The Tidyverse for Archaeologists

# The Tidyverse for Archaeologists
## A Guide for Beginners
### Ben Marwick, November 2020

---

.pull-right[
# Find me at...
.left[
[<svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @benmarwick](http://twitter.com/benmarwick)  
[<svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @benmarwick](http://github.com/benmarwick)  
[<svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> faculty.washington.edu/bmarwick](http://faculty.washington.edu/bmarwick)  
[<svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 512 512"><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> bmarwick@uw.edu](mailto:bmarwick@uw.edu)
]
]

---

# Welcome

- The **[tidyverse](https://www.tidyverse.org/)** is an opinionated collection of R packages designed for data science. They are efficient to learn because they share an underlying design philosophy, grammar, and data structures.

- Many common tasks of archaeological data analysis can be done **easily** with the tidyverse.

- When we write R code to analyse archaeological data, our analysis is **transparent and reproducible**. These are two vital characteristics of good scientific work.

- We should [**share our code and data**](https://github.com/benmarwick/ctv-archaeology/blob/master/README.md) with our research publications for others to study and learn

---

# What are we going to do today?

I will demonstrate and you will practice...

- Importing and inspecting archaeological data

- Five main verbs of the dplyr package to analyse data

- Five main plots of the ggplot2 package to visualise data

---

# Let's import some archaeological data 💽

---

---

```r
*library(rio)
```
]
 
.right-output-import-auto[

]

---
class: split-40
count: false

```r
library(rio)

# read in the data
*j_data <- import("https://bit.ly/j_data_xlsx", setclass = "tbl_df")
```
]
 
.right-output-import-auto[

]

---
class: split-40
count: false

```r
library(rio)

# read in the data
j_data <- import("https://bit.ly/j_data_xlsx", setclass = "tbl_df")

# take a look
*j_data
```
]
 
.right-output-import-auto[

```
# A tibble: 9,752 x 48
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <chr>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,742 more rows, and 37 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>
```
]

---

background-image: url(https://images.unsplash.com/photo-1515490480959-ce9152f7ea2b?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=2850&q=80)
background-size: cover

---

# Your turn

```r
library(rio)

# read in the data
j_data <- import("https://bit.ly/j_data_xlsx", setclass = "tbl_df")

# take a look
j_data
```

<div class="countdown" id="timer_5fb4caa5" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
---

# Let's inspect our archaeological data 🔎

---

```r
# check the class
*class(j_data)
```
]
 
.right-output-inspect1-auto[

```
[1] "tbl_df"     "tbl"        "data.frame"
```
]

---
class: split-40
count: false

```r
# check the class
class(j_data)

# check the size: rows
*nrow(j_data)
```
]
 
.right-output-inspect1-auto[

```
[1] "tbl_df"     "tbl"        "data.frame"
```

```
[1] 9752
```
]

---
class: split-40
count: false

```r
# check the class
class(j_data)

# check the size: rows
nrow(j_data)

# check the size: columns
*ncol(j_data)
```
]
 
.right-output-inspect1-auto[

```
[1] "tbl_df"     "tbl"        "data.frame"
```

```
[1] 9752
```

```
[1] 48
```
]

---
class: split-40
count: false

```r
# check the class
class(j_data)

# check the size: rows
nrow(j_data)

# check the size: columns
ncol(j_data)

# check the column names
*names(j_data)
```
]
 
.right-output-inspect1-auto[

```
[1] "tbl_df"     "tbl"        "data.frame"
```

```
[1] 9752
```

```
[1] 48
```

```
 [1] "Site"       "Square"     "Spit"       "Group"      "Artno"     
 [6] "Material"   "Colour"     "Weight"     "Length"     "Artclas"   
[11] "Cortex"     "Cortype"    "Initiat"    "Breaks"     "Noseg"     
[16] "Platwid"    "Plat"       "Focal"      "Overhang"   "NoDS"      
[21] "NoPS"       "Rtch"       "Plat_2"     "Focal_2"    "Overhang_2"
[26] "NoDS_2"     "NoPS_2"     "Rtch_2"     "RetOri"     "Retype"    
[31] "Retloc"     "Retlen"     "Retdep"     "Portion"    "Heat"      
[36] "EdgeDam"    "Weathering" "Corerot"    "RetScNo"    "Term"      
[41] "Janus"      "Recycled"   "Redirect"   "Width"      "Thick"     
[46] "Platthic"   "area"       "elongation"
```
]

---

```r
# check the first few rows
*head(j_data)
```
]
 
.right-output-inspect2-auto[

```
# A tibble: 6 x 48
  Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
  <chr> <chr>  <dbl> <dbl> <dbl> <chr>    <chr>   <dbl>  <dbl> <chr>    <dbl>
1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
# … with 37 more variables: Cortype <chr>, Initiat <chr>, Breaks <chr>,
#   Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>, Overhang <chr>,
#   NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>, Focal_2 <lgl>,
#   Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>, RetOri <chr>,
#   Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>, Portion <chr>,
#   Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>, RetScNo <dbl>,
#   Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>, Width <dbl>,
#   Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>
```
]

---
class: split-40
count: false

```r
# check the first few rows
head(j_data)

# check the structure
*str(j_data)
```
]
 
