ma206, lesson 2

class: inverse, left, bottom
background-image: url(https://images.unsplash.com/photo-1543286386-713bdd548da4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1470&q=80)
background-size: cover
# .Large[ma206data Lesson 3]
## .small[Data Wrangling and Tidyverse Tutorial]
#### .tiny[Dr. Evangeline Reynolds | 2022-08-18 |Image credit: William Iven, Upsplash]
???

---

# Warm-up

```r
pressure
```

```
##    temperature pressure
## 1            0   0.0002
## 2           20   0.0012
## 3           40   0.0060
## 4           60   0.0300
## 5           80   0.0900
## 6          100   0.2700
## 7          120   0.7500
## 8          140   1.8500
## 9          160   4.2000
## 10         180   8.8000
## 11         200  17.3000
## 12         220  32.1000
## 13         240  57.0000
## 14         260  96.0000
## 15         280 157.0000
## 16         300 247.0000
## 17         320 376.0000
## 18         340 558.0000
## 19         360 806.0000
```

---

### try out more geoms (more than one in a chart works)

- #### geom_point()
- #### geom_col() 
- #### geom_tile()

### use one of the pressure variables (temperature or pressure) instead of 'var_name' to explore a few more visual channels that can represent variables:

- #### aes(alpha = var_name)
- #### aes(fill = var_name) vs aes(color = var_name)

## Check how your neighbor is doing.

---

# Attendance question

## What's your favorite data viz/analysis tool up to this point? (pen and paper, excel?, something else?)

## 'Bonus': What company, w/ data viz production based on 'the grammar of graphics' was bought for 15.7 billion dollars.

# Add myself...

---

# The "Grammar of Graphics" (1999)

---

![](images/paste-99474543.png)
Third wave data visualization, Meeks: https://www.youtube.com/watch?v=itChfcTx7aos

---

# A data visualization is made up of ...
--

### .left[... geometric objects ...]
--

### .center[...that take on aesthetics (color, line width, x-position) ...]
--

### .center[...which represent variables ...]
--

### .right[...from a dataset (dataframe)]

---

## Elements of the Grammar of Graphics (choices)
<img src="images/wilkinson_layers_grammar_of_graphics.png" width="957" />

---

@media print {
  .has-continuation {
    display: block;
  }
}

code.r.hljs.remark-code{
  position: relative;
  overflow-x: hidden;
}

code.r.hljs.remark-code:hover{
  overflow-x:visible;
  width: 500px;
  border-style: solid;
}
</style>

---

| function |	action |
| --- | --- |
| filter() |	keep rows (if true) |
| select() |	keep variables (or drop them -var) |
| mutate() |	create a new variable |
| case_when() |	is used for “recoding” variable, often  used with mutate() |
| rename()	| renaming variables |
| arrange()	| order rows based on a variable |
| slice()	| *keep or drop rows based on row number |

