slides_03.html

<!DOCTYPE html>
<html lang="" xml:lang="">
  <head>
    <title>Sesión II: Manipulando datos</title>
    <meta charset="utf-8" />
    <meta name="author" content="Guillermo de Anda-Jáuregui y Laura Gómez-Romero" />
    <meta name="date" content="2022-01-01" />
    <script src="libs/header-attrs/header-attrs.js"></script>
    <link href="libs/remark-css/default.css" rel="stylesheet" />
    <link href="libs/remark-css/default-fonts.css" rel="stylesheet" />
  </head>
  <body>
    <textarea id="source">
class: center, middle, inverse, title-slide

# Sesión II: Manipulando datos
### Guillermo de Anda-Jáuregui y Laura Gómez-Romero
### 2022

---


class: inverse, center

# ¿Qué es tener datos **limpios**?

---
class: inverse, center, middle

&lt;img src="figures/untidy_data.png"&gt;

---
class: inverse, center, middle

# Los datos limpios


## - Cada columna es una variable
## - Cada renglón es una observación

---
class: inverse, center, middle

&lt;img src="figures/tidy_data.png"&gt;

---
class: inverse, center, middle


```r
my_data
```

```
## # A tibble: 7 x 10
##      ID direccion  subdireccion puesto  apellido_paterno apellido_materno nombre
##   &lt;dbl&gt; &lt;chr&gt;      &lt;chr&gt;        &lt;chr&gt;   &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt; 
## 1     1 administr… limpieza     limpie… Sanchez          Pérez            Santi…
## 2     2 jurídico   legal        practi… Lopez            Domínguez        Luisa 
## 3     3 jurídico   legal        practi… Romero           Madero           Agust…
## 4     4 operativo  ventas       ventas  Jimenez          Ordoñez          Luis  
## 5     5 administr… transporte   chofer  Perez            Rubio            Diana 
## 6     6 administr… finanzas     analis… García           Moreno           Danie…
## 7     7 operativo  compras      compras Cabrera          NA               Miguel
## # … with 3 more variables: fecha_inicio &lt;chr&gt;, fecha_fin &lt;chr&gt;, interno &lt;chr&gt;
```

---
class: inverse, center

# El paquete tidyR

&lt;img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/tidyr.png", height = "90"&gt;

---
class: inverse, center

# Datos limpios, código limpio

.left[
### - Si tenemos datos limpios, podemos pensar en usar código parecido para diferentes problemas.

### - Código limpio: más legible.

### - Más tiempo para pensar en nuestros análisis.

]

---
class: inverse, center

# El paquete magrittR 

&lt;img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/pipe.png", height = "90"&gt;

---
class: inverse, center

# Funciones anidadas


```r
x &lt;- 1:10

(exp(sqrt(log(x))))
```

```
##  [1] 1.000000 2.299185 2.852361 3.245956 3.556000 3.813572 4.034808 4.229259
##  [9] 4.403078 4.560477
```


---
class: inverse, center

# Objetos intermedios


```r
x &lt;- 1:10

y &lt;- log(x)

z &lt;- sqrt(y)

w &lt;- exp(z)

w
```

```
##  [1] 1.000000 2.299185 2.852361 3.245956 3.556000 3.813572 4.034808 4.229259
##  [9] 4.403078 4.560477
```
---
class: inverse, center

# Sobreescribir mi objeto


```r
x &lt;- 1:10

x &lt;- log(x)

x &lt;- sqrt(x)

x &lt;- exp(x)

x
```

```
##  [1] 1.000000 2.299185 2.852361 3.245956 3.556000 3.813572 4.034808 4.229259
##  [9] 4.403078 4.560477
```

---
class: inverse, center

# El pipe 

# %&gt;% 

---
class: inverse, center

# El pipe  %&gt;% 


```r
x &lt;- 1:10

my_result &lt;-
  
  x %&gt;%
  log() %&gt;% 
  sqrt() %&gt;% 
  exp 
  
my_result
```

```
##  [1] 1.000000 2.299185 2.852361 3.245956 3.556000 3.813572 4.034808 4.229259
##  [9] 4.403078 4.560477
```