.right-output-inspect2-auto[

```
tibble [9,752 × 48] (S3: tbl_df/tbl/data.frame)
 $ Site      : chr [1:9752] "J" "J" "J" "J" ...
 $ Square    : chr [1:9752] "B" "B" "B" "B" ...
 $ Spit      : num [1:9752] 1 1 1 1 1 1 1 1 1 1 ...
 $ Group     : num [1:9752] 1 1 1 1 1 1 1 1 1 1 ...
 $ Artno     : num [1:9752] 1 2 3 4 5 6 7 8 9 10 ...
 $ Material  : chr [1:9752] "Chert" "Chert" "Chert" "Chert" ...
 $ Colour    : chr [1:9752] "Brown" "Yellow" "Dk Grey" "Grey" ...
 $ Weight    : num [1:9752] 1.83 0.61 3.81 0.13 0.05 0.58 0.03 0.02 0.21 0.25 ...
 $ Length    : num [1:9752] 22.03 14.59 29.59 5.83 7.07 ...
 $ Artclas   : chr [1:9752] "Flake" "RetF" "Core" "RetFrag" ...
 $ Cortex    : num [1:9752] 0 0 0 0 0 NA 0 0 0 0 ...
 $ Cortype   : chr [1:9752] NA NA NA NA ...
 $ Initiat   : chr [1:9752] "Hertz" "Hertz" NA NA ...
 $ Breaks    : chr [1:9752] NA NA NA "Both" ...
 $ Noseg     : num [1:9752] NA 3 NA 1 NA NA NA NA NA NA ...
 $ Platwid   : num [1:9752] 20.31 10.97 NA NA 6.05 ...
 $ Plat      : chr [1:9752] "2-scars" "3-scars" NA NA ...
 $ Focal     : logi [1:9752] NA NA NA NA NA NA ...
 $ Overhang  : chr [1:9752] "Yes" "Yes" NA NA ...
 $ NoDS      : num [1:9752] 5 5 10 2 3 11 3 1 3 5 ...
 $ NoPS      : num [1:9752] 2 3 NA NA 2 NA 1 NA NA 1 ...
 $ Rtch      : chr [1:9752] NA "Yes" NA "Yes" ...
 $ Plat_2    : chr [1:9752] "2-scars" "3-scars" NA NA ...
 $ Focal_2   : logi [1:9752] NA NA NA NA NA NA ...
 $ Overhang_2: chr [1:9752] "Yes" "Yes" NA NA ...
 $ NoDS_2    : num [1:9752] 5 5 10 2 3 11 3 1 3 5 ...
 $ NoPS_2    : num [1:9752] 2 3 NA NA 2 NA 1 NA NA 1 ...
 $ Rtch_2    : chr [1:9752] NA "Yes" NA "Yes" ...
 $ RetOri    : chr [1:9752] NA "Both" NA "V-D" ...
 $ Retype    : chr [1:9752] NA "Percus" NA "Press" ...
 $ Retloc    : chr [1:9752] NA "Rlat" NA "Dist" ...
 $ Retlen    : num [1:9752] NA 10.75 NA 2.05 NA ...
 $ Retdep    : num [1:9752] NA 3.04 NA 2.04 NA NA NA NA NA NA ...
 $ Portion   : chr [1:9752] NA NA NA "Dist" ...
 $ Heat      : num [1:9752] NA NA 1 NA NA NA NA NA NA NA ...
 $ EdgeDam   : num [1:9752] NA NA NA NA 1 NA NA NA NA NA ...
 $ Weathering: num [1:9752] NA NA 1 NA NA NA NA NA NA NA ...
 $ Corerot   : num [1:9752] NA NA 4 NA NA 4 NA NA NA NA ...
 $ RetScNo   : num [1:9752] NA 3 NA 1 NA NA NA NA NA NA ...
 $ Term      : chr [1:9752] "Feather" "Feather" NA "Retouched" ...
 $ Janus     : logi [1:9752] NA NA NA NA NA NA ...
 $ Recycled  : logi [1:9752] NA NA NA NA NA NA ...
 $ Redirect  : logi [1:9752] NA NA NA NA NA NA ...
 $ Width     : num [1:9752] 20.37 19.11 11.58 6.54 6.56 ...
 $ Thick     : num [1:9752] 3.34 2.74 7.25 2.05 1.13 4.79 1.16 1.09 3.91 3.13 ...
 $ Platthic  : num [1:9752] 2 1.62 NA NA 1.63 NA 0.69 NA NA 2.01 ...
 $ area      : num [1:9752] 448.8 278.8 342.7 38.1 46.4 ...
 $ elongation: num [1:9752] 1.081 0.763 2.555 0.891 1.078 ...
```
]

---

background-image: url(https://images.unsplash.com/photo-1494129935429-873eafa78178?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=2878&q=80)
background-size: cover

---

background-image: url(https://images.unsplash.com/photo-1507652955-f3dcef5a3be5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1650&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
# check the class
class(j_data)

# check the size: rows
nrow(j_data)

# check the size: columns
ncol(j_data)

# check the column names
names(j_data)
```

```r
# check the first few rows
head(j_data)

# check the structure
str(j_data)
```

---

# Let's analyse our archaeological data with the dplyr & ggplot2 pkgs 🛠️

Just for fun, we will do one main dplyr verb followed by one main type of ggplot. But you can combine and mix up the verbs and plots however you want to.

Verb         | Plot
------------- | -------------
  **select** 🔜 | **histogram** 🔜
  
---

background-image: url(https://images.unsplash.com/photo-1540660290370-8aa90e451e8a?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=934&q=80)
background-size: cover

---

```r
*library(tidyverse)
```
]
 
.right-output-dplyr1-auto[

]

---
class: split-40
count: false

```r
library(tidyverse)  
# `select` helps us
# choose columns
*j_data   # say 'then'
```
]
 
.right-output-dplyr1-auto[

---
class: split-40
count: false

```r
library(tidyverse)  
# `select` helps us
# choose columns
j_data %>%  # say 'then'
* select(Square,
*        Spit,
*        Weight)
```
]
 
.right-output-dplyr1-auto[

```
# A tibble: 9,752 x 3
   Square  Spit Weight
   <chr>  <dbl>  <dbl>
 1 B          1  1.83 
 2 B          1  0.61 
 3 B          1  3.81 
 4 B          1  0.13 
 5 B          1  0.05 
 6 B          1  0.580
 7 B          1  0.03 
 8 B          1  0.02 
 9 B          1  0.21 
10 B          1  0.25 
# … with 9,742 more rows
```
]

---
class: split-40
count: false

```r
library(tidyverse)  
# `select` helps us
# choose columns
j_data %>%  # say 'then'
  select(Square,  
         Spit,  
         Weight)

