Fix NAs for age band, and added new colours and gradients

Author: viv3ckj

analysis/measures_definition_pf_codes_conditions.py

@@ -66,14 +66,16 @@
 # Age bands for age breakdown
 age = patients.age_on(INTERVAL.start_date)
 age_band = case(
+    when(age.is_null()).then("Missing"),
     when((age >= 0) & (age < 20)).then("0-19"),
     when((age >= 20) & (age < 40)).then("20-39"),
     when((age >= 40) & (age < 60)).then("40-59"),
     when((age >= 60) & (age < 80)).then("60-79"),
     when(age >= 80).then("80+"),
-    when(age.is_null()).then("Missing"),
+    otherwise="Missing"
 )
 
+
 # IMD groupings for IMD breakdown
 imd = addresses.for_patient_on(INTERVAL.start_date).imd_rounded
 max_imd = 32844

lib/functions/function_plot_measures.R

@@ -73,7 +73,11 @@ plot_measures <- function(
       plot.title = element_text(hjust = 0.5)
     ) +
 
-    scale_colour_brewer(palette = "Set1")
+    scale_colour_manual(values = c(
+      "navy", "red", "#3cb44b", "#ff00ff",
+      "#f58231",
+      "#dcdc00", "#911eb4", "#469990", "#8000009d", "grey50"
+    ), na.value = "grey50")
 
   # Automatically change y scale depending selected value
   if (rlang::as_label(enquo(select_value)) %in% c("numerator", "denominator")) {

reports/pharmacy_first_report.Rmd

@@ -107,6 +107,8 @@ df_measures$region <- factor(
 
 df_measures <- df_measures %>%
   mutate(sex = factor(sex, levels = c("female", "male"), labels = c("Female", "Male")))
+
+df_measures$age_band[is.na(df_measures$age_band)] <- "Missing"  
 ```
 
 # Background
@@ -150,7 +152,7 @@ plot_measures(
   facet_var = NULL,
   title = "Number of consultations for each clinical service per month",
   y_label = "Number of codes for consultations",
-)
+) 
 ```
 
 ### Breakdown by age
@@ -172,7 +174,8 @@ plot_measures(
   facet_var = measure,
   title = "Number of consultations for each clinical service by age band per month",
   y_label = "Number of codes for consultations",
-)
+) + scale_color_manual(values = c("#FFCCCC", "#fa7070", "#fc0e0e", "#9c0000", "#4c0000", "grey"))
+
 ```
 
 ### Breakdown by sex
@@ -216,7 +219,7 @@ plot_measures(
   facet_var = measure,
   title = "Number of consultations for each clinical service by IMD per month",
   y_label = "Number of codes for consultations",
-)
+) + scale_color_manual(values = c("#001F4D", "#0056B3", "#007BFF", "#66B3E2", "#A4D8E1", "grey"))
 ```
 
 ### Breakdown by region
@@ -308,7 +311,8 @@ plot_measures(
   facet_var = measure,
   title = "Number of consultations for each clinical condition by age band per month",
   y_label = "Number of codes for consultations",
-)
+) + scale_color_manual(values = c("#FFCCCC", "#fa7070", "#fc0e0e", "#9c0000", "#4c0000", "grey"))
+
 ```
 
 ### Breakdown by sex
@@ -352,7 +356,7 @@ plot_measures(
   facet_var = measure,
   title = "Number of consultations for each clinical condition by IMD per month",
   y_label = "Number of codes for consultations",
-)
+) + scale_color_manual(values = c("#001F4D", "#0056B3", "#007BFF", "#66B3E2", "#A4D8E1", "grey"))
 ```
 
 ### Breakdown by region