Summarizing Data

class: center, middle, inverse, title-slide

# Summarizing Data
## Computational Mathematics and Statistics
### Jason Bryer, Ph.D.
### September 3, 2024

---
# One Minute Paper Results

.pull-left[
**What was the most important thing you learned during this class?**
<img src="02-Summarizing_Data_files/figure-html/unnamed-chunk-2-1.png" style="display: block; margin: auto;" />
]
.pull-right[
**What important question remains unanswered for you?**
<img src="02-Summarizing_Data_files/figure-html/unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
]

---
# Familiarity with Statistical Topics <img src="images/hex/likert.png" class="title-hex"><img src="images/hex/googlesheets4.png" class="title-hex">

``` r
likert(stats.results) %>% plot(center = 2.5)
```

---
# Math Anxiety Survey Scale <img src="images/hex/likert.png" class="title-hex"><img src="images/hex/googlesheets4.png" class="title-hex">

``` r
likert(mass.results) %>% plot()
```

---
class: font80
# About `legosets` <img src="images/hex/brickset.png" class="title-hex">

To install the `brickset` package:

``` r
remotes::install_github('jbryer/brickset')
```

To load the load the `legosets` dataset.

``` r
data('legosets', package = 'brickset')
```

The `legosets` data has 19409 observations of 36 variables.

.code70[

``` r
names(legosets)
```

```
##  [1] "setID"                 "number"                "numberVariant"        
##  [4] "name"                  "year"                  "theme"                
##  [7] "themeGroup"            "subtheme"              "category"             
## [10] "released"              "pieces"                "minifigs"             
## [13] "bricksetURL"           "rating"                "reviewCount"          
## [16] "packagingType"         "availability"          "agerange_min"         
## [19] "thumbnailURL"          "imageURL"              "US_retailPrice"       
## [22] "US_dateFirstAvailable" "US_dateLastAvailable"  "UK_retailPrice"       
## [25] "UK_dateFirstAvailable" "UK_dateLastAvailable"  "CA_retailPrice"       
## [28] "CA_dateFirstAvailable" "CA_dateLastAvailable"  "DE_retailPrice"       
## [31] "DE_dateFirstAvailable" "DE_dateLastAvailable"  "height"               
## [34] "width"                 "depth"                 "weight"
```
]

---
# Structure (`str`) <img src="images/hex/brickset.png" class="title-hex">

.code50[

``` r
str(legosets)
```

```
## 'data.frame':	19409 obs. of  36 variables:
##  $ setID                : int  7693 7695 7697 7698 25534 7418 7419 6020 22704 7421 ...
##  $ number               : chr  "1" "2" "3" "4" ...
##  $ numberVariant        : int  8 8 6 4 6 1 1 1 3 4 ...
##  $ name                 : chr  "Small house set" "Medium house set" "Medium house set" "Large house set" ...
##  $ year                 : int  1970 1970 1970 1970 1970 1970 1970 1970 1970 1970 ...
##  $ theme                : chr  "Minitalia" "Minitalia" "Minitalia" "Minitalia" ...
##  $ themeGroup           : chr  "Vintage" "Vintage" "Vintage" "Vintage" ...
##  $ subtheme             : chr  NA NA NA NA ...
##  $ category             : chr  "Normal" "Normal" "Normal" "Normal" ...
##  $ released             : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ pieces               : int  67 109 158 233 NA 1 1 60 65 NA ...
##  $ minifigs             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ bricksetURL          : chr  "https://brickset.com/sets/1-8" "https://brickset.com/sets/2-8" "https://brickset.com/sets/3-6" "https://brickset.com/sets/4-4" ...
##  $ rating               : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ reviewCount          : int  0 0 1 0 0 0 0 0 0 0 ...
##  $ packagingType        : chr  "{Not specified}" "{Not specified}" "{Not specified}" "{Not specified}" ...
##  $ availability         : chr  "{Not specified}" "{Not specified}" "{Not specified}" "{Not specified}" ...
##  $ agerange_min         : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ thumbnailURL         : chr  "https://images.brickset.com/sets/small/1-8.jpg" "https://images.brickset.com/sets/small/2-8.jpg" "https://images.brickset.com/sets/small/3-6.jpg" "https://images.brickset.com/sets/small/4-4.jpg" ...
##  $ imageURL             : chr  "https://images.brickset.com/sets/images/1-8.jpg" "https://images.brickset.com/sets/images/2-8.jpg" "https://images.brickset.com/sets/images/3-6.jpg" "https://images.brickset.com/sets/images/4-4.jpg" ...
##  $ US_retailPrice       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ US_dateFirstAvailable: Date, format: NA NA ...
##  $ US_dateLastAvailable : Date, format: NA NA ...
##  $ UK_retailPrice       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ UK_dateFirstAvailable: Date, format: NA NA ...
##  $ UK_dateLastAvailable : Date, format: NA NA ...
##  $ CA_retailPrice       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ CA_dateFirstAvailable: Date, format: NA NA ...
##  $ CA_dateLastAvailable : Date, format: NA NA ...
##  $ DE_retailPrice       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ DE_dateFirstAvailable: Date, format: NA NA ...
##  $ DE_dateLastAvailable : Date, format: NA NA ...
##  $ height               : num  NA NA NA NA NA ...
##  $ width                : num  NA NA NA NA NA ...
##  $ depth                : num  NA NA NA NA NA NA NA NA 5.08 NA ...
##  $ weight               : num  NA NA NA NA NA NA NA NA NA NA ...
```

