data_challenge_project.R

# Sorting the raw data based on requirement 
# Load required libraries
library(dplyr)
library(magrittr)
library(tidyr)

# Read the data from the CSV file
# Replace "/path/to/your/csvfile.csv" with the actual path to your CSV file
raw_data <- read.csv("/path/to/your/csvfile.csv")

# Filter the data for a specific species and country
# Replace "Species" and "Country" with the appropriate column names
# Replace "Species_Name" and "Country_Name" with the desired species and country names
filtered_data <- raw_data %>%
  filter(Species == "Species_Name" & Country == "Country_Name")

# Save the filtered data to a new CSV file
# Replace "/path/to/your/output/file.csv" with the desired output path and file name
write.csv(filtered_data, '/path/to/your/output/file.csv')




# Remove any empty columns 
# Load required libraries
library(dplyr)

# Read the data from the CSV file
# Replace "/path/to/your/input/file.csv" with the actual path to your input CSV file
data <- read.csv("/path/to/your/input/file.csv")

# Identify and remove empty columns
empty_cols <- which(colSums(is.na(data)) == nrow(data))
data <- data[, -empty_cols]

# Save the modified data to a new CSV file
# Replace "/path/to/your/output/modified_file.csv" with the desired output path and file name
write.csv(data, "/path/to/your/output/modified_file.csv", row.names = FALSE)




# Creating a distribution plot of samples across the world from the provided dataset 
# Load required libraries
library(ggplot2)
library(maps)

# Read the CSV file into a data frame
# Replace "/path/to/your/input/file.csv" with the actual path to your input CSV file
data <- read.csv("/path/to/your/input/file.csv")

# Create a world map
world <- map_data("world")

# Check for missing or non-unique country names in the data
if (any(is.na(data$Country))) {
  stop("There are missing values in the 'Country' column.")
}

if (any(duplicated(data$Country))) {
  stop("There are non-unique values in the 'Country' column.")
}

# Merge the data frame with the world map
data_map <- inner_join(world, data, by = c("region" = "Country"))

# Create the plot using ggplot2
plot <- ggplot() +
  geom_polygon(data = data_map, aes(x = long, y = lat, group = group, fill = SampleID)) +
  coord_map("miller") +
  theme_minimal() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  scale_fill_viridis_c() +
  labs(title = "Distribution of Samples on World Map")

# Save the plot as a PNG file
# Replace "/path/to/your/output/sample_map.png" with the desired output path and file name
ggsave("/path/to/your/output/sample_map.png", plot, width = 10, height = 6, dpi = 300)






# To generate a plot for prevalence of species by year 
# Load required libraries
library(ggplot2)
library(dplyr)

# Read the CSV file into a data frame
# Replace "/path/to/your/input/file.csv" with the actual path to your input CSV file
data_counts <- read.csv("/path/to/your/input/file.csv")

# Check for missing or non-unique values in the data
if (any(is.na(data_counts$Year)) || any(is.na(data_counts$n))) {
  stop("There are missing values in the 'Year' or 'n' columns.")
}

if (any(duplicated(data_counts$Year))) {
  stop("There are non-unique values in the 'Year' column.")
}

# Create the line plot using ggplot2
line_plot <- ggplot(data_counts, aes(x = Year, y = n, group = Species, color = Species)) +
  geom_line() +
  geom_point() +
  labs(title = "Prevalence of ESKAPE pathogens by species and year",
       x = "Year",
       y = "Number of Samples",
       color = "Species") +
  theme_minimal()

# Display the line plot
print(line_plot)

# Save the plot as a PNG file
# Replace "/path/to/your/output/line_plot.png" with the desired output path and file name
ggsave("/path/to/your/output/line_plot.png", line_plot, width = 10, height = 6, dpi = 300)



# preparing the data for MDR analysis using the AMR package 
# First convert the entries in the dataset columns into R,I,S format from Resistant,Intermediate,Susceptible format 
# Load required libraries
library(dplyr)

# Read the raw data from CSV file
# Replace "/path/to/your/input/raw_data.csv" with the actual path to your input CSV file
raw_data <- read.csv("/path/to/your/input/raw_data.csv")