# `select` can use
# rules to save time
*j_data   # say 'then'
```
]
 
.right-output-dplyr1-auto[

```
# A tibble: 9,752 x 3
   Square  Spit Weight
   <chr>  <dbl>  <dbl>
 1 B          1  1.83 
 2 B          1  0.61 
 3 B          1  3.81 
 4 B          1  0.13 
 5 B          1  0.05 
 6 B          1  0.580
 7 B          1  0.03 
 8 B          1  0.02 
 9 B          1  0.21 
10 B          1  0.25 
# … with 9,742 more rows
```

---
class: split-40
count: false

```r
library(tidyverse)  
# `select` helps us
# choose columns
j_data %>%  # say 'then'
  select(Square,  
         Spit,  
         Weight)

# `select` can use
# rules to save time
j_data %>%  # say 'then'
* select_if(is.numeric)
```
]
 
.right-output-dplyr1-auto[

```
# A tibble: 9,752 x 24
    Spit Group Artno Weight Length Cortex Noseg Platwid  NoDS  NoPS NoDS_2
   <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl>   <dbl> <dbl> <dbl>  <dbl>
 1     1     1     1  1.83   22.0       0    NA   20.3      5     2      5
 2     1     1     2  0.61   14.6       0     3   11.0      5     3      5
 3     1     1     3  3.81   29.6       0    NA   NA       10    NA     10
 4     1     1     4  0.13    5.83      0     1   NA        2    NA      2
 5     1     1     5  0.05    7.07      0    NA    6.05     3     2      3
 6     1     1     6  0.580  11.1      NA    NA   NA       11    NA     11
 7     1     1     7  0.03    5.72      0    NA    2.91     3     1      3
 8     1     1     8  0.02    8.37      0    NA   NA        1    NA      1
 9     1     1     9  0.21   10.7       0    NA   NA        3    NA      3
10     1     1    10  0.25   14.3       0    NA    3.78     5     1      5
# … with 9,742 more rows, and 13 more variables: NoPS_2 <dbl>, Retlen <dbl>,
#   Retdep <dbl>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>,
#   elongation <dbl>
```
]

---

background-image: url(https://images.unsplash.com/photo-1565936795196-4f283b0ae59b?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=992&q=80)
background-size: cover

---

background-image: url(https://images.unsplash.com/photo-1553250135-606ca563bcb3?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=2081&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
library(tidyverse)
# `select` helps us 
# choose columns 
j_data %>%  # say 'then'
  select(Square,
         Spit, 
         Weight)

# `select` can use 
# rules to save time
j_data %>% # say 'then'
  select_if(is.numeric)
```

---

# Let's make a histogram <img src="figures/noun_histogram_2312161.png" style="display: inline-block; margin: 0" width="50px" height="50px" />

---

```r
# we create a new
# data frame for
# plotting
*j_data1 <- j_data
```
]
 
.right-output-ggplot1a-auto[

]

---
class: split-40
count: false

```r
# we create a new
# data frame for
# plotting
j_data1 <- j_data %>%  
* select(Square,
*        Spit,
*        Material,
*        Weight,
*        Thick)
```
]
 
.right-output-ggplot1a-auto[

]

---
class: split-40
count: false

```r
# we create a new
# data frame for
# plotting
j_data1 <- j_data %>%  
  select(Square,  
         Spit,  
         Material,  
         Weight,  
         Thick)

# take a look
*j_data1
```
]
 
.right-output-ggplot1a-auto[

```
# A tibble: 9,752 x 5
   Square  Spit Material Weight Thick
   <chr>  <dbl> <chr>     <dbl> <dbl>
 1 B          1 Chert     1.83   3.34
 2 B          1 Chert     0.61   2.74
 3 B          1 Chert     3.81   7.25
 4 B          1 Chert     0.13   2.05
 5 B          1 Chert     0.05   1.13
 6 B          1 Chert     0.580  4.79
 7 B          1 Chert     0.03   1.16
 8 B          1 Chert     0.02   1.09
 9 B          1 Chert     0.21   3.91
10 B          1 Chert     0.25   3.13
# … with 9,742 more rows
```
]

---

```r
*ggplot(data = j_data1)
```
]
 
.right-output-ggplot1b-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1b_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
*      aes(x = Weight)
```
]
 
.right-output-ggplot1b-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1b_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
* geom_histogram()
```
]
 
.right-output-ggplot1b-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1b_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram()  
```
]
 
.right-output-ggplot1b-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1b_auto_4_output-1.png" width="432" />
]

---

```r
*ggplot(data = j_data1) +
*      aes(x = Weight) +
* geom_histogram()
```
]
 
.right-output-ggplot1c-user[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1c_user_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
* scale_x_log10()
```
]
 
.right-output-ggplot1c-user[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1c_user_2_output-1.png" width="432" />
]

---

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
  scale_x_log10() +  
  NULL  
```
]
 
.right-output-ggplot1d-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1d_rotate_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
* scale_x_log10(labels = scales::comma) +
  NULL  
```
]
 
.right-output-ggplot1d-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1d_rotate_2_output-1.png" width="432" />
]

---

```r
*ggplot(data = j_data1)
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
*      aes(x = Weight)
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
* geom_histogram()
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
* scale_x_log10(labels = scales::comma)
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
  scale_x_log10(labels = scales::comma) +  
* theme_minimal()
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
  scale_x_log10(labels = scales::comma) +  
  theme_minimal()  
```
]
 
.right-output-ggplot1e-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1e_auto_6_output-1.png" width="432" />
]

---

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
  scale_x_log10(labels = scales::comma) +  
  theme_minimal() +  
  NULL  
```
]
 
.right-output-ggplot1f-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1f_rotate_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data1) +  
       aes(x = Weight) +  
  geom_histogram() +  
  scale_x_log10(labels = scales::comma) +  
* theme_minimal(base_size = 20) +
  NULL  
```
]
 
.right-output-ggplot1f-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot1f_rotate_2_output-1.png" width="432" />
]

---
background-image: url(https://images.unsplash.com/photo-1567602612078-2f1504cbe535?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1567&q=80)
background-size: cover

