使用r，如何将不是基本聚合函数的函数（moments :: skewness）应用于分组表？

Question

I have in Redshift a set of crowdsourced weather data: many stations, each many days, each day 20 to 100 observations, each observation several variables. I am computing daily statistics. It works like this;

dailyn<-cwoparchive %>%
filter(stationname=="EW2020" %>%
group_by (archivedate) %>%
summarise(ncount=n(),meanlat=mean(latitude),meanlon=mean(longitude)) %>%
collect() %>%
data.frame ()

returns this, just what I want:

• archivedate ncount meanlat meanlon
• 1 2013-02-06 2 38.82667 -76.79884
• 2 2013-03-19 22 38.82700 -76.79816
• 3 2013-03-21 45 38.82700 -76.79816
• 4 2013-03-22 49 38.82699 -76.79819
• 5 2013-03-24 63 38.82690 -76.79836
• 6 2013-03-27 62 38.82691 -76.79834
• 7 2013-03-28 48 38.82700 -76.79816
• 8 2013-03-29 45 38.82700 -76.79816
• 9 2013-03-30 39 38.82700 -76.79816
• 10 2013-04-01 49 38.82697 -76.79823 etc.

Next I want to know if mornings are sunnier. But when I do the same thing as above, only replacing that summarise line with this one:

summarise(askew=skewness(linterpreted))

I get an error message, "Error in postgresqlExecStatement(conn, statement, ...) : RS-DBI driver: (could not Retrieve the result : ERROR: function skewness(integer) does not exist HINT: No function matches the given name and argument types. You may need to add explicit type casts." Yet the skewness function works fine on the same data in normal, non-grouped data frames.

Answer 1

With apologies for bothering everybody, I worked it out. First download, secondly group-by in a follow-on step - this seems irrational but it is necessary, and then summarise on that.

stationdata<-cwoparchive %>%
filter(stationname=="EW2020") %>%
collect() %>%
data.frame ()

station_by_day <- group_by(stationdata, archivedate)

skew_by_day <- summarise(station_by_day, count = n(),askew=skewness(linterpreted))

produces the ideal result:

• archivedate count askew
  • (date) (int) (dbl)
  • 1 2013-02-06 2 0.0000000
  • 2 2013-03-01 5 -0.3755537
  • 3 2013-03-19 22 -0.2498925
  • 4 2013-03-20 38 -0.3328628
  • 5 2013-03-21 45 0.7237873 etc.