42 lines
2.0 KiB
Plaintext
42 lines
2.0 KiB
Plaintext
# Import {#sec-import .unnumbered}
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```{r}
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#| echo: false
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source("_common.R")
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```
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In this part of the book, you'll learn how to import a wider range of data into R, as well as how to get it into a form useful for analysis.
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Sometimes this is just a matter of calling a function from the appropriate data import package.
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But in more complex cases it might require both tidying and transformation in order to get to the tidy rectangle that you'd prefer to work with.
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```{r}
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#| label: fig-ds-import
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#| echo: false
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#| fig-cap: |
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#| Data import is the beginning of the data science process; without
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#| data you can't do data science!
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#| fig-alt: |
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#| Our data science model with import highlighted in blue.
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#| out.width: NULL
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knitr::include_graphics("diagrams/data-science/import.png", dpi = 270)
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```
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In this part of the book you'll learn how to access data stored in the following ways:
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- In @sec-import-spreadsheets, you'll learn how to import data from Excel spreadsheets and Google Sheets.
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- In @sec-import-databases, you'll learn about getting data out of a database and into R (and you'll also learn a little about how to get data out of R and into a database).
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- In @sec-arrow, you'll learn about Arrow, a powerful tool for working with out-of-memory data, particularly when it's stored in the parquet format.
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- In @sec-rectangling, you'll learn how to work with hierarchical data, including the deeply nested lists produced by data stored in the JSON format.
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- In @sec-scraping, you'll learn web "scraping", the art and science of extracting data from web pages.
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There are two important tidyverse packages that we don't discuss here: haven and xml2.
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If you're working with data from SPSS, Stata, and SAS files, check out the **haven** package, <https://haven.tidyverse.org>.
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If you're working with XML data, check out the **xml2** package, <https://xml2.r-lib.org>.
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Otherwise, you'll need to do some research to figure which package you'll need to use; google is your friend here 😃.
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