---

class: inverse, center

# Otros pipes

## la TE


```r
rnorm(100) %&gt;%
  matrix(ncol = 2) %T&gt;%
  plot() %&gt;%
  str()
```

![](slides_03_files/figure-html/unnamed-chunk-8-1.png)&lt;!-- --&gt;

```
##  num [1:50, 1:2] 0.286 -0.859 -0.125 -0.58 1.793 ...
```

---

class: inverse, center

# Otros pipes

## la EXPLOSIÓN


```r
mtcars %$%
  cor(disp, mpg)
```

```
## [1] -0.8475514
```
---

class: inverse, center

# ¿Qué queremos manipular?

.left[

## En un set de datos rectangulares...

### - Queremos modificar variables completas.

### - Queremos hacer operaciones y sacar nuevas variables.

### - Queremos seleccionar observaciones que cumplan características.

### - Queremos agrupar.

### - Queremos sacar descripciones de los datos. 

### - Queremos reordenar variables. 

]
 

---
class: inverse, center

# Dataset: nycflights13

&lt;img src ="figures/nyc_plane.jpg"&gt;

https://thumbs.dreamstime.com/b/airplane-take-off-new-york-skyline-travel-concept-64524867.jpg
---
class: inverse, center

# Filtrados

# El paquete dplyr

&lt;img src="https://raw.githubusercontent.com/rstudio/hex-stickers/master/PNG/dplyr.png", height = "90"&gt;

--

## Una *gramática* para la manipulación de datos

---
class: inverse, center, middle

# Tidyverse =&gt; Verbos 

--

###  *mutate*

###  *select*

###  *filter*

###  *group_by*

###  *summarise*

###  *arrange*

---
class: inverse, center, middle

#Analicemos flights

---
class: inverse, center, middle


```r
flights &lt;- vroom::vroom(file = "data/flights.txt")
```

```
## Rows: 336776 Columns: 19
```

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr   (4): carrier, tailnum, origin, dest
## dbl  (14): year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, ...
## dttm  (1): time_hour
```

```
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

---
class: inverse, center, middle


```r
flights 
```

```
## # A tibble: 336,776 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # … with 336,766 more rows, and 11 more variables: arr_delay &lt;dbl&gt;,
## #   carrier &lt;chr&gt;, flight &lt;dbl&gt;, tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;,
## #   air_time &lt;dbl&gt;, distance &lt;dbl&gt;, hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```


---
class: inverse, center

# Filtrados

### Buscamos observaciones (renglones) que cumplan una condición lógica

# Verbo *filter*
---
class: inverse, center

# filter


```r
filter(.data = flights, 
       dest  == "SBN")
```

```
## # A tibble: 10 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013    10    18     1820           1745        35     2030           2011
##  2  2013    11     1     2012           1905        67     2221           2131
##  3  2013    11    22     2013           1905        68     2224           2131
##  4  2013    12     1     1241           1215        26     1431           1431
##  5  2013     8    30     1909           1910        -1     2117           2136
##  6  2013     9     1      833            840        -7     1030           1040
##  7  2013     9     8      847            840         7     1043           1040
##  8  2013     9    20     1948           1950        -2     2207           2216
##  9  2013     9    22      837            840        -3     1025           1040
## 10  2013     9    27     2011           1950        21     2209           2216
## # … with 11 more variables: arr_delay &lt;dbl&gt;, carrier &lt;chr&gt;, flight &lt;dbl&gt;,
## #   tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;, air_time &lt;dbl&gt;, distance &lt;dbl&gt;,
## #   hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```

---
class: inverse, center

# filter


```r
flights %&gt;% 
  filter(dest  == "HDN")
```

```
## # A tibble: 15 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     5      829            830        -1     1047           1111
##  2  2013     1    12      827            830        -3     1112           1111
##  3  2013     1    19      843            830        13     1123           1111
##  4  2013     1    26      828            830        -2     1114           1111
##  5  2013    12    21      916            830        46     1149           1117
##  6  2013    12    28      913            829        44     1128           1116
##  7  2013     2     2      858            830        28     1124           1111
##  8  2013     2     9       NA            830        NA       NA           1111
##  9  2013     2    16      834            830         4     1114           1111
## 10  2013     2    23      826            830        -4     1050           1111
## 11  2013     3     2      854            830        24     1104           1111
## 12  2013     3     9      838            830         8     1107           1111
## 13  2013     3    16      845            830        15     1154           1111
## 14  2013     3    23      835            830         5     1104           1111
## 15  2013     3    30      825            830        -5     1045           1111
## # … with 11 more variables: arr_delay &lt;dbl&gt;, carrier &lt;chr&gt;, flight &lt;dbl&gt;,
## #   tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;, air_time &lt;dbl&gt;, distance &lt;dbl&gt;,
## #   hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```

---
class: inverse, center

# filter


