## ---- message=FALSE, warning=FALSE-------------------------------------------- # Load packages library(NHANES) # NHANES dataset library(dplyr) # Data manipulation library(ExclusionTable) # Attach data data("NHANES") ## ----------------------------------------------------------------------------- # Subset NHANES data using dplyr::filter NHANES_subset <- NHANES %>% filter(Gender == "female", Age >= 65, !is.na(BMI)) # Print number of observations nrow(NHANES_subset) ## ----------------------------------------------------------------------------- exclusion_table(NHANES, inclusion_criteria = c("Gender == 'female'", "Age >= 65"), exclusion_criteria = "is.na(BMI)", keep_data = FALSE) ## ----------------------------------------------------------------------------- exclusion_table(NHANES, inclusion_criteria = c("Gender == 'female'", "Age >= 65"), exclusion_criteria = "is.na(BMI)", labels_inclusion = c("Get females", "Age is >= 65"), labels_exclusion = "Missing BMI", keep_data = FALSE) ## ---- message=FALSE----------------------------------------------------------- NHANES_ex_tab <- exclusion_table(NHANES, inclusion_criteria = c("Gender == 'female'", "Age >= 65"), exclusion_criteria = "is.na(BMI)", labels_inclusion = c("Get females", "Age is >= 65"), labels_exclusion = "Missing BMI", keep_data = TRUE) # Print structure str(NHANES_ex_tab, 1) ## ----------------------------------------------------------------------------- NAHANES_cleaned <- NHANES_ex_tab[["dataset"]] nrow(NAHANES_cleaned) ## ----------------------------------------------------------------------------- room_selection <- c(2, 4, 9) exclusion_table(NHANES, inclusion_criteria = c("HomeRooms %in% obj$room_selection"), labels_inclusion = c("2, 4, 9 rooms"), obj = list(room_selection = room_selection))