---
title: "Code Snippets Audit"
output:
  html_document:
    df_print: paged
---




<!--This code imports the data from a validly labelled and named csv file, runs mathematical calculations on the data, and also generates charts to present the data. It is provided with my audit for reproducibility purposes if necessary. It requires the 'R' runtime environment to work and Rstudio is the recommended program to run it. -->

# collection of snippets for the data collection

# Setup and calculations



```{r setup, include=TRUE, echo=TRUE}
library(readr)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(gridExtra)
library(ggflowchart)
library(tibble)


# Read the HtnData.csv file with specified column types
HtnData <- read_csv("HtnData.csv", col_types = cols(
  row.names = col_integer(),
  Patient_ID = col_integer(),
  Progress_Note_Date = col_character(),
  Age = col_integer(),
  Patient_Female = col_logical(),
  Patient_ATSI_Status = col_character(),
  Presentation_Symptoms = col_character(),
  Organisation_Name = col_integer(),
  Inclusion_Crit = col_logical(),
  HbA1c = col_logical(),
  Lipid = col_logical(),
  U_E = col_logical(),
  Dip_Urine = col_logical(),
  ACR_Urine = col_logical(),
  Lifestyle_Discussion = col_logical(),
  HTN_MGMT_PLN = col_logical(),
  c715_Check = col_logical(),
  c3_MO_FLWP_STBL = col_logical(),
  c6_MO_BP = col_logical(),
  c3_MO_RVW_LFSTL = col_logical(),
  c2_4_WK_RVW_ACE = col_logical(),
))



# cleans the data somewhat. If there are inexplicable rows of NA, they will be removed
HtnData <- HtnData[!is.na(HtnData$Patient_ID), ]

# Display the dataframe for perusal
print(HtnData)

# Show data outputs below

## Calculate the average age of patients
average_age <- mean(HtnData$Age)
print(paste("The average age of patients is:", round(average_age, 0)))


# Convert Inclusion_Crit to numeric if it's not already
HtnData$Inclusion_Crit <- as.numeric(HtnData$Inclusion_Crit)

# Calculates the percentage of patients that met the inclusion criteria
inclusion_percentage <- mean(HtnData$Inclusion_Crit) * 100
print(paste("The percentage of patients who met the inclusion criteria is:", round(inclusion_percentage, 2), "%"))

# everything below this point only deals with data where Inclusion_Crit is equal to 1
HtnDataold <- HtnData
HtnData <- HtnData[HtnData$Inclusion_Crit == 1, ]

## Calculate the average age of patients
average_age_included <- mean(HtnData$Age)
print(paste("The average age of patients is:", round(average_age_included, 0)))

##histogram of the Age data

#New column for age categories
HtnData$Age_Group <- cut(HtnData$Age,
                         breaks = c(-Inf, 14, 24, 44, 64, 80, Inf),
                         labels = c("1-14", "15-24", "25-44", "45-64", "65-80","80+" ),
                         include.lowest = TRUE, right = FALSE)

# Create a histogram of the Age data. unused in final report
ggplot(HtnData, aes(x = Age_Group)) +
  geom_bar(color = "black", fill = "lightblue") +
  scale_x_discrete(drop = FALSE) +
  theme_minimal() +
  labs(title = "Age Distribution of Patients diagnosed with Hypertension between audited dates",
       x = "Age Group", y = "Count")



### Male female ratio

# Create data frame with counts of males and females - unused testing out techniques
gender_count <- data.frame(
  gender = c("Female", "Male"),
  count = c(sum(HtnData$Patient_Female, na.rm = TRUE),