```r
flights %&gt;% 
  filter(dest  == "HDN",
       !is.na(dep_time)
       )
```

```
## # A tibble: 14 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     5      829            830        -1     1047           1111
##  2  2013     1    12      827            830        -3     1112           1111
##  3  2013     1    19      843            830        13     1123           1111
##  4  2013     1    26      828            830        -2     1114           1111
##  5  2013    12    21      916            830        46     1149           1117
##  6  2013    12    28      913            829        44     1128           1116
##  7  2013     2     2      858            830        28     1124           1111
##  8  2013     2    16      834            830         4     1114           1111
##  9  2013     2    23      826            830        -4     1050           1111
## 10  2013     3     2      854            830        24     1104           1111
## 11  2013     3     9      838            830         8     1107           1111
## 12  2013     3    16      845            830        15     1154           1111
## 13  2013     3    23      835            830         5     1104           1111
## 14  2013     3    30      825            830        -5     1045           1111
## # … with 11 more variables: arr_delay &lt;dbl&gt;, carrier &lt;chr&gt;, flight &lt;dbl&gt;,
## #   tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;, air_time &lt;dbl&gt;, distance &lt;dbl&gt;,
## #   hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```
---
class: inverse, center

# slice

### Nos deja seleccionar renglones por posición


```r
flights %&gt;% 
  slice(14:17)
```

```
## # A tibble: 4 x 19
##    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##   &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
## 1  2013     1     1      558            600        -2      923            937
## 2  2013     1     1      559            600        -1      941            910
## 3  2013     1     1      559            559         0      702            706
## 4  2013     1     1      559            600        -1      854            902
## # … with 11 more variables: arr_delay &lt;dbl&gt;, carrier &lt;chr&gt;, flight &lt;dbl&gt;,
## #   tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;, air_time &lt;dbl&gt;, distance &lt;dbl&gt;,
## #   hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```

---
class: inverse, center

# select

## Permite escoger algunas variables (columnas)

---
class: inverse, center

# select


```r
flights %&gt;% 
  select(flight, carrier, origin, dest, distance)
```

```
## # A tibble: 336,776 x 5
##    flight carrier origin dest  distance
##     &lt;dbl&gt; &lt;chr&gt;   &lt;chr&gt;  &lt;chr&gt;    &lt;dbl&gt;
##  1   1545 UA      EWR    IAH       1400
##  2   1714 UA      LGA    IAH       1416
##  3   1141 AA      JFK    MIA       1089
##  4    725 B6      JFK    BQN       1576
##  5    461 DL      LGA    ATL        762
##  6   1696 UA      EWR    ORD        719
##  7    507 B6      EWR    FLL       1065
##  8   5708 EV      LGA    IAD        229
##  9     79 B6      JFK    MCO        944
## 10    301 AA      LGA    ORD        733
## # … with 336,766 more rows
```

---
class: inverse, center


```r
flights %&gt;% 
  select(contains("time"))
```

```
## # A tibble: 336,776 x 6
##    dep_time sched_dep_time arr_time sched_arr_time air_time time_hour          
##       &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt; &lt;dttm&gt;             
##  1      517            515      830            819      227 2013-01-01 10:00:00
##  2      533            529      850            830      227 2013-01-01 10:00:00
##  3      542            540      923            850      160 2013-01-01 10:00:00
##  4      544            545     1004           1022      183 2013-01-01 10:00:00
##  5      554            600      812            837      116 2013-01-01 11:00:00
##  6      554            558      740            728      150 2013-01-01 10:00:00
##  7      555            600      913            854      158 2013-01-01 11:00:00
##  8      557            600      709            723       53 2013-01-01 11:00:00
##  9      557            600      838            846      140 2013-01-01 11:00:00
## 10      558            600      753            745      138 2013-01-01 11:00:00
## # … with 336,766 more rows
```

---
class: inverse, center


