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This is a more conceptual question, but in class today, I was told by my professor that it would be preferable to use read_csv rather than read.csv. For more context, we are working with tidyverse in this class.
As such, since read_csv and read.csv (as far as I'm aware) both read CSV files, what are the objective benefits and drawbacks of using one function versus the other?
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base R. Here read_csv() is far superior to read. csv() .
csv() Function. read. csv() function in R Language is used to read “comma separated value” files. It imports data in the form of a data frame.
csv is used for data where commas are used as separators and periods are used as decimals, while read. csv2 is for data where semicolons are used as separators and commas are used as decimals.
Use read_csv as a versatile tool One of the most widely used functions of Pandas is read_csv which reads comma-separated values (csv) files and creates a DataFrame. In this post, I will focus on many different parameters of read_csv function and how to efficiently use them.
read_csv is significantly faster for large .csv files. See here for more information. Personally, I pretty much always use read_csv by default.
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