]

---
# RStudio Eenvironment tab can help <img src="images/hex/rstudio.png" class="title-hex">

---
class: hide-logo
# Table View

.font60[

<div class="datatables html-widget html-fill-item" id="htmlwidget-acd8881be81b2fbef135" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-acd8881be81b2fbef135">{"x":{"filter":"none","vertical":false,"fillContainer":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70","71","72","73","74","75","76","77","78","79","80","81","82","83","84","85","86","87","88","89","90","91","92","93","94","95","96","97","98","99","100"],[1137,31564,6832,1654,6383,29916,5571,26320,4795,9247,1813,6327,29304,8233,34511,23192,23469,22530,33677,8029,31248,813,2337,31991,9941,4046,32,712,8487,3476,30810,1199,4395,4601,31702,23943,10037,4399,5923,6885,25611,26594,8468,9254,9615,30322,2135,1599,31436,4370,24652,8571,33692,30796,31662,24290,27495,34075,10223,29870,8151,6321,47252,24917,23792,881,24626,8133,22761,23305,26924,2079,1080,4693,1887,2534,9826,3075,28197,33638,1601,28438,7467,4660,8147,25655,25632,3906,7899,8145,7265,4329,24219,3809,24225,34135,31628,3412,590,2689],["The Meadowsweet's' Home","Spider-Man & Doctor Octopus Mech Battle","Garbage Truck","NHL Championship Challenge","LEGO Star Wars: The Video Game","Bricks and Animals","Deep Sea Treasure Hunter","Journal with White Band","Hans Christian Andersen Bucket","Racing Car","Boris Bulldog","Aeroplane","SHAZAM!","Deep Sea Raider","Black Panther with Jet","Heartlake Horse Show","2 stud Yellow Storage Brick","Freestyle Set","Barn & Farm Animals","Court Jester Key Chain","Adidas Sport Infant Shoes","Copter","Defense Tower","Hulk vs. Rhino Truck Showdown","Lunch Box","Transformer / Speed Controller 12V","Imperial Star Destroyer","Baby Walker","Surfer Girl","Red Four Wheel Driver","Roadwork Truck","Sarah","Rahkshi Turahk","Harry Potter Key Chain","Llama Girl Key Chain","Nurp-Naut","Friends school starter pack","Takanuva","LEGO Brick Sports Bag Blue","Western Town","The Fortress","My First Number Train","Isla De Muerta","Ganrash","LEGO Minifigures - Series 4 - Complete","Andy Warhol's Marilyn Monroe","Secret Set B","Basic Building Set, 3+","Amusement Park","Pohatu Nuva","LEGO Minifigures - Series 14 - Monsters - Complete","Blacksmith Attack","The Rolling Stones","Piranha Plant Puzzling Challenge","Emma's Magical Box","Technic Beams with Snaps","A-Wing vs. TIE Silencer Microfighters","Fantasy Adventure Ride","Homing Spider Droid","Jay","LEGO Mindstorms Education Resource Set","Basic Bricks","Flower Bouquet Art Print","Beast Master's Chaos Chariot","ARC-170 Starfighter Microfighter","Peter's Holiday Building Set","Arendelle Castle Celebration","Halloween Ghost","Legends of Chima Cragger Minifigure Clock","Roaring Power","Kiss Kiss Tuxedo Batman Key Chain","Story Builder - Farmyard Fun","DUPLO Family","Belt","Create Your Dreams","Basic Bricks Blue","Pink Hippo Key Chain","Spy Shark","Luna Lovegood","Dragon","Jet-Car","Emma's Art Studio","Basic Set","The Princess and the Pea","E-Motor","Miles' Stellosphere Hangar","Ultimate Flama","Soccer Watch","Star Wars Magnet Set","Energy Display","Airport","Car Chassis","Flash Speeder","Ewok Attack","First Order Transporter","LEGO Star Wars: The Skywalker Saga - PlayStation 