In iecastro/tidyVA: Tidy simple features of VHA geographies

knitr::opts_chunk$set(warning = FALSE,
                      message = FALSE,
                      collapse = TRUE,
                      comment = "#>"
)

devtools::load_all()

VA SAIL metrics are a way of summarizing VA hospital performance data. Individual data sets by location are available for download from VA.gov. These are excel files formatted with banners and special characters that produce a large amount of dirtiness when the data are imported into R:

file <- curl::curl_download("https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_albany_fy19q1.xlsx", 
                            destfile = tempfile())

data_dirty <- suppressMessages(readxl::read_excel(file))

dplyr::glimpse(data_dirty)

sail_tidy

To make these data easier to handle in R, sail_tidy will download, import, and wrangle the dataset into a clean data frame that's easier to analyze in R.

library(tidyVA)
library(ggplot2)
library(dplyr)


albany_sail <- sail_tidy("https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_albany_fy19q1.xlsx")

glimpse(albany_sail)

Using map_df from the purrr package you can create a data frame of metrics from several sites:

albany <-  "https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_albany_fy19q1.xlsx"
syracuse <- "https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_syracuse_fy19q1.xlsx"
bath <- "https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_bath_fy19q1.xlsx"
buffalo <- "https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_buffalo_fy19q1.xlsx"
bronx <- "https://www.va.gov/QUALITYOFCARE/SAIL_FY19_Q1/SAIL-2_bronx_fy19q1.xlsx"

links <- c(albany, syracuse, bath,
           buffalo,bronx)

data <- purrr::map_df(links, sail_tidy)

This can then easily be explored within the tidyverse

data %>% 
  distinct(measure)

data %>%
  filter(measure =="AcuteCareStandardizedMortalityRatioSmr") %>%
  mutate(SMR = as.numeric(value)) %>% 
  ggplot(aes(reorder(site, SMR), SMR)) +
  geom_point() +
  geom_segment(aes(x = site, y = 0, 
                   xend = site, yend = SMR)) +
  coord_flip() + 
  geom_hline(yintercept = 0.409, lty = "dashed", color = "darkred") +
  labs(x = NULL, y = "Standardized Ratio") +
  ggtitle("Acute Care Standardized Mortality Ratio (SMR)",
          subtitle = "VISN 2 - 2019 Q1") +
  theme_va(grid = FALSE)

data %>% 
  filter(measure == "PopulationCoverage") %>%
  mutate(value = as.numeric(value)) %>%
  arrange(desc(value)) %>% 
  ggplot(aes(forcats::fct_inorder(site), value)) +
  geom_col(alpha = .75, fill = "#fdbf11", width = .75 ) +
  labs(y = "Standardized Score", x = "",
       color= "Site") +
  ggtitle("Population Coverage - Mental Health",
          subtitle = "SAIL Metrics for VISN 2") +
  theme_va(grid = "XY")

data %>% 
  filter(measure == "PopulationCoverage") %>%
  mutate(value = as.numeric(value),
         best_10 = as.numeric(`best_10_percent`)) %>% 
  arrange(desc(value)) %>% 
  ggplot(aes(forcats::fct_inorder(site), value)) +
  geom_point() +
  geom_segment(aes(x = site, y = best_10, 
                   xend = site, yend = value)) +
  coord_flip()  +
  geom_hline(aes(yintercept = best_10), 
             lty = "solid", color = "#fdbf11") +
  labs(x = NULL, y = "Standardized Score") +
  ggtitle("VISN 2 Mental Health Coverage",
          subtitle = "Distance from Best 10%") +
  theme_va(grid = "Y")