---

---
class: inverse, center, middle

# What manipulation is required?

---

```r
# Original, full dataset
library(gapminder)
gapminder %>% 
  filter(continent == "Europe")
```

```
# A tibble: 360 × 6
   country                continent  year lifeExp      pop gdpPercap
   <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
 1 Albania                Europe     1952    55.2  1282697     1601.
 2 Albania                Europe     1957    59.3  1476505     1942.
 3 Albania                Europe     1962    64.8  1728137     2313.
 4 Albania                Europe     1967    66.2  1984060     2760.
 5 Albania                Europe     1972    67.7  2263554     3313.
 6 Albania                Europe     1977    68.9  2509048     3533.
 7 Albania                Europe     1982    70.4  2780097     3631.
 8 Albania                Europe     1987    72    3075321     3739.
 9 Albania                Europe     1992    71.6  3326498     2497.
10 Albania                Europe     1997    73.0  3428038     3193.
11 Albania                Europe     2002    75.7  3508512     4604.
12 Albania                Europe     2007    76.4  3600523     5937.
13 Austria                Europe     1952    66.8  6927772     6137.
14 Austria                Europe     1957    67.5  6965860     8843.
15 Austria                Europe     1962    69.5  7129864    10751.
16 Austria                Europe     1967    70.1  7376998    12835.
17 Austria                Europe     1972    70.6  7544201    16662.
18 Austria                Europe     1977    72.2  7568430    19749.
19 Austria                Europe     1982    73.2  7574613    21597.
20 Austria                Europe     1987    74.9  7578903    23688.
21 Austria                Europe     1992    76.0  7914969    27042.
22 Austria                Europe     1997    77.5  8069876    29096.
23 Austria                Europe     2002    79.0  8148312    32418.
24 Austria                Europe     2007    79.8  8199783    36126.
25 Belgium                Europe     1952    68    8730405     8343.
26 Belgium                Europe     1957    69.2  8989111     9715.
27 Belgium                Europe     1962    70.2  9218400    10991.
28 Belgium                Europe     1967    70.9  9556500    13149.
29 Belgium                Europe     1972    71.4  9709100    16672.
30 Belgium                Europe     1977    72.8  9821800    19118.
31 Belgium                Europe     1982    73.9  9856303    20980.
32 Belgium                Europe     1987    75.4  9870200    22526.
33 Belgium                Europe     1992    76.5 10045622    25576.
34 Belgium                Europe     1997    77.5 10199787    27561.
35 Belgium                Europe     2002    78.3 10311970    30486.
36 Belgium                Europe     2007    79.4 10392226    33693.
37 Bosnia and Herzegovina Europe     1952    53.8  2791000      974.
38 Bosnia and Herzegovina Europe     1957    58.4  3076000     1354.
39 Bosnia and Herzegovina Europe     1962    61.9  3349000     1710.
40 Bosnia and Herzegovina Europe     1967    64.8  3585000     2172.
41 Bosnia and Herzegovina Europe     1972    67.4  3819000     2860.
42 Bosnia and Herzegovina Europe     1977    69.9  4086000     3528.
43 Bosnia and Herzegovina Europe     1982    70.7  4172693     4127.
44 Bosnia and Herzegovina Europe     1987    71.1  4338977     4314.
45 Bosnia and Herzegovina Europe     1992    72.2  4256013     2547.
46 Bosnia and Herzegovina Europe     1997    73.2  3607000     4766.
47 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
48 Bosnia and Herzegovina Europe     2007    74.9  4552198     7446.
49 Bulgaria               Europe     1952    59.6  7274900     2444.
50 Bulgaria               Europe     1957    66.6  7651254     3009.
51 Bulgaria               Europe     1962    69.5  8012946     4254.
52 Bulgaria               Europe     1967    70.4  8310226     5577.
53 Bulgaria               Europe     1972    70.9  8576200     6597.
54 Bulgaria               Europe     1977    70.8  8797022     7612.
55 Bulgaria               Europe     1982    71.1  8892098     8224.
# … with 305 more rows
```