4","Bandmates Series 2 - Sealed Box","Crisis News Crew","Pleasure Cruiser","Steering Accessories"],[1999,2021,2009,2004,2005,2020,2007,2016,2005,2012,1982,1972,2019,2011,2023,2014,2014,1996,2022,2010,2021,2000,2005,2022,2012,1980,2002,1997,2011,2000,2021,2001,2003,2004,2021,2015,2012,2003,2007,1998,2016,2017,2011,2012,2011,2020,2002,1985,2021,2002,2015,2011,2022,2021,2021,2015,2018,2022,2013,2019,2010,2008,2023,2016,2015,1991,2016,2010,2013,2014,2017,2002,1986,2004,2003,1996,2012,1996,2018,2019,1998,2019,1973,2005,2010,2016,2016,2002,2010,2010,2010,1977,2015,2002,2015,2022,2021,1997,1994,1998],["Duplo","Marvel Super Heroes","Duplo","Sports","Gear","Classic","Aqua Raiders","Gear","Creator","City","Fabuland","LEGOLAND","DC Comics Super Heroes","Atlantis","Marvel Super Heroes","Friends","Gear","Freestyle","City","Gear","Gear","Basic","Duplo","Marvel Super Heroes","Gear","Trains","Star Wars","Primo","Collectable Minifigures","Town","City","Duplo","Bionicle","Gear","Gear","Mixels","Gear","Bionicle","Gear","Duplo","Minecraft","Duplo","Pirates of the Caribbean","Games","Collectable Minifigures","Art","Sports","Basic","Duplo","Bionicle","Collectable Minifigures","Castle","Art","Super Mario","Friends","Education","Star Wars","Promotional","Star Wars","Ninjago","Education","Bricks and More","Gear","Nexo Knights","Star Wars","Duplo","Disney","Seasonal","Gear","Creator","Gear","Explore","Duplo","Gear","Creator","Service Packs","Gear","Aquazone","Collectable Minifigures","Promotional","Znap","Friends","Universal Building Set","Belville","Education","Duplo","Nexo Knights","Gear","Gear","Education","City","Technic","Star Wars","Star Wars","Star Wars","Gear","Vidiyo","Town","Town","Service Packs"],["Pre-school","Licensed","Pre-school","Modern day","Miscellaneous","Basic","Action/Adventure","Miscellaneous","Model making","Modern day","Junior","Vintage","Licensed","Action/Adventure","Licensed","Modern day","Miscellaneous","Basic","Modern day","Miscellaneous","Miscellaneous","Basic","Pre-school","Licensed","Miscellaneous","Modern day","Licensed","Pre-school","Miscellaneous","Modern day","Modern day","Pre-school","Constraction","Miscellaneous","Miscellaneous","Miscellaneous","Miscellaneous","Constraction","Miscellaneous","Pre-school","Licensed","Pre-school","Licensed","Miscellaneous","Miscellaneous","Art and crafts","Modern day","Basic","Pre-school","Constraction","Miscellaneous","Historical","Art and crafts","Licensed","Modern day","Educational","Licensed","Miscellaneous","Licensed","Action/Adventure","Educational","Basic","Miscellaneous","Action/Adventure","Licensed","Pre-school","Licensed","Miscellaneous","Miscellaneous","Model making","Miscellaneous","Pre-school","Pre-school","Miscellaneous","Model making","Miscellaneous","Miscellaneous","Action/Adventure","Miscellaneous","Miscellaneous","Technical","Modern day","Basic","Girls","Educational","Pre-school","Action/Adventure","Miscellaneous","Miscellaneous","Educational","Modern day","Technical","Licensed","Licensed","Licensed","Miscellaneous","Licensed","Modern day","Modern