import_cpi updates and test fixes

R/import_CPI.R

@@ -12,7 +12,7 @@
 #'
 #' @examples
 #' #cpi_data <- import_CPI()
-import_CPI <- function(path = "C:/Users/lclem/Downloads/CPI_Data_DashboardExtract.xlsx", sheet = "Data_Price", skip = 2){
+import_CPI <- function(path, sheet = "Data_Price", skip = 2){
   data <- readxl::read_excel(path, sheet = sheet, skip = skip)
   data <- data %>%
     dplyr::filter(`Instrument Type` == "ETS") %>% 

tests/testthat/test-import_CPI.R

@@ -0,0 +1,27 @@
+library(readxl)
+library(dplyr)
+library(tidyr)
+library(stringr)
+
+# Assuming you have a test Excel file located in your test directory called "test_CPI_Data.xlsx"
+path_to_test_file <- "tests/testthat/testdata/CPI_Data_DashboardExtract.xlsx"
+
+test_data <- import_CPI(path = path_to_test_file, sheet = "Data_Price", skip = 2)
+
+test_that("import_CPI returns a dataframe with correct structure and data", {
+  # Check if the output is a dataframe
+  expect_true(is.data.frame(test_data))
+
+  # Check for correct columns in the dataframe
+  expected_columns <- c("Jurisdiction", "Period", "Year", "Price ($)")
+  expect_true(all(expected_columns %in% names(test_data)))
+
+  # Here, check that only ETS instruments are included
+
+  # Check the Year and Period transformations
+  expect_true(all(!is.na(as.numeric(test_data$Year))))
+  expect_true(all(!is.na(as.numeric(test_data$Period))))
+
+  # Check for non-NA prices
+  expect_false(any(is.na(test_data$`Price ($)`)))
+})