```r
flights %&gt;% 
  select_if(is_character)
```

```
## # A tibble: 336,776 x 4
##    carrier tailnum origin dest 
##    &lt;chr&gt;   &lt;chr&gt;   &lt;chr&gt;  &lt;chr&gt;
##  1 UA      N14228  EWR    IAH  
##  2 UA      N24211  LGA    IAH  
##  3 AA      N619AA  JFK    MIA  
##  4 B6      N804JB  JFK    BQN  
##  5 DL      N668DN  LGA    ATL  
##  6 UA      N39463  EWR    ORD  
##  7 B6      N516JB  EWR    FLL  
##  8 EV      N829AS  LGA    IAD  
##  9 B6      N593JB  JFK    MCO  
## 10 AA      N3ALAA  LGA    ORD  
## # … with 336,766 more rows
```

---
class: inverse, center

#Podemos concatenar verbos usando el pipe


```r
flights %&gt;% 
  select("dest", contains("time")) %&gt;% 
  filter(dest == "HDN") %&gt;% 
  tidyr::drop_na() %&gt;% 
  slice(1:7)
```

```
## # A tibble: 7 x 7
##   dest  dep_time sched_dep_time arr_time sched_arr_time air_time
##   &lt;chr&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;
## 1 HDN        829            830     1047           1111      243
## 2 HDN        827            830     1112           1111      259
## 3 HDN        843            830     1123           1111      260
## 4 HDN        828            830     1114           1111      265
## 5 HDN        916            830     1149           1117      254
## 6 HDN        913            829     1128           1116      239
## 7 HDN        858            830     1124           1111      246
## # … with 1 more variable: time_hour &lt;dttm&gt;
```


---
class: inverse, center

# pull

### Permite sacar una variable como vector 


```r
flights %&gt;% 
  pull("dest") %&gt;% 
  head
```

```
## [1] "IAH" "IAH" "MIA" "BQN" "ATL" "ORD"
```
---
class: inverse, center

# Agrupamientos

### *group_by* me permite agregar por variables categóricas (o coercible a variables categóricas).

### Esto me servirá para hacer resúmenes después. 

---
class: inverse, center

# Agrupamientos


```r
flights %&gt;% 
  group_by(dest)
```

```
## # A tibble: 336,776 x 19
## # Groups:   dest [105]
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # … with 336,766 more rows, and 11 more variables: arr_delay &lt;dbl&gt;,
## #   carrier &lt;chr&gt;, flight &lt;dbl&gt;, tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;,
## #   air_time &lt;dbl&gt;, distance &lt;dbl&gt;, hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```

---
class: inverse, center

# Agrupamientos


```r
flights %&gt;% 
  group_by(dest, origin)
```

```
## # A tibble: 336,776 x 19
## # Groups:   dest, origin [224]
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # … with 336,766 more rows, and 11 more variables: arr_delay &lt;dbl&gt;,
## #   carrier &lt;chr&gt;, flight &lt;dbl&gt;, tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;,
## #   air_time &lt;dbl&gt;, distance &lt;dbl&gt;, hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;
```

---
class: inverse, center

# Funciones de resumen

### Nos van a dar descripciones de nuestro conjunto de datos 

### Funcionan en el conjunto original, filtrado, agrupado, o manipulado

---
class: inverse, center


```r
flights %&gt;% 
  summarise(mean_distance = mean(distance))
```

```
## # A tibble: 1 x 1
##   mean_distance
##           &lt;dbl&gt;
## 1         1040.
```
---
class: inverse, center


```r
flights %&gt;% 
  select(contains("time")) %&gt;% 
  summarise(mean_deptime = mean(dep_time),
            mean_sched_deptime = mean(sched_dep_time),
            mean_arr_time = mean(arr_time),
            mean_sched_arr_time = mean(sched_arr_time),
            mean_air_time = mean(air_time),
            mean_time_hour = mean(time_hour)
            )
```

```
## # A tibble: 1 x 6
##   mean_deptime mean_sched_deptime mean_arr_time mean_sched_arr_ti… mean_air_time
##          &lt;dbl&gt;              &lt;dbl&gt;         &lt;dbl&gt;              &lt;dbl&gt;         &lt;dbl&gt;
## 1           NA              1344.            NA              1536.            NA
## # … with 1 more variable: mean_time_hour &lt;dttm&gt;
```