# Your turn

---

background-image: url(https://images.unsplash.com/photo-1484100356142-db6ab6244067?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1562&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
j_data1 <- j_data %>%  
  select(Square,
         Spit, 
         Material,
         Weight,
         Thick)
```

```r
ggplot(data = j_data1) + 
       aes(x = Weight) + 
  geom_histogram() + 
  scale_x_log10(labels = scales::comma) +
  theme_minimal(base_size = 20) 
```

---

# What have we done?

Verb         | Plot
------------- | -------------
  select ✅ | histogram ✅
      **filter** 🔜 | **scatterplot** 🔜
      
---

```r
# `filter` helps us
# choose rows
*j_data1
```
]
 
.right-output-dplyr2-auto[

---
class: split-40
count: false

```r
# `filter` helps us
# choose rows
j_data1 %>%  
# is equivalent to...
* filter(Square == "A")
```
]
 
.right-output-dplyr2-auto[

```
# A tibble: 4,436 x 5
   Square  Spit Material Weight Thick
   <chr>  <dbl> <chr>     <dbl> <dbl>
 1 A          1 Chert      1.74  4.37
 2 A          1 Chert      1.64  4.09
 3 A          1 Obsidian   0.12  1.41
 4 A          1 Chert      2.12  5.96
 5 A          1 Chert      2.25  7.62
 6 A          1 Chert      4.88  8.97
 7 A          1 Chert      8.95 16.0 
 8 A          1 Chert      3.02  8.15
 9 A          1 Chert      2.71  6.44
10 A          1 Chert      1.31  5.84
# … with 4,426 more rows
```
]

---
class: split-40
count: false

```r
# `filter` helps us
# choose rows
j_data1 %>%  
# is equivalent to...
  filter(Square == "A") %>%  
# is less than...
* filter(Weight < 10)
```
]
 
.right-output-dplyr2-auto[

```
# A tibble: 4,416 x 5
   Square  Spit Material Weight Thick
   <chr>  <dbl> <chr>     <dbl> <dbl>
 1 A          1 Chert      1.74  4.37
 2 A          1 Chert      1.64  4.09
 3 A          1 Obsidian   0.12  1.41
 4 A          1 Chert      2.12  5.96
 5 A          1 Chert      2.25  7.62
 6 A          1 Chert      4.88  8.97
 7 A          1 Chert      8.95 16.0 
 8 A          1 Chert      3.02  8.15
 9 A          1 Chert      2.71  6.44
10 A          1 Chert      1.31  5.84
# … with 4,406 more rows
```
]

---
class: split-40
count: false

```r
# `filter` helps us
# choose rows
j_data1 %>%  
# is equivalent to...
  filter(Square == "A") %>%  
# is less than...
  filter(Weight < 10) %>%  
# is between x and y
* filter(between(Thick, 0.2, 20))
```
]
 
.right-output-dplyr2-auto[

```
# A tibble: 4,392 x 5
   Square  Spit Material Weight Thick
   <chr>  <dbl> <chr>     <dbl> <dbl>
 1 A          1 Chert      1.74  4.37
 2 A          1 Chert      1.64  4.09
 3 A          1 Obsidian   0.12  1.41
 4 A          1 Chert      2.12  5.96
 5 A          1 Chert      2.25  7.62
 6 A          1 Chert      4.88  8.97
 7 A          1 Chert      8.95 16.0 
 8 A          1 Chert      3.02  8.15
 9 A          1 Chert      2.71  6.44
10 A          1 Chert      1.31  5.84
# … with 4,382 more rows
```
]

---
class: split-40
count: false

```r
# `filter` helps us
# choose rows
j_data1 %>%  
# is equivalent to...
  filter(Square == "A") %>%  
# is less than...
  filter(Weight < 10) %>%  
# is between x and y
  filter(between(Thick, 0.2, 20)) %>%  
# is not NA
* filter(!is.na(Material))
```
]
 
.right-output-dplyr2-auto[

---

# Let's make a scatterplot <img src="figures/noun_Scatter Plot_105485.png" style="display: inline-block; margin: 0" width="50px" height="50px" />

---

```r
*ggplot(data = j_data2)
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
*      aes(x = Weight,
*          y = Thick)
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
* geom_point()
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
  geom_point() +  
* scale_x_log10()
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
  geom_point() +  
  scale_x_log10() +  
* scale_y_log10()
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
  geom_point() +  
  scale_x_log10() +  
  scale_y_log10() +  
* theme_bw(base_size = 30)
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_6_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
  geom_point() +  
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 30) +  
* labs(x = "Mass (g)",
*      y = "Thickness (mm)")
```
]
 
.right-output-ggplot2a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2a_auto_7_output-1.png" width="432" />
]

---

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
  geom_point() +  
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 30) +  
  labs(x = "Mass (g)",  
       y = "Thickness (mm)")  
```
]
 
.right-output-ggplot2b-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2b_rotate_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
* geom_point(size = 4) +
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 30) +  
  labs(x = "Mass (g)",  
       y = "Thickness (mm)")  
```
]
 
.right-output-ggplot2b-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2b_rotate_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick) +  
* geom_point(size = 4, alpha = 0.1) +
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 30) +  
  labs(x = "Mass (g)",  
       y = "Thickness (mm)")  
```
]
 
.right-output-ggplot2b-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2b_rotate_3_output-1.png" width="432" />
]

---

```r
*ggplot(data = j_data2)
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
*      aes(x = Weight,
*          y = Thick,
*          colour = Material)
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
* geom_point(size = 4)
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
  geom_point(size = 4) +  
* scale_x_log10()
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
  geom_point(size = 4) +  
  scale_x_log10() +  
* scale_y_log10()
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
  geom_point(size = 4) +  
  scale_x_log10() +  
  scale_y_log10() +  
* theme_bw(base_size = 10)
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_6_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
  geom_point(size = 4) +  
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 10) +  
* labs(x = "Mass (g)",
*      y = "Thickness (mm)")
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_7_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Weight,  
           y = Thick,  
           colour = Material) +  
  geom_point(size = 4) +  
  scale_x_log10() +  
  scale_y_log10() +  
  theme_bw(base_size = 10) +  
  labs(x = "Mass (g)",  
       y = "Thickness (mm)") +  
* theme(legend.position = c(0.85, 0.3))
```
]
 
