search for: final_result

...x of open, high, low, close, and volume prices returns <- function(fund) { p_12ago = as.vector(fund[nrow(fund)-252,6]) perc_diff_12mo = ((p_last - p_12ago) / p_last)*100 #This is the line that's giving me problems: funds=c(as.character(fund)) mo12=c(perc_diff_12mo) final_results=data.frame(funds,mo12) return(final_results) } I can't figure out how to insert the original argument from the function (in this case the name of the fund). Ultimately I want to be able to do returns("GOOG"), and have the output in this format: funds 12mo GOOG 14% I...

...setcolorder(out1, all_cols) > setcolorder(out2, all_cols) > > # Combine by ID_Key (since they share same columns now) > final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > > # Step E: If needed, summarize across ID_Key to sum presence > indicators > final_result <- final_dt[, lapply(.SD, sum, na.rm = TRUE), by = > ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")] > > # note that final_result should now contain summed presence/absence > (0/1) indicators. Hope this helps! gregg somewhereinArizona -------------- next part...

...setcolorder(out1, all_cols) > setcolorder(out2, all_cols) > > # Combine by ID_Key (since they share same columns now) > final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > > # Step E: If needed, summarize across ID_Key to sum presence > indicators > final_result <- final_dt[, lapply(.SD, sum, na.rm = TRUE), by = > ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")] > > # note that final_result should now contain summed presence/absence > (0/1) indicators. Hope this helps! gregg somewhereinArizona The information in this e-m...

...rder(out2, all_cols) > > > > # Combine by ID_Key (since they share same columns now) > > final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > > > > # Step E: If needed, summarize across ID_Key to sum presence > > > indicators > > final_result <- final_dt[, lapply(.SD, sum, na.rm = TRUE), by = > > > ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")] > > > > # note that final_result should now contain summed presence/absence > > > (0/1) indicators. > > > > > Hope thi...

...> > # Combine by ID_Key (since they share same columns now) > > > final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > > > > > > # Step E: If needed, summarize across ID_Key to sum presence > > > > > indicators > > > final_result <- final_dt[, lapply(.SD, sum, na.rm = TRUE), by = > > > > > ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")] > > > > > > # note that final_result should now contain summed presence/absence > > > > > (0/1) indicators. > >...

...) out2_missing <- setdiff(all_cols, names(out2)) for (col in out1_missing) out1[, (col) := 0] for (col in out2_missing) out2[, (col) := 0] setcolorder(out1, all_cols) setcolorder(out2, all_cols) final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) final_result <- as_tibble(final_dt[, lapply(.SD, sum, na.rm = TRUE), by = ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")]) Worth noting however: * I unfortunately had to keep the multicore parameters for the janitor package to use make_clean_names() because it just took to long to run i...

...setcolorder(out1, all_cols) > setcolorder(out2, all_cols) > > # Combine by ID_Key (since they share same columns now) > final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > > # Step E: If needed, summarize across ID_Key to sum presence > indicators > final_result <- final_dt[, lapply(.SD, sum, na.rm = TRUE), by = > ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")] > > # note that final_result should now contain summed presence/absence > (0/1) indicators. Hope this helps! gregg somewhereinArizona The information in this e-...

...ff(all_cols, names(out2)) > ? ? for (col in out1_missing) out1[, (col) := 0] > ? ? for (col in out2_missing) out2[, (col) := 0] > ? ? setcolorder(out1, all_cols) > ? ? setcolorder(out2, all_cols) > ? ? final_dt <- rbindlist(list(out1, out2), use.names = TRUE, fill = TRUE) > ? ? final_result <- as_tibble(final_dt[, lapply(.SD, sum, na.rm = TRUE), by = ID_Key, .SDcols = setdiff(names(final_dt), "ID_Key")]) > > > Worth noting however: > > > - I unfortunately had to keep the `multicore`?parameters for the `janitor`?package to use `make_clean_names(...

...request, res->msg); (gdb) bt full #0 db_ldap_handle_request_result (res=0x0, idx=<optimized out>, request=<optimized out>, conn=<optimized out>) at db-ldap.c:819 _data_stack_cur_id = 3 srequest = <optimized out> ret = <optimized out> final_result = <optimized out> named_res = <optimized out> #1 db_ldap_handle_result (res=<optimized out>, conn=<optimized out>) at db-ldap.c:886 auth_request = 0x7f3704fa2620 idx = <optimized out> msgid = <optimized out> #2 ldap_input (conn=&...

Hi R users. Apologies for the lack of concrete examples because the dataset is large, and it being so I believe is the issue. I multiple, very large datasets for which I need to generate 0/1 absence/presence columns Some include over 200M rows, with two columns that need presence/absence columns based on the strings contained within them, as an example, one set has ~29k unique values and the

...0x00007f279faa8255 in db_ldap_handle_request_result (res=<optimized out>, idx=<optimized out>, request=<optimized out>, conn=<optimized out>) at db-ldap.c:841 _data_stack_cur_id = 3 srequest = <optimized out> ret = <optimized out> final_result = <optimized out> #19 db_ldap_handle_result (res=<optimized out>, conn=<optimized out>) at db-ldap.c:908 auth_request = 0x55c932881628 idx = <optimized out> msgid = <optimized out> #20 ldap_input (conn=<optimized out>) at db-ldap.c:940...

Hi, currently we deploying Dovecot as imap/pop3 proxy. Every few minutes some panic/assert occurred (we connect roughly 7k - 8k user at one imap proxy with a connection rate of 200/s). We activate core dumps. Concerning the sensitive information in the dump we would prefer to not share the dump (e.g. i found our ssl private key in the dump). Log/Stack trace: Mar 30 15:54:06 imap16 dovecot: