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  • Alright.

  • So, now we know how read.table() works, and that paves our way to learning some shortcuts.

  • The file we loaded into R the previous lesson is a CSV file; it is a simple text document

  • in which the values are separated by commas.

  • CSV files are extremely common, so R’s brainy developers have given us a shortcut function

  • with which to load them faster...

  • This function is from the read.table() family, and it is called read.csv().

  • read.csv() takes fewer arguments than read.table(), because its defaults are set in a very

  • convenient way; headers are set to TRUE, and separators are set to a comma. All we need to do in order to read

  • a table is pass the name of the file, and specify whether we want our strings to be

  • factors or not.

  • Sweet!

  • Apart from comma-separated, values in a text data file can be separated by tabs; these

  • types of documents are called tab-delimited files.

  • And just as with CSVs, there is a read.table() shortcut to reading them: read.delim().

  • What’s happening behind the scenes here is that the sep = argument is set to \t, header

  • is again TRUE, and a bunch of other useful arguments are set to default to their most

  • commonly used values.

  • Now, just before we wrap this up, I want to mention a few important things.

  • First, for those of you in Europe or anywhere else in the world where the notation for the

  • decimal is a comma, and therefore CSV files don’t really work for you, there is a read.csv2()

  • function designed to deal with this problem.

  • It reads CSV files with a semi-colon as a separator.

  • The same goes for read.delim()which also has a version 2 with the exact same purpose.

  • Second.

  • Often, data files from external sources come with additional text, either as an introduction

  • or a sign-off, which will only cause havoc in your data if your end up importing it.

  • Therefore, it is excellent that we can tell R to completely ignore the first few lines

  • of text in our data file.

  • If you want to restrict where R stops reading the data file, you can tell it to read a precise

  • number of rows with the nrow = argument.

  • For example, our Pokémon data is way too large, and I may only be interested in the

  • first 100 Pokémon.

  • If I set nrow = 100, this is exactly what I will get.

  • Pay attention to what happened here: the heather doesn’t count towards the number of rows

  • specified.

  • nrow = stands for rows of observations.

  • Okay, let’s break it off here.

  • Super good job, everyone!

  • The next lesson will be very short, and it will complete the data import/export circle:

  • we will be talking about exporting data.

  • See you there!

  • And

  • May the Force Be with You

Alright.

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B1 中級

Rでのデータフレーム - RでのCSVのインポート (Data frames in R - Import a CSV in R)

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    林宜悉 に公開 2021 年 01 月 14 日
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