Data manipulation practice

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# .column.Large[Wrangle - Answers!]
## April 23, 2020
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#### .column[Gina Reynolds<br>  Photo Credit: Joshua Coleman]

---
class: inverse, middle center

# Set up

---

The following exercises are designed to help you practice your data wrangling "vocabulary". In the exercises, you'll always start from the full gapminder data and wrangle to a target using functions from {dplyr} (one of the packages made available in the tidyverse).

Load the following packages:

``` r
library(gapminder)
library(tidyverse)
```

---

# Remember: 'RC cola'

![](images/clipboard-2017109601.png)

for tables: Rows X Columns

```
# 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.
# ℹ 1,694 more rows
```

--_

Make sure to that the number of observations and columns in your result match the target outcome.  Let's recall the full dimensions of the gapminder data:

``` r
# dim gives number of rows, then nubmer of columns
dim(gapminder)
```

```
[1] 1704    6
```

Some other features to have in mind:

- {gapminder} only reports data every 5 years, from 1952 to 2007
- only {142} countries are included in the data

---
class: center, inverse, middle

# Wrangle to it. #1

---

## Target #1

```
# A tibble: 1,704 × 3
   country      year lifeExp
   <fct>       <int>   <dbl>
 1 Afghanistan  1952    28.8
 2 Afghanistan  1957    30.3
 3 Afghanistan  1962    32.0
 4 Afghanistan  1967    34.0
 5 Afghanistan  1972    36.1
 6 Afghanistan  1977    38.4
 7 Afghanistan  1982    39.9
 8 Afghanistan  1987    40.8
 9 Afghanistan  1992    41.7
10 Afghanistan  1997    41.8
# ℹ 1,694 more rows
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* select(country, year, lifeExp)
```
]
 
.panel2-selection-auto[

---
class: center, inverse, middle

# Wrangle to it. #2

---

## Target #2

```
# 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.
# ℹ 132 more rows
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* filter(year == 2002)
```
]
 
.panel2-filter-auto[

---
class: center, inverse, middle

# Wrangle to it. #3

---

## Target #3

```
# A tibble: 12 × 6
   country continent  year lifeExp      pop gdpPercap
   <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
 1 Belgium Europe     1952    68    8730405     8343.
 2 Belgium Europe     1957    69.2  8989111     9715.
 3 Belgium Europe     1962    70.2  9218400    10991.
 4 Belgium Europe     1967    70.9  9556500    13149.
 5 Belgium Europe     1972    71.4  9709100    16672.
 6 Belgium Europe     1977    72.8  9821800    19118.
 7 Belgium Europe     1982    73.9  9856303    20980.
 8 Belgium Europe     1987    75.4  9870200    22526.
 9 Belgium Europe     1992    76.5 10045622    25576.
10 Belgium Europe     1997    77.5 10199787    27561.
11 Belgium Europe     2002    78.3 10311970    30486.
12 Belgium Europe     2007    79.4 10392226    33693.
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder  |>
* filter(country == "Belgium")
```
]
 
.panel2-filterselect-auto[

---
class: center, inverse, middle

# Wrangle to it. #4

---

## Target #4

```
# A tibble: 8 × 3
  country  year        pop
  <fct>   <int>      <int>
1 China    2007 1318683096
2 China    2002 1280400000
3 China    1997 1230075000
4 China    1992 1164970000
5 India    2007 1110396331
6 China    1987 1084035000
7 India    2002 1034172547
8 China    1982 1000281000
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* select(country, year, pop)
```
]
 
.panel2-filterarrselect-auto[

```
# A tibble: 1,704 × 3
   country      year      pop
   <fct>       <int>    <int>
 1 Afghanistan  1952  8425333
 2 Afghanistan  1957  9240934
 3 Afghanistan  1962 10267083
 4 Afghanistan  1967 11537966
 5 Afghanistan  1972 13079460
 6 Afghanistan  1977 14880372
 7 Afghanistan  1982 12881816
 8 Afghanistan  1987 13867957
 9 Afghanistan  1992 16317921
10 Afghanistan  1997 22227415
# ℹ 1,694 more rows
```
]

---
count: false

``` r
gapminder |>
  select(country, year, pop) |>
* filter(pop > 1000000000)
```
]
 
