dplyr-ejercicios2.utf8

class: inverse, center

# <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>  para análisis de datos 🚀<br> <br>

## 🔧Ejercicios de dplyr - gapminder 💻 
<br> <br> <br>

.large[Roxana N. Villafañe | LEMyP | <a href='http://twitter.com/data_datum'><svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @data_datum</a>] <br> 
.large[Florencia D'Andrea | INTA-CONICET | <a href="http://twitter.com/cantoflor_87"> <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @cantoflor_87</a><br>]

✨
<br> Página web del curso en <https://flor14.github.io/Curso_r_unne_2020/> 🌟

---

# 1. De gapminder seleccionar los datos correspondientes a Argentina.

```
## # A tibble: 12 x 6
##    country   continent  year lifeExp      pop gdpPercap
##    <fct>     <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Argentina Americas   1952    62.5 17876956     5911.
##  2 Argentina Americas   1957    64.4 19610538     6857.
##  3 Argentina Americas   1962    65.1 21283783     7133.
##  4 Argentina Americas   1967    65.6 22934225     8053.
##  5 Argentina Americas   1972    67.1 24779799     9443.
##  6 Argentina Americas   1977    68.5 26983828    10079.
##  7 Argentina Americas   1982    69.9 29341374     8998.
##  8 Argentina Americas   1987    70.8 31620918     9140.
##  9 Argentina Americas   1992    71.9 33958947     9308.
## 10 Argentina Americas   1997    73.3 36203463    10967.
## 11 Argentina Americas   2002    74.3 38331121     8798.
## 12 Argentina Americas   2007    75.3 40301927    12779.
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  filter(country=="Argentina")
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 2. Eliminar la columna correspondiente a la población en gapminder

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

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  select(-pop)
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

## 3. Seleccionar los datos correspondientes al continente americano, correspondientes del año 80 en adelante. Los datos deben estar ordenados según los años más actuales.

```
## # A tibble: 150 x 6
##    country            continent  year lifeExp       pop gdpPercap
##    <fct>              <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Argentina          Americas   2007    75.3  40301927    12779.
##  2 Bolivia            Americas   2007    65.6   9119152     3822.
##  3 Brazil             Americas   2007    72.4 190010647     9066.
##  4 Canada             Americas   2007    80.7  33390141    36319.
##  5 Chile              Americas   2007    78.6  16284741    13172.
##  6 Colombia           Americas   2007    72.9  44227550     7007.
##  7 Costa Rica         Americas   2007    78.8   4133884     9645.
##  8 Cuba               Americas   2007    78.3  11416987     8948.
##  9 Dominican Republic Americas   2007    72.2   9319622     6025.
## 10 Ecuador            Americas   2007    75.0  13755680     6873.
## # ... with 140 more rows
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  filter(continent=="Americas", year >= 1980) %>%
  arrange(desc(year))
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 4. Seleccionar las columnas correspondientes a ingresos per capita y esperanza de vida

```
## # A tibble: 1,704 x 2
##    lifeExp gdpPercap
##      <dbl>     <dbl>
##  1    28.8      779.
##  2    30.3      821.
##  3    32.0      853.
##  4    34.0      836.
##  5    36.1      740.
##  6    38.4      786.
##  7    39.9      978.
##  8    40.8      852.
##  9    41.7      649.
## 10    41.8      635.
## # ... with 1,694 more rows
```
---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  select(lifeExp, gdpPercap)
```

# 5. Calcular la media por continente del ingreso per cápita y la esperanza de vida. Ordenarlos de mayor a menor según la esperanza de vida.

```
## # A tibble: 5 x 3
##   continent    gpd lifeE
##   <fct>      <dbl> <dbl>
## 1 Oceania   18622.  74.3
## 2 Europe    14469.  71.9
## 3 Americas   7136.  64.7
## 4 Asia       7902.  60.1
## 5 Africa     2194.  48.9
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  group_by(continent) %>%
  summarize(gpd=mean(gdpPercap), lifeE=mean(lifeExp)) %>%
  arrange(desc(lifeE))
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 6. ¿Cuáles son los 7 países con mayor esperanza de vida en el año 2002?

```
## # A tibble: 7 x 6
##   country          continent  year lifeExp       pop gdpPercap
##   <fct>            <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Japan            Asia       2002    82   127065841    28605.
## 2 Hong Kong, China Asia       2002    81.5   6762476    30209.
## 3 Switzerland      Europe     2002    80.6   7361757    34481.
## 4 Iceland          Europe     2002    80.5    288030    31163.
## 5 Australia        Oceania    2002    80.4  19546792    30688.
## 6 Italy            Europe     2002    80.2  57926999    27968.
## 7 Sweden           Europe     2002    80.0   8954175    29342.
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  filter(year==2002) %>%
  arrange(desc(lifeExp)) %>%
  head(7)
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 7. ¿Cuáles son los países con menor esperanza de vida y menor ingreso en el año 2002?