day","Miscellaneous"],["Normal","Normal","Normal","Normal","Gear","Normal","Normal","Gear","Normal","Normal","Normal","Normal","Normal","Normal","Other","Normal","Gear","Normal","Normal","Gear","Gear","Normal","Normal","Normal","Gear","Normal","Normal","Normal","Normal","Normal","Normal","Normal","Normal","Gear","Gear","Normal","Gear","Normal","Gear","Normal","Normal","Normal","Normal","Normal","Collection","Normal","Normal","Normal","Normal","Normal","Collection","Normal","Normal","Normal","Normal","Extended","Normal","Extended","Normal","Other","Normal","Normal","Gear","Normal","Normal","Normal","Normal","Normal","Gear","Normal","Gear","Normal","Normal","Gear","Normal","Normal","Gear","Normal","Normal","Other","Normal","Normal","Normal","Normal","Normal","Normal","Normal","Gear","Gear","Normal","Normal","Normal","Normal","Normal","Normal","Gear","Collection","Normal","Normal","Normal"],[null,19.99,16.99,null,null,59.99,4.99,19.99,null,null,null,null,null,29.99,null,39.99,null,null,69.98999999999999,4.99,null,null,24.99,19.99,15.99,null,269.99,null,2.99,null,9.99,null,null,null,5.99,4.99,null,null,24.99,null,109.99,19.99,19.99,null,null,119.99,null,null,109.99,null,null,9.99,149.99,29.99,null,null,19.99,null,29.99,null,null,12.99,null,29.99,9.99,null,59.99,1.99,29.99,29.99,null,null,null,null,null,null,4.99,null,null,null,null,24.99,null,19.99,null,34.99,9.99,null,14.99,null,99.98999999999999,null,29.99,null,89.98999999999999,59.99,4.99,null,null,null],[41,305,12,374,null,1500,75,null,500,35,8,28,5,265,25,355,null,25,230,null,null,10,64,110,null,33,3096,14,6,20,58,32,45,null,null,52,null,200,null,37,984,23,152,55,110,3341,4,311,95,40,86,104,1998,267,67,30,188,null,295,5,817,280,null,314,95,17,477,18,null,374,null,9,7,null,100,62,null,57,10,37,27,235,184,55,1,44,67,null,null,1,703,601,312,121,792,null,null,133,66,12],[4,2,1,8,null,null,1,null,null,1,1,null,1,2,1,2,null,null,4,null,null,null,2,3,null,null,null,1,1,1,1,2,null,null,null,null,null,null,null,4,4,2,4,2,16,null,1,4,7,null,16,2,null,3,1,null,2,null,5,1,null,null,null,2,1,1,6,null,null,null,null,null,6,null,null,null,null,1,1,null,null,2,null,1,null,1,1,null,3,null,5,null,5,4,7,null,null,3,1,null],[0,3.4,0,0,0,4,4,0,0,3,0,0,4.3,4.2,0,0,0,0,3.9,0,0,0,0,3.3,0,4.1,4.5,0,3.1,0,3.7,0,4,0,0,3.7,0,4.3,0,0,3.9,3.7,4,0,4.3,4,0,0,0,3.9,4.3,4.1,0,3.6,3.5,0,3.7,0,3.8,3.8,0,0,0,3.7,3.8,0,3.7,0,0,3.9,3.8,0,0,0,0,0,0,3.6,4.3,0,0,3.8,0,0,0,0,3.5,0,0,0,4.1,4.3,4,3.9,4.1,0,0,4.2,0,0]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>setID<\/th>\n      <th>name<\/th>\n      <th>year<\/th>\n      <th>theme<\/th>\n      <th>themeGroup<\/th>\n      <th>category<\/th>\n      <th>US_retailPrice<\/th>\n      <th>pieces<\/th>\n      <th>minifigs<\/th>\n      <th>rating<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":10,"columnDefs":[{"className":"dt-right","targets":[1,3,7,8,9,10]},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"setID","targets":1},{"name":"name","targets":2},{"name":"year","targets":3},{"name":"theme","targets":4},{"name":"themeGroup","targets":5},{"name":"category","targets":6},{"name":"US_retailPrice","targets":7},{"name":"pieces","targets":8},{"name":"minifigs","targets":9},{"name":"rating","targets":10}],"order":[],"autoWidth":false,"orderClasses":false},"selection":{"mode":"multiple","selected":null,"target":"row","selectable":null}},"evals":[],"jsHooks":[]}</script>

