How to use echo: false and eval: false in chunks to hide and stop running data cleaning code? #166

Robinlovelace · 2025-02-06T13:59:22Z

Robinlovelace
Feb 6, 2025
Maintainer

See the below code that I used to generate the output shown in the OSM section of Practical 2 (note that the long and perhaps boring data cleaning code is hidden): https://itsleeds.github.io/tds/p2/#getting-openstreetmap-data

tds/p2/index.qmd

Lines 61 to 134 in 907a66f

    
           ```{r} 
        
           #| eval: false 
        
           #| echo: false 
        
           library(tidyverse) 
        
           library(osmextract) 
        
           region_name = "west yorkshire" 
        
           supermarket_points = oe_get( 
        
             place = region_name, 
        
             extra_tags = "shop", 
        
             query = "SELECT * FROM points WHERE shop='supermarket'" 
        
           ) 
        
           supermarket_polygons = oe_get( 
        
             place = region_name, 
        
             extra_tags = "shop", 
        
             query = "SELECT * FROM multipolygons WHERE shop='supermarket'" 
        
           ) 
        
           nrow(supermarket_points) # 81 
        
           nrow(supermarket_polygons) # 261 
        
           # Deduplicate points 
        
           supermarket_points_in_polygons = supermarket_points |> 
        
             sf::st_filter(supermarket_polygons) 
        
           nrow(supermarket_points_in_polygons)  # none 
        
           supermarket_polygon_centroids = supermarket_polygons |> 
        
             sf::st_centroid() 
        
           waldo::compare(names(supermarket_points), names(supermarket_polygons)) 
        
           supermarket_points_combined = rbind( 
        
             supermarket_points |> 
        
               select(osm_id, name), 
        
             supermarket_polygon_centroids |> 
        
               select(osm_id, name)   
        
           ) 
        
           supermarket_frequency = supermarket_points_combined |> 
        
             count(name, sort = TRUE) 
        
           length(supermarket_frequency$name) # 77 
        
           supermarket_frequency$name[1:20] 
        
           supermarket_points_cleaned = supermarket_points_combined |> 
        
             mutate( 
        
               name = case_when( 
        
                 name == "" ~ "Unnamed", 
        
                 str_detect(name, "Tesco") ~ "Tesco", 
        
                 str_detect(name, "Sainsbury") ~ "Sainsbury's", 
        
                 str_detect(name, "Asda") ~ "Asda", 
        
                 str_detect(name, "Morrisons") ~ "Morrisons", 
        
                 str_detect(name, "Aldi|ALDI") ~ "ALDI", 
        
                 str_detect(name, "Lidl") ~ "Lidl", 
        
                 str_detect(name, "Co-op|Coop") ~ "Co-op", 
        
                 str_detect(name, "M&S|Marks & Spencer") ~ "M&S", 
        
                 TRUE ~ name 
        
               ) 
        
             ) 
        
           supermarket_frequency_cleaned = supermarket_points_cleaned |> 
        
             count(name, sort = TRUE) 
        
           length(supermarket_frequency_cleaned$name) # 66 
        
           supermarket_top_6 = supermarket_frequency_cleaned |> 
        
             slice_max(n, n = 6) 
        
           supermarket_points_cleaned = supermarket_points_cleaned |> 
        
             mutate(name_simplified = case_when( 
        
               name %in% supermarket_top_6$name ~ name, 
        
               TRUE ~ "Other" 
        
             ))  
        
           # Save the result: 
        
           sf::st_write(supermarket_points_cleaned, "supermarkets_points_cleaned.geojson", delete_dsn = TRUE) 
        
           system("gh release upload 2025 supermarkets_points_cleaned.geojson --clobber") 
        
           ``` 
        
           Import and visualise a dataset with supermarket names and locations with the following code (see the source code of the practical to see how the supermarket data was obtained with `osmextract`): 
        
           ```{r} 
        
           supermarkets = sf::read_sf("https://github.com/ITSLeeds/tds/releases/download/2025/supermarkets_points_cleaned.geojson") 
        
           library(tmap) 
        
           tmap_mode("view") 
        
           tm_shape(supermarkets) + 
        
             tm_dots("name_simplified") 
        
           ```

Robinlovelace · 2025-02-06T14:04:59Z

Robinlovelace
Feb 6, 2025
Maintainer Author

Specifically the #| eval: false bits, what other chunk settings could we use here?

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