            sum(!HtnData$Patient_Female, na.rm = TRUE))
)

# Create pie chart - unused
ggplot(gender_count, aes(x = "", y = count, fill = gender)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start = 0) +
  theme_void() +
  labs(title = "Gender Distribution") +
  geom_label(aes(label = round((count/sum(count))*100, 1)),
             position = position_stack(vjust = 0.5)) +
  scale_fill_brewer(palette = "Set2")



#### checks if all investigations are done, then adds a column
# Create a new column `Investig_Met`
HtnData$Investig_Met <- HtnData$HbA1c & HtnData$Lipid & HtnData$U_E & HtnData$Dip_Urine & HtnData$ACR_Urine

HtnData$Investig_Sine_Urine_Met <- HtnData$HbA1c & HtnData$Lipid & HtnData$U_E & HtnData$ACR_Urine

#####

# Import previous audit results


HtnData$Old_audit_Std_1_Met <- 0.28

HtnData$Old_audit_Std_2_Met <- 0.28


#create new column for followup breakdown that doesn't switch the NA values
HtnData$c3_MO_FLWP_STBL_WITHNA <-HtnData$c3_MO_FLWP_STBL


# Sometimes 3 month followup not applicable due to never getting stable, so as not to disturb the score for the unbroken down pie chart, any null value is changed to whatever the 2-4week followup value is
# finds NA in c3_MO_FLWP_STBL and replaces it with the corresponding value from c2_4_WK_RVW_ACE
na_index <- which(is.na(HtnData$c3_MO_FLWP_STBL))
HtnData$c3_MO_FLWP_STBL[na_index] <- HtnData$c2_4_WK_RVW_ACE[na_index]


# Check **OLD** standard 1 met

HtnData$Old_Std_1_Met <- HtnData$Investig_Met & (HtnData$c3_MO_RVW_LFSTL | HtnData$c2_4_WK_RVW_ACE ) & HtnData$Lifestyle_Discussion & HtnData$c3_MO_FLWP_STBL

# Check **OLD** standard 2 met

HtnData$Old_Std_2_Met <- HtnData$HTN_MGMT_PLN & HtnData$c715_Check & HtnData$c3_MO_FLWP_STBL & HtnData$c6_MO_BP


# Check ***NEW*** standard 1 met

HtnData$New_Std_1_Met <- HtnData$Investig_Met & HtnData$Lifestyle_Discussion & HtnData$c715_Check & HtnData$HTN_MGMT_PLN

# Check ***NEW Standard 1 sine lifestsyle met*** - unused. for comprehension purposes only

HtnData$New_Std_1_Sine_Lifestyle_Met <- HtnData$Investig_Met & HtnData$c715_Check & HtnData$HTN_MGMT_PLN

# Check **NEW** standard 2 met


HtnData$New_Std_2_Met <- HtnData$c3_MO_FLWP_STBL & HtnData$c6_MO_BP & (HtnData$c3_MO_RVW_LFSTL | HtnData$c2_4_WK_RVW_ACE)






## Pie charts for each standard - ended up unused, bar graph better representation to compare by year

generate_pie_chart <- function(data, column) {
    # Count TRUE and FALSE instances
    counts <- table(data[[column]])

    # Name counts for plotting
    df <- data.frame(labels = names(counts), counts = as.vector(counts))
    df$labels <- ifelse(df$labels == "TRUE", "Standard Met", "Standard Not Met")

    # Calculate percentages for labeling
    df$perc <- round((df$counts/sum(df$counts))*100, 1)

    # Generate pie chart
    p <- ggplot(df, aes(x = "", y = counts, fill = labels)) +
        geom_bar(width = 1, stat = "identity", colour = 'black') +
        coord_polar("y", start = 0) +
        scale_fill_manual(values = c("Standard Not Met" = "#CC0000", "Standard Met" = "#FFFF00")) +
        labs(fill = "") +
        geom_text(data = subset(df, labels == "Standard Met"), aes(label = paste0("Standard Met: ", perc, "%")),
            position = position_stack(vjust = 0.5), color = "black") +
        ggtitle(paste("Percent of old standard 2 met")) +
        theme_minimal() +
