search for: guess_max

...he data type from boolean to numeric. I tried doing dataset$my_field = as.numeric(dataset$my_field), I also tried to do dataset <- dataset[complete.cases(dataset), ], didn't work either. The only thing that worked for me was to take a single sheed and through the read_excel function use the guess_max parameter and set it to a sufficiently large number (a number >= to the total amount of the full merged dataset). I want to automate the merging of the N number of Excel sheets so that I don't have to be manually doing it. Unless there is a way to accomplish something similar to what rio'...

...numeric. I tried doing dataset$my_field = > as.numeric(dataset$my_field), I also tried to do dataset <- > dataset[complete.cases(dataset), ], didn't work either. > > The only thing that worked for me was to take a single sheed and through > the read_excel function use the guess_max parameter and set it to a > sufficiently large number (a number >= to the total amount of the full > merged dataset). I want to automate the merging of the N number of Excel > sheets so that I don't have to be manually doing it. Unless there is a > way to accomplish something...

...that has 46 sheets on it. I basically combined all 46 sheets > and read them as a single dataframe in R using package rio. > > I read a solution using package readlx, as suggested in a StackOverflow > discussion as follows: > df <- read_excel(path = filepath, sheet = sheet_name, guess_max = 100000). > Now, when you have so many sheets (46 in my case) in an Excel file, the rio > methodology is more practical. > > This is what I did: > path = > "C:/Users/myuser/Documents/DataScienceF/Forecast_and_Econometric_Analysis_FIGI > (4).xlsx" > figidat = impo...

...d doing dataset$my_field = >> as.numeric(dataset$my_field), I also tried to do dataset <- >> dataset[complete.cases(dataset), ], didn't work either. >> >> The only thing that worked for me was to take a single sheed and through >> the read_excel function use the guess_max parameter and set it to a >> sufficiently large number (a number >= to the total amount of the full >> merged dataset). I want to automate the merging of the N number of Excel >> sheets so that I don't have to be manually doing it. Unless there is a way >> to accompli...

...ield = >>> as.numeric(dataset$my_field), I also tried to do dataset <- >>> dataset[complete.cases(dataset), ], didn't work either. >>> >>> The only thing that worked for me was to take a single sheed and through >>> the read_excel function use the guess_max parameter and set it to a >>> sufficiently large number (a number >= to the total amount of the full >>> merged dataset). I want to automate the merging of the N number of Excel >>> sheets so that I don't have to be manually doing it. Unless there is a >>>...

...uot; = "c", "Comentarios" = "c"), locale = default_locale(), na = c("", " "), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = FALSE)) } # C.2) parallel Package: Environment Settings no_cores <- detectCores() c1 <- makeCluster(no_cores) invisible(clusterEvalQ(c1, library(readr))) setDefaultCluster(c1) # C.3) parRapply Function Application: DISPOIN_CSV_List &...

...and I have a .xlsx file that has 46 sheets on it. I basically combined all 46 sheets and read them as a single dataframe in R using package rio. I read a solution using package readlx, as suggested in a StackOverflow discussion as follows: df <- read_excel(path = filepath, sheet = sheet_name, guess_max = 100000). Now, when you have so many sheets (46 in my case) in an Excel file, the rio methodology is more practical. This is what I did: path = "C:/Users/myuser/Documents/DataScienceF/Forecast_and_Econometric_Analysis_FIGI (4).xlsx" figidat = import_list(path, rbind = TRUE) #here figida...