Data and Vizualization

# Data and Vizualization
## Visualizing different types of data
### Becky Tang
### 05.19.2021

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<div class="my-footer">
<span>
<a href="http://datasciencebox.org" target="_blank">datasciencebox.org</a>
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</div>

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# Identifying variables

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## Number of variables involved

- .vocab[Univariate data analysis]: distribution of single variable

<br>

- .vocab[Bivariate data analysis]: relationship between two variables

<br>

- .vocab[Multivariate data analysis]: relationship between many variables at once, usually focusing on the relationship between two while conditioning for others

---

## Types of variables

- .vocab[Numerical variables] can be classified as .vocab[continuous] or .vocab[discrete] based on whether or not the variable can take on an infinite number of values or only non-negative whole numbers, respectively.
    - *height* is continuous
    - *number of siblings* is discrete

- If the variable is .vocab[categorical], we can determine if it is .vocab[ordinal] based on whether or not the levels have a natural ordering.
    - *hair color* is unordered 
    - *year in school* is ordinal

---

# Visualizing numerical data

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## Describing numerical distributions

- .vocab[shape:]
    - skewness: right-skewed, left-skewed, symmetric
    - modality: unimodal, bimodal, multimodal, uniform
    
- .vocab[center:] mean (`mean`), median (`median`), mode (not always useful)

- .vocab[spread:] range (`range`), standard deviation (`sd`), inter-quartile range (`IQR`)

- .vocab[outliers:] observations outside of the usual pattern

---

## Diamonds data

```r
diamonds
```

```
## # A tibble: 53,940 x 11
##    carat cut   color clarity depth table price     x     y     z price_per_carat
##    <dbl> <ord> <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>           <dbl>
##  1  0.23 Ideal E     SI2      61.5    55   326  3.95  3.98  2.43           1417.
##  2  0.21 Prem… E     SI1      59.8    61   326  3.89  3.84  2.31           1552.
##  3  0.23 Good  E     VS1      56.9    65   327  4.05  4.07  2.31           1422.
##  4  0.29 Prem… I     VS2      62.4    58   334  4.2   4.23  2.63           1152.
##  5  0.31 Good  J     SI2      63.3    58   335  4.34  4.35  2.75           1081.
##  6  0.24 Very… J     VVS2     62.8    57   336  3.94  3.96  2.48           1400 
##  7  0.24 Very… I     VVS1     62.3    57   336  3.95  3.98  2.47           1400 
##  8  0.26 Very… H     SI1      61.9    55   337  4.07  4.11  2.53           1296.
##  9  0.22 Fair  E     VS2      65.1    61   337  3.87  3.78  2.49           1532.
## 10  0.23 Very… H     VS1      59.4    61   338  4     4.05  2.39           1470.
## # … with 53,930 more rows
```

---

## Diamonds data, glimpse

```r
glimpse(diamonds)
```

```
## Rows: 53,940
## Columns: 11
## $ carat           <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, …
## $ cut             <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very G…
## $ color           <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, …
## $ clarity         <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI…
## $ depth           <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, …
## $ table           <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54…
## $ price           <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339,…
## $ x               <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, …
## $ y               <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, …
## $ z               <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, …
## $ price_per_carat <dbl> 1417.391, 1552.381, 1421.739, 1151.724, 1080.645, 1400…
```

---

## Diamonds help file
.pull-left[
<img src="img/03/diamonds_help1.png" width="80%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="img/03/diamonds_help2.png" width="80%" style="display: block; margin: auto;" />
]

---

## Diamonds: clarity

---

## Diamonds: color

---

## Histograms

```r
ggplot(data = diamonds, mapping = aes(x = price)) +
  geom_histogram()
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-8-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Histograms

```r
ggplot(data = diamonds, mapping = aes(x = price)) +
  geom_histogram(binwidth = 1000)
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-9-1.png" width="80%" style="display: block; margin: auto;" />
]

```r
ggplot(data = diamonds, mapping = aes(x = price)) +
  geom_histogram(bins = 12)
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-10-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Density plots

```r
ggplot(data = diamonds, mapping = aes(x = price)) +
  geom_density()
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-11-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Side-by-side box plots

```r
ggplot(data = diamonds, mapping = aes(y = price, x = cut)) +
  geom_boxplot()
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-12-1.png" width="80%" style="display: block; margin: auto;" />
]

---

# Visualizing categorical data

---

## Bar plots

```r
ggplot(data = diamonds, mapping = aes(x = clarity)) + 
  geom_bar()
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-13-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Segmented bar plots, counts

```r
ggplot(data = diamonds, mapping = aes(x = clarity, fill = cut)) + 
  geom_bar() 
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-14-1.png" width="70%" style="display: block; margin: auto;" />
]

---

## Segmented bar plots, proportions

