FPV DATA SET

### 0-6 MOnth= Kitten 
### 6-24 Month = Young
### 24 Month above = Adult

### Age Wise Prevalence of FPV 
library(readxl)
fpv <- read_excel("fpv.xlsx")
View(fpv)

# Create age groups based on the 'Months' column
fpv$age_group = cut(fpv$Months,
                    breaks = c(-Inf, 6, 24, Inf),
                    labels = c("Kitten (<= 6 Months)", "Young (>6 to 24 Months)", "Adult (> 24 Months)"),
                    right = TRUE)

# Count samples in each age group
age_group_counts = table(fpv$age_group)

# Convert the table to a data frame
age_group_df = as.data.frame(age_group_counts)

# Rename the columns for clarity
colnames(age_group_df) = c("Age Group", "Total")

# Display the counts in table form
print(age_group_df)

# Create age groups based on the 'Months' column
fpv$age_group = cut(fpv$Months,
                    breaks = c(-Inf, 6, 24, Inf),
                    labels = c("Kitten (<= 6 Months)", "Young (>6 to 24 Months)", "Adult (> 24 Months)"),
                    right = TRUE)

# Create a subset of the data with "FPV" diagnoses
fpv_fpv = fpv[fpv$`Diagnosis based on clinical signs and rapid test` == "FPV", ]

# Count the number of FPV cases in each age group
fpv_age_group_counts = table(fpv_fpv$age_group)

# Convert the table to a data frame
fpv_age_group_df = as.data.frame(fpv_age_group_counts)

# Rename the columns
colnames(fpv_age_group_df) = c("Age Group", "Positive")

# Display the counts in table form
print(fpv_age_group_df)

# Create age groups based on the 'Months' column
fpv$age_group = cut(fpv$Months,
                    breaks = c(-Inf, 6, 24, Inf),
                    labels = c("Kitten (<= 6 Months)", "Young (>6 to 24 Months)", "Adult (> 24 Months)"),
                    right = TRUE)

# Count total samples in each age group
age_group_counts = table(fpv$age_group)

# Create a subset of the data with "FPV" diagnoses
fpv_fpv = fpv[fpv$`Diagnosis based on clinical signs and rapid test` == "FPV", ]

# Count the number of FPV cases in each age group
fpv_age_group_counts = table(fpv_fpv$age_group)

# Convert the counts to data frames
age_group_df = as.data.frame(age_group_counts)
fpv_age_group_df = as.data.frame(fpv_age_group_counts)

# Rename the columns
colnames(age_group_df) = c("Age Group", "Total")
colnames(fpv_age_group_df) = c("Age Group", "Positive")

# Merge the total and positive counts into one data frame
merged_df = merge(age_group_df, fpv_age_group_df, by = "Age Group", all.x = TRUE)

# Fill NAs with 0 for age groups with no positive cases
merged_df$Positive[is.na(merged_df$Positive)] = 0

# Calculate prevalence
merged_df$Prevalence = (merged_df$Positive / merged_df$Total) * 100

# Display the final counts with prevalence
print(merged_df)

# Count samples in each sex group
sex_group_counts = table(fpv$Sex)

# Convert the table to a data frame
sex_group_df = as.data.frame(sex_group_counts)

# Rename the columns for clarity
colnames(sex_group_df) = c("Sex", "Total")

# Display the counts in table form
print(sex_group_df)

NA

# Create a subset of the data with "FPV" diagnoses
fpv_fpv = fpv[fpv$`Diagnosis based on clinical signs and rapid test` == "FPV", ]

# Count the number of FPV cases in each sex group
fpv_sex_counts = table(fpv_fpv$Sex)

# Convert the table to a data frame
fpv_sex_df = as.data.frame(fpv_sex_counts)

# Rename the columns for clarity
colnames(fpv_sex_df) = c("Sex", "Positive")

# Display the counts in table form
print(fpv_sex_df)

NA

# Count samples with "Diarrhoea"
diarrhoea_only = sum(fpv$Signs == "Diarrhoea")
print(diarrhoea_only)

[1] 10

# Count how many entries in the "Signs" column contain "diarrhoea"
##Mistake in Data Entry, contains both diarrhoea and Diarrhorea in spelling 
diarrhoea_count = sum(grepl("diarrhoea", fpv$Signs, ignore.case = TRUE))
diarrhorea_count = sum(grepl("Diarrhorea", fpv$Signs, ignore.case = TRUE))
Diarrhoetic_Cat = diarrhoea_count + diarrhorea_count
# Count the total number of entries in the "Signs" column
total_entries = nrow(fpv)
# Calculate the non-diarrhoetic count
non_diarrhoetic_count = total_entries - Diarrhoetic_Cat
# Print the result
print(Diarrhoetic_Cat)