---
class: inverse, center


```r
flights %&gt;% 
  select(contains("time")) %&gt;% 
  summarise_all(mean, na.rm=TRUE)
```

```
## # A tibble: 1 x 6
##   dep_time sched_dep_time arr_time sched_arr_time air_time time_hour          
##      &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt; &lt;dttm&gt;             
## 1    1349.          1344.    1502.          1536.     151. 2013-07-03 09:22:54
```
---
class: inverse, center

# Puedo usarlas sobre datos agrupados


```r
flights %&gt;% 
  group_by(dest) %&gt;% 
  select(contains("time")) %&gt;% 
  summarise_all(mean, na.rm=TRUE)
```

```
## Adding missing grouping variables: `dest`
```

```
## # A tibble: 105 x 7
##    dest  dep_time sched_dep_time arr_time sched_arr_time air_time
##    &lt;chr&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;    &lt;dbl&gt;
##  1 ABQ      2006.          2001.    2049.          2278.    249. 
##  2 ACK      1033.          1033.    1145.          1139.     42.1
##  3 ALB      1627.          1610.    1702.          1718.     31.8
##  4 ANC      1635.          1618.    1968           1966.    413. 
##  5 ATL      1293.          1287.    1513.          1529.    113. 
##  6 AUS      1521.          1506.    1614.          1816.    213. 
##  7 AVL      1175.          1169.    1373.          1362.     89.9
##  8 BDL      1490.          1506.    1549.          1610.     25.5
##  9 BGR      1690.          1673.    1715.          1836.     54.1
## 10 BHM      1944.          1909.    2028.          2078.    123. 
## # … with 95 more rows, and 1 more variable: time_hour &lt;dttm&gt;
```
---
class: inverse, center

# Puedo usarlas sobre datos agrupados


```r
flights %&gt;% 
  group_by(origin, dest) %&gt;% 
  select(contains("time")) %&gt;% 
  summarise_all(list(med = median,
                     avg = mean
                     ),
                na.rm = TRUE
                     )
```

```
## Adding missing grouping variables: `origin`, `dest`
```

```
## # A tibble: 224 x 14
## # Groups:   origin [3]
##    origin dest  dep_time_med sched_dep_time_med arr_time_med sched_arr_time_med
##    &lt;chr&gt;  &lt;chr&gt;        &lt;dbl&gt;              &lt;dbl&gt;        &lt;dbl&gt;              &lt;dbl&gt;
##  1 EWR    ALB          1738                1721        1832                1822
##  2 EWR    ANC          1618                1615        1957                1953
##  3 EWR    ATL          1257                1300        1513                1519
##  4 EWR    AUS          1503                1459        1746                1805
##  5 EWR    AVL          1156                1200        1339                1350
##  6 EWR    BDL          1328.               1327        1414.               1422
##  7 EWR    BNA          1306.               1300        1421                1425
##  8 EWR    BOS          1400                1400        1458                1511
##  9 EWR    BQN          2054                2038         105                  42
## 10 EWR    BTV          1231                1209        1316                1320
## # … with 214 more rows, and 8 more variables: air_time_med &lt;dbl&gt;,
## #   time_hour_med &lt;dttm&gt;, dep_time_avg &lt;dbl&gt;, sched_dep_time_avg &lt;dbl&gt;,
## #   arr_time_avg &lt;dbl&gt;, sched_arr_time_avg &lt;dbl&gt;, air_time_avg &lt;dbl&gt;,
## #   time_hour_avg &lt;dttm&gt;
```
---
class: inverse, center

# Contar: n, count, tally

### n


```r
flights %&gt;% 
  group_by(month, origin, dest) %&gt;% 
  summarize(cuantos = n())
```

```
## `summarise()` has grouped output by 'month', 'origin'. You can override using the `.groups` argument.
```

```
## # A tibble: 2,313 x 4
## # Groups:   month, origin [36]
##    month origin dest  cuantos
##    &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt;   &lt;int&gt;
##  1     1 EWR    ALB        64
##  2     1 EWR    ATL       362
##  3     1 EWR    AUS        51
##  4     1 EWR    AVL         2
##  5     1 EWR    BDL        37
##  6     1 EWR    BNA       111
##  7     1 EWR    BOS       430
##  8     1 EWR    BQN        31
##  9     1 EWR    BTV       100
## 10     1 EWR    BUF       119
## # … with 2,303 more rows
```
---
class: inverse, center
### count


```r
flights %&gt;% 
  count()
```

```
## # A tibble: 1 x 1
##        n
##    &lt;int&gt;
## 1 336776
```
---
class: inverse, center

