Visualizing Data with R

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background-image: url(images/oupoppies.jpeg)
background-size: cover

# .large[.fancy[Visualizing Data with R]]

## .fancy[Ani Ruhil]

---

## Agenda

- Graphics with `ggplot2`

- Interactive graphics with `highcharter`

- Interactive graphics with `plotly`

- Maps 
  - with `ggplot2` 
  - with `leaflet`

---

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---

# [the grammar of graphics](http://vita.had.co.nz/papers/layered-grammar.html)

.pull-left[
<center><img src = "images/grammarofgraphics.png", width = 400px></center>
]

.pull-right[
`data =` cleaned and mutated or summarized to give us what we'd like to visualize

`geom =` what kind of a visual do you want? A map, bar-chart, line-chart, scatter-plot, something else?

`coordinate system =` what should go on the x-axis? y-axis?

What other `aesthetics` should be used, border colors, fill colors, text or other annotations, plotting symbols, facet the plot to show breakouts by some attribute, something else?  
]

---

.pull-left[

```r
load("data/multkey.merge.RData")

my.df <- multkey.merge

library(tidyverse)
ggplot(data = my.df)
```

<img src="Module03_files/figure-html/unnamed-chunk-1-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[

The canvas is blank because we have not specified what goes on the x-axis, y-axis

That can be specified via `aes` ... the aesthetics 
]

---

.pull-left[

```r
ggplot(data = my.df, aes(x = college_desc)) 
```

Aha! Now we see the labels on the x-axis but nothing more. Why?

Because we have not specified what type of a graphic we want ... a bar-chart perhaps?

]

.pull-right[

```r
ggplot(data = my.df, aes(x = college_desc)) +
  geom_bar() 
```

Notice that `geom_bar()` generates a bar-chart

The y-axis is mapping the frequency 
]

---

We could do better of course, by customizing the labels for the x-axis and y-axis, adding a title and/or subtitle, a caption, maybe even coloring the bars

```r
ggplot(data = my.df, 
       aes(x = college_desc, fill = college_desc)) +
  geom_bar() + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research") +
  theme(legend.position = "bottom")
```

.plot-callout[
<img src="Module03_files/figure-html/fig01-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

The x-axis labels are hard to read so we could flip the x- and y-axis

.plot-callout[
<img src="Module03_files/figure-html/fig02-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

How about ordering the colleges in terms of increasing/decreasing frequency?

```r
library(forcats)

my.df %>%
  group_by(college_desc) %>%
  summarise(frequency = n()) %>%
  ggplot(aes(x = fct_reorder(college_desc, frequency),
           y = frequency,
           fill = college_desc)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research") +
  theme(legend.position = "bottom") +
  coord_flip()
```

Note how we are using the pipe operator `%>%` to do some calculations before seamlessly rolling into the plotting commands

`fct_reorder(college_desc, frequency)` is ordering the bars for us

.plot-callout[
<img src="Module03_files/figure-html/fig03-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

```r
library(forcats)

my.df %>%
  group_by(college_desc) %>%
  summarise(frequency = n()) %>%
  ggplot(aes(x = fct_reorder(college_desc, -frequency),
           y = frequency,
           fill = college_desc)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research") +
  theme(legend.position = "bottom") +
  coord_flip()
```

.center[Note: `fct_reorder(college_desc, -frequency)`]

.plot-callout[
<img src="Module03_files/figure-html/fig03b-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

We could still do better ... do we need a legend? No. How about better axis labels?

```r
my.df %>%
  group_by(college_desc) %>%
  summarise(frequency = n()) %>%
  ggplot(aes(x = fct_reorder(college_desc, frequency),
           y = frequency,
           fill = college_desc)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research",
       x = "Number of Students",
       y = "College") +
  theme(legend.position = "hide") +
  coord_flip() 
```

.plot-callout[
<img src="Module03_files/figure-html/fig04-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

### What if I want to look at enrollment numbers by sex?

```r
my.df %>%
  filter(sex.f %in% c("Male", "Female")) %>%
  group_by(college_desc, sex.f) %>%
  summarise(frequency = n()) %>%
  ggplot(aes(x = fct_reorder(college_desc, frequency),
           y = frequency,
           fill = college_desc)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research",
       y = "Number of Students",
       x = "College") +
  theme(legend.position = "hide") +
  coord_flip() + 
  facet_wrap(~ sex.f)
```

.plot-callout[
<img src="Module03_files/figure-html/fig05-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

