dplyr-trucos.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>

## 🔧Tips de dplyr 💻 
<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/> 🌟

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

#Tip 1: si tenemos que seleccionar las mismas columnas varias veces

```r
library(dplyr) 
library(gapminder) 
cols<-c("country", "lifeExp", "gdpPercap") #selecciono variables
gapminder %>%
  select(!!cols) #selecciono según el vector creado
```

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

#Tip 2: seleccionar según una expresión regular (regex)

```r
gapminder %>%
  select(matches("gdp"))%>% #la expresión regular es "gdp"
  head
```

```
## # A tibble: 6 x 1
##   gdpPercap
##       <dbl>
## 1      779.
## 2      821.
## 3      853.
## 4      836.
## 5      740.
## 6      786.
```

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

#Tip 3: para reordenar columnas

```r
gapminder %>%
  select("lifeExp", "gdpPercap", everything())%>%
  head
```

```
## # A tibble: 6 x 6
##   lifeExp gdpPercap country     continent  year      pop
##     <dbl>     <dbl> <fct>       <fct>     <int>    <int>
## 1    28.8      779. Afghanistan Asia       1952  8425333
## 2    30.3      821. Afghanistan Asia       1957  9240934
## 3    32.0      853. Afghanistan Asia       1962 10267083
## 4    34.0      836. Afghanistan Asia       1967 11537966
## 5    36.1      740. Afghanistan Asia       1972 13079460
## 6    38.4      786. Afghanistan Asia       1977 14880372
```
---

# Tip 4: Si quisiera borrar una columna

Con la función select y como argumento el nombre de la columna, antecedida por el signo menos.

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

```
## # 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
```

---

# `select_all()`

- Permite seleccionar todas las columnas y aplicar una operación a todas las columnas

```r
gapminder %>%
  select_all(toupper) %>%
  head
```

```
## # A tibble: 6 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.
```

- Para deshacer el cambio anterior

```r
gapminder %>%
    select_all(tolower)
```

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

# Tip 5: usar `between()` para especificar rangos
- También podemos hacerlo combinando con between():

```r
gapminder %>%
  select (country, lifeExp, year) %>%
  filter(between(lifeExp, 60, 85)) %>%
  head
```

```
## # A tibble: 6 x 3
##   country lifeExp  year
##   <fct>     <dbl> <int>
## 1 Albania    64.8  1962
## 2 Albania    66.2  1967
## 3 Albania    67.7  1972
## 4 Albania    68.9  1977
## 5 Albania    70.4  1982
## 6 Albania    72    1987
```

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

# Tip 6: Si queremos conservar sólo la nueva columna; usamos `transmute()`

```r
gapminder %>%
  transmute(gdp = pop * gdpPercap) %>%
  head
```

```
## # A tibble: 6 x 1
##            gdp
##          <dbl>
## 1  6567086330.
## 2  7585448670.
## 3  8758855797.
## 4  9648014150.
## 5  9678553274.
## 6 11697659231.
```

---

#Funciones útiles para combinar con `summarise()`:
.pull-left[
#### rbase
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> funciones </th>
   <th style="text-align:left;"> descripción </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> min(), max() </td>
   <td style="text-align:left;"> valores mínimos y máximos </td>
  </tr>
  <tr>
   <td style="text-align:left;"> mean() </td>
   <td style="text-align:left;"> media </td>
  </tr>
  <tr>
   <td style="text-align:left;"> median() </td>
   <td style="text-align:left;"> mediana </td>
  </tr>
  <tr>
   <td style="text-align:left;"> sum() </td>
   <td style="text-align:left;"> suma de los valores </td>
  </tr>
  <tr>
   <td style="text-align:left;"> var(), sd() </td>
   <td style="text-align:left;"> varianza y desviación típica </td>
  </tr>
</tbody>
</table>
]
.pull-right[
#### dplyr
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> dplyr </th>
   <th style="text-align:left;"> descripción </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> first() </td>
   <td style="text-align:left;"> primer valor de un vector </td>
  </tr>
  <tr>
   <td style="text-align:left;"> last() </td>
   <td style="text-align:left;"> último valor de un vector </td>
  </tr>
  <tr>
   <td style="text-align:left;"> n() </td>
   <td style="text-align:left;"> el numero de valores en un vector </td>
  </tr>
  <tr>
   <td style="text-align:left;"> n_distinct() </td>
   <td style="text-align:left;"> número de valores distintos en un vector </td>
  </tr>
  <tr>
   <td style="text-align:left;"> nth() </td>
   <td style="text-align:left;"> extraer el valor que ocupa la posición n en un vector </td>
  </tr>
</tbody>
</table>
]