.panel2-filterarrselect-auto[

```
# A tibble: 8 × 3
  country  year        pop
  <fct>   <int>      <int>
1 China    1982 1000281000
2 China    1987 1084035000
3 China    1992 1164970000
4 China    1997 1230075000
5 China    2002 1280400000
6 China    2007 1318683096
7 India    2002 1034172547
8 India    2007 1110396331
```
]

---
count: false

``` r
gapminder |>
  select(country, year, pop) |>
  filter(pop > 1000000000) |>
* arrange(-pop)
```
]
 
.panel2-filterarrselect-auto[

---
class: center, inverse, middle

# Wrangle to it. #5

---

## Target #5

```
# A tibble: 7 × 3
  country          lifeExp  year
  <fct>              <dbl> <int>
1 Japan               82.6  2007
2 Hong Kong, China    82.2  2007
3 Japan               82    2002
4 Iceland             81.8  2007
5 Switzerland         81.7  2007
6 Hong Kong, China    81.5  2002
7 Australia           81.2  2007
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* select(country, lifeExp, year)
```
]
 
.panel2-filterage-auto[

```
# A tibble: 1,704 × 3
   country     lifeExp  year
   <fct>         <dbl> <int>
 1 Afghanistan    28.8  1952
 2 Afghanistan    30.3  1957
 3 Afghanistan    32.0  1962
 4 Afghanistan    34.0  1967
 5 Afghanistan    36.1  1972
 6 Afghanistan    38.4  1977
 7 Afghanistan    39.9  1982
 8 Afghanistan    40.8  1987
 9 Afghanistan    41.7  1992
10 Afghanistan    41.8  1997
# ℹ 1,694 more rows
```
]

---
count: false

``` r
gapminder |>
  select(country, lifeExp, year) |>
* filter(lifeExp > 81)
```
]
 
.panel2-filterage-auto[

```
# A tibble: 7 × 3
  country          lifeExp  year
  <fct>              <dbl> <int>
1 Australia           81.2  2007
2 Hong Kong, China    81.5  2002
3 Hong Kong, China    82.2  2007
4 Iceland             81.8  2007
5 Japan               82    2002
6 Japan               82.6  2007
7 Switzerland         81.7  2007
```
]

---
count: false

``` r
gapminder |>
  select(country, lifeExp, year) |>
  filter(lifeExp > 81) |>
* arrange(-lifeExp)
```
]
 
.panel2-filterage-auto[

---
class: center, inverse, middle

# Wrangle to it. #6

---

## Target #6

```
# A tibble: 2 × 3
  country              gdp  year
  <fct>              <dbl> <int>
1 Australia   87256254102.  1952
2 New Zealand 21058193787.  1952
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* mutate(gdp = pop * gdpPercap)
```
]
 
.panel2-foursteps-auto[

```
# A tibble: 1,704 × 7
   country     continent  year lifeExp      pop gdpPercap          gdp
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>        <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.  6567086330.
 2 Afghanistan Asia       1957    30.3  9240934      821.  7585448670.
 3 Afghanistan Asia       1962    32.0 10267083      853.  8758855797.
 4 Afghanistan Asia       1967    34.0 11537966      836.  9648014150.
 5 Afghanistan Asia       1972    36.1 13079460      740.  9678553274.
 6 Afghanistan Asia       1977    38.4 14880372      786. 11697659231.
 7 Afghanistan Asia       1982    39.9 12881816      978. 12598563401.
 8 Afghanistan Asia       1987    40.8 13867957      852. 11820990309.
 9 Afghanistan Asia       1992    41.7 16317921      649. 10595901589.
10 Afghanistan Asia       1997    41.8 22227415      635. 14121995875.
# ℹ 1,694 more rows
```
]

---
count: false

``` r
gapminder |>
  mutate(gdp = pop * gdpPercap) |>
* filter(year == 1952)
```
]
 
.panel2-foursteps-auto[

```
# A tibble: 142 × 7
   country     continent  year lifeExp      pop gdpPercap           gdp
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.   6567086330.
 2 Albania     Europe     1952    55.2  1282697     1601.   2053669902.
 3 Algeria     Africa     1952    43.1  9279525     2449.  22725632678.
 4 Angola      Africa     1952    30.0  4232095     3521.  14899557133.
 5 Argentina   Americas   1952    62.5 17876956     5911. 105676319105.
 6 Australia   Oceania    1952    69.1  8691212    10040.  87256254102.
 7 Austria     Europe     1952    66.8  6927772     6137.  42516266683.
 8 Bahrain     Asia       1952    50.9   120447     9867.   1188460759.
 9 Bangladesh  Asia       1952    37.5 46886859      684.  32082059995.
10 Belgium     Europe     1952    68    8730405     8343.  72838686716.
# ℹ 132 more rows
```
]