---

---
count: false

```
# A tibble: 1,704 × 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.
11 Afghanistan Asia       2002    42.1 25268405      727.
12 Afghanistan Asia       2007    43.8 31889923      975.
13 Albania     Europe     1952    55.2  1282697     1601.
14 Albania     Europe     1957    59.3  1476505     1942.
15 Albania     Europe     1962    64.8  1728137     2313.
16 Albania     Europe     1967    66.2  1984060     2760.
17 Albania     Europe     1972    67.7  2263554     3313.
18 Albania     Europe     1977    68.9  2509048     3533.
19 Albania     Europe     1982    70.4  2780097     3631.
20 Albania     Europe     1987    72    3075321     3739.
21 Albania     Europe     1992    71.6  3326498     2497.
22 Albania     Europe     1997    73.0  3428038     3193.
23 Albania     Europe     2002    75.7  3508512     4604.
24 Albania     Europe     2007    76.4  3600523     5937.
25 Algeria     Africa     1952    43.1  9279525     2449.
26 Algeria     Africa     1957    45.7 10270856     3014.
27 Algeria     Africa     1962    48.3 11000948     2551.
28 Algeria     Africa     1967    51.4 12760499     3247.
29 Algeria     Africa     1972    54.5 14760787     4183.
30 Algeria     Africa     1977    58.0 17152804     4910.
31 Algeria     Africa     1982    61.4 20033753     5745.
32 Algeria     Africa     1987    65.8 23254956     5681.
33 Algeria     Africa     1992    67.7 26298373     5023.
34 Algeria     Africa     1997    69.2 29072015     4797.
35 Algeria     Africa     2002    71.0 31287142     5288.
36 Algeria     Africa     2007    72.3 33333216     6223.
37 Angola      Africa     1952    30.0  4232095     3521.
38 Angola      Africa     1957    32.0  4561361     3828.
39 Angola      Africa     1962    34    4826015     4269.
40 Angola      Africa     1967    36.0  5247469     5523.
41 Angola      Africa     1972    37.9  5894858     5473.
42 Angola      Africa     1977    39.5  6162675     3009.
43 Angola      Africa     1982    39.9  7016384     2757.
44 Angola      Africa     1987    39.9  7874230     2430.
45 Angola      Africa     1992    40.6  8735988     2628.
46 Angola      Africa     1997    41.0  9875024     2277.
47 Angola      Africa     2002    41.0 10866106     2773.
48 Angola      Africa     2007    42.7 12420476     4797.
49 Argentina   Americas   1952    62.5 17876956     5911.
50 Argentina   Americas   1957    64.4 19610538     6857.
51 Argentina   Americas   1962    65.1 21283783     7133.
52 Argentina   Americas   1967    65.6 22934225     8053.
53 Argentina   Americas   1972    67.1 24779799     9443.
54 Argentina   Americas   1977    68.5 26983828    10079.
55 Argentina   Americas   1982    69.9 29341374     8998.
# … with 1,649 more rows
```

---
count: false

```
# A tibble: 142 × 6
   country                  continent  year lifeExp        pop gdpPercap
   <fct>                    <fct>     <int>   <dbl>      <int>     <dbl>
 1 Afghanistan              Asia       2002    42.1   25268405      727.
 2 Albania                  Europe     2002    75.7    3508512     4604.
 3 Algeria                  Africa     2002    71.0   31287142     5288.
 4 Angola                   Africa     2002    41.0   10866106     2773.
 5 Argentina                Americas   2002    74.3   38331121     8798.
 6 Australia                Oceania    2002    80.4   19546792    30688.
 7 Austria                  Europe     2002    79.0    8148312    32418.
 8 Bahrain                  Asia       2002    74.8     656397    23404.
 9 Bangladesh               Asia       2002    62.0  135656790     1136.
10 Belgium                  Europe     2002    78.3   10311970    30486.
11 Benin                    Africa     2002    54.4    7026113     1373.
12 Bolivia                  Americas   2002    63.9    8445134     3413.
13 Bosnia and Herzegovina   Europe     2002    74.1    4165416     6019.
14 Botswana                 Africa     2002    46.6    1630347    11004.
15 Brazil                   Americas   2002    71.0  179914212     8131.
16 Bulgaria                 Europe     2002    72.1    7661799     7697.
17 Burkina Faso             Africa     2002    50.6   12251209     1038.
18 Burundi                  Africa     2002    47.4    7021078      446.
19 Cambodia                 Asia       2002    56.8   12926707      896.
20 Cameroon                 Africa     2002    49.9   15929988     1934.
21 Canada                   Americas   2002    79.8   31902268    33329.
22 Central African Republic Africa     2002    43.3    4048013      739.
23 Chad                     Africa     2002    50.5    8835739     1156.
24 Chile                    Americas   2002    77.9   15497046    10779.
25 China                    Asia       2002    72.0 1280400000     3119.
26 Colombia                 Americas   2002    71.7   41008227     5755.
27 Comoros                  Africa     2002    63.0     614382     1076.
28 Congo, Dem. Rep.         Africa     2002    45.0   55379852      241.
29 Congo, Rep.              Africa     2002    53.0    3328795     3484.
30 Costa Rica               Americas   2002    78.1    3834934     7723.
31 Cote d'Ivoire            Africa     2002    46.8   16252726     1649.
32 Croatia                  Europe     2002    74.9    4481020    11628.
33 Cuba                     Americas   2002    77.2   11226999     6341.
34 Czech Republic           Europe     2002    75.5   10256295    17596.
35 Denmark                  Europe     2002    77.2    5374693    32167.
36 Djibouti                 Africa     2002    53.4     447416     1908.
37 Dominican Republic       Americas   2002    70.8    8650322     4564.
38 Ecuador                  Americas   2002    74.2   12921234     5773.
39 Egypt                    Africa     2002    69.8   73312559     4755.
40 El Salvador              Americas   2002    70.7    6353681     5352.
41 Equatorial Guinea        Africa     2002    49.3     495627     7703.
42 Eritrea                  Africa     2002    55.2    4414865      765.
43 Ethiopia                 Africa     2002    50.7   67946797      530.
44 Finland                  Europe     2002    78.4    5193039    28205.
45 France                   Europe     2002    79.6   59925035    28926.
46 Gabon                    Africa     2002    56.8    1299304    12522.
47 Gambia                   Africa     2002    58.0    1457766      661.
48 Germany                  Europe     2002    78.7   82350671    30036.
49 Ghana                    Africa     2002    58.5   20550751     1112.
50 Greece                   Europe     2002    78.3   10603863    22514.
51 Guatemala                Americas   2002    69.0   11178650     4858.
52 Guinea                   Africa     2002    53.7    8807818      946.
53 Guinea-Bissau            Africa     2002    45.5    1332459      576.
54 Haiti                    Americas   2002    58.1    7607651     1270.
55 Honduras                 Americas   2002    68.6    6677328     3100.
# … with 87 more rows
```

