Geography 13

# Geography 13
## Lecture 06: Data Visualization
### Mike Johnson

---

# Picking back up!

---

# Table Functions:

- **select()**  _Select relevant columns of your data_
- **filter()**	_Filter your data according to logical statements_
- **arrange()**	_Sort your data on a certain column_
- **mutate()**	_Create new variables and add them to your dataset_
- **rename()**	_Rename the columns of your data_

# Split-apply

**group_by()** _declare subsets in data_
**summarize()** _summarize the data, by groups if they have been declared_

# The "glue"

The pipe **%>%** is used to feed in the output that precedes it.

---

**Operators**  
`+`	plus  
`-`	minus  
`*`	multiplication  
`/`	division  
`^`	exponential

**Logical Operator**  
`==`	equal, tests equality  
`!=`	not equal, tests inequality  
`>` greater than, tests greater than (also >=)  
`<`	less than, tests less than (also <=)  
`%in%`	contains, tests inclusion  
`&`	and, returns true if preceeding and following are both true, else FALSE   
`|`	or, returns true if either preceeding and following are true, else FALSE

**Function**  
max()  
min()  
sum()  
custom ...  
 
---

# Yesterdays Assignment

```r
library(tidyverse)
url = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties-recent.csv'
covid = read_csv(url)
head(covid)
```

```
# A tibble: 6 x 6
  date       county  state   fips  cases deaths
  <date>     <chr>   <chr>   <chr> <dbl>  <dbl>
1 2021-05-31 Autauga Alabama 01001  7142    110
2 2021-05-31 Baldwin Alabama 01003 21620    311
3 2021-05-31 Barbour Alabama 01005  2334     59
4 2021-05-31 Bibb    Alabama 01007  2664     64
5 2021-05-31 Blount  Alabama 01009  6864    139
6 2021-05-31 Bullock Alabama 01011  1233     42
```

---
count: false
 
# Question 1: Counties with most cases
.panel1-q1-auto[

```r
*covid
```
]
 
.panel2-q1-auto[

```
# A tibble: 97,395 x 6
   date       county   state   fips  cases deaths
   <date>     <chr>    <chr>   <chr> <dbl>  <dbl>
 1 2021-05-31 Autauga  Alabama 01001  7142    110
 2 2021-05-31 Baldwin  Alabama 01003 21620    311
 3 2021-05-31 Barbour  Alabama 01005  2334     59
 4 2021-05-31 Bibb     Alabama 01007  2664     64
 5 2021-05-31 Blount   Alabama 01009  6864    139
 6 2021-05-31 Bullock  Alabama 01011  1233     42
 7 2021-05-31 Butler   Alabama 01013  2219     71
 8 2021-05-31 Calhoun  Alabama 01015 14622    324
 9 2021-05-31 Chambers Alabama 01017  3665    123
10 2021-05-31 Cherokee Alabama 01019  1862     45
# … with 97,385 more rows
```
]

---
count: false
 
# Question 1: Counties with most cases
.panel1-q1-auto[

```r
covid %>%
* filter(date == max(date))
```
]
 
.panel2-q1-auto[

```
# A tibble: 3,246 x 6
   date       county   state   fips  cases deaths
   <date>     <chr>    <chr>   <chr> <dbl>  <dbl>
 1 2021-06-29 Autauga  Alabama 01001  7247    113
 2 2021-06-29 Baldwin  Alabama 01003 21985    314
 3 2021-06-29 Barbour  Alabama 01005  2345     60
 4 2021-06-29 Bibb     Alabama 01007  2687     64
 5 2021-06-29 Blount   Alabama 01009  6975    139
 6 2021-06-29 Bullock  Alabama 01011  1249     42
 7 2021-06-29 Butler   Alabama 01013  2255     71
 8 2021-06-29 Calhoun  Alabama 01015 14766    330
 9 2021-06-29 Chambers Alabama 01017  3734    124
10 2021-06-29 Cherokee Alabama 01019  1874     45
# … with 3,236 more rows
```
]

---
count: false
 
# Question 1: Counties with most cases
.panel1-q1-auto[

```r
covid %>%
  filter(date == max(date)) %>%
* arrange(-cases)
```
]
 