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

# `summarise_all()`
- Requiere una función que se aplicará a todas las columnas

```r
iris %>%
  group_by(Species) %>%
  summarise_all(mean)%>%
  head
```

```
## # A tibble: 3 x 5
##   Species    Sepal.Length Sepal.Width Petal.Length Petal.Width
##   <fct>             <dbl>       <dbl>        <dbl>       <dbl>
## 1 setosa             5.01        3.43         1.46       0.246
## 2 versicolor         5.94        2.77         4.26       1.33 
## 3 virginica          6.59        2.97         5.55       2.03
```

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

# `summarise_at()`
- Requiere dos argumentos, uno indicando las columnas que se tendrán en cuenta, y luego la operación con la que se resumirán los datos.

```r
iris %>%
  group_by(Species) %>%
  summarise_at(vars(contains("Sepal")), mean) 
```

```
## # A tibble: 3 x 3
##   Species    Sepal.Length Sepal.Width
##   <fct>             <dbl>       <dbl>
## 1 setosa             5.01        3.43
## 2 versicolor         5.94        2.77
## 3 virginica          6.59        2.97
```

```r
#resumo variables que contengan #Sepal
```

---
<img src="img/hex-dplyr.png" width="10%" align="right" />

# `summarise_if()`:
- Requiere dos argumentos

```r
gapminder %>%
  group_by(continent) %>%
  summarise_if(is.numeric, mean)
```

```
## # A tibble: 5 x 5
##   continent  year lifeExp       pop gdpPercap
##   <fct>     <dbl>   <dbl>     <dbl>     <dbl>
## 1 Africa    1980.    48.9  9916003.     2194.
## 2 Americas  1980.    64.7 24504795.     7136.
## 3 Asia      1980.    60.1 77038722.     7902.
## 4 Europe    1980.    71.9 17169765.    14469.
## 5 Oceania   1980.    74.3  8874672.    18622.
```

---

# Si tenemos dudas 
### Podemos consultar la documentación
--

```r
?dplyr::select
?dplyr::filter
?dplyr::mutate
?dplyr::arrange
?dplyr::summarise
?dplyr::group_by
```

