---
title: "GP practices"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    theme: 
        version: 5
        bootswatch: minty
date: "2026-07-01"

---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F)
library(dplyr)
library(readr)
library(ggplot2)
library(flexdashboard)

thematic::thematic_rmd()

options(scipen=8) # to stop scientific notation kicking in

boards <- read_rds("data/boards.rds")

gps <- read_csv("data/practice_contact_details_20260401_opendata.csv") |>
        select(PracticeCode, 
               GPPracticeName, 
               PracticeListSize, 
               Postcode, 
               PracticeType, 
               HB) |>
         left_join(boards) |>
         select(-HB)


```

Row {data-height=200}
--------------------------------------

### Summary text

+ Scotland has `r nrow(gps)` practices
+ The largest (the `r gps[which(gps$PracticeListSize == max(gps$PracticeListSize)),]$GPPracticeName`) has `r max(gps$PracticeListSize)` patients
+ The smallest (the `r gps[which(gps$PracticeListSize == min(gps$PracticeListSize)),]$GPPracticeName`) has `r min(gps$PracticeListSize)` patients
+ The average practice size is `r round(mean(gps$PracticeListSize))`

Row {data-height=800}
--------------------------------------

### Summary data

```{r}
gps |>
  group_by(HBName) |>
  summarise(`Number of Practices` = n(),
            `Mean Practice List Size` = round(mean(PracticeListSize)),
            range = paste(min(PracticeListSize), "-", max(PracticeListSize))) |>
  knitr::kable(align = "lccc")
```



### Summary plot
```{r message=FALSE, warning=FALSE}
gps |>
  ggplot() +
  geom_histogram(aes(x = PracticeListSize)) +
  scale_x_log10() +
  xlab("Total list size (log10)") +
  ylab("Total number of practices") +
  labs(fill="Health board name") 
```