---
count: false

``` r
gapminder |>
  mutate(gdp = pop * gdpPercap) |>
  filter(year == 1952) |>
* filter(continent == "Oceania")
```
]
 
.panel2-foursteps-auto[

```
# A tibble: 2 × 7
  country     continent  year lifeExp     pop gdpPercap          gdp
  <fct>       <fct>     <int>   <dbl>   <int>     <dbl>        <dbl>
1 Australia   Oceania    1952    69.1 8691212    10040. 87256254102.
2 New Zealand Oceania    1952    69.4 1994794    10557. 21058193787.
```
]

---
count: false

``` r
gapminder |>
  mutate(gdp = pop * gdpPercap) |>
  filter(year == 1952) |>
  filter(continent == "Oceania") |>
* select(country, gdp, year)
```
]
 
.panel2-foursteps-auto[

```
# A tibble: 2 × 3
  country              gdp  year
  <fct>              <dbl> <int>
1 Australia   87256254102.  1952
2 New Zealand 21058193787.  1952
```
]

---
class: center, inverse, middle

# Wrangle to it. #7

---

## Target #7

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

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* mutate(asia = continent == "Asia")
```
]
 
.panel2-indicator-auto[

---
class: center, inverse, middle

# Summarizing (collapsing across rows) 🚨😬

---
class: center, inverse, middle

# Wrangle to it. #8
---

## Target #8

```
# A tibble: 5 × 1
  continent
  <fct>    
1 Asia     
2 Europe   
3 Africa   
4 Americas 
5 Oceania  
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* distinct(continent)
```
]
 
.panel2-collapse-auto[

```
# A tibble: 5 × 1
  continent
  <fct>    
1 Asia     
2 Europe   
3 Africa   
4 Americas 
5 Oceania  
```
]

---
class: center, inverse, middle

# Wrangle to it. #9
---

## Target #9

```
# A tibble: 5 × 2
  continent     n
  <fct>     <int>
1 Africa       52
2 Americas     25
3 Asia         33
4 Europe       30
5 Oceania       2
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* distinct(continent, country)
```
]
 
.panel2-count-auto[

```
# A tibble: 142 × 2
   continent country    
   <fct>     <fct>      
 1 Asia      Afghanistan
 2 Europe    Albania    
 3 Africa    Algeria    
 4 Africa    Angola     
 5 Americas  Argentina  
 6 Oceania   Australia  
 7 Europe    Austria    
 8 Asia      Bahrain    
 9 Asia      Bangladesh 
10 Europe    Belgium    
# ℹ 132 more rows
```
]

---
count: false

``` r
gapminder |>
  distinct(continent, country) |>
* count(continent)
```
]
 
.panel2-count-auto[

```
# A tibble: 5 × 2
  continent     n
  <fct>     <int>
1 Africa       52
2 Americas     25
3 Asia         33
4 Europe       30
5 Oceania       2
```
]

---
class: center, inverse, middle

# Wrangle to it. #10
---

## Target #10

```
# A tibble: 12 × 1
    year
   <int>
 1  1952
 2  1957
 3  1962
 4  1967
 5  1972
 6  1977
 7  1982
 8  1987
 9  1992
10  1997
11  2002
12  2007
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* distinct(year)
```
]
 
.panel2-distinctyear-auto[

```
# A tibble: 12 × 1
    year
   <int>
 1  1952
 2  1957
 3  1962
 4  1967
 5  1972
 6  1977
 7  1982
 8  1987
 9  1992
10  1997
11  2002
12  2007
```
]

---
class: center, inverse, middle

# Wrangle to it. #11
---

## Target #11

```
# A tibble: 60 × 3
# Groups:   continent [5]
   continent  year mean_life_exp
   <fct>     <int>         <dbl>
 1 Africa     1952          39.1
 2 Africa     1957          41.3
 3 Africa     1962          43.3
 4 Africa     1967          45.3
 5 Africa     1972          47.5
 6 Africa     1977          49.6
 7 Africa     1982          51.6
 8 Africa     1987          53.3
 9 Africa     1992          53.6
10 Africa     1997          53.6
# ℹ 50 more rows
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* group_by(continent, year)
```
]
 
.panel2-mean-auto[