```
## # A tibble: 5 x 6
##   country      continent  year lifeExp      pop gdpPercap
##   <fct>        <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Zambia       Africa     2002    39.2 10595811     1072.
## 2 Zimbabwe     Africa     2002    40.0 11926563      672.
## 3 Angola       Africa     2002    41.0 10866106     2773.
## 4 Sierra Leone Africa     2002    41.0  5359092      699.
## 5 Afghanistan  Asia       2002    42.1 25268405      727.
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

#Solución

```r
gapminder %>%
  filter(year==2002) %>%
  arrange(lifeExp, gdpPercap) %>%
  head(5)
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 8. En el dataset iris, poner la columna species en primer lugar

### Vamos a reordenar columnas

```
##   Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1  setosa          5.1         3.5          1.4         0.2
## 2  setosa          4.9         3.0          1.4         0.2
## 3  setosa          4.7         3.2          1.3         0.2
## 4  setosa          4.6         3.1          1.5         0.2
## 5  setosa          5.0         3.6          1.4         0.2
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

#Solución

```r
iris %>%
  select(Species, everything()) %>%
  head(5)
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 9. Seleccionar la mitad (50%) del dataset gapminder

```
## # A tibble: 852 x 6
##    country                continent  year lifeExp       pop gdpPercap
##    <fct>                  <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Philippines            Asia       1997    68.6  75012988     2537.
##  2 Germany                Europe     1977    72.5  78160773    20513.
##  3 Belgium                Europe     2002    78.3  10311970    30486.
##  4 Colombia               Americas   1982    66.7  27764644     4398.
##  5 United States          Americas   1962    70.2 186538000    16173.
##  6 Denmark                Europe     1977    74.7   5088419    20423.
##  7 Jordan                 Asia       1957    45.7    746559     1886.
##  8 Bosnia and Herzegovina Europe     1977    69.9   4086000     3528.
##  9 Canada                 Americas   1952    68.8  14785584    11367.
## 10 Kuwait                 Asia       1997    76.2   1765345    40301.
## # ... with 842 more rows
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  sample_frac(size=0.5)
```

```
## # A tibble: 852 x 6
##    country            continent  year lifeExp      pop gdpPercap
##    <fct>              <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Oman               Asia       1962    43.2   628164     2925.
##  2 Zimbabwe           Africa     1982    60.4  7636524      789.
##  3 Saudi Arabia       Asia       1987    66.3 14619745    21198.
##  4 Cameroon           Africa     1997    52.2 14195809     1694.
##  5 Slovak Republic    Europe     2002    73.8  5410052    13639.
##  6 Germany            Europe     1957    69.1 71019069    10188.
##  7 Panama             Americas   1952    55.2   940080     2480.
##  8 Benin              Africa     1962    42.6  2151895      949.
##  9 West Bank and Gaza Asia       1952    43.2  1030585     1516.
## 10 Albania            Europe     1992    71.6  3326498     2497.
## # ... with 842 more rows
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# 10. Elegir los casos que están entre las filas 20 y 30.

```
## # A tibble: 11 x 6
##    country continent  year lifeExp      pop gdpPercap
##    <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Albania Europe     1987    72    3075321     3739.
##  2 Albania Europe     1992    71.6  3326498     2497.
##  3 Albania Europe     1997    73.0  3428038     3193.
##  4 Albania Europe     2002    75.7  3508512     4604.
##  5 Albania Europe     2007    76.4  3600523     5937.
##  6 Algeria Africa     1952    43.1  9279525     2449.
##  7 Algeria Africa     1957    45.7 10270856     3014.
##  8 Algeria Africa     1962    48.3 11000948     2551.
##  9 Algeria Africa     1967    51.4 12760499     3247.
## 10 Algeria Africa     1972    54.5 14760787     4183.
## 11 Algeria Africa     1977    58.0 17152804     4910.
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Solución

```r
gapminder %>%
  slice(20:30)
```

---
<img src="C://Users/Roxana/curso-r-analisis-datos/img/hex-dplyr.png" width="10%" align="right" />

# Para seguir practicando...

https://garthtarr.github.io/meatR/dplyr_ex1.html

### R4DS Capítulo 5: Data Transformation

https://r4ds.had.co.nz/transform.html

### R4DS Capítulo 5 (en español)

https://es.r4ds.hadley.nz/transform.html

### Soluciones (en inglés)

https://jrnold.github.io/r4ds-exercise-solutions/transform.html