        theme(axis.title.x=element_blank(),
            axis.title.y=element_blank(),
            panel.border = element_blank(),
            panel.grid=element_blank(),
            axis.ticks = element_blank(),
            plot.title=element_text(hjust=0.5),
            axis.text = element_blank())

    print(p)
}

generate_pie_chart(HtnData, "Old_Std_2_Met")
generate_pie_chart(HtnData, "New_Std_1_Sine_Lifestyle_Met")






###### Single bar chart for 715

# Calculate adherence for the 715 check
counts <- table(HtnData$c715_Check)
percentage_adherence <- sum(counts["TRUE"]) / sum(counts) * 100

# Create a data frame for the chart
df <- data.frame(label = "715 Check", adherence = percentage_adherence)

# Single Horizontal Bar Chart
p <- ggplot(df, aes(x = "", y = adherence)) +
  geom_bar(stat = "identity", width = 0.2, fill = "steelblue") +
  coord_flip() +
  geom_hline(yintercept = 60, linetype = "dashed", color = "red",
             aes(label = "60% standard")) +
  geom_hline(yintercept = 0, color = "black", size = 1.0) +
  geom_text(data = df, aes(label = paste0(round(adherence, 1), "%")), vjust = -1.5, color = "black") +
  labs(x = "", y = "Adherence Percentage (%)",
       title = "Adherence to 715 Check Standard") +
  theme_minimal() +
  ylim(0,100)

print(p)

###### Single bar chart for lifestyle discussion

# Calculate adherence for the 715 check
counts <- table(HtnData$Lifestyle_Discussion)
percentage_adherence <- sum(counts["TRUE"]) / sum(counts) * 100

# Create a data frame for the chart
df <- data.frame(label = "Lifestyle Discussion", adherence = percentage_adherence)

# Single Horizontal Bar Chart
p <- ggplot(df, aes(x = "", y = adherence)) +
  geom_bar(stat = "identity", width = 0.2, fill = "steelblue") +
  coord_flip() +
  geom_hline(yintercept = 60, linetype = "dashed", color = "red",
             aes(label = "60% standard")) +
  geom_hline(yintercept = 0, color = "black", size = 1.0) +
  geom_text(data = df, aes(label = paste0(round(adherence, 1), "%")), vjust = -1.5, color = "black") +
  labs(x = "", y = "Adherence Percentage (%)",
       title = "Adherence to Lifestyle Discussion Standard") +
  theme_minimal() +
  ylim(0,100)

print(p)

#### single bar chart for htn mgmt plan


# Calculate adherence for the htn mgmt plan
counts <- table(HtnData$HTN_MGMT_PLN)
percentage_adherence <- sum(counts["TRUE"]) / sum(counts) * 100

# Make a data frame for the chart
df <- data.frame(label = "Hypertension Management Plan", adherence = percentage_adherence)

# Single Horizontal Bar Chart
p <- ggplot(df, aes(x = "", y = adherence)) +
  geom_bar(stat = "identity", width = 0.2, fill = "steelblue") +
  coord_flip() +
  geom_hline(yintercept = 60, linetype = "dashed", color = "red",
             aes(label = "60% standard")) +
  geom_hline(yintercept = 0, color = "black", size = 1.0) +
  geom_text(data = df, aes(label = paste0(round(adherence, 1), "%")), vjust = -1.5, color = "black") +
  labs(x = "", y = "Adherence Percentage (%)",
       title = "Adherence to Hypertension Management Plan Standard") +
  theme_minimal() +
  ylim(0,100)

print(p)






#### Variables for use in the text
NoOfFemales <- (gender_count$count[gender_count$gender == "Female"])
NoOfMales <- (gender_count$count[gender_count$gender == "Male"])
AverageAge <- (round(average_age_included, 1))
FinalIncluded <- nrow(HtnDataold[HtnDataold$Inclusion_Crit == 1, ])