# Function to replace "Resistant", "Susceptible", and "Intermediate" with "R", "S", and "I" respectively
replace_resistant_susceptible_intermediate <- function(x) {
  x <- gsub("Resistant", "R", x)
  x <- gsub("Susceptible", "S", x)
  gsub("Intermediate", "I", x)
}

# Automatically detect and replace columns containing "Resistant", "Susceptible", and "Intermediate"
raw_data <- raw_data %>%
  mutate(across(where(is.character), replace_resistant_susceptible_intermediate))

# Save the updated data to a new CSV file if needed
# Replace "/path/to/your/output/modified_data.csv" with the desired output path and file name
write.csv(raw_data, "/path/to/your/output/modified_data.csv", row.names = FALSE)



# Determine the MDR isolates count from the prepared dataset usinf AMR package https://msberends.github.io/AMR/reference/mdro.html

mdro(
  # A dataframe antibiotic columns like AMX or amox.
  x = NULL, 
  # A specefic supported National/International/Custom guideline to be followed 
  guideline = "CMI2012",
  # column name of the names or codes of the microorganisms
  col_mo = NULL,
  # a logical to indicate whether progress should be printed to the console
  info = interactive(),
  # minimal required percentage of antimicrobial classes that must be available per isolate
  pct_required_classes = 0.5,
  # a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. 
  combine_SI = TRUE,
  # a logical to turn Verbose mode on and off (default is off).
  verbose = FALSE,
  # a logical to indicate whether only antibiotic columns must be detected that were transformed to class sir
  only_sir_columns = FALSE,
  ...
)



# Generating a plot for the MDR analysis 
# Load required libraries
library(ggplot2)
library(tidyverse)

# Example data: Replace these with your actual data
pathogen_names <- c("E. faecium", "S. aureus", "K. pneumoniae", "A. baumannii", "P. aeruginosa", "Enterobacter spp")
total_counts <- c(16661, 147921, 88712, 38036, 94460, 48041)
mdr_counts <- c(5710, 1596, 27327, 23077, 17165, 7549)

# Combine vectors into a data frame
mdr_data <- data.frame(pathogen = pathogen_names, total_count = total_counts, mdr_count = mdr_counts)

# Calculate the proportion of MDR isolates relative to total isolates
mdr_data$mdr_proportion <- mdr_data$mdr_count / mdr_data$total_count

# Create the grouped bar plot with text labels
ggplot(mdr_data, aes(x = reorder(pathogen, total_count), y = total_count, fill = "Total")) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_bar(aes(y = mdr_count, fill = "MDR"), stat = "identity", position = "dodge") +
  geom_text(aes(label = paste0(round(mdr_proportion * 100), "%")),
            position = position_dodge(width = 0.9), vjust = -0.5, size = 3.5) +
  labs(title = "MDR Isolate Count Among Total Isolates for ESKAPE Pathogens",
       x = "Pathogen",
       y = "Count",
       fill = "Isolate Type") +
  scale_fill_manual(values = c("Total" = "gray", "MDR" = "red")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.title = element_blank(),
        axis.text = element_text(color = "black"))




# Future antimicrobial resistance prediction using regression models 
# AMR package (https://msberends.github.io/AMR/articles/resistance_predict.html)
# Load the necessary libraries
library(dplyr)
library(ggplot2)
library(AMR)

# Read the CSV file into a data frame
# Replace "/path/to/your/input/your_file_path.csv" with the actual path to your input CSV file
data <- read.csv("/path/to/your/input/your_file_path.csv")

# Specify the name of the antibiotic for resistance prediction
# Replace "Your_Antibiotic_Column_Name" with the actual column name containing antibiotic resistance information
antibiotic_column <- "Your_Antibiotic_Column_Name"

# Perform resistance prediction for the specified antibiotic
# Replace "Year" and "Your_Antibiotic_Column_Name" with the actual column names for date and antibiotic resistance information
predict_antibiotic <- data %>%
  resistance_predict(
    col_date = "Year",
    col_ab = antibiotic_column,
    model = "binomial"
  )

# Plot the resistance prediction results
plot(predict_antibiotic)

# Plot the predicted SIR (Susceptible, Intermediate, Resistant) proportions
ggplot_sir_predict(predict_antibiotic)