---
count: false

```
# A tibble: 30 × 6
   country                continent  year lifeExp      pop gdpPercap
   <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
 1 Albania                Europe     2002    75.7  3508512     4604.
 2 Austria                Europe     2002    79.0  8148312    32418.
 3 Belgium                Europe     2002    78.3 10311970    30486.
 4 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
 5 Bulgaria               Europe     2002    72.1  7661799     7697.
 6 Croatia                Europe     2002    74.9  4481020    11628.
 7 Czech Republic         Europe     2002    75.5 10256295    17596.
 8 Denmark                Europe     2002    77.2  5374693    32167.
 9 Finland                Europe     2002    78.4  5193039    28205.
10 France                 Europe     2002    79.6 59925035    28926.
11 Germany                Europe     2002    78.7 82350671    30036.
12 Greece                 Europe     2002    78.3 10603863    22514.
13 Hungary                Europe     2002    72.6 10083313    14844.
14 Iceland                Europe     2002    80.5   288030    31163.
15 Ireland                Europe     2002    77.8  3879155    34077.
16 Italy                  Europe     2002    80.2 57926999    27968.
17 Montenegro             Europe     2002    74.0   720230     6557.
18 Netherlands            Europe     2002    78.5 16122830    33725.
19 Norway                 Europe     2002    79.0  4535591    44684.
20 Poland                 Europe     2002    74.7 38625976    12002.
21 Portugal               Europe     2002    77.3 10433867    19971.
22 Romania                Europe     2002    71.3 22404337     7885.
23 Serbia                 Europe     2002    73.2 10111559     7236.
24 Slovak Republic        Europe     2002    73.8  5410052    13639.
25 Slovenia               Europe     2002    76.7  2011497    20660.
26 Spain                  Europe     2002    79.8 40152517    24835.
27 Sweden                 Europe     2002    80.0  8954175    29342.
28 Switzerland            Europe     2002    80.6  7361757    34481.
29 Turkey                 Europe     2002    70.8 67308928     6508.
30 United Kingdom         Europe     2002    78.5 59912431    29479.
```