.panel2-q1-auto[

```
# A tibble: 3,246 x 6
   date       county         state      fips    cases deaths
   <date>     <chr>          <chr>      <chr>   <dbl>  <dbl>
 1 2021-06-29 Los Angeles    California 06037 1250503  24496
 2 2021-06-29 New York City  New York   <NA>   955505  33415
 3 2021-06-29 Maricopa       Arizona    04013  559688  10276
 4 2021-06-29 Cook           Illinois   17031  556951  11045
 5 2021-06-29 Miami-Dade     Florida    12086  501540   6472
 6 2021-06-29 Harris         Texas      48201  403149   6577
 7 2021-06-29 Dallas         Texas      48113  306108   4121
 8 2021-06-29 Riverside      California 06065  301655   4627
 9 2021-06-29 San Bernardino California 06071  299730   4928
10 2021-06-29 San Diego      California 06073  282480   3780
# … with 3,236 more rows
```
]

---
count: false
 
# Question 1: Counties with most cases
.panel1-q1-auto[

```r
covid %>%
  filter(date == max(date)) %>%
  arrange(-cases) %>%
* select(county, state, cases)
```
]
 
.panel2-q1-auto[

```
# A tibble: 3,246 x 3
   county         state        cases
   <chr>          <chr>        <dbl>
 1 Los Angeles    California 1250503
 2 New York City  New York    955505
 3 Maricopa       Arizona     559688
 4 Cook           Illinois    556951
 5 Miami-Dade     Florida     501540
 6 Harris         Texas       403149
 7 Dallas         Texas       306108
 8 Riverside      California  301655
 9 San Bernardino California  299730
10 San Diego      California  282480
# … with 3,236 more rows
```
]

---
count: false
 
# Question 1: Counties with most cases
.panel1-q1-auto[

```r
covid %>%
  filter(date == max(date)) %>%
  arrange(-cases) %>%
  select(county, state, cases) %>%
* head(5)
```
]
 
.panel2-q1-auto[

```
# A tibble: 5 x 3
  county        state        cases
  <chr>         <chr>        <dbl>
1 Los Angeles   California 1250503
2 New York City New York    955505
3 Maricopa      Arizona     559688
4 Cook          Illinois    556951
5 Miami-Dade    Florida     501540
```
]

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
*covid
```
]
 
.panel2-q2-auto[

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
covid %>%
* filter(date == max(date))
```
]
 
.panel2-q2-auto[

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
covid %>%
  filter(date == max(date)) %>%
* group_by(state)
```
]
 
.panel2-q2-auto[

```
# A tibble: 3,246 x 6
# Groups:   state [55]
   date       county   state   fips  cases deaths
   <date>     <chr>    <chr>   <chr> <dbl>  <dbl>
 1 2021-06-29 Autauga  Alabama 01001  7247    113
 2 2021-06-29 Baldwin  Alabama 01003 21985    314
 3 2021-06-29 Barbour  Alabama 01005  2345     60
 4 2021-06-29 Bibb     Alabama 01007  2687     64
 5 2021-06-29 Blount   Alabama 01009  6975    139
 6 2021-06-29 Bullock  Alabama 01011  1249     42
 7 2021-06-29 Butler   Alabama 01013  2255     71
 8 2021-06-29 Calhoun  Alabama 01015 14766    330
 9 2021-06-29 Chambers Alabama 01017  3734    124
10 2021-06-29 Cherokee Alabama 01019  1874     45
# … with 3,236 more rows
```
]

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
covid %>%
  filter(date == max(date)) %>%
  group_by(state) %>%
* summarize(cases = sum(cases, na.rm = TRUE))
```
]
 
.panel2-q2-auto[

```
# A tibble: 55 x 2
   state                  cases
   <chr>                  <dbl>
 1 Alabama               550451
 2 Alaska                 70581
 3 Arizona               894106
 4 Arkansas              348699
 5 California           3818219
 6 Colorado              560407
 7 Connecticut           349301
 8 Delaware              109712
 9 District of Columbia   49347
10 Florida              2321929
# … with 45 more rows
```
]

