorials/R_drop_in_session_26_may_Maps_in_R/data/Clinical_Commissioning_Groups_April_2021/CCG_APR_2021_EN_BGC.shp' \n  using driver `ESRI Shapefile'\nSimple feature collection with 106 features and 7 fields\nGeometry type: MULTIPOLYGON\nDimension:     XY\nBounding box:  xmin: 82668.52 ymin: 5352.6 xmax: 655653.8 ymax: 657539.2\nProjected CRS: OSGB36 / British National Grid\n```\n:::\n\n```{.r .cell-code}\nCCG_map <- create_CCG_map(CCG_boundaries)\nprint(CCG_map)\n```\n\n::: {.cell-output-display}\n![](mapsinR26May2023_files/figure-html/unnamed-chunk-3-1.png){width=672}\n:::\n:::\n\n\n## 5. Obtain NHS Indicators {background=\"#43464B\"}\n\nFor this presentation I will download `CCG Outcomes Indicator` set for March 2022 from Digital website: \nhttps://digital.nhs.uk/data-and-information/publications/statistical/ccg-outcomes-indicator-set/march-2022\n\nThe Office for National Statistics provides a free and open access to several geographic produts. There is a specific section for Health boundaries on their website: \n\n![Health Boundaries](Figures_maps_slides/CCG outcomes indicator set.png)\n\n## 6. Download CCG Outcomes Indicators {background=\"#43464B\"}\n\nAfter loading the shapefile we `download` the `indicators` to be plotted in the map \n\n![CCG Indicators](Figures_maps_slides/05 Load ccg indicators.png)\n\n## 7. Data wrangling {background=\"#43464B\"}\n\nNow we `combine` ONS `shapefiles` with CCG Outcomes `Indicators` data ready to be plotted in the map.\n\nWe will plot this indicator: 1.17 - Percentage of new cases of cancer for which a valid stage is recorded at the time of diagnosis.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n# Function to load and preprocess cancer data\nload_and_preprocess_cancer_data <- function() {\n  # Load cancer data from Excel file\n  cancer_data <- readxl::read_excel(here::here(\"data\", \"CCG_1.17_I01968_D.xlsx\"), sheet = 3, skip = 13) \n  \n  # Clean column names, select necessary columns, and filter rows\n  cleaned_cancer_data <- cancer_data %>%\n    janitor::clean_names() %>%\n    dplyr::select(reporting_period, breakdown, ons_code, level, level_description, indicator_value) %>%\n    dplyr::filter(level_description != \"England\")\n  \n  return(cleaned_cancer_data)\n}\n\n# Main function for data wrangling\ncancer_data_analysis <- function() {\n  # Load and preprocess cancer data\n  cancer_data <- load_and_preprocess_cancer_data()\n  \n  # Additional processing or analysis can be performed here\n  \n  # Print the resulting dataframe\n  print(cancer_data)\n}\n\n# Call the main function for data analysis\ncancer_new_sel <- cancer_data_analysis()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 742 × 6\n   reporting_period breakdown ons_code  level level_description  indicator_value\n              <dbl> <chr>     <chr>     <chr> <chr>                        <dbl>\n 1             2019 CCG       E38000006 02P   NHS Barnsley CCG              68  \n 2             2019 CCG       E38000007 99E   NHS Basildon and …            73.5\n 3             2019 CCG       E38000008 02Q   NHS Bassetlaw CCG             68.5\n 4             2019 CCG       E38000231 92G   NHS Bath and Nort…            78.7\n 5             2019 CCG       E38000249 M1J4Y NHS Bedfordshire,…            80.2\n 6             2019 CCG       E38000221 15A   NHS Berkshire Wes…            70  \n 7             2019 CCG       E38000220 15E   NHS Birmingham an…            75.4\n 8             2019 CCG       E38000250 D2P2L NHS Black Country…            76.8\n 9             2019 CCG       E38000014 00Q   NHS Blackburn wit…            83.6\n10             2019 CCG       E38000015 00R   NHS Blackpool CCG             76.8\n# ℹ 732 more rows\n```\n:::\n:::\n\n\n## 8. Merge shapefile and Outcome Indicators {background=\"#43464B\"} \n\nThis is the final