---
count: false

```
# A tibble: 30 × 5
   country                continent  year      pop gdpPercap
   <fct>                  <fct>     <int>    <int>     <dbl>
 1 Albania                Europe     2002  3508512     4604.
 2 Austria                Europe     2002  8148312    32418.
 3 Belgium                Europe     2002 10311970    30486.
 4 Bosnia and Herzegovina Europe     2002  4165416     6019.
 5 Bulgaria               Europe     2002  7661799     7697.
 6 Croatia                Europe     2002  4481020    11628.
 7 Czech Republic         Europe     2002 10256295    17596.
 8 Denmark                Europe     2002  5374693    32167.
 9 Finland                Europe     2002  5193039    28205.
10 France                 Europe     2002 59925035    28926.
11 Germany                Europe     2002 82350671    30036.
12 Greece                 Europe     2002 10603863    22514.
13 Hungary                Europe     2002 10083313    14844.
14 Iceland                Europe     2002   288030    31163.
15 Ireland                Europe     2002  3879155    34077.
16 Italy                  Europe     2002 57926999    27968.
17 Montenegro             Europe     2002   720230     6557.
18 Netherlands            Europe     2002 16122830    33725.
19 Norway                 Europe     2002  4535591    44684.
20 Poland                 Europe     2002 38625976    12002.
21 Portugal               Europe     2002 10433867    19971.
22 Romania                Europe     2002 22404337     7885.
23 Serbia                 Europe     2002 10111559     7236.
24 Slovak Republic        Europe     2002  5410052    13639.
25 Slovenia               Europe     2002  2011497    20660.
26 Spain                  Europe     2002 40152517    24835.
27 Sweden                 Europe     2002  8954175    29342.
28 Switzerland            Europe     2002  7361757    34481.
29 Turkey                 Europe     2002 67308928     6508.
30 United Kingdom         Europe     2002 59912431    29479.
```

---
count: false

```
# A tibble: 30 × 5
   country                continent  year population gdpPercap
   <fct>                  <fct>     <int>      <int>     <dbl>
 1 Albania                Europe     2002    3508512     4604.
 2 Austria                Europe     2002    8148312    32418.
 3 Belgium                Europe     2002   10311970    30486.
 4 Bosnia and Herzegovina Europe     2002    4165416     6019.
 5 Bulgaria               Europe     2002    7661799     7697.
 6 Croatia                Europe     2002    4481020    11628.
 7 Czech Republic         Europe     2002   10256295    17596.
 8 Denmark                Europe     2002    5374693    32167.
 9 Finland                Europe     2002    5193039    28205.
10 France                 Europe     2002   59925035    28926.
11 Germany                Europe     2002   82350671    30036.
12 Greece                 Europe     2002   10603863    22514.
13 Hungary                Europe     2002   10083313    14844.
14 Iceland                Europe     2002     288030    31163.
15 Ireland                Europe     2002    3879155    34077.
16 Italy                  Europe     2002   57926999    27968.
17 Montenegro             Europe     2002     720230     6557.
18 Netherlands            Europe     2002   16122830    33725.
19 Norway                 Europe     2002    4535591    44684.
20 Poland                 Europe     2002   38625976    12002.
21 Portugal               Europe     2002   10433867    19971.
22 Romania                Europe     2002   22404337     7885.
23 Serbia                 Europe     2002   10111559     7236.
24 Slovak Republic        Europe     2002    5410052    13639.
25 Slovenia               Europe     2002    2011497    20660.
26 Spain                  Europe     2002   40152517    24835.
27 Sweden                 Europe     2002    8954175    29342.
28 Switzerland            Europe     2002    7361757    34481.
29 Turkey                 Europe     2002   67308928     6508.
30 United Kingdom         Europe     2002   59912431    29479.
```

