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Dictation and grammar corrections for chapters 1-4 #776

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Oct 3, 2020
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Dictation and grammar corrections for chapters 1-4 #776

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960d55b
Spell out what API stands for
salmasian Apr 24, 2019
101139c
Proper use of punctuation
salmasian Apr 24, 2019
f6ec7c1
Which Garrett?
salmasian Apr 24, 2019
ee28db4
Do not repeat the output
salmasian Apr 24, 2019
77b0a20
Replace I with we
salmasian Apr 24, 2019
a647df2
Linkify `#rstats`
salmasian Apr 24, 2019
c61365c
Replace I with we
salmasian Apr 25, 2019
8ee6e51
Undoing f6ec7c1 per pull request comments
salmasian Jun 18, 2019
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Spell out what API stands for
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@salmasian
salmasian authored Apr 24, 2019
commit 960d55b0c27aaa01527c47018f3bc12eb9a10139
2 changes: 1 addition & 1 deletion intro.Rmd
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Expand Up @@ -10,7 10,7 @@ Data science is a huge field, and there's no way you can master it by reading a
knitr::include_graphics("diagrams/data-science.png")
```

First you must __import__ your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it!
First you must __import__ your data into R. This typically means that you take data stored in a file, database, or web application programming interface (API), and load it into a data frame in R. If you can't get your data into R, you can't do data science on it!

Once you've imported your data, it is a good idea to __tidy__ it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.

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