*Format* the data to use appropriate variable names; *fill in missing values* with data where appropriate (as indicated in the header information) ```{r} posture_clean_df = posture_df %>% mutate( age_group = case_when( !is.na(age_group) ~ as.numeric(age_group), # Keep existing value if not NA age >= 10 & age <= 17 ~ 1, age >= 18 & age <= 30 ~ 2, age >= 31 & age <= 40 ~ 3, age >= 41 & age <= 50 ~ 4, age >= 51 & age <= 60 ~ 5, age >= 61 & age <= 70 ~ 6, age > 70 ~ 7, TRUE ~ NA_real_ ), fhp_category = case_when( !is.na(fhp_category) ~ as.numeric(fhp_category), fhp_size_mm >= 0 & fhp_size_mm < 10 ~ 0, fhp_size_mm >= 10 & fhp_size_mm < 20 ~ 1, fhp_size_mm >= 20 & fhp_size_mm < 30 ~ 2, fhp_size_mm >= 30 & fhp_size_mm < 40 ~ 3, fhp_size_mm >= 40 & fhp_size_mm < 50 ~ 4, fhp_size_mm >= 50 & fhp_size_mm < 60 ~ 5, fhp_size_mm >= 60 & fhp_size_mm < 70 ~ 6, fhp_size_mm >= 70 ~ 7, TRUE ~ NA_real_ ), eop_size = case_when( !is.na(eop_size) ~ as.numeric(eop_size), eop_size_mm >= 0 & eop_size_mm < 5 ~ 0, eop_size_mm >= 5 & eop_size_mm < 10 ~ 1, eop_size_mm >= 10 & eop_size_mm < 15 ~ 2, eop_size_mm >= 15 & eop_size_mm < 20 ~ 3, eop_size_mm >= 20 & eop_size_mm < 25 ~ 4, eop_size_mm >= 25 ~ 5, TRUE ~ NA_real_ ) ) ```