---
count: false

```
# A tibble: 30 × 6
   country                continent  year population gdpPercap gdp_billions
   <fct>                  <fct>     <int>      <int>     <dbl>        <dbl>
 1 Albania                Europe     2002    3508512     4604.        16.2 
 2 Austria                Europe     2002    8148312    32418.       264.  
 3 Belgium                Europe     2002   10311970    30486.       314.  
 4 Bosnia and Herzegovina Europe     2002    4165416     6019.        25.1 
 5 Bulgaria               Europe     2002    7661799     7697.        59.0 
 6 Croatia                Europe     2002    4481020    11628.        52.1 
 7 Czech Republic         Europe     2002   10256295    17596.       180.  
 8 Denmark                Europe     2002    5374693    32167.       173.  
 9 Finland                Europe     2002    5193039    28205.       146.  
10 France                 Europe     2002   59925035    28926.      1733.  
11 Germany                Europe     2002   82350671    30036.      2473.  
12 Greece                 Europe     2002   10603863    22514.       239.  
13 Hungary                Europe     2002   10083313    14844.       150.  
14 Iceland                Europe     2002     288030    31163.         8.98
15 Ireland                Europe     2002    3879155    34077.       132.  
16 Italy                  Europe     2002   57926999    27968.      1620.  
17 Montenegro             Europe     2002     720230     6557.         4.72
18 Netherlands            Europe     2002   16122830    33725.       544.  
19 Norway                 Europe     2002    4535591    44684.       203.  
20 Poland                 Europe     2002   38625976    12002.       464.  
21 Portugal               Europe     2002   10433867    19971.       208.  
22 Romania                Europe     2002   22404337     7885.       177.  
23 Serbia                 Europe     2002   10111559     7236.        73.2 
24 Slovak Republic        Europe     2002    5410052    13639.        73.8 
25 Slovenia               Europe     2002    2011497    20660.        41.6 
26 Spain                  Europe     2002   40152517    24835.       997.  
27 Sweden                 Europe     2002    8954175    29342.       263.  
28 Switzerland            Europe     2002    7361757    34481.       254.  
29 Turkey                 Europe     2002   67308928     6508.       438.  
30 United Kingdom         Europe     2002   59912431    29479.      1766.  
```

---
count: false

```
# A tibble: 30 × 6
   country                continent  year population gdpPercap gdp_billions
   <fct>                  <fct>     <int>      <int>     <dbl>        <dbl>
 1 Montenegro             Europe     2002     720230     6557.         4.72
 2 Iceland                Europe     2002     288030    31163.         8.98
 3 Albania                Europe     2002    3508512     4604.        16.2 
 4 Bosnia and Herzegovina Europe     2002    4165416     6019.        25.1 
 5 Slovenia               Europe     2002    2011497    20660.        41.6 
 6 Croatia                Europe     2002    4481020    11628.        52.1 
 7 Bulgaria               Europe     2002    7661799     7697.        59.0 
 8 Serbia                 Europe     2002   10111559     7236.        73.2 
 9 Slovak Republic        Europe     2002    5410052    13639.        73.8 
10 Ireland                Europe     2002    3879155    34077.       132.  
11 Finland                Europe     2002    5193039    28205.       146.  
12 Hungary                Europe     2002   10083313    14844.       150.  
13 Denmark                Europe     2002    5374693    32167.       173.  
14 Romania                Europe     2002   22404337     7885.       177.  
15 Czech Republic         Europe     2002   10256295    17596.       180.  
16 Norway                 Europe     2002    4535591    44684.       203.  
17 Portugal               Europe     2002   10433867    19971.       208.  
18 Greece                 Europe     2002   10603863    22514.       239.  
19 Switzerland            Europe     2002    7361757    34481.       254.  
20 Sweden                 Europe     2002    8954175    29342.       263.  
21 Austria                Europe     2002    8148312    32418.       264.  
22 Belgium                Europe     2002   10311970    30486.       314.  
23 Turkey                 Europe     2002   67308928     6508.       438.  
24 Poland                 Europe     2002   38625976    12002.       464.  
25 Netherlands            Europe     2002   16122830    33725.       544.  
26 Spain                  Europe     2002   40152517    24835.       997.  
27 Italy                  Europe     2002   57926999    27968.      1620.  
28 France                 Europe     2002   59925035    28926.      1733.  
29 United Kingdom         Europe     2002   59912431    29479.      1766.  
30 Germany                Europe     2002   82350671    30036.      2473.  
```

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_12_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_13_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_14_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_15_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_16_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_17_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_18_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_19_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_20_output-1.png)

---
count: false

![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_21_output-1.png)

---

```r
*library(gapminder)
```
]
 
.panel2-wrangle-auto[

]

---
count: false

```r
library(gapminder)
*library(tidyverse)
```
]
 
.panel2-wrangle-auto[

]

---
count: false

```r
library(gapminder)
library(tidyverse)
*gapminder
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
* filter(year == 2002)
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
* filter(continent == "Europe")
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
* select(-lifeExp)
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
* rename(population = pop)
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
* mutate(gdp_billions =
*          gdpPercap * population /
*          1000000000)
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
* arrange(gdp_billions)
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
* mutate(country =
*          fct_reorder(country,
*                      gdp_billions))
```
]
 