]

---
# Data Wrangling Cheat Sheet <img src="images/hex/dplyr.png" class="title-hex">

.center[
<a href='https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf' target='_new'><img src='images/data-transformation.png' width='700' /></a>
]

---
# Tidyverse vs Base R <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/pipe.png" class="title-hex">

.center[
<a href='images/R_Syntax_Comparison.jpeg' target='_new'><img src="images/R_Syntax_Comparison.jpeg" width='700' /></a>
]

---
# Pipes `%>%` and `|>` <img src="images/hex/magrittr.png" class="title-hex">

.font90[
The pipe operator (`%>%`) introduced with the `magrittr` R package allows for the chaining of R operations. Base R has now added their own pipe operator (`|>`). They take the output from the left-hand side and passes it as the first parameter to the function on the right-hand side.
]

.pull-left[
You can do this in two steps:

``` r
tab_out <- table(legosets$category)
prop.table(tab_out)
```

Or as nested function calls.

``` r
prop.table(table(legosets$category))
```
]
.pull-right[
Using the pipe (`|>`) operator we can chain these calls in a what is arguably a more readable format:

``` r
table(legosets$category) |> prop.table()
```
]

<hr />

```
## 
##        Book  Collection    Extended        Gear      Normal       Other 
## 0.034468546 0.031377196 0.028749549 0.154515946 0.684682364 0.062599825 
##      Random 
## 0.003606574
```

---
# Filter <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

.center[
<img src='images/dplyr_filter_sm.png' width='800' />
]

---
# Logical Operators

* `!a` - TRUE if a is FALSE
* `a == b` - TRUE if a and be are equal
* `a != b` - TRUE if a and b are not equal
* `a > b` - TRUE if a is larger than b, but not equal
* `a >= b` - TRUE if a is larger or equal to b
* `a < b` - TRUE if a is smaller than be, but not equal
* `a <= b` - TRUE if a is smaller or equal to b
* `a %in% b` - TRUE if a is in b where b is a vector

``` r
which( letters %in% c('a','e','i','o','u') )
```

```
## [1]  1  5  9 15 21
```
* `a | b` - TRUE if a *or* b are TRUE
* `a & b` - TRUE if a *and* b are TRUE
* `isTRUE(a)` - TRUE if a is TRUE

---
# Filter <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

### `dplyr`

``` r
mylego <- legosets %>% filter(themeGroup == 'Educational' & year > 2015)
```

### Base R

``` r
mylego <- legosets[legosets$themeGroups == 'Educaitonal' & legosets$year > 2015,]
```

<hr />

``` r
nrow(mylego)
```

```
## [1] 99
```

---
# Select <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

### `dplyr`

``` r
mylego <- mylego %>% select(setID, pieces, theme, availability, US_retailPrice, minifigs)
```

### Base R

``` r
mylego <- mylego[,c('setID', 'pieces', 'theme', 'availability', 'US_retailPrice', 'minifigs')]
```

<hr />

``` r
head(mylego, n = 4)
```

```
##   setID pieces     theme    availability US_retailPrice minifigs
## 1 26803    103 Education {Not specified}             NA        6
## 2 26689    142 Education {Not specified}             NA        4
## 3 26804     98 Education {Not specified}             NA        6
## 4 26277    188 Education     Educational          94.95       NA
```

---
# Relocate <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

.center[
<img src='images/dplyr_relocate.png' width='800' />
]

---
# Relocate <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

### `dplyr`

``` r
mylego %>% relocate(where(is.numeric), .after = where(is.character)) %>% head(n = 3)
```

```
##       theme    availability setID pieces US_retailPrice minifigs
## 1 Education {Not specified} 26803    103             NA        6
## 2 Education {Not specified} 26689    142             NA        4
## 3 Education {Not specified} 26804     98             NA        6
```

### Base R

``` r
mylego2 <- mylego[,c('theme', 'availability', 'setID', 'pieces', 'US_retailPrice', 'minifigs')]
head(mylego2, n = 3)
```

---
# Rename <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

.center[
<img src='images/rename_sm.jpg' width='1000' />
]

---
# Rename <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

### `dplyr`

``` r
mylego %>% dplyr::rename(USD = US_retailPrice) %>% head(n = 3)
```

```
##   setID pieces     theme    availability USD minifigs
## 1 26803    103 Education {Not specified}  NA        6
## 2 26689    142 Education {Not specified}  NA        4
## 3 26804     98 Education {Not specified}  NA        6
```

### Base R

``` r
names(mylego2)[5] <- 'USD'
head(mylego2, n = 3)
```

```
##       theme    availability setID pieces USD minifigs
## 1 Education {Not specified} 26803    103  NA        6
## 2 Education {Not specified} 26689    142  NA        4
## 3 Education {Not specified} 26804     98  NA        6
```

---
# Mutate <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

.center[
<img src='images/dplyr_mutate.png' width='700' />
]

