Response time too long, Error Handling in R

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question

ty

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Response time too long, Error Handling in R

R code:

library(eikonapir)

eikonapir::set_app_id("my-key")
time.start <- Sys.time()
tickers <- c("BRGV5YUSAC=R", "AAHR5YEUAM=R", "ANZ5YUSAR=R", "BBVA5YEUAM=R", "SAN5YEUAM=R", "BAC5YUSAX=R", "BKCH5YUSAC=R", "BNS5YUSAX=MP", "BARC5YEUAM=R", "BNPP5YEUAM=R", "CNDA5YUSAC=R", "C5YUSAX=R", "CBKG5YEUAM=R", "CBA5YUSAR=R", "RABO5YEUAM=R", "CAGR5YEUAM=R", "CSGN5YEUAM=R", "DB5YEUAM=R", "GS5YUSAX=R", "HBCA5YEUAM=R", "INGB5YEUAM=R", "BCIN5YEUAM=R", "JPM5YUSAX=R", "LLTS5YEUAM=R", "MBL5YUSAR=R", "MZFC5YUSAC=R", "MS5YUSAX=R", "MTFC5YUSAC=R", "CNAT5YEUAM=MG", "NDAA5YEUAM=MG", "FINA5YUSAC=MP", "SHIA5YUSAC=R", "SG5YEUAM=R", "STAN5YEUAM=R", "SUMA5YUSAC=R", "SHB5YEUAM=R", "UBSN5YEUAM=R", "UNIC5YEUAM=R", "UOBH5YUSAC=R", "WFC5YUSAX=R", "WBC5YUSAR=R", "WOOR5YUSAC=R", "OCBC5YUSAC=R", "ICBC5YUSAC=MG", "RBS5YEUAM=R")
df <- data.frame(Data = NA, CDS = NA, Ticker = NA)[c(-1),]
for (t in tickers) {
  df_aux <- get_timeseries(rics = as.list(t),
                           fields = list("TIMESTAMP", "CLOSE"),
                           start_date = format(Sys.Date() - 7, "%Y-%m-%dT%H:%M:%SZ"),
                           end_date = format(Sys.Date(), "%Y-%m-%dT%H:%M:%SZ"),
                           interval = "daily")
  if (ncol(df_aux) == 3) {
      colnames(df_aux) <- c("Data", "CDS", "Ticker")
      df <- rbind(df, df_aux)
  }
}
time.end <- Sys.time()
(time.taken <- time.end - time.start)

Result: 21.6 minutes

So, my questions:

I) First time I run that script, I didn't mesure, but it took less than 4 minutes, but nowadays it's so slow.. Is there any reason for that long execution?

II) I have to use that if (ncol(df_aux) == 3) for treat no data or no access data since it return a 2 column dataframe instead of 3 on those cases. Is this the best practice?

III) How to error handling in R? How to get the error message in get_data and get_timeseries?

eikon eikon-data-api python refinitiv-dataplatform-eikon workspace workspace-data-api r error-handling

Aug 19, 2020 at 02:47 PM

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veerapath.rungruengrayubkul Aug 28, 2020 at 04:22 AM

@ty

Hi,

Thank you for your participation in the forum. Is the reply below satisfactory in answering your question? If yes please click the 'Accept' text next to the most appropriate reply. This will guide all community members who have a similar question. Otherwise please post again offering further insight into your question. Thanks,

AHS

chavalit-jintamalit Sep 04, 2020 at 05:29 AM

Please be informed that a reply has been verified as correct in answering the question, and has been marked as such. Thanks, AHS

Answer 1 · 2020-08-20T04:34:34Z

Jirapongse

78.8k ●250 ●52 ●74

@ty

The efficient way is getting raw data and then create a data frame from the raw data.

You can send one request of all items if it doesn't exceed 3000 datapoints as mentioned in the EIKON DATA API USAGE AND LIMITS GUIDELINE.

The code looks like:

get_formatted_data_frame  <- function(data)
{
  input_data_frame = data$timeseriesData
  data_frames <- list()
  for (i in 1:nrow(input_data_frame))
  { current_row = input_data_frame[i,]    
    if (is.na(current_row$errorCode)==FALSE) {
        next
    }   
    current_fields  = current_row$fields[[1]]$name
    ric_column = rep( current_row$ric,nrow(input_data_frame[i,]))
    data_frame  <- as.data.frame(current_row$dataPoints[[1]],stringsAsFactors = FALSE)   
    data_frame <- cbind(data_frame, Security=ric_column)
    names(data_frame) <- current_fields
    data_frames[[i]] = data_frame
  }
  return (do.call("rbind", data_frames))
}

raw <- get_timeseries(rics = list("BRGV5YUSAC=R", "AAHR5YEUAM=R", "ANZ5YUSAR=R", "BBVA5YEUAM=R", "SAN5YEUAM=R", "BAC5YUSAX=R", "BKCH5YUSAC=R", "BNS5YUSAX=MP", "BARC5YEUAM=R", "BNPP5YEUAM=R", "CNDA5YUSAC=R", "C5YUSAX=R", "CBKG5YEUAM=R", "CBA5YUSAR=R", "RABO5YEUAM=R", "CAGR5YEUAM=R", "CSGN5YEUAM=R", "DB5YEUAM=R", "GS5YUSAX=R", "HBCA5YEUAM=R", "INGB5YEUAM=R", "BCIN5YEUAM=R", "JPM5YUSAX=R", "LLTS5YEUAM=R", "MBL5YUSAR=R", "MZFC5YUSAC=R", "MS5YUSAX=R", "MTFC5YUSAC=R", "CNAT5YEUAM=MG", "NDAA5YEUAM=MG", "FINA5YUSAC=MP", "SHIA5YUSAC=R", "SG5YEUAM=R", "STAN5YEUAM=R", "SUMA5YUSAC=R", "SHB5YEUAM=R", "UBSN5YEUAM=R", "UNIC5YEUAM=R", "UOBH5YUSAC=R", "WFC5YUSAX=R", "WBC5YUSAR=R", "WOOR5YUSAC=R", "OCBC5YUSAC=R", "ICBC5YUSAC=MG", "RBS5YEUAM=R"),
                           fields = list("TIMESTAMP", "CLOSE"),
                           start_date = format(Sys.Date() - 7, "%Y-%m-%dT%H:%M:%SZ"),
                           end_date = format(Sys.Date(), "%Y-%m-%dT%H:%M:%SZ"),
                           interval = "daily",
                           raw_output = TRUE)
data = jsonlite::fromJSON(raw)
get_formatted_data_frame(data)

Aug 20, 2020 at 04:34 AM

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Answer 2 · 2020-09-08T17:47:35Z

ty

5 ●3 ●4 ●3

Sorry for the delay, it does solve my problem with the "raw_output = TRUE" option! Thanks a lot!

Sep 08, 2020 at 05:47 PM

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Answer 3 · 2021-02-06T08:50:16Z

krunallathiya21.kl

1 ●0 ●0 ●0

When you are writing big programs, sometimes something goes wrong with your R code. What do you do if the program stops unexpectedly? What tools do you have to address the problem? This is where the tryCatch() function will help you. Debugging is the art and science of fixing unexpected problems in your code.

You can use the tryCatch() exception handling method to resolve most of your issues.

Thanks,

Feb 06, 2021 at 08:50 AM

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