10/29/2023 0 Comments Load data in r studio![]() Of course, what is the best solution and what you can improve depends a lot on your data and approach. Perhaps you don't need millions of records for your question anyway. There is a good chance that a single step is particularly slow, and that you can do something about it and make the whole process a lot faster.Īnd finally, another possibility: for many analyses it makes sense to just take a subsample of the original data and run code on these. For a small number of records (a number that takes a few seconds to run), you can run code profiling and see what is the longest step. Of course that could also be necessary for your analysis. But if the time increases faster than the number of records (for example doubling the number of records multiplies the processing time by 4), then there could be something to improve (that suggests you're using nested loops, and for each record you read, you re-read all the other records). If time and number of records are proportional (doubling the number of records doubles the processing time), it means your code is basically performing one operation on each record. Second, it gives you an idea of how efficient your code is.First, that gives you an estimate of how long it takes for a given number of records.I would recommend that you run your readLines()and processing on sections with 10, 50, 100, 500, 1000, 5000 and 10,000 records (or until it becomes too long), and plot how the processing speed depends on the number of records. The question is about the processing speed. That could work well with numeric columns and standard dplyr operations, I'm not sure it would go great with text mining.Īnother possibility is your approach 4: to process the input in chunks. One is to use a database for on-disk storage, and use dbplyr or SQL to perform operations on the database. (small technical note: there could be a way, let's say you can use 6GB for R and you have 5,000,000 records, that still gives 1 MB per record, so perhaps there would be a way to reduce the amount of data in one record when loading it to fit each one in less than 1 MB, which is roughly the size of 1,000 characters of text) The memory (RAM) of your computer is fixed, apparently you don't have enough to store all records at once, there is nothing you can do about that, so methods 1, 2, 3 won't work no matter what package you use. You have two separate problems: memory, and speed. I can try with a smaller number but I feel like it won't be viable.įYI, my computer is running R on 64bit and has 8gb of RAM. Using the readLines command to break the dataset down into a smaller section - I tried subsetting a million entries but it's still processing (about an hour now). It sat there for 2 hours before saying it ran out of memory as well Using the ndjson package, which is supposed to be faster and more efficient, but basically the same. The same option but with pagesize = 100k or a million - pretty much the same thing since the problem is the overall memory This works until like 3 million, when it slows down drastically, and gives up at 4 million Using jsonlite's stream_in function to process it. The file is a ndjson file and is a set of yelp reviews from their dataset challenge. However, despite several trials, I keep getting these errorsĮrror: 'hotel_bookings.csv' does not exist in current working directory ('/cloud/project').īookings_df <- read_csv("hotel_bookings.csv")Įrror in vroom::vroom(file, delim = ",", col_names = col_names, col_types = col_types, :Īrgument "file" is missing, with no defaultĮrror in head(bookings_df) : object 'bookings_df' not foundĮrror in setwd("projects/Course 7/Week 3") :So I'm really at my wit's end here, but I have a large dataset that I'm trying to import into R, but my computer takes hours to try and out it in before running out of memory and failing to process it. Here is the code chunk: bookings_df <- read_csv("hotel_bookings.csv") The results will display as column specifications: If this line causes an error, copy in the line setwd("projects/Course 7/Week 3") before it. csv in the project folder called "hotel_bookings.csv" and save it as a data frame called bookings_df. In the chunk below, use the read_csv() function to import data from a. The tidyverse library package readr has a number of functions for "reading in" or importing data, including. One of the most common file types data analysts import into R is comma separated values files, or. Here are the course note and instructions. I have been unable to import data file from the Coursera learning course that I am currently into.
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