### count


```r
flights %&gt;% 
  count(origin)
```

```
## # A tibble: 3 x 2
##   origin      n
##   &lt;chr&gt;   &lt;int&gt;
## 1 EWR    120835
## 2 JFK    111279
## 3 LGA    104662
```
---
class: inverse, center

### count - datos agrupados


```r
flights %&gt;% 
  group_by(month) %&gt;% 
  count(origin)
```

```
## # A tibble: 36 x 3
## # Groups:   month [12]
##    month origin     n
##    &lt;dbl&gt; &lt;chr&gt;  &lt;int&gt;
##  1     1 EWR     9893
##  2     1 JFK     9161
##  3     1 LGA     7950
##  4     2 EWR     9107
##  5     2 JFK     8421
##  6     2 LGA     7423
##  7     3 EWR    10420
##  8     3 JFK     9697
##  9     3 LGA     8717
## 10     4 EWR    10531
## # … with 26 more rows
```

---
class: inverse, center

#tally


```r
flights %&gt;% 
  group_by(month, origin, dest) %&gt;% 
  tally()
```

```
## # A tibble: 2,313 x 4
## # Groups:   month, origin [36]
##    month origin dest      n
##    &lt;dbl&gt; &lt;chr&gt;  &lt;chr&gt; &lt;int&gt;
##  1     1 EWR    ALB      64
##  2     1 EWR    ATL     362
##  3     1 EWR    AUS      51
##  4     1 EWR    AVL       2
##  5     1 EWR    BDL      37
##  6     1 EWR    BNA     111
##  7     1 EWR    BOS     430
##  8     1 EWR    BQN      31
##  9     1 EWR    BTV     100
## 10     1 EWR    BUF     119
## # … with 2,303 more rows
```

---

class: inverse, center

# Rangos y disperciones


```r
flights %&gt;% 
  group_by(origin, dest) %&gt;% 
  summarise(min_airtime = min(air_time, na.rm = TRUE),
            max_airtime = max(air_time, na.rm = TRUE),
            std_dev_airtime = sd(air_time, na.rm = TRUE),
            cuantos = n()
            )
```

```
## Warning in min(air_time, na.rm = TRUE): no non-missing arguments to min;
## returning Inf
```

```
## Warning in max(air_time, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf
```

```
## `summarise()` has grouped output by 'origin'. You can override using the `.groups` argument.
```

```
## # A tibble: 224 x 6
## # Groups:   origin [3]
##    origin dest  min_airtime max_airtime std_dev_airtime cuantos
##    &lt;chr&gt;  &lt;chr&gt;       &lt;dbl&gt;       &lt;dbl&gt;           &lt;dbl&gt;   &lt;int&gt;
##  1 EWR    ALB            24          50            3.08     439
##  2 EWR    ANC           388         434           14.7        8
##  3 EWR    ATL            88         176            9.99    5022
##  4 EWR    AUS           174         301           17.9      968
##  5 EWR    AVL            76         119            7.45     265
##  6 EWR    BDL            20          56            3.29     443
##  7 EWR    BNA            70         176           10.9     2336
##  8 EWR    BOS            30         112            4.92    5327
##  9 EWR    BQN           173         231            8.88     297
## 10 EWR    BTV            34          69            3.67     931
## # … with 214 more rows
```
---
class: inverse, center

# dplyr + pipe + ggplot2


```r
flights %&gt;% 
  group_by(origin, dest) %&gt;% 
  summarise(mean_dep_delay = mean(dep_delay, na.rm = TRUE),
            mean_arr_delay = mean(arr_delay, na.rm = TRUE)
            ) %&gt;% 
  ggplot(mapping = aes(x = mean_dep_delay, 
                       y = mean_arr_delay, 
                       colour = as_factor(origin)
                       )
         ) +
  geom_point()
```

---
class: inverse, center

# dplyr + pipe + ggplot2


```
## `summarise()` has grouped output by 'origin'. You can override using the `.groups` argument.
```

![](slides_03_files/figure-html/unnamed-chunk-36-1.png)&lt;!-- --&gt;
---
class: inverse, center

# ¡¡¡Nota!!!
# dplyr + %&gt;%  + ggplot2 != Tidyverse !!!!

## ¡Hay muchas otros paquetes!

## Lo importante es la filosofía: datos limpios, usar verbos...

---
class: inverse, center
# Ejercicio: 

.left[ 
### Queremos saber como se comportaron las aerolíneas a lo largo del año 2013, mes a mes, en términos del tiempo promedio de vuelo. 