```


# Flow Chart


```{r flowchart, include=TRUE, echo=TRUE, fig.cap="Case Selection for Kimberley Hypertension Audit of 2022 Patients"}
# Define ineligibles, all others defined before
Ineligibles <- nrow(HtnDataold) - FinalIncluded

# Define edge data
edge_data <- tibble::tibble(
  from = c("patients_attending","patients_attending","met_criteria"),
  to = c("met_criteria","participants_not_eligible","study_sample")
)

# Define data for each box
node_data <- tibble::tibble(
  name = c("patients_attending","met_criteria","participants_not_eligible","study_sample"),
  label = c(paste0("Patients aged 10 or above attending KAMS clinics where hypertension\n was recorded as the presenting complaint between 01/01/22 and 01/06/22.\nn=", nrow(HtnDataold)),
            paste0("Patients with no previously recorded\n diagnosis or treatment of hypertension \nn=", FinalIncluded),
            paste0("Patients with a recorded diagnosis\n or treatment of hypertension\n outside of the specified dates \nn=", Ineligibles),
            paste0("Audit sample n=", FinalIncluded)),
  x_nudge = c(1.0, 0.48, 0.48, 0.5),
  y_nudge = c(0.25, 0.3, 0.3, 0.25)
)

# Generates the flowchart
ggflowchart(
  data = edge_data,
  node_data = node_data,
  fill = 'white',
  colour = 'black',
  text_colour = 'black',
  text_size = 3.88,
  arrow_colour = "black",
  arrow_size = 0.3,
  family = "sans",
  horizontal = FALSE
)


```
  


# Table of age categories

```{r tableofage, echo=TRUE, include=TRUE, warning=FALSE, fig.cap='Age Distribution of Patient Sample', fig.pos ='H', message=FALSE}
library(kableExtra)
# Summarise data
AgeTable <- HtnData %>% 
  group_by(Age_Group) %>% 
  summarise(Count = n(),
            Percentage = (Count / nrow(HtnData))*100) %>% 
  # formatting columns
  mutate(Age_Group = as.character(Age_Group),
         Percentage = paste0(round(Percentage, 2), "%")) %>% 
  rename(`Age Group` = Age_Group)

# making the table
kable(AgeTable, digits = 2, caption = "Age distribution of Patient Sample", align = 'c') %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, latex_options = "HOLD_position")
```





# Converting variables to in text references


```{r calculatingstandards-results, echo=TRUE}

OldSt1Text <- round(mean(as.numeric(HtnData$Old_Std_1_Met)) * 100, 1)
OldSt2Text <- round(mean(as.numeric(HtnData$Old_Std_2_Met)) * 100, 1)
OldASt1Text <- mean(as.numeric(HtnData$Old_audit_Std_1_Met)) * 100
OldASt2Text <- mean(as.numeric(HtnData$Old_audit_Std_2_Met)) * 100

```

 
# Bar graph comparing previous and current audit results

```{r bargraphStandardOld, echo=TRUE, include=TRUE, fig.cap="Comparison of Previous and Present Audit Based on Previous Standard"}
# Unite the columns into one dataframe
standards <- HtnData %>%
  select(Old_Std_1_Met, Old_Std_2_Met, Old_audit_Std_1_Met, Old_audit_Std_2_Met) %>%
  pivot_longer(cols = everything(), names_to = "Standard", values_to = "Met") %>%
  mutate(Met = as.numeric(Met)) %>%
  group_by(Standard) %>%
  summarise(Percentage = mean(Met, na.rm = TRUE) * 100)


standards$Condition <- ifelse(grepl("Old_audit", standards$Standard), "2022", "2023")
standards$Standard_No <- ifelse(grepl("1", standards$Standard), "Standard 1", "Standard 2")

# Create the bar plot
ggplot(standards, aes(x = Standard_No, y = Percentage, fill = Condition)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.5, color = "black") + 
  geom_text(aes(label = paste0(formatC(Percentage, format = "f", digits =  1), "%")), 
            position = position_dodge(width = 0.5), vjust = -0.5, color = "black") +
  geom_hline(yintercept = 60, linetype = "dashed", color = "black") +
  geom_hline(yintercept = 0, color = "black", size = 1.0) +
  scale_fill_manual(values = c("2023" = "#c71d22", "2022" = "#ffd503")) +
  labs(x = "Standards", y = "Percentage Adherence (%)",
       fill = "Condition",
       title = "") +
  theme_minimal() +
  ylim(0,100)
```
  