---

```r
devtools::session_info()
```

```
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.3 (2020-02-29)
##  os       Ubuntu 20.04 LTS            
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language es_AR:es                    
##  collate  es_AR.UTF-8                 
##  ctype    es_AR.UTF-8                 
##  tz       America/Argentina/Cordoba   
##  date     2020-06-05                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version    date       lib source                              
##  assertthat    0.2.1      2019-03-21 [1] CRAN (R 3.6.3)                      
##  backports     1.1.7      2020-05-13 [1] CRAN (R 3.6.3)                      
##  callr         3.4.3      2020-03-28 [1] CRAN (R 3.6.3)                      
##  cli           2.0.2      2020-02-28 [1] CRAN (R 3.6.3)                      
##  crayon        1.3.4      2017-09-16 [1] CRAN (R 3.6.3)                      
##  desc          1.2.0      2018-05-01 [1] CRAN (R 3.6.3)                      
##  devtools      2.2.2      2020-02-17 [3] CRAN (R 3.6.3)                      
##  digest        0.6.25     2020-02-23 [1] CRAN (R 3.6.3)                      
##  dplyr       * 0.8.5      2020-03-07 [1] CRAN (R 3.6.3)                      
##  ellipsis      0.3.1      2020-05-15 [1] CRAN (R 3.6.3)                      
##  emo         * 0.0.0.9000 2020-05-12 [1] Github (hadley/emo@3f03b11)         
##  evaluate      0.14       2019-05-28 [1] CRAN (R 3.6.3)                      
##  fansi         0.4.1      2020-01-08 [1] CRAN (R 3.6.3)                      
##  fontawesome * 0.1.0      2020-05-12 [1] Github (rstudio/fontawesome@2b64e31)
##  fs            1.4.1      2020-04-04 [1] CRAN (R 3.6.3)                      
##  gapminder   * 0.3.0      2017-10-31 [1] CRAN (R 3.6.3)                      
##  generics      0.0.2      2018-11-29 [1] CRAN (R 3.6.3)                      
##  glue          1.4.1      2020-05-13 [1] CRAN (R 3.6.3)                      
##  highr         0.8        2019-03-20 [1] CRAN (R 3.6.3)                      
##  htmltools     0.4.0      2019-10-04 [1] CRAN (R 3.6.3)                      
##  knitr         1.28       2020-02-06 [1] CRAN (R 3.6.3)                      
##  lifecycle     0.2.0      2020-03-06 [1] CRAN (R 3.6.3)                      
##  lubridate     1.7.8      2020-04-06 [1] CRAN (R 3.6.3)                      
##  magrittr      1.5        2014-11-22 [1] CRAN (R 3.6.3)                      
##  memoise       1.1.0      2017-04-21 [3] CRAN (R 3.5.0)                      
##  pillar        1.4.4      2020-05-05 [1] CRAN (R 3.6.3)                      
##  pkgbuild      1.0.8      2020-05-07 [1] CRAN (R 3.6.3)                      
##  pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 3.6.3)                      
##  pkgload       1.1.0      2020-05-29 [1] CRAN (R 3.6.3)                      
##  prettyunits   1.1.1      2020-01-24 [1] CRAN (R 3.6.3)                      
##  processx      3.4.2      2020-02-09 [1] CRAN (R 3.6.3)                      
##  ps            1.3.3      2020-05-08 [1] CRAN (R 3.6.3)                      
##  purrr         0.3.4      2020-04-17 [1] CRAN (R 3.6.3)                      
##  R6            2.4.1      2019-11-12 [1] CRAN (R 3.6.3)                      
##  Rcpp          1.0.4.6    2020-04-09 [1] CRAN (R 3.6.3)                      
##  remotes       2.1.1      2020-02-15 [1] CRAN (R 3.6.3)                      
##  rlang         0.4.6      2020-05-02 [1] CRAN (R 3.6.3)                      
##  rmarkdown     2.1        2020-01-20 [1] CRAN (R 3.6.3)                      
##  rprojroot     1.3-2      2018-01-03 [1] CRAN (R 3.6.3)                      
##  sessioninfo   1.1.1      2018-11-05 [3] CRAN (R 3.5.1)                      
##  stringi       1.4.6      2020-02-17 [1] CRAN (R 3.6.3)                      
##  stringr       1.4.0      2019-02-10 [1] CRAN (R 3.6.3)                      
##  testthat      2.3.2      2020-03-02 [1] CRAN (R 3.6.3)                      
##  tibble        3.0.1      2020-04-20 [1] CRAN (R 3.6.3)                      
##  tidyselect    1.0.0      2020-01-27 [1] CRAN (R 3.6.3)                      
##  usethis       1.5.1      2019-07-04 [3] CRAN (R 3.6.2)                      
##  utf8          1.1.4      2018-05-24 [1] CRAN (R 3.6.3)                      
##  vctrs         0.3.1      2020-06-05 [1] CRAN (R 3.6.3)                      
##  withr         2.2.0      2020-04-20 [1] CRAN (R 3.6.3)                      
##  xaringan      0.16       2020-03-31 [1] CRAN (R 3.6.3)                      
##  xfun          0.13       2020-04-13 [1] CRAN (R 3.6.3)                      
##  yaml          2.2.1      2020-02-01 [1] CRAN (R 3.6.3)                      
## 
## [1] /home/roxana/R/x86_64-pc-linux-gnu-library/3.6
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library
```