.right-output-ggplot2c-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot2c_auto_8_output-1.png" width="432" />
]

---

background-image: url(https://images.unsplash.com/photo-1572866511072-44aae0768e85?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=934&q=80)
background-size: cover

# Your turn

---
background-image: url(https://images.unsplash.com/photo-1489533119213-66a5cd877091?ixlib=rb-1.2.1&auto=format&fit=crop&w=1651&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
j_data2 <- 
j_data1 %>%  
  filter(Square == "A") %>% 
  filter(Weight < 10) %>% 
  filter(between(Thick, 0.2, 20)) %>% 
  filter(!is.na(Material))
```

---
background-image: url(https://images.unsplash.com/photo-1524901548305-08eeddc35080?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1950&q=80)
background-size: cover

# Let's take a short break

---

background-image: url(https://images.unsplash.com/photo-1533745848184-3db07256e163?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=2689&q=80)
background-size: cover

---

# What have we done?

Verb          | Plot
------------- | -------------
  select ✅   |     histogram ✅
  filter ✅  | scatterplot ✅ 
  **mutate** 🔜  | **boxplot** 🔜
  
---

```r
# `mutate` helps us
# change columns
*j_data
```
]
 
.right-output-dplyr3-auto[

---
class: split-40
count: false

```r
# `mutate` helps us
# change columns
j_data %>%  
# create a new column
* mutate(plat_area = Platwid * Platthic)
```
]
 
.right-output-dplyr3-auto[

```
# A tibble: 9,752 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <chr>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,742 more rows, and 38 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>,
#   plat_area <dbl>
```
]

---
class: split-40
count: false

```r
# `mutate` helps us
# change columns
j_data %>%  
# create a new column
  mutate(plat_area = Platwid * Platthic) %>%  
# update an existing column
* mutate(Colour =
*  case_when(Colour == "Dk Grey"   ~ "Dark Grey",
*            Colour == "Lt Brown"  ~ "Light Brown",
*            TRUE ~ Colour))
```
]
 
.right-output-dplyr3-auto[

```
# A tibble: 9,752 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <chr>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dark …  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Light…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,742 more rows, and 38 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>,
#   plat_area <dbl>
```
]

---
class: split-40
count: false

```r
# `mutate` helps us
# change columns
j_data %>%  
# create a new column
  mutate(plat_area = Platwid * Platthic) %>%  
# update an existing column
  mutate(Colour =  
   case_when(Colour == "Dk Grey"   ~ "Dark Grey",  
             Colour == "Lt Brown"  ~ "Light Brown",  
             TRUE ~ Colour)) %>%  
# update many columns
* mutate_at(vars(Length, Width), scale)
```
]
 
.right-output-dplyr3-auto[

```
# A tibble: 9,752 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length[,1] Artclas
   <chr> <chr>  <dbl> <dbl> <dbl> <chr>    <chr>   <dbl>      <dbl> <chr>  
 1 J     B          1     1     1 Chert    Brown   1.83      1.80   Flake  
 2 J     B          1     1     2 Chert    Yellow  0.61      0.629  RetF   
 3 J     B          1     1     3 Chert    Dark …  3.81      3.00   Core   
 4 J     B          1     1     4 Chert    Grey    0.13     -0.755  RetFrag
 5 J     B          1     1     5 Chert    Grey    0.05     -0.559  Flake  
 6 J     B          1     1     6 Chert    Light…  0.580     0.0821 Core   
 7 J     B          1     1     7 Chert    Dk Br…  0.03     -0.772  Flake  
 8 J     B          1     1     8 Chert    Cream   0.02     -0.354  FFrag  
 9 J     B          1     1     9 Chert    Grey    0.21      0.0142 Flake-b
10 J     B          1     1    10 Chert    Grey    0.25      0.578  Flake  
# … with 9,742 more rows, and 39 more variables: Cortex <dbl>, Cortype <chr>,
#   Initiat <chr>, Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>,
#   Focal <lgl>, Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>,
#   Plat_2 <chr>, Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>,
#   Rtch_2 <chr>, RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>,
#   Retdep <dbl>, Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>,
#   Corerot <dbl>, RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>,
#   Redirect <lgl>, Width[,1] <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>,
#   elongation <dbl>, plat_area <dbl>
```
]

---
class: split-40
count: false

```r
# `mutate` helps us
# change columns
j_data %>%  
# create a new column
  mutate(plat_area = Platwid * Platthic) %>%  
# update an existing column
  mutate(Colour =  
   case_when(Colour == "Dk Grey"   ~ "Dark Grey",  
             Colour == "Lt Brown"  ~ "Light Brown",  
             TRUE ~ Colour)) %>%  
# update many columns
  mutate_at(vars(Length, Width), scale) %>%  
# update an existing column
* mutate(Material = fct_lump(Material, 5))
```
]
 
.right-output-dplyr3-auto[

```
# A tibble: 9,752 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length[,1] Artclas
   <chr> <chr>  <dbl> <dbl> <dbl> <fct>    <chr>   <dbl>      <dbl> <chr>  
 1 J     B          1     1     1 Chert    Brown   1.83      1.80   Flake  
 2 J     B          1     1     2 Chert    Yellow  0.61      0.629  RetF   
 3 J     B          1     1     3 Chert    Dark …  3.81      3.00   Core   
 4 J     B          1     1     4 Chert    Grey    0.13     -0.755  RetFrag
 5 J     B          1     1     5 Chert    Grey    0.05     -0.559  Flake  
 6 J     B          1     1     6 Chert    Light…  0.580     0.0821 Core   
 7 J     B          1     1     7 Chert    Dk Br…  0.03     -0.772  Flake  
 8 J     B          1     1     8 Chert    Cream   0.02     -0.354  FFrag  
 9 J     B          1     1     9 Chert    Grey    0.21      0.0142 Flake-b
10 J     B          1     1    10 Chert    Grey    0.25      0.578  Flake  
# … with 9,742 more rows, and 39 more variables: Cortex <dbl>, Cortype <chr>,
#   Initiat <chr>, Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>,
#   Focal <lgl>, Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>,
#   Plat_2 <chr>, Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>,
#   Rtch_2 <chr>, RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>,
#   Retdep <dbl>, Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>,
#   Corerot <dbl>, RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>,
#   Redirect <lgl>, Width[,1] <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>,
#   elongation <dbl>, plat_area <dbl>
```
]

---

# Let's make a boxplot <img src="figures/noun_Box Plot_991170.png" style="display: inline-block; margin: 0" width="50px" height="50px" />

---