---
# Mutate <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

### `dplyr`

``` r
mylego %>% filter(!is.na(pieces) & !is.na(US_retailPrice)) %>% 
	mutate(Price_per_piece = US_retailPrice / pieces) %>% head(n = 3)
```

```
##   setID pieces     theme availability US_retailPrice minifigs Price_per_piece
## 1 26277    188 Education  Educational          94.95       NA       0.5050532
## 2 25949    280 Education  Educational         224.95       NA       0.8033929
## 3 25954      1 Education  Educational          14.95       NA      14.9500000
```

### Base R

``` r
mylego2 <- mylego[!is.na(mylego$US_retailPrice) & !is.na(mylego$Price_per_piece),]
mylego2$Price_per_piece <- mylego2$Price_per_piece / mylego2$US_retailPrice
head(mylego2, n = 3)
```

```
## [1] setID           pieces          theme           availability   
## [5] US_retailPrice  minifigs        Price_per_piece
## <0 rows> (or 0-length row.names)
```

---
# Group By and Summarize <img src="images/hex/tidyverse.png" class="title-hex"><img src="images/hex/dplyr.png" class="title-hex">

.code80[

``` r
legosets %>% group_by(themeGroup) %>% summarize(mean_price = mean(US_retailPrice, na.rm = TRUE),
												sd_price = sd(US_retailPrice, na.rm = TRUE),
												median_price = median(US_retailPrice, na.rm = TRUE),
												n = n(),
												missing = sum(is.na(US_retailPrice)))
```

```
## # A tibble: 17 × 6
##    themeGroup       mean_price sd_price median_price     n missing
##    <chr>                 <dbl>    <dbl>        <dbl> <int>   <int>
##  1 Action/Adventure       40.2     38.9         30.0  1474     779
##  2 Art and crafts         34.9     47.7         17.5    97       9
##  3 Basic                  21.6     19.2         15.0   873     733
##  4 Constraction           16.4     12.4         13.0   502     284
##  5 Educational           182.     188.         130.    503     465
##  6 Girls                  35.8     24.0         23.0   240     227
##  7 Historical             34.2     32.4         20.0   473     400
##  8 Junior                 22.0     10.1         20.0   228     165
##  9 Licensed               53.3     71.7         30.0  2775    1066
## 10 Miscellaneous          20.7     29.2         13.0  6253    3961
## 11 Model making           74.3     92.1         40.0   771     384
## 12 Modern day             38.2     35.6         30.0  2469    1535
## 13 Pre-school             30.8     22.7         25.0  1562    1103
## 14 Racing                 26.8     26.5         15.0   270     176
## 15 Technical              82.8     95.3         50.0   607     327
## 16 Vintage               NaN       NA           NA     306     306
## 17 <NA>                  NaN       NA           NA       6       6
```
]

---
# Describe and Describe By

``` r
library(psych)
describe(legosets$US_retailPrice)
```

```
##    vars    n  mean   sd median trimmed   mad  min    max range skew kurtosis
## X1    1 7483 38.96 56.5  19.99    27.7 17.79 1.49 849.99 848.5 5.32    44.74
##      se
## X1 0.65
```

``` r
describeBy(legosets$US_retailPrice, group = legosets$availability, mat = TRUE, skew = FALSE)
```

```
##      item                group1 vars    n      mean        sd median   min    max range         se
## X11     1       {Not specified}    1 1831  26.84733  39.96747  19.99  1.49 789.99 788.5  0.9340335
## X12     2           Educational    1   12 212.86667 105.88283 222.45 14.95 399.95 385.0 30.5657410
## X13     3        LEGO exclusive    1 1039  57.21203 106.63125  12.99  1.99 849.99 848.0  3.3080857
## X14     4    LEGOLAND exclusive    1    2   4.99000   0.00000   4.99  4.99   4.99   0.0  0.0000000
## X15     5              Not sold    1    1  12.99000        NA  12.99 12.99  12.99   0.0         NA
## X16     6           Promotional    1    5   4.79000   0.83666   4.99  3.99   5.99   2.0  0.3741657
## X17     7 Promotional (Airline)    1    0       NaN        NA     NA   Inf   -Inf  -Inf         NA
## X18     8                Retail    1 4290  37.55889  38.44918  24.99  1.99 699.99 698.0  0.5870275
## X19     9      Retail - limited    1  302  63.54381  70.91908  39.99  2.49 449.99 447.5  4.0809343
## X110   10               Unknown    1    1   3.99000        NA   3.99  3.99   3.99   0.0         NA
```

---
# Additional Resources

For data wrangling:

* `dplyr` website: https://dplyr.tidyverse.org
* R for Data Science book: https://r4ds.had.co.nz/wrangle-intro.html
* Wrangling penguins tutorial: https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome
* Data transformation cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf

---
class: middle
# Grammer of Graphics

.center[
<img src="images/ggplot2_masterpiece.png" height="550" />
]

---
# Data Visualizations with ggplot2 <img src="images/hex/ggplot2.png" class="title-hex">

* `ggplot2` is an R package that provides an alternative framework based upon Wilkinson’s (2005) Grammar of Graphics.