What if I want to show percentages instead of frequencies?

```r
my.df %>%
  filter(sex.f %in% c("Male", "Female")) %>%
  group_by(college_desc, sex.f) %>%
  summarise(frequency = n()) %>%
  mutate(percent = (frequency / sum(frequency)) * 100) %>%
  ggplot(aes(x = fct_reorder(college_desc, percent),
           y = percent,
           fill = sex.f)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by Sex and College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research",
       y = "Percent",
       x = "College",
       fill = "") +
  theme(legend.position = "bottom") +
  coord_flip() 
```

.plot-callout[
<img src="Module03_files/figure-html/fig06-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

What if I want to store the summarized data as a data frame and then plot?

```r
tab1 <- my.df %>%
  filter(sex.f %in% c("Male", "Female")) %>%
  group_by(college_desc, sex.f) %>%
  summarise(frequency = n()) %>%
  mutate(percent = (frequency / sum(frequency)) * 100)

ggplot(data = tab1, aes(x = fct_reorder(college_desc, percent),
           y = percent,
           fill = sex.f)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by Sex and College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research",
       y = "Percent",
       x = "College",
       fill = "") +
  theme(legend.position = "bottom") +
  coord_flip() 
```

.center[This approach is handy if you need to print or make available the table]

.plot-callout[
<img src="Module03_files/figure-html/fig07-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

```r
tab1p <- tab1 %>% mutate_at(vars(starts_with("percent")), funs(round(., 2)))

DT::datatable(tab1p, caption = "Distribution of Students by Sex and College",
              rownames = FALSE,
              colnames = c("College", "Sex", "Number", "Percent"))
```

```{=html}
<div id="htmlwidget-5b5b5f9eadd376fe290a" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-5b5b5f9eadd376fe290a">{"x":{"filter":"none","caption":"<caption>Distribution of Students by Sex and College<\/caption>","data":[["Arts &amp; Sciences","Arts &amp; Sciences","Business","Business","Communication","Communication","Education","Education","Engineering &amp; Technology","Engineering &amp; Technology","Fine Arts","Fine Arts","George Voinovich School","George Voinovich School","Health Sciences &amp; Professions","Health Sciences &amp; Professions","Honors Tutorial","Honors Tutorial","International Studies","International Studies","Miscellaneous","Miscellaneous","Osteopathic Medicine","Osteopathic Medicine","Regional Higher Ed","Regional Higher Ed","University College","University College"],["Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male","Female","Male"],[98267,75574,19875,33731,18844,14468,25939,11605,4542,22552,20396,13900,877,440,62345,14740,315,166,506,415,198,110,5608,6562,10774,8269,9164,8378],[56.53,43.47,37.08,62.92,56.57,43.43,69.09,30.91,16.76,83.24,59.47,40.53,66.59,33.41,80.88,19.12,65.49,34.51,54.94,45.06,64.29,35.71,46.08,53.92,56.58,43.42,52.24,47.76]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>College<\/th>\n      <th>Sex<\/th>\n      <th>Number<\/th>\n      <th>Percent<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3]}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
```

---

What if we'd like to break out the preceding plot by students' rank?

```r
my.df %>%
  filter(sex.f %in% c("Male", "Female")) %>%
  group_by(college_desc, rank_desc, sex.f) %>%
  summarise(frequency = n()) %>%
  mutate(percent = (frequency / sum(frequency)) * 100) %>%
  ggplot(aes(x = fct_reorder(college_desc, percent),
           y = percent,
           fill = sex.f)) +
  geom_bar(stat = "identity") + 
  labs(title = "Distribution of Students by College",
       subtitle = "(Multiple Terms)",
       caption = "Source: Ohio University's Office of Institutional Research",
       y = "Percent",
       x = "College",
       fill = "Student's Sex at Birth") +
  theme(legend.position = "bottom") +
  coord_flip() +
  facet_wrap(~ rank_desc)
```
.plot-callout[
<img src="Module03_files/figure-html/fig08-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

## `geom_line() and geom_point()`

Let us calculate enrollments by college and term

```r
tab2 <- my.df %>%
  group_by(term_code, college_desc) %>%
  summarise(frequency = n_distinct(anon_id))

ggplot() +
  geom_point(data = tab2, aes(x = term_code, y = frequency,
                              group = college_desc,
                              color = college_desc,
                              size = frequency,
                              shape = college_desc)) +
  geom_line(data = tab2, aes(x = term_code, y = frequency,
                             group = college_desc,
                             color = college_desc,
                             linetype = college_desc)) +
  labs(x = "Term",
       y = "Number Enrolled",
       color = "")
```

.plot-callout[
<img src="Module03_files/figure-html/fig09-inset-1.png" width="100%" height="99%" style="display: block; margin: auto;" />
]

---

---

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