Introduction to the Tidyverse

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# Introduction to the Tidyverse

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# Tidyverse

The `tidyverse` is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

--
***

These packages are designed to support the natural workflow of any data analysis project, as depicted below

.center[
<img src="images/data_science_workflow.png" width="689" />
]

.footnote[Image from [R for Data Science](https://r4ds.had.co.nz/introduction.html)]

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# Core tidyverse packages

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# Core tidyverse packages

.center[
<img src="images/tidyverse_packages.png" width="2516" />
]

.footnote[Image from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

---
# Core tidyverse packages

.center[
<img src="images/tidyverse_packages_purpose.png" width="90%" />
]

.footnote[Image from [Mine Çetinkaya-Rundel](https://education.rstudio.com/blog/2020/07/teaching-the-tidyverse-in-2020-part-1-getting-started/)]
---
background-image: url(images/hex/readr.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]
  
# `readr`

.panelset[

.panel[.panel-name[Overview]

.pull-left[
Importing data is the very first step! <br/> You can use `readr` to import rectangular data.
]
.pull-right[
Functions for different file types
+ comma separated (CSV) files with `read_csv()`
+ tab separated files with `read_tsv()`
+ general delimited files with `read_delim()`
+ fixed width files with `read_fwf()`
+ tabular files where columns are separated by white-space with `read_table()`
+ web log files with `read_log()`

]
]

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-import-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 11: Data import](https://r4ds.had.co.nz/data-import.html)
]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/tibble.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `tibble`

.panelset[

.panel[.panel-name[Overview]

.pull-left[
A `tibble` is much like the data frame in base R, but optimized for use in the tidyverse.

Among other features, it has nicer printing methods. 
]
]

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-import-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 10: Tibbles](https://r4ds.had.co.nz/tibbles.html)

]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `ggplot2`

.panelset[
.panel[.panel-name[Overview]

`ggplot2` uses the "Grammar of Graphics" to create a plot. Check out some examples of what's possible from the [ggplot2 extensions gallery](https://exts.ggplot2.tidyverse.org/gallery/).

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-visualization-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 3: Data visualization](https://r4ds.had.co.nz/data-visualisation.html)

]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/dplyr.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `dplyr`

.panelset[
.panel[.panel-name[Overview]

.pull-left[
`dplyr` includes a host of functions that perform specific and incremental data transformation steps to help you to wrangle your data into exactly the right form you need. 
]

.pull-right[
+ Pick observations by their values with `filter()`.

+ Reorder the rows with `arrange()`.

+ Pick variables by their names `select()`.

+ Create new variables with functions of existing variables `mutate()`.

+ Collapse many values down to a single summary `summarize()`.

]
]

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 5: Data transformation](https://r4ds.had.co.nz/transform.html)
]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/forcats.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `forcats`

.panelset[

.panel[.panel-name[Overview]
.pull-left[
`forcats` is great for working with **categorical variables** or factors. 
]

.pull-right[
A few key functions:
+ `fct_reorder()`: Reordering a factor by another variable

+ `fct_infreq()`: Reordering a factor by the frequency of values

+ `fct_relevel()`: Changing the order of a factor by hand

+ `fct_lump()`: Collapsing the least/most frequent values of a factor into “other”
]
]

.panel[.panel-name[Cheatsheet]

.center[
<img src="https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/forcats-cheatsheet-thumbs.png" width="60%" />

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/factors.pdf)
]
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 15: Factors](https://r4ds.had.co.nz/factors.html)
]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/stringr.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `stringr`

.panelset[

.panel[.panel-name[Overview]
.pull-left[
`stringr` helps us manipulate strings! The package includes many functions to help us with **regular expressions**, which are a concise language for describing patterns in strings.
]

.pull-right[
`stringr` allows you to

-  detect matches
-  subset strings
-  manage string lengths
-  mutate strings
-  join and split strings
-  order strings
-  ...and more!
]
]

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/strings-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/strings.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 14: Strings](https://r4ds.had.co.nz/strings.html)
]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/tidyr.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `tidyr`

.panelset[

.panel[.panel-name[Overview]

.pull-left[
`tidyr` allows you to reshape raw "untidy" data into a "tidy" format.

Here is an example of what a tidy dataframe looks like:

![](https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)
]

.pull-right[
There are three interrelated rules which make a dataset tidy:

- Each variable must have its own column.
- Each observation must have its own row.
- Each value must have its own cell.
]
]

.panel[.panel-name[Cheatsheet]

![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/data-import-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf)
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 12: Tidy data](https://r4ds.had.co.nz/tidy-data.html)
]
]

.panel[.panel-name[Reference]

---
background-image: url(images/hex/purrr.png)
background-position: 90% 5%
background-size: 10%

.footnote[Slide adapted from [Sylvia Canelón](https://github.com/spcanelon/2020-rladies-chi-tidyverse)]

# `purrr`

.panelset[

.panel[.panel-name[Overview]
.pull-left[
`purrr` provides tools for working with functions and vectors -- in particular, repeating the same operations many times concisely and efficiently

The `map()` family of functions helps us replace for loops, <br/> making our code easier to read and more succinct.
]
]

.panel[.panel-name[Cheatsheet]

.center[
![](https://raw.githubusercontent.com/rstudio/cheatsheets/master/pngs/thumbnails/purrr-cheatsheet-thumbs.png)

<i class="fas  fa-file-pdf "></i> [Download PDF](https://github.com/rstudio/cheatsheets/raw/master/purrr.pdf)
]
]

.panel[.panel-name[Reading]

.left-column[
<img src="images/r4ds-cover.png" width="667" />
]

.right-column[
## [Ch 21: Iteration](https://r4ds.had.co.nz/iteration.html)
]
]

.panel[.panel-name[Reference]

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# Q & A

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# Next up...

## Importing data & Project-oriented workflows

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# Break!