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
covid %>%
  filter(date == max(date)) %>%
  group_by(state) %>%
  summarize(cases = sum(cases, na.rm = TRUE)) %>%
* ungroup()
```
]
 
.panel2-q2-auto[

---
count: false
 
# Question 2: States with most cases
.panel1-q2-auto[

```r
covid %>%
  filter(date == max(date)) %>%
  group_by(state) %>%
  summarize(cases = sum(cases, na.rm = TRUE)) %>%
  ungroup() %>%
* slice_max(cases, n = 5)
```
]
 
.panel2-q2-auto[

```
# A tibble: 5 x 2
  state        cases
  <chr>        <dbl>
1 California 3818219
2 Texas      2991200
3 Florida    2321929
4 New York   2112872
5 Illinois   1395442
```
]

---

# The hidden need for ungrouping

- `ungroup()` should always be  applied after the grouping calcuations.. 
- If you forget to `ungroup()` data, future data management will likely produce errors.
-  Even if you do not plan on performing additional calculations, it’s a good habit to keep. 
- `ungroup()` is especially important when creating objects!!

- Think about the dimision of your data structure!

```r
gapminder %>% 
  filter(year == 2007) %>% 
  group_by(continent) %>% 
  mutate(mp = mean(pop)) %>%   
  mutate(mle = mean(lifeExp)) %>% 
* ungroup()  %>%
  select(country, year, mp, mle) 
```

```
# A tibble: 142 x 4
   country      year         mp   mle
   <fct>       <int>      <dbl> <dbl>
 1 Afghanistan  2007 115513752.  70.7
 2 Albania      2007  19536618.  77.6
 3 Algeria      2007  17875763.  54.8
 4 Angola       2007  17875763.  54.8
 5 Argentina    2007  35954847.  73.6
 6 Australia    2007  12274974.  80.7
 7 Austria      2007  19536618.  77.6
 8 Bahrain      2007 115513752.  70.7
 9 Bangladesh   2007 115513752.  70.7
10 Belgium      2007  19536618.  77.6
# … with 132 more rows
```
]

```r
gapminder %>% 
  filter(year == 2007) %>% 
  group_by(continent) %>%  
  mutate(mp = mean(pop)) %>%  
* ungroup() %>%
  mutate(mle = mean(lifeExp))  %>% 
  select(country, year, mp, mle)
```

```
# A tibble: 142 x 4
   country      year         mp   mle
   <fct>       <int>      <dbl> <dbl>
 1 Afghanistan  2007 115513752.  67.0
 2 Albania      2007  19536618.  67.0
 3 Algeria      2007  17875763.  67.0
 4 Angola       2007  17875763.  67.0
 5 Argentina    2007  35954847.  67.0
 6 Australia    2007  12274974.  67.0
 7 Austria      2007  19536618.  67.0
 8 Bahrain      2007 115513752.  67.0
 9 Bangladesh   2007 115513752.  67.0
10 Belgium      2007  19536618.  67.0
# … with 132 more rows
```
]

---
count: false
 
# Question 3 (1): County death ratio
.panel1-q31-auto[

```r
*covid
```
]
 
.panel2-q31-auto[

---
count: false
 
# Question 3 (1): County death ratio
.panel1-q31-auto[

```r
covid %>%
* filter(date == max(date), county != "Unknown", cases != 0)
```
]
 
.panel2-q31-auto[

```
# A tibble: 3,221 x 6
   date       county   state   fips  cases deaths
   <date>     <chr>    <chr>   <chr> <dbl>  <dbl>
 1 2021-06-29 Autauga  Alabama 01001  7247    113
 2 2021-06-29 Baldwin  Alabama 01003 21985    314
 3 2021-06-29 Barbour  Alabama 01005  2345     60
 4 2021-06-29 Bibb     Alabama 01007  2687     64
 5 2021-06-29 Blount   Alabama 01009  6975    139
 6 2021-06-29 Bullock  Alabama 01011  1249     42
 7 2021-06-29 Butler   Alabama 01013  2255     71
 8 2021-06-29 Calhoun  Alabama 01015 14766    330
 9 2021-06-29 Chambers Alabama 01017  3734    124
10 2021-06-29 Cherokee Alabama 01019  1874     45
# … with 3,211 more rows
```
]

---
count: false
 
# Question 3 (1): County death ratio
.panel1-q31-auto[