### Genere un gráfico de puntos que le permita contestar: 

### ¿Qué aerolínea tiene el tiempo de viaje más largo? ¿Es la misma todo el año?

### ¿Qué aerolínea tiene el tiempo de viaje más corto? ¿Es la misma todo el año?

### Si el gráfico no es suficiente, apóyese en resúmenes de su análisis de datos.
]

---
class: inverse, center, middle

# Mutate

## Transformaciones de variables

---
class: inverse, center, middle

# Mutate

## Quiero saber el tiempo que estaba esperado para los vuelos


```r
flights %&gt;% 
  select(carrier, flight, air_time, dep_delay, arr_delay)
```

```
## # A tibble: 336,776 x 5
##    carrier flight air_time dep_delay arr_delay
##    &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;
##  1 UA        1545      227         2        11
##  2 UA        1714      227         4        20
##  3 AA        1141      160         2        33
##  4 B6         725      183        -1       -18
##  5 DL         461      116        -6       -25
##  6 UA        1696      150        -4        12
##  7 B6         507      158        -5        19
##  8 EV        5708       53        -3       -14
##  9 B6          79      140        -3        -8
## 10 AA         301      138        -2         8
## # … with 336,766 more rows
```
### *air_time* me dice cuanto paso en el aire

### *dep_delay* y *arr_delay* me dicen cuanto se demoró

--

### ¡Tengo que generar una nueva variable!

---
class: inverse, center, middle


```r
flights %&gt;% 
  select(carrier, flight, air_time, dep_delay, arr_delay) %&gt;% 
  mutate(expected_airtime = air_time - dep_delay - arr_delay)
```

```
## # A tibble: 336,776 x 6
##    carrier flight air_time dep_delay arr_delay expected_airtime
##    &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;            &lt;dbl&gt;
##  1 UA        1545      227         2        11              214
##  2 UA        1714      227         4        20              203
##  3 AA        1141      160         2        33              125
##  4 B6         725      183        -1       -18              202
##  5 DL         461      116        -6       -25              147
##  6 UA        1696      150        -4        12              142
##  7 B6         507      158        -5        19              144
##  8 EV        5708       53        -3       -14               70
##  9 B6          79      140        -3        -8              151
## 10 AA         301      138        -2         8              132
## # … with 336,766 more rows
```

---
class: inverse, center, middle

Tambien puedo hacer variables intermedias


```
## # A tibble: 336,776 x 7
##    carrier flight air_time dep_delay arr_delay total_delay expected_airtime
##    &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;       &lt;dbl&gt;            &lt;dbl&gt;
##  1 UA        1545      227         2        11          13              214
##  2 UA        1714      227         4        20          24              203
##  3 AA        1141      160         2        33          35              125
##  4 B6         725      183        -1       -18         -19              202
##  5 DL         461      116        -6       -25         -31              147
##  6 UA        1696      150        -4        12           8              142
##  7 B6         507      158        -5        19          14              144
##  8 EV        5708       53        -3       -14         -17               70
##  9 B6          79      140        -3        -8         -11              151
## 10 AA         301      138        -2         8           6              132
## # … with 336,766 more rows
```

---
class: inverse, center, middle

# case_when para condicionales

Quiero una variable que diga si se adelantó o se atrasó 


```r
my_flights &lt;- 
flights %&gt;% 
  mutate(total_delay = dep_delay + arr_delay,
         expected_airtime = air_time - total_delay
         ) %&gt;% 
  mutate(net_gainloss = case_when(total_delay &gt; 0 ~ "retraso",
                             total_delay == 0 ~ "a_tiempo",
                             total_delay &lt; 0 ~ "adelanto"
                             )
         )
```
---
class: inverse, center, middle

