creating data frame from large data pool in r

To create a data frame from a large data pool in R, you can use read.csv() or read.table() functions to read in the data. These functions can handle large datasets by reading in the data in chunks and then combining them into a data frame.

If you have a large dataset that is too big to fit into memory, you can use the ff package to create a data frame that can be stored on disk. This package provides a memory-efficient way of handling large datasets and allows you to perform operations on the data that are typical of a data frame.

Once you have your data in a data frame, you can use the various data manipulation functions available in R such as subset(), filter(), arrange(), group_by() and so on to manipulate the data as required.

Here is an example code to create a data frame from a large data pool using read.csv() function:

main.r
# read in the data in chunks
data_chunks <- read.csv("large_data.csv", header = TRUE, sep = ",",
                  nrows = 1000, # number of rows to read in each chunk
                  skip = 0 # number of rows to skip before reading
                  )

# combine the chunks into a single data frame
data <- do.call("rbind", data_chunks)
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In this example, we are reading in the data in chunks of 1000 rows each and then combining them into a single data frame using do.call() function with "rbind" as the operation.

Alternatively, you could use data.table package for creating a data frame from a large data pool since it can handle large datasets efficiently.