```r
covid %>%
  filter(date == max(date), county != "Unknown", cases != 0) %>%
* mutate(ratio = 100*(deaths/cases))
```
]
 
.panel2-q31-auto[

```
# A tibble: 3,221 x 7
   date       county   state   fips  cases deaths ratio
   <date>     <chr>    <chr>   <chr> <dbl>  <dbl> <dbl>
 1 2021-06-29 Autauga  Alabama 01001  7247    113  1.56
 2 2021-06-29 Baldwin  Alabama 01003 21985    314  1.43
 3 2021-06-29 Barbour  Alabama 01005  2345     60  2.56
 4 2021-06-29 Bibb     Alabama 01007  2687     64  2.38
 5 2021-06-29 Blount   Alabama 01009  6975    139  1.99
 6 2021-06-29 Bullock  Alabama 01011  1249     42  3.36
 7 2021-06-29 Butler   Alabama 01013  2255     71  3.15
 8 2021-06-29 Calhoun  Alabama 01015 14766    330  2.23
 9 2021-06-29 Chambers Alabama 01017  3734    124  3.32
10 2021-06-29 Cherokee Alabama 01019  1874     45  2.40
# … with 3,211 more rows
```
]

---
count: false
 
# Question 3 (1): County death ratio
.panel1-q31-auto[

```r
covid %>%
  filter(date == max(date), county != "Unknown", cases != 0) %>%
  mutate(ratio = 100*(deaths/cases)) %>%
* slice_max(ratio, n = 5)
```
]
 
.panel2-q31-auto[

```
# A tibble: 5 x 7
  date       county    state      fips  cases deaths ratio
  <date>     <chr>     <chr>      <chr> <dbl>  <dbl> <dbl>
1 2021-06-29 Grant     Nebraska   31075    36      4 11.1 
2 2021-06-29 Sabine    Texas      48403   525     45  8.57
3 2021-06-29 Petroleum Montana    30069    12      1  8.33
4 2021-06-29 Foard     Texas      48155   124     10  8.06
5 2021-06-29 Harding   New Mexico 35021    13      1  7.69
```
]

---
count: false
 
# Question 3 (2): County death ratio
.panel1-q32-auto[

```r
*covid
```
]
 
.panel2-q32-auto[

---
count: false
 
# Question 3 (2): County death ratio
.panel1-q32-auto[

```r
covid %>%
* filter(date == max(date), cases > 0, county != "Unknown")
```
]
 
.panel2-q32-auto[

---
count: false
 
# Question 3 (2): County death ratio
.panel1-q32-auto[

```r
covid %>%
  filter(date == max(date), cases > 0, county != "Unknown") %>%
* slice_max(100*(deaths/cases), n = 5)
```
]
 
.panel2-q32-auto[

```
# A tibble: 5 x 6
  date       county    state      fips  cases deaths
  <date>     <chr>     <chr>      <chr> <dbl>  <dbl>
1 2021-06-29 Grant     Nebraska   31075    36      4
2 2021-06-29 Sabine    Texas      48403   525     45
3 2021-06-29 Petroleum Montana    30069    12      1
4 2021-06-29 Foard     Texas      48155   124     10
5 2021-06-29 Harding   New Mexico 35021    13      1
```
]

---

```r
*filter(covid, date == max(date))
```
]
 
.panel2-q4-auto[

---
count: false
 
# Question 4: State death ratio
.panel1-q4-auto[

```r
filter(covid, date == max(date)) %>%
* group_by(state)
```
]
 
.panel2-q4-auto[

---
count: false
 
# Question 4: State death ratio
.panel1-q4-auto[

```r
filter(covid, date == max(date)) %>%
  group_by(state) %>%
* summarize(totCases = sum(cases),
*           totDeaths = sum(deaths),
*           ratio = 100 * (totDeaths/totCases))
```
]
 
.panel2-q4-auto[