* `ggplot2` is, in general, more flexible for creating "prettier" and complex plots.

* Works by creating layers of different types of objects/geometries (i.e. bars, points, lines, polygons, etc.)
`ggplot2` has at least three ways of creating plots:
     1. `qplot`
     2. `ggplot(...) + geom_XXX(...) + ...`
     3. `ggplot(...) + layer(...)`

* We will focus only on the second.

---
# Parts of a `ggplot2` Statement <img src="images/hex/ggplot2.png" class="title-hex">

* Data  
`ggplot(myDataFrame, aes(x=x, y=y))`

* Layers  
`geom_point()`, `geom_histogram()`

* Facets  
`facet_wrap(~ cut)`, `facet_grid(~ cut)`

* Scales  
`scale_y_log10()`

* Other options  
`ggtitle('my title')`, `ylim(c(0, 10000))`, `xlab('x-axis label')`

---
# Lots of geoms <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ls('package:ggplot2')[grep('^geom_', ls('package:ggplot2'))]
```

```
##  [1] "geom_abline"            "geom_area"              "geom_bar"               "geom_bin_2d"           
##  [5] "geom_bin2d"             "geom_blank"             "geom_boxplot"           "geom_col"              
##  [9] "geom_contour"           "geom_contour_filled"    "geom_count"             "geom_crossbar"         
## [13] "geom_curve"             "geom_density"           "geom_density_2d"        "geom_density_2d_filled"
## [17] "geom_density2d"         "geom_density2d_filled"  "geom_dotplot"           "geom_errorbar"         
## [21] "geom_errorbarh"         "geom_freqpoly"          "geom_function"          "geom_hex"              
## [25] "geom_histogram"         "geom_hline"             "geom_jitter"            "geom_label"            
## [29] "geom_line"              "geom_linerange"         "geom_map"               "geom_path"             
## [33] "geom_point"             "geom_pointrange"        "geom_polygon"           "geom_qq"               
## [37] "geom_qq_line"           "geom_quantile"          "geom_raster"            "geom_rect"             
## [41] "geom_ribbon"            "geom_rug"               "geom_segment"           "geom_sf"               
## [45] "geom_sf_label"          "geom_sf_text"           "geom_smooth"            "geom_spoke"            
## [49] "geom_step"              "geom_text"              "geom_tile"              "geom_violin"           
## [53] "geom_vline"
```

---
# Data Visualization Cheat Sheet <img src="images/hex/ggplot2.png" class="title-hex">

.center[
<a href='https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf'><img src='images/data-visualization-2.1.png' width='700' /></a>
]

---
# Scatterplot  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=pieces, y=US_retailPrice)) + geom_point()
```

---
# Scatterplot (cont.)  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=pieces, y=US_retailPrice, color=availability)) + geom_point()
```

---
# Scatterplot (cont.)  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=pieces, y=US_retailPrice, size=minifigs, color=availability)) + geom_point()
```

---
# Scatterplot (cont.)  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=pieces, y=US_retailPrice, size=minifigs)) + geom_point() + facet_wrap(~ availability)
```

---
# Boxplots  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x='Lego', y=US_retailPrice)) + geom_boxplot()
```

---
# Boxplots (cont.)  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=availability, y=US_retailPrice)) + geom_boxplot()
```

---
# Boxplot (cont.)  <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x=availability, y=US_retailPrice)) + geom_boxplot() + coord_flip()
```

---
# Histograms <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x = US_retailPrice)) + geom_histogram(binwidth = 25)
```

---
# Histograms (cont.)<img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x = US_retailPrice)) + geom_histogram(bins = 15) + scale_x_log10()
```

---
# Histograms (cont.) <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x = US_retailPrice)) + geom_histogram(binwidth = 25) + facet_wrap(~ availability)
```

---
# Density Plots <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x = US_retailPrice, color = availability)) + geom_density()
```