# New standard Pie Chart

```{r newstandard1, echo=TRUE, include=TRUE, fig.cap = "Proportion Of Present Audit Adherence With Standard 1"}
generate_pie_chart <- function(data, column, title) {
  # Count TRUE and FALSE instances
  counts <- table(data[[column]])

  # Name counts for plotting
  df <- data.frame(labels = names(counts), counts = as.vector(counts))
  df$labels <- ifelse(df$labels == "TRUE", "Adherent", "Non-Adherent")

  # Calculate percentages for labeling
  df$perc <- round((df$counts/sum(df$counts))*100, 1)

  df$newlabels <- paste(df$labels, ": ", df$perc, "%", sep = "")

  # Generate pie chart
  p <- ggplot(df, aes(x = "", y = counts, fill = newlabels)) +
        geom_bar(width = 1, stat = "identity", colour = 'black') +
        coord_polar("y", start = 0) +
        scale_fill_manual(values = c("#ffd503", "#c71d22")) +
        labs(fill = "") +
        ggtitle(title) +
        theme_minimal() +
        theme(axis.title.x=element_blank(),
            axis.title.y=element_blank(),
            panel.border = element_blank(),
            panel.grid=element_blank(),
            axis.ticks = element_blank(),
            plot.title=element_text(hjust=0.0),
            axis.text = element_blank())

  return(p)
}

# Create the pie chart for New_Std_1_Met
newstandard1 <- generate_pie_chart(HtnData, "New_Std_1_Met", "")

# Print the pie chart
print(newstandard1)

```
  

# Baseline investigations 

```{r AdherenceBaselineFull, echo=TRUE, include=TRUE, fig.cap="Adherence to Standard for Baseline Investigations"}


columns_to_run <- c("HbA1c" = "HbA1c", "Lipid" = "Lipids", "U_E" = "U&Es", "Dip_Urine" = "Urine Dipstick", "ACR_Urine" = "Urine ACR")

# make new data frame to store the results
adherence_inv_perc <- data.frame()

# calculate adherence percentages for each column
for(column in names(columns_to_run)) {
    counts <- table(HtnData[[column]])
    df <- data.frame(label = column, adherence = sum(counts["TRUE"]) / sum(counts) * 100,
                     pretty_label = columns_to_run[[column]])
    adherence_inv_perc <- rbind(adherence_inv_perc, df)
}

# calculate the adherence percentage for ALL the investigations
all_investigations <- HtnData %>%
   rowwise() %>%
   mutate(all_investigations_done = all(c(HbA1c, Lipid, U_E, Dip_Urine, ACR_Urine))) %>%
   ungroup() %>%
   summarize(all_adherence = mean(all_investigations_done) * 100)

# Add row for "All investigations" into the adherence_inv_perc df
df <- data.frame(label = "All", adherence = all_investigations$all_adherence, pretty_label = "All investigations")
adherence_inv_perc <- rbind(adherence_inv_perc, df)

# Reorder  based on 'adherence'
adherence_inv_perc <- adherence_inv_perc[order(adherence_inv_perc$adherence, decreasing = TRUE), ]
adherence_inv_perc$pretty_label <- factor(adherence_inv_perc$pretty_label, levels = adherence_inv_perc$pretty_label)


adherence_inv_perc$color <- ifelse(adherence_inv_perc$pretty_label == "All investigations", "All investigations", "Single investigation")

# Make the bar chart
ggplot(adherence_inv_perc, aes(x = pretty_label, y = adherence, fill = color)) +
    geom_bar(stat = "identity", width = 0.7, color="black") +
    geom_hline(yintercept = 60, linetype = "dashed", color = "red") +
    geom_hline(yintercept = 0, color = "black", size = 1.0) +
    geom_text(aes(label = paste0(formatC(adherence, format = "f", digits = 1), "%")), vjust = -0.3, color = "black") +
    labs(x = "Investigations", y = "Adherence Percentage (%)",
         title = "") +
    scale_fill_manual(values = c("Single investigation" = "#009aa6", "All investigations" = "#c54b00")) +
    ylim(0, 100) +
    theme_minimal() +
    theme(legend.position = "none")  


