Out of pocket, data_filtering

data_filtering.R

# Data Filtering Code
library(dplyr)

# Sample Data
sample_data <- data.frame(
  Country = c("CountryA", "CountryA", "CountryB", "CountryB", "CountryB"),
  Code = c("A", "A", "B", "B", "B"),
  Year = c(2010, 2011, 2010, 2011, 2012),
  `Out-of-pocket expenditure (% of current health expenditure)` = c(10, 12, 8, 9, 11)
)

# Data Manipulation for Sample Data
filtered_data <- sample_data %>%
  # Your data manipulation code here

# Assuming dataset is loaded into a data frame named df
# You can load it using: df <- read.csv("share-of-out-of-pocket-expenditure-on-healthcare.csv")

# Count the number of years each country has data for
years_per_country <- df %>%
  group_by(Country) %>%
  summarize(NumYears = n())

# Identify countries with less than 10 years of data
countries_to_remove <- years_per_country$Country[years_per_country$NumYears < 10]

# Filter the dataset to exclude countries with less than 10 years of data
filtered_df <- df[!(df$Country %in% countries_to_remove), ]

# Now, filtered_df contains the dataset with countries providing at least 10 years of data

# Print the Result
print(filtered_data)

Test the dataset with sample data only.