```
# A tibble: 1,704 × 6
# Groups:   continent, year [60]
   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.
# ℹ 1,694 more rows
```
]

---
count: false

``` r
gapminder |>
  group_by(continent, year) |>
* summarise(mean_life_exp = mean(lifeExp))
```
]
 
.panel2-mean-auto[

---
class: center, inverse, middle

# Wrangle to it. #12
---

## Target #12

```
# A tibble: 142 × 1
   country    
   <fct>      
 1 Afghanistan
 2 Albania    
 3 Algeria    
 4 Angola     
 5 Argentina  
 6 Australia  
 7 Austria    
 8 Bahrain    
 9 Bangladesh 
10 Belgium    
# ℹ 132 more rows
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* distinct(country)
```
]
 
.panel2-countries-auto[

```
# A tibble: 142 × 1
   country    
   <fct>      
 1 Afghanistan
 2 Albania    
 3 Algeria    
 4 Angola     
 5 Argentina  
 6 Australia  
 7 Austria    
 8 Bahrain    
 9 Bangladesh 
10 Belgium    
# ℹ 132 more rows
```
]

---
class: center, inverse, middle

# Wrangle to it. #13
---

## Target #13

```
# A tibble: 60 × 3
# Groups:   continent [5]
   continent  year median_life_exp
   <fct>     <int>           <dbl>
 1 Africa     1952            38.8
 2 Africa     1957            40.6
 3 Africa     1962            42.6
 4 Africa     1967            44.7
 5 Africa     1972            47.0
 6 Africa     1977            49.3
 7 Africa     1982            50.8
 8 Africa     1987            51.6
 9 Africa     1992            52.4
10 Africa     1997            52.8
# ℹ 50 more rows
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* group_by(continent, year)
```
]
 
.panel2-median-auto[

---
count: false

``` r
gapminder |>
  group_by(continent, year) |>
* summarise(median_life_exp = median(lifeExp))
```
]
 
.panel2-median-auto[

---
class: center, inverse, middle

# Wrangle to it. #14
---

## Target #1

```
# A tibble: 5 × 3
  continent median_life_exp mean_life_exp
  <fct>               <dbl>         <dbl>
1 Africa               51.2          53.3
2 Americas             72.0          72.4
3 Asia                 71.0          69.2
4 Europe               77.5          76.7
5 Oceania              79.7          79.7
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* filter(year == 2002)
```
]
 
.panel2-twosummaries-auto[

---
count: false

``` r
gapminder |>
  filter(year == 2002) |>
* group_by(continent)
```
]
 
.panel2-twosummaries-auto[

```
# A tibble: 142 × 6
# Groups:   continent [5]
   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.
# ℹ 132 more rows
```
]

---
count: false

``` r
gapminder |>
  filter(year == 2002) |>
  group_by(continent) |>
* summarise(median_life_exp = median(lifeExp),
*           mean_life_exp = mean(lifeExp))
```
]
 
.panel2-twosummaries-auto[

```
# A tibble: 5 × 3
  continent median_life_exp mean_life_exp
  <fct>               <dbl>         <dbl>
1 Africa               51.2          53.3
2 Americas             72.0          72.4
3 Asia                 71.0          69.2
4 Europe               77.5          76.7
5 Oceania              79.7          79.7
```
]

---
class: center, inverse, middle

# Wrangle to it.15
---

## Target #15

```
# A tibble: 1 × 2
  median_life_exp mean_life_exp
            <dbl>         <dbl>
1            70.8          65.7
```

---
class: center, middle

### Walk through

---

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

---
count: false

``` r
gapminder |>
* filter(year == 2002)
```
]
 
.panel2-overall-auto[

---
count: false

``` r
gapminder |>
  filter(year == 2002) |>
* summarise(median_life_exp = median(lifeExp),
*           mean_life_exp = mean(lifeExp))
```
]
 
.panel2-overall-auto[

```
# A tibble: 1 × 2
  median_life_exp mean_life_exp
            <dbl>         <dbl>
1            70.8          65.7
```
]