```
  
# Other management bar graph

```{r OtherManagement, include=TRUE, echo=TRUE, fig.cap="Adherence of Remaining Criteria"}

# Calculate adherence
counts_715 <- table(HtnData$c715_Check)
counts_life <- table(HtnData$Lifestyle_Discussion)
counts_htn <- table(HtnData$HTN_MGMT_PLN)

percentage_adherence_715 <- sum(counts_715["TRUE"]) / sum(counts_715) * 100
percentage_adherence_life <- sum(counts_life["TRUE"]) / sum(counts_life) * 100
percentage_adherence_htn <- sum(counts_htn["TRUE"]) / sum(counts_htn) * 100

# combine the different variables 
df <- data.frame(label = c("715 Check", "Lifestyle Discussion", "Hypertension Management Plan"),
                 adherence = c(percentage_adherence_715, percentage_adherence_life, percentage_adherence_htn))

# Combined Bar Chart
p <- ggplot(df, aes(x = label, y = adherence, fill = label)) +
  geom_bar(stat = "identity", width = 0.4, color="black") +
  coord_flip() +
  geom_hline(yintercept = 60, linetype = "dashed", color = "red") +
  geom_hline(yintercept = 0, color = "black", size = 1.0) +
  geom_text(aes(label = paste0(formatC(adherence, format = "f", digits =  1), "%")), vjust = -2.5, color = "black") +
  labs(x = "", y = "Adherence Percentage (%)",
       title = "") +
  scale_fill_manual(values = c("Lifestyle Discussion" = "#231f20", "Hypertension Management Plan" = "#bc2026", "715 Check" = "#008596")) +
  theme_minimal() +
  theme(legend.position = "none") +  # line added to remove the legend
  ylim(0, 100)

print(p)


```


# Standard 2 Pie chart

```{r newstandard2, echo=TRUE, include=TRUE, fig.cap="Proportion of Present Audit Adherence with Standard 2"}
generate_pie_chart <- function(data, column, title) {
  # Get proportions of true and false
  counts <- table(data[[column]])

  # Name counts for plotting
  df <- data.frame(labels = names(counts), counts = as.vector(counts))
  df$labels <- ifelse(df$labels == "TRUE", "Adherent", "Non-Adherent")

  # Percentages for labeling
  df$perc <- round((df$counts/sum(df$counts))*100, 1)

  df$newlabels <- paste(df$labels, ": ", df$perc, "%", sep = "")

  # Generate pie chart
  p <- ggplot(df, aes(x = "", y = counts, fill = newlabels)) +
        geom_bar(width = 1, stat = "identity", colour = 'black') +
        coord_polar("y", start = 0) +
        scale_fill_manual(values = c("#ffd503", "#c71d22")) +
        labs(fill = "") +
        ggtitle(title) +
        theme_minimal() +
        theme(axis.title.x=element_blank(),
            axis.title.y=element_blank(),
            panel.border = element_blank(),
            panel.grid=element_blank(),
            axis.ticks = element_blank(),
            plot.title=element_text(hjust=0.0),
            axis.text = element_blank())

  return(p)
}

# Create the pie chart for New_Std_1_Met
newstandard1 <- generate_pie_chart(HtnData, "New_Std_2_Met", "")

# show the pie chart
print(newstandard1)