```
# A tibble: 55 x 4
   state                totCases totDeaths ratio
   <chr>                   <dbl>     <dbl> <dbl>
 1 Alabama                550451     11338 2.06 
 2 Alaska                  70581       357 0.506
 3 Arizona                894106     17930 2.01 
 4 Arkansas               348699      5905 1.69 
 5 California            3818219     63606 1.67 
 6 Colorado               560407      6940 1.24 
 7 Connecticut            349301      8276 2.37 
 8 Delaware               109712      1694 1.54 
 9 District of Columbia    49347      1141 2.31 
10 Florida               2321929     37772 1.63 
# … with 45 more rows
```
]

---
count: false
 
# Question 4: State death ratio
.panel1-q4-auto[

```r
filter(covid, date == max(date)) %>%
  group_by(state) %>%
  summarize(totCases = sum(cases),
            totDeaths = sum(deaths),
            ratio = 100 * (totDeaths/totCases)) %>%
* ungroup()
```
]
 
.panel2-q4-auto[

---
count: false
 
# Question 4: State death ratio
.panel1-q4-auto[

```r
filter(covid, date == max(date)) %>%
  group_by(state) %>%
  summarize(totCases = sum(cases),
            totDeaths = sum(deaths),
            ratio = 100 * (totDeaths/totCases)) %>%
  ungroup() %>%
* slice_max(ratio, n = 5)
```
]
 
.panel2-q4-auto[

```
# A tibble: 5 x 4
  state                totCases totDeaths ratio
  <chr>                   <dbl>     <dbl> <dbl>
1 New Jersey            1023200     26444  2.58
2 Massachusetts          709873     17993  2.53
3 New York              2112872     53093  2.51
4 Connecticut            349301      8276  2.37
5 District of Columbia    49347      1141  2.31
```
]

---
class: center, middle

---
class: middle, center, inverse
# Data Visualization
---
# ggplot

- `ggplot2` is a library is based on the **grammar of graphics**

- the idea is you can build every graph from the same
few components: 
  1. a data set 
  2. geom(s)
  3. a coordinate system

- `ggplot2` provides a programmatic interface for specifying
 - what variables to plot, 
 - how they are displayed, 
 - general visual properties. 
 
--

- Therefore, we only need _minimal_ changes if the underlying data changes or if we decide to change our visual.

- This helps create publication quality plots with minimal amounts of adjustments and tweaking.

- ggplot likes data in the ‘long’ format: i.e., a column for every dimension, and a row for every observation. (more on this tommorrow!!)

---

# Components of a ggplot:

- ggplot graphics are built step by step by adding new elements and layers

1. Data
  2. Geometry (geom)
  3. Aesthetic mapping
  4. Theme

- Elements of a plot are layered by iteritavly adding elements

- These can be added in a series of 5 steps:

1. Setup
2. Layers
3. Labels
4. Facets
5. Themes

---

# Example Data for today ...

```r
(gm2007 = filter(gapminder, year == 2007))
```

```
# A tibble: 142 x 6
   country     continent  year lifeExp       pop gdpPercap
   <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
 1 Afghanistan Asia       2007    43.8  31889923      975.
 2 Albania     Europe     2007    76.4   3600523     5937.
 3 Algeria     Africa     2007    72.3  33333216     6223.
 4 Angola      Africa     2007    42.7  12420476     4797.
 5 Argentina   Americas   2007    75.3  40301927    12779.
 6 Australia   Oceania    2007    81.2  20434176    34435.
 7 Austria     Europe     2007    79.8   8199783    36126.
 8 Bahrain     Asia       2007    75.6    708573    29796.
 9 Bangladesh  Asia       2007    64.1 150448339     1391.
10 Belgium     Europe     2007    79.4  10392226    33693.
# … with 132 more rows
```

---

# 1. The Setup: canvas

```r
ggplot()
```

<img src="lecture-06_files/figure-html/unnamed-chunk-7-1.png" width="432" />
]

---

# 1. The Setup: data