---
# Density Plots (cont.) <img src="images/hex/ggplot2.png" class="title-hex">

``` r
ggplot(legosets, aes(x = US_retailPrice, color = availability)) + geom_density() + scale_x_log10()
```

---
# `ggplot2` aesthetics <img src="images/hex/ggplot2.png" class="title-hex">

.center[
<a href='images/ggplot_aesthetics_cheatsheet.png' target='_new'> <img src='images/ggplot_aesthetics_cheatsheet.png' height='550' /></a>
]

---
# Likert Scales <img src="images/hex/likert.png" class="title-hex">

Likert scales are a type of questionnaire where respondents are asked to rate items on scales usually ranging from four to seven levels (e.g. strongly disagree to strongly agree).

``` r
library(likert)
library(reshape)
data(pisaitems)
items24 <- pisaitems[,substr(names(pisaitems), 1,5) == 'ST24Q']
items24 <- rename(items24, c(
			ST24Q01="I read only if I have to.",
			ST24Q02="Reading is one of my favorite hobbies.",
			ST24Q03="I like talking about books with other people.",
			ST24Q04="I find it hard to finish books.",
			ST24Q05="I feel happy if I receive a book as a present.",
			ST24Q06="For me, reading is a waste of time.",
			ST24Q07="I enjoy going to a bookstore or a library.",
			ST24Q08="I read only to get information that I need.",
			ST24Q09="I cannot sit still and read for more than a few minutes.",
			ST24Q10="I like to express my opinions about books I have read.",
			ST24Q11="I like to exchange books with my friends."))
```

---
# `likert` R Package <img src="images/hex/likert.png" class="title-hex">

``` r
l24 <- likert(items24)
summary(l24)
```

```
##                                                        Item      low neutral     high     mean        sd
## 10   I like to express my opinions about books I have read. 41.07516       0 58.92484 2.604913 0.9009968
## 5            I feel happy if I receive a book as a present. 46.93475       0 53.06525 2.466751 0.9446590
## 8               I read only to get information that I need. 50.39874       0 49.60126 2.484616 0.9089688
## 7                I enjoy going to a bookstore or a library. 51.21231       0 48.78769 2.428508 0.9164136
## 3             I like talking about books with other people. 54.99129       0 45.00871 2.328049 0.9090326
## 11                I like to exchange books with my friends. 55.54115       0 44.45885 2.343193 0.9609234
## 2                    Reading is one of my favorite hobbies. 56.64470       0 43.35530 2.344530 0.9277495
## 1                                 I read only if I have to. 58.72868       0 41.27132 2.291811 0.9369023
## 4                           I find it hard to finish books. 65.35125       0 34.64875 2.178299 0.8991628
## 9  I cannot sit still and read for more than a few minutes. 76.24524       0 23.75476 1.974736 0.8793028
## 6                       For me, reading is a waste of time. 82.88729       0 17.11271 1.810093 0.8611554
```

---
# `likert` Plots  <img src="images/hex/likert.png" class="title-hex">

``` r
plot(l24)
```

---
# `likert` Plots  <img src="images/hex/likert.png" class="title-hex">

``` r
plot(l24, type='heat')
```

---
# `likert` Plots  <img src="images/hex/likert.png" class="title-hex">

``` r
plot(l24, type='density')
```

---
# Pie Charts

There is only one pie chart in *OpenIntro Statistics* (Diez, Barr, & Çetinkaya-Rundel, 2015, p. 48). Consider the following three pie charts that represent the preference of five different colors. Is there a difference between the three pie charts? This is probably a difficult to answer.

---
# Pie Charts

Source: [https://en.wikipedia.org/wiki/Pie_chart](https://en.wikipedia.org/wiki/Pie_chart).

---
class: middle
# Just say NO to pie charts!

.font150[
"There is no data that can be displayed in a pie chart that cannot better be displayed in some other type of chart"]
.right[.font130[John Tukey]]

---
# Additional Resources

For data wrangling:

For data visualization:

* `ggplot2` website: https://ggplot2.tidyverse.org
* R for Data Science book: https://r4ds.had.co.nz/data-visualisation.html
* R Graphics Cookbook: https://r-graphics.org
* Data visualization cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf

---
class: left, font140
# One Minute Paper

.pull-left[
1. What was the most important thing you learned during this class?
2. What important question remains unanswered for you?
]
.pull-right[
<img src="02-Summarizing_Data_files/figure-html/unnamed-chunk-52-1.png" style="display: block; margin: auto;" />
]

https://forms.gle/U4UXAosdjHorxY919