```r
ggplot(data = diamonds, mapping = aes(x = clarity, fill = cut)) + 
* geom_bar(position = "fill") +
  labs(y = "proportion") 
```

<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-15-1.png" width="70%" style="display: block; margin: auto;" />
]

---

## Which bar plot is more appropriate?

.question[
Which plot is more useful for visualizing the relationship between clarity and cut? Why?
]

.pull-left[
<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-16-1.png" style="display: block; margin: auto;" />
]

.pull-right[
<img src="03-data-and-viz2_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
]
---

# Data visualization

---

## What is data visualization?

Anything	that	converts	data	sources	into	a	visual	representation

- charts
- plots
- maps
- tables
- etc.

---

# Why do we visualize?

---

## Data: `datasaurus_dozen`

Below is an excerpt from the `datasaurus_dozen` dataset:

```
## # A tibble: 142 x 8
##    away_x away_y bullseye_x bullseye_y circle_x circle_y dino_x dino_y
##     <dbl>  <dbl>      <dbl>      <dbl>    <dbl>    <dbl>  <dbl>  <dbl>
##  1   32.3   61.4       51.2       83.3     56.0     79.3   55.4   97.2
##  2   53.4   26.2       59.0       85.5     50.0     79.0   51.5   96.0
##  3   63.9   30.8       51.9       85.8     51.3     82.4   46.2   94.5
##  4   70.3   82.5       48.2       85.0     51.2     79.2   42.8   91.4
##  5   34.1   45.7       41.7       84.0     44.4     78.2   40.8   88.3
##  6   67.7   37.1       37.9       82.6     45.0     77.9   38.7   84.9
##  7   53.3   97.5       39.5       80.8     48.6     78.8   35.6   79.9
##  8   63.5   25.1       39.6       82.7     42.1     76.9   33.1   77.6
##  9   68.0   81.0       34.8       80.0     41.0     76.4   29.0   74.5
## 10   67.4   29.7       27.6       72.8     34.6     72.7   26.2   71.4
## # … with 132 more rows
```

---

## Summary statistics

```r
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(r = cor(x, y))
```

```
## # A tibble: 13 x 2
##    dataset          r
##    <chr>        <dbl>
##  1 away       -0.0641
##  2 bullseye   -0.0686
##  3 circle     -0.0683
##  4 dino       -0.0645
##  5 dots       -0.0603
##  6 h_lines    -0.0617
##  7 high_lines -0.0685
##  8 slant_down -0.0690
##  9 slant_up   -0.0686
## 10 star       -0.0630
## 11 v_lines    -0.0694
## 12 wide_lines -0.0666
## 13 x_shape    -0.0656
```

---
.question[
How similar do the relationships between `x` and `y` look based on the plots? Based on the summary statistics? 
]

---

## Anscombe's quartet

```r
library(Tmisc)
quartet
```

```
##    set  x     y
## 1    I 10  8.04
## 2    I  8  6.95
## 3    I 13  7.58
## 4    I  9  8.81
## 5    I 11  8.33
## 6    I 14  9.96
## 7    I  6  7.24
## 8    I  4  4.26
## 9    I 12 10.84
## 10   I  7  4.82
## 11   I  5  5.68
## 12  II 10  9.14
## 13  II  8  8.14
## 14  II 13  8.74
## 15  II  9  8.77
## 16  II 11  9.26
## 17  II 14  8.10
## 18  II  6  6.13
## 19  II  4  3.10
## 20  II 12  9.13
## 21  II  7  7.26
## 22  II  5  4.74
```
]
.pull-right[

```
##    set  x     y
## 23 III 10  7.46
## 24 III  8  6.77
## 25 III 13 12.74
## 26 III  9  7.11
## 27 III 11  7.81
## 28 III 14  8.84
## 29 III  6  6.08
## 30 III  4  5.39
## 31 III 12  8.15
## 32 III  7  6.42
## 33 III  5  5.73
## 34  IV  8  6.58
## 35  IV  8  5.76
## 36  IV  8  7.71
## 37  IV  8  8.84
## 38  IV  8  8.47
## 39  IV  8  7.04
## 40  IV  8  5.25
## 41  IV 19 12.50
## 42  IV  8  5.56
## 43  IV  8  7.91
## 44  IV  8  6.89
```
]

---

## Summarising Anscombe's quartet

```r
quartet %>%
  group_by(set) %>%
  summarise(
    mean_x = mean(x), mean_y = mean(y),
    sd_x = sd(x), sd_y = sd(y),
    r = cor(x, y)
  )
```

```
## # A tibble: 4 x 6
##   set   mean_x mean_y  sd_x  sd_y     r
##   <fct>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 I          9   7.50  3.32  2.03 0.816
## 2 II         9   7.50  3.32  2.03 0.816
## 3 III        9   7.5   3.32  2.03 0.816
## 4 IV         9   7.50  3.32  2.03 0.817
```

---

## Visualizing Anscombe's quartet

```r
ggplot(quartet, aes(x = x, y = y)) +
  geom_point() +
  facet_wrap(~ set, ncol = 4)
```

---

## Do you see anything out of the ordinary?

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## Reporting lower vs. higher values

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# Designing effective visualizations

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## Keep it simple

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## Use	color	to	draw	attention

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## Tell a story