[1] 53

print(non_diarrhoetic_count)

[1] 284

# Create subsets for diarrhoetic and non-diarrhoetic cases
diarrhoetic_cases = fpv[grepl("diarrhoea", fpv$Signs, ignore.case = TRUE) | grepl("Diarrhorea", fpv$Signs, ignore.case = TRUE), ]
non_diarrhoetic_cases = fpv[!(grepl("diarrhoea", fpv$Signs, ignore.case = TRUE) | grepl("Diarrhorea", fpv$Signs, ignore.case = TRUE)), ]

# Count how many FPV cases are in the diarrhoetic subset
fpv_diarrhoetic_count = sum(diarrhoetic_cases$`Diagnosis based on clinical signs and rapid test` == "FPV")

# Count how many FPV cases are in the non-diarrhoetic subset
fpv_non_diarrhoetic_count = sum(non_diarrhoetic_cases$`Diagnosis based on clinical signs and rapid test` == "FPV")

# Print the results
cat("FPV cases among Diarrhoetic:", fpv_diarrhoetic_count, "\n")

FPV cases among Diarrhoetic: 23 

cat("FPV cases among Non-Diarrhoetic:", fpv_non_diarrhoetic_count, "\n")

FPV cases among Non-Diarrhoetic: 10 

# Count how many entries in the "Breed" column contain "Local"
local_count = sum(grepl("local", fpv$Breed, ignore.case = TRUE))
Persian_count = sum(grepl("Persian", fpv$Breed, ignore.case = TRUE))
Domestic_short_hair_count = sum(grepl("short hair", fpv$Breed, ignore.case = TRUE)) 
Domestic_long_hair_count = sum(grepl("long hair", fpv$Breed, ignore.case = TRUE))
Others_count = sum(grepl("Others", fpv$Breed, ignore.case = TRUE))
#Mistake in Data Entry
Ohers_count = sum(grepl("Ohers", fpv$Breed, ignore.case = TRUE))

Others = Others_count + Ohers_count

total_type = local_count + Persian_count + Domestic_short_hair_count + Domestic_long_hair_count + Others
#Print
print(local_count)

[1] 153

print(Persian_count)

[1] 101

print(Domestic_short_hair_count)

[1] 21

print(Domestic_long_hair_count)

[1] 15

print(Others)

[1] 47

print(total_type)

[1] 337

# Initialize counts for each breed with FPV
local_fpv_count = sum(grepl("local", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")
persian_fpv_count = sum(grepl("Persian", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")
domestic_short_hair_fpv_count = sum(grepl("short hair", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")
domestic_long_hair_fpv_count = sum(grepl("long hair", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")
others_fpv_count = sum(grepl("Others", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")
ohers_fpv_count = sum(grepl("Ohers", fpv$Breed, ignore.case = TRUE) & fpv$`Diagnosis based on clinical signs and rapid test` == "FPV")

# Calculate total FPV cases for Others category
total_others_fpv = others_fpv_count + ohers_fpv_count

# Print results
cat("FPV Count in Local Breed:", local_fpv_count, "\n")

FPV Count in Local Breed: 14 

cat("FPV Count in Persian Breed:", persian_fpv_count, "\n")

FPV Count in Persian Breed: 10 

cat("FPV Count in Domestic Short Hair Breed:", domestic_short_hair_fpv_count, "\n")

FPV Count in Domestic Short Hair Breed: 4 

cat("FPV Count in Domestic Long Hair Breed:", domestic_long_hair_fpv_count, "\n")

FPV Count in Domestic Long Hair Breed: 3 

cat("Total FPV Count in Others Breed:", total_others_fpv, "\n")

Total FPV Count in Others Breed: 2