step prior to plotting the map. We `merge` shapefile with `indicator` files. \n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Rename columns in cancer_new_sel\ncancer_MAP_rename <- cancer_new_sel %>%\n  dplyr::select(\n    reporting_period,\n    breakdown,\n    CCG21CD = ons_code,\n    level,\n    level_description,\n    indicator_value\n  )\n\n# Merge shapefile and metric data using left_join from dplyr\nmapdata <- dplyr::left_join(CCG_boundaries, cancer_MAP_rename, by = \"CCG21CD\")\n\n# Apply projection (EPSG:4326) to merged data to plot the map\nmapdata_coord <- sf::st_transform(mapdata, crs = 4326)\n```\n:::\n\n\n## 9. Plot map in ggplot2 {background=\"#43464B\"}\n\nFinally we can add Title and labs to the map in ggplot we have just created\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Plot map combining shapefile and CCG indicator\ncancer_map_blues <- mapdata_coord %>% \n  ggplot2::ggplot(ggplot2::aes(fill = indicator_value)) +\n  ggplot2::geom_sf() +\n  ggplot2::labs(\n    title = \"CCG OIS Indicator 1.17 - Record of Stage of Cancer at Diagnosis\",\n    subtitle = \"Percentage of New Cases of Cancer for Which a Valid Stage is Recorded \\n at the Time of Diagnosis (95% CI)\",\n    caption = \"Data source: NHS Digital National Disease Registration Service (NDRS)\"\n  ) +\n  ggplot2::theme_minimal() +\n  ggplot2::scale_fill_viridis_c(name = \"Indicator Value\")\n\n# Print the map\nprint(cancer_map_blues)\n```\n\n::: {.cell-output-display}\n![](mapsinR26May2023_files/figure-html/unnamed-chunk-6-1.png){width=672}\n:::\n:::\n\n\n\n## 10. Open street maps using OSM package {background=\"#43464B\"}\n\nThe second part of this presentation will cover how to build city maps in R using `osmdata` package in R. This package allows us to download and use data from the `OpenStreetMap (OSM)`\n\nPackage details: <https://cran.r-project.org/web/packages/osmdata/vignettes/osmdata.html>\nOpenStreepMap. OSM is a global open access mapping project, which is free and open under the ODbL licence. OpenStreetMap contributors 2017: <https://www.openstreetmap.org/#map=6/54.910/-3.432>\n\n\nFollow all the details for this second map from my website: <https://pablo-source.github.io/City_maps.html>\n\n\n\n## Online resources to build maps in R {background=\"#43464B\"}\n\nThis presentation only covers a handful of options, please check this repo for facets and grid options\n\n- Maps-in-R> GitHub repo: <https://github.com/Pablo-source/Maps-in-R>\n- Examples and Shapefiles:<https://github.com/Pablo-source/Maps-in-R/tree/main>\n- OSM-maps> GitHub repo: <https://github.com/Pablo-source/Maps-in-R/tree/main/City_maps>\n- CCG Outcomes Indicato Set - March 2022: <https://digital.nhs.uk/data-and-information/publications/statistical/ccg-outcomes-indicator-set/march-2022>\n- Map projections using the “sf” package in R: <https://cran.r-project.org/web/packages/oce/vignettes/D_map_projections.html>\n- R Spatial Workshop Notes: <https://spatialanalysis.github.io/workshop-notes/spatial-clustering.html>\n- NHS-R  NHS Colour Guidelines: <https://nhsengland.github.io/nhs-r-reporting/tutorials/nhs-colours.html>\n\n\n\n\n## Shapefiles {background=\"#43464B\"}\n\n- The Open Geography portal from the Office for National Statistics (ONS): \n<https://geoportal.statistics.gov.uk/>\n- The London Datastore:Shapefiles and plenty of social indicators to plot (OA,LSOA,MSOA,Wards)\n<https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london>\n\nOnline books about building maps in R\n- Geocomputation with R: <https://bookdown.org/robinlovelace/geocompr/spatial-class.html>\n- Chapter 16. Geospatial – Bigbook of r: <https://www.bigbookofr.com/geospatial.html>\n\n- Any questions? [email protected], <https://github.com/Pablo-source>",
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