```r
ggplot(data = gm2007)
```

<img src="lecture-06_files/figure-html/unnamed-chunk-8-1.png" width="432" />
]

---

# 1. The Setup: Aesthetic Mappings

- Denoted by the `aes` argument

- Aesthetic mappings can be set in ggplot() and/or in individual layers.

- Aesthetic mappings in the ggplot() call, can be seen by all geom layers.

- The X and Y axis of the plot as well colors, sizes, shapes, fills are all aesthetic.

- If you want to have an aesthetic fixed (that is **not** vary based on a variable) you need to specify it _outside_ the aes()
]

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp))
```

<img src="lecture-06_files/figure-html/unnamed-chunk-9-1.png" width="432" />
]

---

# 2. Layers

- The `+` sign is used to add layers to a `ggplot` setup

- Layers can define geometries, compute summary statistics, define what scales to use, or even change styles.

- In general a plot construction will look like this:

```r
DATA %>% 
  ggplot(aes(x, y)) + 
  LAYER 1 + 
  LAYER 2 + 
  … 
```

---

# 2. Layers: Geometry

- Many layers in `ggplot2` are called ‘geoms’.

- `geoms` are the geometric objects (points, lines, bars, etc.) that can be placed on a graph to visualize the `X-Y mapping` of the input `data`

- They are called using functions that start with `geom_*`.

- Examples include:
  - points (`geom_point`, for scatter plots, dot plots, etc)
  - lines (`geom_line`, for time series, trend lines, etc)
  - boxplots (`geom_boxplot`)
  - … and many more!

```
 [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"            
```
  
  
---

# 2. Layers: Geometry

- A plot **must** have at least one geom, but there is no maximum.

- Adding geoms to a ggplot follows the pattern:

```r
ggplot(data = <DATA>, aes(X, Y)) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

- Note again that the aesthetics placed in the `ggplot` call are the **global** parameters for the plot, and the aethetics placed in each `geom` are specific to that `geom`.

---

```r
*ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-geom1-auto[
<img src="lecture-06_files/figure-html/geom1_auto_01_output-1.png" width="432" />
]

---
count: false
 
# A first geom_* ...
.panel1-geom1-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
* geom_point()
```
]
 
.panel2-geom1-auto[
<img src="lecture-06_files/figure-html/geom1_auto_02_output-1.png" width="432" />
]

---

# 2. Layers: Geometry

- position (i.e., on the x and y axes)
- color (“outside” color)
- fill (“inside” color)
- shape
- line type
- size

Each `geom` accepts only a **subset** of these aesthetics

(refer to the `geom` help pages (e.g. `?geom_point`) to see what mappings each `geom` accepts. 
]

---
# 2. Layers: data.frame driven or fixed?

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) + 
  geom_point(col = "red")
```

<img src="lecture-06_files/figure-html/unnamed-chunk-13-1.png" width="432" />
]

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) + 
  geom_point(aes(col = continent))
```

<img src="lecture-06_files/figure-html/unnamed-chunk-14-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-geoms-auto[

```r
*ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-geoms-auto[
<img src="lecture-06_files/figure-html/geoms_auto_01_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-geoms-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
* geom_point(aes(color = continent, size = pop))
```
]
 
.panel2-geoms-auto[
<img src="lecture-06_files/figure-html/geoms_auto_02_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-geoms-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
* geom_smooth(color = "black")
```
]
 
.panel2-geoms-auto[
<img src="lecture-06_files/figure-html/geoms_auto_03_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-geoms-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black") +
* geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray")
```
]
 
.panel2-geoms-auto[
<img src="lecture-06_files/figure-html/geoms_auto_04_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-geoms-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black") +
  geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray") +
* geom_vline(xintercept = mean(gm2007$gdpPercap), color = "gray")
```
]
 
.panel2-geoms-auto[
<img src="lecture-06_files/figure-html/geoms_auto_05_output-1.png" width="432" />
]

---

# 3. Labels

- Now that you have drawn the main parts of the graph. You might want to add labs that clarify what is being shown.

- This can be done using the `labs` layer.

- The most typical are: `title`, `x`, and `y` but other options exist!