```

# Follow up criteria

```{r followupfinal, include=TRUE, echo=TRUE, fig.cap="Adherence to Criteria for Followup"}
# Calculate adherence to followup
HtnData$initial_flwp <- (HtnData$c3_MO_RVW_LFSTL | HtnData$c2_4_WK_RVW_ACE)
HtnData$complete_followup_adherence <- HtnData$initial_flwp & HtnData$c3_MO_FLWP_STBL_WITHNA & HtnData$c6_MO_BP

columns_to_run <- c("initial_flwp" = "Initial Followup",
                    "c3_MO_FLWP_STBL_WITHNA" = "Three Month Followup",
                    "c6_MO_BP" = "Six Month BP",
                    "complete_followup_adherence" = "Complete Followup")  # add complete followup data here

adherence_flwp_perc <- data.frame()

for(column in names(columns_to_run)) {
    counts <- table(HtnData[[column]])
    df <- data.frame(label = column, adherence = sum(counts["TRUE"]) / sum(counts) * 100,
                     pretty_label = columns_to_run[[column]])
    adherence_flwp_perc <- rbind(adherence_flwp_perc, df)
}


adherence_flwp_perc$pretty_label <- factor(adherence_flwp_perc$pretty_label,
                                           levels = c("Initial Followup",
                                                      "Three Month Followup",
                                                      "Six Month BP",
                                                      "Complete Followup")) 

# Bar chart below here
ggplot(adherence_flwp_perc, aes(x = pretty_label, y = adherence, fill = pretty_label)) +
    geom_bar(stat = "identity", width = 0.5, color="black") +
    scale_fill_manual(values=c("Initial Followup" = "#009aa6",
                               "Three Month Followup" = "#009aa6",
                               "Six Month BP" = "#009aa6",
                               "Complete Followup" = "#c54b00")) +
    geom_hline(yintercept = 60, linetype = "dashed", color = "red") +
    geom_hline(yintercept = 0, color = "black", size = 1.0) +
    geom_text(aes(label = paste0(formatC(adherence, format ="f", digits =  1),"%")), vjust = -0.3, color = "black") +
    labs(x = "Follow up Criteria", y = "Adherence Percentage (%)", title = "") +
    theme_minimal() +
    theme(legend.position = "none") + # line added to remove the legend
    ylim(0,100)
```



# More in text references for variables

```{r calculatingstandards-discussion, echo=TRUE}

OldSt1Text <- round(mean(as.numeric(HtnData$Old_Std_1_Met)) * 100)
OldSt2Text <- round(mean(as.numeric(HtnData$Old_Std_2_Met)) * 100)
OldASt1Text <- round(mean(as.numeric(HtnData$Old_audit_Std_1_Met)) * 100, 1)
OldASt2Text <- round(mean(as.numeric(HtnData$Old_audit_Std_2_Met)) * 100, 1)
# for new standards
NewSt1Text <- round(mean(as.numeric(HtnData$New_Std_1_Met)) * 100, 1)
NewSt2Text <- round(mean(as.numeric(HtnData$New_Std_2_Met)) * 100, 1)

counts_715 <- table(HtnData$c715_Check)
counts_life <- table(HtnData$Lifestyle_Discussion)
counts_htn <- table(HtnData$HTN_MGMT_PLN)
counts_urin <- table(HtnData$Dip_Urine)

text715 <- round(sum(counts_715["TRUE"]) / sum(counts_715) * 100, 1)
textlfstl <- round(sum(counts_life["TRUE"]) / sum(counts_life) * 100, 1)
texturin <- round(sum(counts_urin["TRUE"]) /sum(counts_urin) * 100, 1)
```



## Action Plan

```{r actionplan, echo=TRUE, include=TRUE, message=FALSE, fig.pos ='H', fig.cap="Action Plan following 2023 Audit of Adherence to Kimberley Hypertension Guidelines"}

library(readxl)
library(kableExtra)

datadictionary <- read_excel("actionplan.xlsx")


datadictionary %>%
kableExtra::kable(df, format = "latex", booktabs = TRUE, linesep = "\\addlinespace", caption = "Data Dictionary for Collection") %>%
  kable_styling(latex_options = c( "scale_down", "bordered", "HOLD_position")) %>%
  column_spec(1, bold = FALSE, width = "15em") %>%
  column_spec(2:5, width = "10em") %>%
  row_spec(0, bold = TRUE, italic = FALSE) 


```


Copyright 2023 Liam Jones

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.