---
title: "Code Snippets Audit"
output: html_notebook
---


# collection of snippets for the data collection

# Setup and calculations



```{r setup, include=FALSE, echo=FALSE}
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=FALSE, 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=FALSE, 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=FALSE}

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=FALSE, 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=FALSE, 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=FALSE, 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=FALSE, 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=FALSE, 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=FALSE, 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=FALSE}

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=FALSE, 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) 


```