---

```r
*ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-labs-auto[
<img src="lecture-06_files/figure-html/labs_auto_01_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-labs-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
* geom_point(aes(color = continent, size = pop))
```
]
 
.panel2-labs-auto[
<img src="lecture-06_files/figure-html/labs_auto_02_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-labs-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
* geom_smooth(color = "black", size = .5)
```
]
 
.panel2-labs-auto[
<img src="lecture-06_files/figure-html/labs_auto_03_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-labs-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
* geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray")
```
]
 
.panel2-labs-auto[
<img src="lecture-06_files/figure-html/labs_auto_04_output-1.png" width="432" />
]

---
count: false
 
# For our example...
.panel1-labs-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
  geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray") +
  geom_vline(xintercept = mean(gm2007$gdpPercap), color = "gray") +
* labs(title = "Per capita GDP versus life expectency in 2007",
*      x = "Per Capita GDP",
*      y = "Life Expectancy",
*      caption = "Based on Hans Rosling Plots",
*      subtitle = 'Data Source: Gapminder',
*      color = "",
*      size = "Population")
```
]
 
.panel2-labs-auto[
<img src="lecture-06_files/figure-html/labs_auto_06_output-1.png" width="432" />
]

---
# 4. Facets

- In the previous chart, we showed a scatterplot for all countries plotted in the same chart. What if you want one chart for each continent?

- Such separation is called `faceting`

- `facet_wrap()` takes in a formula as the argument.

- Formulas look like this `RHS ~ LHS` (where RHS = right hand side, LHS = left hand side)

The item on the RHS corresponds to the column. The item on the LHS defines the rows.

- In `facet_wrap`, the scales of the X and Y axis are fixed to accommodate **all** points by default.

- This makes the comparison of values more meaningful because they would be in the same scale.

- The scales can be made `free` by setting the argument `scales=free`.

---

```r
*ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_01_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
* geom_point(aes(color = continent, size = pop))
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_02_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
* geom_smooth(color = "black", size = .5)
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_03_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
* geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray")
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_04_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
  geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray") +
  geom_vline(xintercept = mean(gm2007$gdpPercap), color = "gray") +
* labs(title = "Per capita GDP versus life expectency in 2007",
*      x = "Per Capita GDP",
*      y = "Life Expectancy",
*      caption = "Based on Hans Rosling Plots",
*      subtitle = 'Data Source: Gapminder',
*      color = "",
*      size = "Population")
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_06_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
  geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray") +
  geom_vline(xintercept = mean(gm2007$gdpPercap), color = "gray") +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
* facet_wrap(~continent)
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_07_output-1.png" width="432" />
]

---
count: false
 
# Facet Wrap...
.panel1-facet-auto[

```r
ggplot(data = gm2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  geom_smooth(color = "black", size = .5) +
  geom_hline(yintercept = mean(gm2007$lifeExp), color = "gray") +
  geom_vline(xintercept = mean(gm2007$gdpPercap), color = "gray") +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  facet_wrap(~continent) +
* facet_wrap(~continent, scales = "free")
```
]
 
.panel2-facet-auto[
<img src="lecture-06_files/figure-html/facet_auto_08_output-1.png" width="432" />
]

---

```r
*gapminder
```
]
 
.panel2-grid-auto[

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows
```
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
* filter(year %in% c(1952, 1977, 2007))
```
]
 
.panel2-grid-auto[

```
# A tibble: 426 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1977    38.4 14880372      786.
 3 Afghanistan Asia       2007    43.8 31889923      975.
 4 Albania     Europe     1952    55.2  1282697     1601.
 5 Albania     Europe     1977    68.9  2509048     3533.
 6 Albania     Europe     2007    76.4  3600523     5937.
 7 Algeria     Africa     1952    43.1  9279525     2449.
 8 Algeria     Africa     1977    58.0 17152804     4910.
 9 Algeria     Africa     2007    72.3 33333216     6223.
10 Angola      Africa     1952    30.0  4232095     3521.
# … with 416 more rows
```
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
* ggplot(aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
* geom_point(aes(size = pop))
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop)) +
* labs(title = "Per capita GDP versus life expectency in 2007",
*      x = "Per Capita GDP",
*      y = "Life Expectancy",
*      caption = "Based on Hans Rosling Plots",
*      subtitle = 'Data Source: Gapminder',
*      color = "",
*      size = "Population")
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
* facet_wrap(year~continent)
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_06_output-1.png" width="432" />
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  facet_wrap(year~continent) +
* facet_grid(year~continent)
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_07_output-1.png" width="432" />
]

