Hi, I am computing a weighted average in R using the below code. I'd like the weighted average result produced in Power BI to match the R code but the results differ -- any thoughts on how I should modify the DAX formula so results in Power BI and R are the same?
R code:
raw_data<-data.frame(group = c(rep("batting sports",3),"contact sports"),
subgroup = c("baseball", "cricket", "softball", "football"),
current_fan_base = c(15873742, 330102240, 92893081, 106040),
popularity_grade = c(.5,.9,NA,.99))
summary <- raw_data %>% group_by(group) %>%
dplyr::summarize(
group_pop_grade = sum(popularity_grade * current_fan_base, na.rm = T)/sum(current_fan_base[!is.na(popularity_grade)]))
R results:
batting sports 0.882
contact sports 0.99
Power BI DAX formula
(created using the quick measure tool, with sum of popularity grade as the base value, current fan base as the weight and group as the category)
popularity_grade weighted by current_fan_base per popularity_grade =
VAR __CATEGORY_VALUES = VALUES('raw_data'[popularity_grade])
RETURN
DIVIDE(
SUMX(
KEEPFILTERS(__CATEGORY_VALUES),
CALCULATE(
SUM('raw_data'[popularity_grade])
* SUM('raw_data'[current_fan_base])
)
),
SUMX(
KEEPFILTERS(__CATEGORY_VALUES),
CALCULATE(SUM('raw_data'[current_fan_base]))
)
)
Power BI DAX results
group | popularity_grade weighted by current_fan_base per popularity_grade |
contact sports | 0.99 |
batting sports | 0.695033924 |