```r
*ggplot(data = j_data2)
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
*      aes(x = Material,
*          y = plat_area)
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
* geom_boxplot()
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
* scale_y_log10()
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
  scale_y_log10() +  
* labs(x = "Raw Material",
*      y =  "Platform Area")
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Platform Area") +  
* theme_classic(base_size = 12)
```
]
 
.right-output-ggplot3a-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3a_auto_6_output-1.png" width="432" />
]

---

```r
library(ggbeeswarm)  
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Platform Area") +  
  theme_classic(base_size = 14)   
```
]
 
.right-output-ggplot3b-non_seq[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3b_non_seq_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
library(ggbeeswarm)  
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
# overlay data points                  
* geom_quasirandom(alpha = 0.1) +
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Platform Area") +  
  theme_classic(base_size = 14)   
```
]
 
.right-output-ggplot3b-non_seq[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3b_non_seq_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
library(ggbeeswarm)  
ggplot(data = j_data2) +  
       aes(x = Material,  
           y = plat_area) +  
  geom_boxplot() +  
# overlay data points                  
  geom_quasirandom(alpha = 0.1) +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Platform Area") +  
  theme_classic(base_size = 14) +  
* coord_flip()
```
]
 
.right-output-ggplot3b-non_seq[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot3b_non_seq_3_output-1.png" width="432" />
]

---

background-image: url(https://images.unsplash.com/photo-1581941274242-25a62bdc9bf9?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1650&q=80)
background-size: cover

# Your turn

---

background-image: url(https://images.unsplash.com/photo-1586084531149-373985a4478e?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=934&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
j_data2 <- 
j_data %>%  
  mutate(plat_area = Platwid * Platthic) %>% 
  mutate(Material = fct_lump(Material, 5)) 
```

```r
ggplot(data = j_data2) +
       aes(x = Material,
           y = plat_area) +
  geom_boxplot() +
  scale_y_log10() +
  labs(x = "Raw Material",
       y =  "Platform Area") + 
  theme_classic(base_size = 12)
```

---

# What have we done?

Verb          | Plot
------------- | -------------
  select ✅    |     histogram ✅
  filter ✅    |     scatterplot ✅ 
  mutate ✅    |     boxplot  ✅   
  **group by** 🔜  |      **barplot** 🔜

---

```r
*j_data2
```
]
 
.right-output-dplyr4-auto[

```
# A tibble: 9,752 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <fct>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,742 more rows, and 38 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>,
#   plat_area <dbl>
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
* group_by(Material)
```
]
 
.right-output-dplyr4-auto[

```
# A tibble: 9,752 x 49
# Groups:   Material [8]
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <fct>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,742 more rows, and 38 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>,
#   plat_area <dbl>
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
* tally()
```
]
 
.right-output-dplyr4-auto[

```
# A tibble: 8 x 2
  Material      n
  <fct>     <int>
1 Chert      9072
2 Quartz       31
3 Quartzite    50
4 Silcrete     94
5 Unknown      31
6 Volcanic    425
7 Other        39
8 <NA>         10
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
  tally() %>%  
* arrange(desc(n))
```
]
 
.right-output-dplyr4-auto[

```
# A tibble: 8 x 2
  Material      n
  <fct>     <int>
1 Chert      9072
2 Volcanic    425
3 Silcrete     94
4 Quartzite    50
5 Other        39
6 Quartz       31
7 Unknown      31
8 <NA>         10
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
  tally() %>%  
  arrange(desc(n))  
```
]
 
.right-output-dplyr4-auto[

---

# Let's make a barplot <img src="figures/noun_bar graph_1393295.png" style="display: inline-block; margin: 0" width="50px" height="50px" />

---

```r
*ggplot(data = j_data3)
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
*      aes(x = Material,
*          y = n)
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_2_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
       aes(x = Material,  
           y = n) +  
* geom_col()
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
       aes(x = Material,  
           y = n) +  
  geom_col() +  
* scale_y_log10()
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
       aes(x = Material,  
           y = n) +  
  geom_col() +  
  scale_y_log10() +  
* labs(x = "Raw Material",
*      y =  "Frequency")
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
       aes(x = Material,  
           y = n) +  
  geom_col() +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Frequency") +  
* theme_minimal(base_size = 14)
```
]
 
.right-output-ggplot4-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4_auto_6_output-1.png" width="432" />
]

---

```r
ggplot(data = j_data3) +  
       aes(x = Material,  
           y = n) +  
  geom_col() +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Frequency") +  
  theme_minimal(base_size = 14)  
```
]
 
.right-output-ggplot4a-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4a_rotate_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
ggplot(data = j_data3) +  
*      aes(x = reorder(Material, n),
           y = n) +  
  geom_col() +  
  scale_y_log10() +  
  labs(x = "Raw Material",  
       y =  "Frequency") +  
  theme_minimal(base_size = 14)  
```
]
 
.right-output-ggplot4a-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot4a_rotate_2_output-1.png" width="432" />
]

---

background-image: url(https://images.unsplash.com/photo-1556484658-6789c325bedf?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1650&q=80)
background-size: cover

# Your turn

---

background-image: url(https://images.unsplash.com/photo-1455849318743-b2233052fcff?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1650&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
j_data3 <- 
j_data2 %>%                   
  group_by(Material) %>%      
  tally() %>%                 
  arrange(desc(n)) %>% 
  drop_na()
```

```r
ggplot(data = j_data3) +
       aes(x = Material,
           y = n) +
  geom_col() +        
  scale_y_log10() +
  labs(x = "Raw Material",
       y =  "Frequency") + 
  theme_minimal(base_size = 14) 
```