---
count: false
 
#Facet Grids...
.panel1-grid-auto[

```r
gapminder %>%
  filter(year %in% c(1952, 1977, 2007)) %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  facet_wrap(year~continent) +
  facet_grid(year~continent) +
* facet_grid(continent~year)
```
]
 
.panel2-grid-auto[
<img src="lecture-06_files/figure-html/grid_auto_08_output-1.png" width="432" />
]

---

# 5. Theme

- Great! Now we just need to polish our plots...

- ggplot offers a themeing system:

1.  `elements` specify the non-data elements that you can control. For example, 
  -  `plot.title` controls the appearance of the plot title; 
  - ` axis.ticks.x` controls the ticks on the x axis; 
  - `legend.key.height`, controls the height of the keys in the legend.

2. Each `element` is associated with an element function, which describes the visual properties. For example, 
  - `element_text()` sets the font size, color and face of text elements like `plot.title`.

3. The `theme()` function which allows you to override  default elements:
   - For example  `theme(plot.title = element_text(color = "red"))`.

---

# Built in themes

Wow! Thats a lot :) Fortunately, ggplot comes with many default themes that set all of the theme elements to values designed to work together harmoniously.

```
 [1] "theme_bw"       "theme_classic"  "theme_dark"     "theme_get"     
 [5] "theme_gray"     "theme_grey"     "theme_light"    "theme_linedraw"
 [9] "theme_minimal"  "theme_replace"  "theme_set"      "theme_test"    
[13] "theme_update"   "theme_void"    
```

---

# theme_bw()

```r
theme_bw
```

```
function (base_size = 11, base_family = "", base_line_size = base_size/22, 
    base_rect_size = base_size/22) 
{
    theme_grey(base_size = base_size, base_family = base_family, 
        base_line_size = base_line_size, base_rect_size = base_rect_size) %+replace% 
        theme(panel.background = element_rect(fill = "white", 
            colour = NA), panel.border = element_rect(fill = NA, 
            colour = "grey20"), panel.grid = element_line(colour = "grey92"), 
            panel.grid.minor = element_line(size = rel(0.5)), 
            strip.background = element_rect(fill = "grey85", 
                colour = "grey20"), legend.key = element_rect(fill = "white", 
                colour = NA), complete = TRUE)
}
<bytecode: 0x7f8dc1f9cc38>
<environment: namespace:ggplot2>
```

---

```r
*gm2007
```
]
 
.panel2-theme-auto[

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
* ggplot(aes(x = gdpPercap, y = lifeExp))
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_02_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
* geom_point(aes(color = continent, size = pop))
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_03_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
* labs(title = "Per capita GDP versus life expectency in 2007",
*      x = "Per Capita GDP",
*      y = "Life Expectancy",
*      caption = "Based on Hans Rosling Plots",
*      subtitle = 'Data Source: Gapminder',
*      color = "",
*      size = "Population")
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_04_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
* theme_bw()
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_05_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  theme_bw() +
* theme_dark()
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_06_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  theme_bw() +
  theme_dark() +
* theme_gray()
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_07_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  theme_bw() +
  theme_dark() +
  theme_gray() +
* theme_minimal()
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_08_output-1.png" width="432" />
]

---
count: false
 
#Built in Themes...
.panel1-theme-auto[

```r
gm2007 %>%
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop)) +
  labs(title = "Per capita GDP versus life expectency in 2007",
       x = "Per Capita GDP",
       y = "Life Expectancy",
       caption = "Based on Hans Rosling Plots",
       subtitle = 'Data Source: Gapminder',
       color = "",
       size = "Population") +
  theme_bw() +
  theme_dark() +
  theme_gray() +
  theme_minimal() +
* theme_light()
```
]
 
.panel2-theme-auto[
<img src="lecture-06_files/figure-html/theme_auto_09_output-1.png" width="432" />
]

---

```r
*library(ggthemes)
```
]
 
.panel2-ggtheme-auto[

]