.panel2-wrangle-auto[

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
*europe_2002
```
]
 
.panel2-wrangle-auto[

]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

*ggplot(data = europe_2002)
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_12_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
* aes(x = gdp_billions)
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_13_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
* aes(y = country)
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_14_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
* geom_col()
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_15_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
* labs(y = NULL)
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_16_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
  labs(y = NULL) +
* labs(x = "US$ Billions")
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_17_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
  labs(y = NULL) +
  labs(x = "US$ Billions") +
* labs(title = "Country GDP in Europe, 2002")
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_18_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
  labs(y = NULL) +
  labs(x = "US$ Billions") +
  labs(title = "Country GDP in Europe, 2002") +
* labs(subtitle = "Source: gapminder package")
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_19_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
  labs(y = NULL) +
  labs(x = "US$ Billions") +
  labs(title = "Country GDP in Europe, 2002") +
  labs(subtitle = "Source: gapminder package") +
* labs(caption = "Dr. Reynolds for MA206")
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_20_output-1.png)
]

---
count: false

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  filter(continent == "Europe") %>%
  select(-lifeExp) %>%
  rename(population = pop) %>%
  mutate(gdp_billions =
           gdpPercap * population /
           1000000000) %>%
  arrange(gdp_billions) %>%
  mutate(country =
           fct_reorder(country,
                       gdp_billions)) ->
europe_2002

ggplot(data = europe_2002) +
  aes(x = gdp_billions) +
  aes(y = country) +
  geom_col() +
  labs(y = NULL) +
  labs(x = "US$ Billions") +
  labs(title = "Country GDP in Europe, 2002") +
  labs(subtitle = "Source: gapminder package") +
  labs(caption = "Dr. Reynolds for MA206") +
* theme_minimal(base_size = 10)
```
]
 
.panel2-wrangle-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/wrangle_auto_21_output-1.png)
]

---

# pressure and cars only

# Getting other data

- # gapminder
- # wages
- # ma206 data

---

# 1. install 'gapminder'
--

# 2. install 'remotes'
--

# 3. install 'Lahman' (baseball statistics back to 1800's!)
--

# 4. in console `remotes::install_github("EvaMaeRey/ma206data")`

---
class: inverse, center, middle

# Facets, Small multiples

---

```r
*library(tidyverse)
```
]
 
.panel2-hans-auto[

]

---
count: false

```r
library(tidyverse)
*gapminder::gapminder
```
]
 
.panel2-hans-auto[

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
* filter(year == 2002)
```
]
 
.panel2-hans-auto[

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
* ggplot()
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_04_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
* aes(x = gdpPercap)
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_05_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
* aes(y = lifeExp)
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_06_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
* geom_point()
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_07_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
* aes(color = continent)
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_08_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
* aes(size = pop/1000000)
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_09_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
* labs(size = "Population\n(millions)")
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_10_output-1.png)
]

---
count: false

---
count: false

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
* labs(y = "Life expecancy (years)")
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_13_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
* labs(title = "Life expectancy vs Per Capita GDP, 2002")
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_14_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
  labs(title = "Life expectancy vs Per Capita GDP, 2002") +
* labs(subtitle = "Data Source: gapminder package")
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_15_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
  labs(title = "Life expectancy vs Per Capita GDP, 2002") +
  labs(subtitle = "Data Source: gapminder package") +
* labs(caption = "Produced for MA206 in Fall AY2023")
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_16_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
  labs(title = "Life expectancy vs Per Capita GDP, 2002") +
  labs(subtitle = "Data Source: gapminder package") +
  labs(caption = "Produced for MA206 in Fall AY2023") +
* facet_wrap(~ continent)
```
]
 
.panel2-hans-auto[
![](lesson_03_data_wrangling_simulation_files/figure-html/hans_auto_17_output-1.png)
]

@media print {
  .has-continuation {
    display: block;
  }
}

code.r.hljs.remark-code{
  position: relative;
  overflow-x: hidden;
}

code.r.hljs.remark-code:hover{
  overflow-x:visible;
  width: 500px;
  border-style: solid;
}
</style>