Summarizing Data Part 2

class: center, middle, inverse, title-slide

# Summarizing Data Part 2
## DATA 606 - Statistics & Probability for Data Analytics
### Jason Bryer, Ph.D., Angela Lui, Ph.D., and George Hagstrom, Ph.D.
### September 18, 2024

---
# One Minute Paper Results

.pull-left[
**What was the most important thing you learned during this class?**
<img src="02-Summarizing_Data2_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_Data2_files/figure-html/unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
]

---
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"              
##  [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
##  [7] "geom_boxplot"           "geom_col"               "geom_contour"          
## [10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
## [13] "geom_curve"             "geom_density"           "geom_density_2d"       
## [16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
## [19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
## [22] "geom_freqpoly"          "geom_function"          "geom_hex"              
## [25] "geom_histogram"         "geom_hline"             "geom_jitter"           
## [28] "geom_label"             "geom_line"              "geom_linerange"        
## [31] "geom_map"               "geom_path"              "geom_point"            
## [34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
## [37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
## [40] "geom_rect"              "geom_ribbon"            "geom_rug"              
## [43] "geom_segment"           "geom_sf"                "geom_sf_label"         
## [46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
## [49] "geom_step"              "geom_text"              "geom_tile"             
## [52] "geom_violin"            "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
## 10   I like to express my opinions about books I have read. 41.07516       0
## 5            I feel happy if I receive a book as a present. 46.93475       0
## 8               I read only to get information that I need. 50.39874       0
## 7                I enjoy going to a bookstore or a library. 51.21231       0
## 3             I like talking about books with other people. 54.99129       0
## 11                I like to exchange books with my friends. 55.54115       0
## 2                    Reading is one of my favorite hobbies. 56.64470       0
## 1                                 I read only if I have to. 58.72868       0
## 4                           I find it hard to finish books. 65.35125       0
## 9  I cannot sit still and read for more than a few minutes. 76.24524       0
## 6                       For me, reading is a waste of time. 82.88729       0
##        high     mean        sd
## 10 58.92484 2.604913 0.9009968
## 5  53.06525 2.466751 0.9446590
## 8  49.60126 2.484616 0.9089688
## 7  48.78769 2.428508 0.9164136
## 3  45.00871 2.328049 0.9090326
## 11 44.45885 2.343193 0.9609234
## 2  43.35530 2.344530 0.9277495
## 1  41.27132 2.291811 0.9369023
## 4  34.64875 2.178299 0.8991628
## 9  23.75476 1.974736 0.8793028
## 6  17.11271 1.810093 0.8611554
```

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

``` r
plot(l24)
```

---
# 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).

---
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# 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:

* `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

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

---
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# 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_Data2_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]

https://forms.gle/ESBAdHRhzT65fW6c6