library(cqcr)
library(purrr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(forcats)
library(tidyr)

CQC Partner Codes

The Care Quality Comission does not require registration or the use of keys to access the API, but they do want users to attach a partner code to their requests. If you haven’t been provided a code by the CQC, you should use a name or acronym that identifies you or your organisation. You can set the code for each session with cqc_partner_code(), or store it in an environmental variable named CQC_PARTNER_CODE.

Using cqcr

Let’s look at the care home options available in a few local authorities in East London. The code below retrieves the ID, name and post code of all care homes in Waltham Forest, Hackney and Tower Hamlets local authorities.

loc1 <- cqc_locations_search(care_home = TRUE, 
                             local_authority = c("Waltham Forest",
                                                 "Hackney", "Tower Hamlets"))

Given the list of care home IDs, we can retrieve more detailed information on each of these locations using cqc_location_details(). We can use some functions from purrr and dplyr to create a data frame with some basic information.

care_home_details <- cqc_location_details(loc1)

care_home_df <- map_dfr(care_home_details, `[`,
                        c("location_id", "name", "number_of_beds", 
                          "onspd_longitude", "onspd_latitude"))

care_home_df <- care_home_df %>% 
  mutate(rating = care_home_details %>%
           map(c("current_ratings", "overall", "rating")) %>%
           as.character(),
         rating = recode(rating, "NULL" = "No published rating"),
         rating = factor(rating, levels = c("Outstanding", "Good",
                                            "Requires improvement", 
                                            "Inadequate",
                                            "No published rating")),
         report_date = care_home_details %>%
           map(c("current_ratings", "reportDate")) %>%
           as.character(),
         report_date = ifelse(report_date == "NULL", NA, report_date),
         specialisms = care_home_details %>% map(c("specialisms", "name")))

care_home_df
#> # A tibble: 193 x 8
#>    location_id name  number_of_beds onspd_longitude onspd_latitude rating
#>    <chr>       <chr>          <int>           <dbl>          <dbl> <fct> 
#>  1 1-105426344 Chec…              3        -0.0499            51.6 No pu…
#>  2 1-105836127 Fore…             24         0.00178           51.6 Good  
#>  3 1-105885228 Heat…             84         0.00269           51.6 Good  
#>  4 1-106916349 Aspr…             64        -0.0169            51.6 Requi…
#>  5 1-107269593 Acor…             98        -0.0533            51.6 Requi…
#>  6 1-108969091 St M…             11         0.00205           51.6 No pu…
#>  7 1-109015718 Ashc…             15         0.0120            51.6 No pu…
#>  8 1-109949478 Rose…             20        -0.0141            51.6 No pu…
#>  9 1-110213739 Alba…             61        -0.0187            51.6 Good  
#> 10 1-11053390… Toby…             10        -0.0401            51.5 Good  
#> # … with 183 more rows, and 2 more variables: report_date <chr>,
#> #   specialisms <list>

Lets take a look at the various specialisms that care homes report. Care homes can report multiple specialisms, so the numbers below add up to more than the number of care homes. I have also used the ratings data to show the number of care homes providing a given specialism with their overall rating.

care_home_df_unnest <- tidyr::unnest(care_home_df, cols = c(specialisms)) %>%
  mutate(specialisms = fct_infreq(specialisms))

theme_set(theme_bw())

p_specialisms_rating <- ggplot(care_home_df_unnest,
                        aes(x = specialisms, fill = rating)) + 
  geom_bar() + 
  scale_x_discrete(labels = scales::wrap_format(33)) + 
  scale_fill_viridis_d(name="", option = "A", end = 0.95) + 
  labs(x = "Specialism(s)", y = "Number of Care Homes") + 
  theme(legend.position = "bottom",
        axis.text.x = element_text(size = 8, angle = 60, hjust = 1))

p_specialisms_rating

Then, we can map our data with the leaflet package. In this case the colours indicate the most recent inspection rating, and the size corresponds to a scaled count of beds.

library(leaflet)

pal <- colorFactor(c("blue", "green", "orange", "red", "gray27"),
                   domain = care_home_df$rating)

labels <- paste0(
  "<strong>Name:</strong> ", care_home_df$name,"</br>",
  "<strong>Number of Beds:</strong> ", care_home_df$number_of_beds, "</br>",
  "<strong>Overall Rating:</strong> ", care_home_df$rating, "</br>",
  "<strong>Report Date:</strong> ", format(as.Date(care_home_df$report_date),
                                           "%e %B %Y"), "</br>",
  "<strong>Specialism(s):</strong><ul><li>",
  lapply(care_home_df$specialisms, paste, collapse='</li><li>'), "</li></ul>"
) %>% lapply(htmltools::HTML)

map <- leaflet(care_home_df) %>%
  addTiles() %>%
    addCircleMarkers(lng = ~onspd_longitude,
             lat = ~onspd_latitude,
             label = labels,
             color = ~pal(rating), 
             radius = ~scales::rescale(care_home_df$number_of_beds, to = c(5, 15)))

map