---

# What have we done?

Verb          | Plot
------------- | -------------
  select ✅    |     histogram ✅
  filter ✅    |     scatterplot ✅ 
  mutate ✅    |     boxplot  ✅   
  group by ✅  |      barplot   ✅     
  summarise 🔜  |    facet 🔜

---

```r
*j_data2
```
]
 
.right-output-dplyr5-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
* group_by(Material)
```
]
 
.right-output-dplyr5-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
* summarise(length_mean = mean(Length),
*           length_sd =   sd(Length))
```
]
 
.right-output-dplyr5-auto[

```
# A tibble: 8 x 3
  Material  length_mean length_sd
  <fct>           <dbl>     <dbl>
1 Chert            NA       NA   
2 Quartz           10.9      8.20
3 Quartzite        14.9      8.83
4 Silcrete         11.0      5.28
5 Unknown          15.2     10.1 
6 Volcanic         14.3     10.0 
7 Other            13.2      5.60
8 <NA>             NA       NA   
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
  summarise(length_mean = mean(Length),  
            length_sd =   sd(Length))  
```
]
 
.right-output-dplyr5-auto[

---

```r
*j_data2
```
]
 
.right-output-dplyr5a-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
* group_by(Material)
```
]
 
.right-output-dplyr5a-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
*   summarise(length_mean = mean(Length, na.rm = TRUE),
*             length_sd =     sd(Length, na.rm = TRUE))
```
]
 
.right-output-dplyr5a-auto[

```
# A tibble: 8 x 3
  Material  length_mean length_sd
  <fct>           <dbl>     <dbl>
1 Chert            10.4      6.00
2 Quartz           10.9      8.20
3 Quartzite        14.9      8.83
4 Silcrete         11.0      5.28
5 Unknown          15.2     10.1 
6 Volcanic         14.3     10.0 
7 Other            13.2      5.60
8 <NA>            NaN       NA   
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
    summarise(length_mean = mean(Length, na.rm = TRUE),  
              length_sd =     sd(Length, na.rm = TRUE)) %>%  
* drop_na()
```
]
 
.right-output-dplyr5a-auto[

```
# A tibble: 7 x 3
  Material  length_mean length_sd
  <fct>           <dbl>     <dbl>
1 Chert            10.4      6.00
2 Quartz           10.9      8.20
3 Quartzite        14.9      8.83
4 Silcrete         11.0      5.28
5 Unknown          15.2     10.1 
6 Volcanic         14.3     10.0 
7 Other            13.2      5.60
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
    summarise(length_mean = mean(Length, na.rm = TRUE),  
              length_sd =     sd(Length, na.rm = TRUE)) %>%  
  drop_na()  
```
]
 
.right-output-dplyr5a-auto[

---

```r
*j_data2
```
]
 
.right-output-dplyr5b-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
* group_by(Material)
```
]
 
.right-output-dplyr5b-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
*   summarise(length_mean = mean(Length, na.rm = TRUE),
*             length_sd =     sd(Length, na.rm = TRUE))
```
]
 
.right-output-dplyr5b-auto[

---
class: split-40
count: false

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
    summarise(length_mean = mean(Length, na.rm = TRUE),  
              length_sd =     sd(Length, na.rm = TRUE)) %>%  
  drop_na() %>%  
* mutate_if(is.numeric, round, 2)
```
]
 
.right-output-dplyr5b-auto[

```
# A tibble: 7 x 3
  Material  length_mean length_sd
  <fct>           <dbl>     <dbl>
1 Chert            10.4      6   
2 Quartz           10.9      8.2 
3 Quartzite        14.9      8.83
4 Silcrete         11.0      5.28
5 Unknown          15.2     10.1 
6 Volcanic         14.3     10.0 
7 Other            13.2      5.6 
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
    summarise(length_mean = mean(Length, na.rm = TRUE),  
              length_sd =     sd(Length, na.rm = TRUE)) %>%  
  drop_na() %>%  
  mutate_if(is.numeric, round, 2) %>%  
* knitr::kable('html')
```
]
 
.right-output-dplyr5b-auto[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Material </th>
   <th style="text-align:right;"> length_mean </th>
   <th style="text-align:right;"> length_sd </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Chert </td>
   <td style="text-align:right;"> 10.38 </td>
   <td style="text-align:right;"> 6.00 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Quartz </td>
   <td style="text-align:right;"> 10.94 </td>
   <td style="text-align:right;"> 8.20 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Quartzite </td>
   <td style="text-align:right;"> 14.90 </td>
   <td style="text-align:right;"> 8.83 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Silcrete </td>
   <td style="text-align:right;"> 11.02 </td>
   <td style="text-align:right;"> 5.28 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unknown </td>
   <td style="text-align:right;"> 15.23 </td>
   <td style="text-align:right;"> 10.10 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Volcanic </td>
   <td style="text-align:right;"> 14.31 </td>
   <td style="text-align:right;"> 10.04 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 13.16 </td>
   <td style="text-align:right;"> 5.60 </td>
  </tr>
</tbody>
</table>
]

---
class: split-40
count: false

```r
j_data2 %>%  
  group_by(Material) %>%  
    summarise(length_mean = mean(Length, na.rm = TRUE),  
              length_sd =     sd(Length, na.rm = TRUE)) %>%  
  drop_na() %>%  
  mutate_if(is.numeric, round, 2) %>%  
  knitr::kable('html')  
```
]
 
.right-output-dplyr5b-auto[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> Material </th>
   <th style="text-align:right;"> length_mean </th>
   <th style="text-align:right;"> length_sd </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Chert </td>
   <td style="text-align:right;"> 10.38 </td>
   <td style="text-align:right;"> 6.00 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Quartz </td>
   <td style="text-align:right;"> 10.94 </td>
   <td style="text-align:right;"> 8.20 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Quartzite </td>
   <td style="text-align:right;"> 14.90 </td>
   <td style="text-align:right;"> 8.83 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Silcrete </td>
   <td style="text-align:right;"> 11.02 </td>
   <td style="text-align:right;"> 5.28 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Unknown </td>
   <td style="text-align:right;"> 15.23 </td>
   <td style="text-align:right;"> 10.10 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Volcanic </td>
   <td style="text-align:right;"> 14.31 </td>
   <td style="text-align:right;"> 10.04 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Other </td>
   <td style="text-align:right;"> 13.16 </td>
   <td style="text-align:right;"> 5.60 </td>
  </tr>
</tbody>
</table>
]

---

# Let's make a facetted plot <img src="figures/noun_grid_2202247.png" style="display: inline-block; margin: 0" width="50px" height="50px" />

---

