Meeting chat log
00:41:00 edgar zamora: https://stackoverflow.com/questions/4862178/remove-rows-with-all-or-some-nas-missing-values-in-data-frame
00:41:14 Mike Haugen: For time series analysis, some of the forecasting functions, e..g exponential smoothing, require a certain approach to dealing with NAs. You can remove NAs for some, for others, you need to impute them
00:42:20 Arami: Can you explain what "time series analysis" is? Is it any analysis that tracks change over time?
00:43:09 Mike Haugen: Yes
00:43:48 Mike Haugen: Like forecasting emergency room presentations based on historical data on emergency room presentations over the last few years
00:44:07 Mike Haugen: or forecasting course attendance based on historical data.
00:45:18 Rob Lucas: Glad to know there is some other list-aversion out there!
00:45:27 Mike Haugen: For R, see Hyndman Forecasting: Principles and Practice: https://otexts.com/fpp3/index.html
00:45:46 Arami: Thanks!
00:45:47 Ronak Patel: I also suffer from severe list-aversion.
00:46:33 Morgan Grovenburg: injuries %>%
mutate(diag = fct_lump(fct_infreq(diag), n = 5)) %>%
group_by(diag) %>%
summarise(n = as.integer(sum(weight)))