```r
*j_data2
```
]
 
.right-output-ggplot5-auto[

---
class: split-40
count: false

```r
j_data2 %>%  
* drop_na(Material)
```
]
 
.right-output-ggplot5-auto[

```
# A tibble: 9,742 x 49
   Site  Square  Spit Group Artno Material Colour Weight Length Artclas Cortex
   <chr> <chr>  <dbl> <dbl> <dbl> <fct>    <chr>   <dbl>  <dbl> <chr>    <dbl>
 1 J     B          1     1     1 Chert    Brown   1.83   22.0  Flake        0
 2 J     B          1     1     2 Chert    Yellow  0.61   14.6  RetF         0
 3 J     B          1     1     3 Chert    Dk Gr…  3.81   29.6  Core         0
 4 J     B          1     1     4 Chert    Grey    0.13    5.83 RetFrag      0
 5 J     B          1     1     5 Chert    Grey    0.05    7.07 Flake        0
 6 J     B          1     1     6 Chert    Lt Br…  0.580  11.1  Core        NA
 7 J     B          1     1     7 Chert    Dk Br…  0.03    5.72 Flake        0
 8 J     B          1     1     8 Chert    Cream   0.02    8.37 FFrag        0
 9 J     B          1     1     9 Chert    Grey    0.21   10.7  Flake-b      0
10 J     B          1     1    10 Chert    Grey    0.25   14.3  Flake        0
# … with 9,732 more rows, and 38 more variables: Cortype <chr>, Initiat <chr>,
#   Breaks <chr>, Noseg <dbl>, Platwid <dbl>, Plat <chr>, Focal <lgl>,
#   Overhang <chr>, NoDS <dbl>, NoPS <dbl>, Rtch <chr>, Plat_2 <chr>,
#   Focal_2 <lgl>, Overhang_2 <chr>, NoDS_2 <dbl>, NoPS_2 <dbl>, Rtch_2 <chr>,
#   RetOri <chr>, Retype <chr>, Retloc <chr>, Retlen <dbl>, Retdep <dbl>,
#   Portion <chr>, Heat <dbl>, EdgeDam <dbl>, Weathering <dbl>, Corerot <dbl>,
#   RetScNo <dbl>, Term <chr>, Janus <lgl>, Recycled <lgl>, Redirect <lgl>,
#   Width <dbl>, Thick <dbl>, Platthic <dbl>, area <dbl>, elongation <dbl>,
#   plat_area <dbl>
```
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
*ggplot()
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_3_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
* aes(x = Length)
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_4_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
* geom_histogram()
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_5_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
  geom_histogram() +  
* facet_wrap( ~ Material)
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_6_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
  geom_histogram() +  
  facet_wrap( ~ Material) +  
* labs(x =  "Length (mm)",
*      y =  "Frequency")
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_7_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
  geom_histogram() +  
  facet_wrap( ~ Material) +  
  labs(x =  "Length (mm)",  
       y =  "Frequency") +  
* theme_minimal(base_size = 12)
```
]
 
.right-output-ggplot5-auto[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5_auto_8_output-1.png" width="432" />
]

---

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
  geom_histogram() +  
  facet_wrap( ~ Material) +  
  labs(x =  "Length (mm)",  
       y =  "Frequency") +  
  theme_minimal(base_size = 12)  
```
]
 
.right-output-ggplot5a-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5a_rotate_1_output-1.png" width="432" />
]

---
class: split-40
count: false

```r
j_data2 %>%  
  drop_na(Material) %>%  
ggplot() +  
  aes(x = Length) +  
  geom_histogram() +  
* facet_wrap( ~ Material, scales = "free_y" ) +
  labs(x =  "Length (mm)",  
       y =  "Frequency") +  
  theme_minimal(base_size = 12)  
```
]
 
.right-output-ggplot5a-rotate[
<img src="tidyverse-for-archaeology_files/figure-html/ggplot5a_rotate_2_output-1.png" width="432" />
]

---

background-image: url(https://images.unsplash.com/photo-1565014237362-c52e04dcf6ad?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=976&q=80)
background-size: cover

# Your turn

---

background-image: url(https://images.unsplash.com/photo-1533561797500-4fad4750814e?ixlib=rb-1.2.1&auto=format&fit=crop&w=934&q=80)
background-size: cover

# Your turn

---

# Your turn

```r
j_data2 %>%                   
  group_by(Material) %>%   
    summarise(length_mean = mean(Length, na.rm = TRUE),        
              length_sd =     sd(Length, na.rm = TRUE)) %>%    
  drop_na()      
```

```r
j_data2 %>% 
  drop_na(Material) %>% 
ggplot() +
  aes(x = Length) +
  geom_histogram() +        
  facet_wrap( ~ Material, scales = "free_y" ) + 
  labs(x =  "Length (mm)",
       y =  "Frequency") + 
  theme_minimal(base_size = 12) 
```

---

# What have we done?

Verb          | Plot
------------- | -------------
  select ✅    |     histogram ✅
  filter ✅    |     scatterplot ✅ 
  mutate ✅    |     boxplot  ✅   
  group by ✅  |      barplot   ✅     
  summarise ✅   |    facet ✅

---

---

The flipbooked portion of this presentation was created with the new {flipbookr} package.  Get it with `remotes::install_github("EvaMaeRey/flipbookr")`

---