# case_when para condicionales

Quiero una variable que diga si se adelantó o se atrasó 


```r
my_flights
```

```
## # A tibble: 336,776 x 22
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;     &lt;dbl&gt;    &lt;dbl&gt;          &lt;dbl&gt;
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # … with 336,766 more rows, and 14 more variables: arr_delay &lt;dbl&gt;,
## #   carrier &lt;chr&gt;, flight &lt;dbl&gt;, tailnum &lt;chr&gt;, origin &lt;chr&gt;, dest &lt;chr&gt;,
## #   air_time &lt;dbl&gt;, distance &lt;dbl&gt;, hour &lt;dbl&gt;, minute &lt;dbl&gt;, time_hour &lt;dttm&gt;,
## #   total_delay &lt;dbl&gt;, expected_airtime &lt;dbl&gt;, net_gainloss &lt;chr&gt;
```

---
class: inverse, center, middle

# case_when para condicionales

Quiero una variable que diga si se adelantó o se atrasó 


```r
my_flights %&gt;% 
  select(carrier, flight, air_time, expected_airtime, total_delay, net_gainloss)
```

```
## # A tibble: 336,776 x 6
##    carrier flight air_time expected_airtime total_delay net_gainloss
##    &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;            &lt;dbl&gt;       &lt;dbl&gt; &lt;chr&gt;       
##  1 UA        1545      227              214          13 retraso     
##  2 UA        1714      227              203          24 retraso     
##  3 AA        1141      160              125          35 retraso     
##  4 B6         725      183              202         -19 adelanto    
##  5 DL         461      116              147         -31 adelanto    
##  6 UA        1696      150              142           8 retraso     
##  7 B6         507      158              144          14 retraso     
##  8 EV        5708       53               70         -17 adelanto    
##  9 B6          79      140              151         -11 adelanto    
## 10 AA         301      138              132           6 retraso     
## # … with 336,766 more rows
```

---

class: inverse, center

# Ejercicio

.left[
## calcule la velocidad promedio de cada aerolínea.  
]

---
class: inverse, center

# Los terribles joins

---
class: inverse, center

# Los ~~terribles joins~~

---
class: inverse, center

# El Hombre Araña

&lt;img src="figures/Amazing_Fantasy_15.jpg"&gt;

### - Tuvo dos series de películas ANTES del Marvel Cinematic Universe.

### - Cada una tuvo diferentes actores.

---
class: inverse, center


```r
spiderman_raimi &lt;- vroom::vroom(file = "data/spiderman_raimi.csv")
```

```
## Rows: 11 Columns: 2
```

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): character, cast
```

```
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

```r
spiderman_amazing &lt;- vroom::vroom(file = "data/amazing_spiderman.csv")
```

```
## Rows: 8 Columns: 2
```

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): character, cast
```

```
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

---
class: inverse, center

```r
spiderman_raimi 
```

```
## # A tibble: 11 x 2
##    character        cast               
##    &lt;chr&gt;            &lt;chr&gt;              
##  1 Spider-man       Tobey Maguire      
##  2 Mary Jane Watson Kirsten Dunst      
##  3 Harry Osborn     James Franco       
##  4 Tía May          Rosemary Harris    
##  5 J. Jonah Jameson J.K. Simmons       
##  6 Norman Osborn    Willem Dafoe       
##  7 Doctor Octopus   Alfred Molina      
##  8 Venom            Topher Grace       
##  9 Sandman          Thomas Haden Church
## 10 Gwen Stacy       Bryce Dallas Howard
## 11 Dr. Curt Connors Dylan Baker
```

---
class: inverse, center

```r
spiderman_amazing 
```

```
## # A tibble: 8 x 2
##   character        cast           
##   &lt;chr&gt;            &lt;chr&gt;          
## 1 Spider-man       Andrew Garfield
## 2 Harry Osborn     Dane DeHaan    
## 3 Tía May          Sally Field    
## 4 Norman Osborn    Chris Cooper   
## 5 Electro          Jamie Foxx     
## 6 Gwen Stacy       Emma Stone     
## 7 Rhino            Paul Giamatti  
## 8 Dr. Curt Connors Rhys Ifans
```

--

.left[
### ¿Qué actores interpretan a cada personaje? 
### ¿Qué personajes aparecen en la primera serie?
### ¿Qué personajes aparecen en la segunda serie?
### ¿Qué personajes aparecen en ambas?
]

---
class: inverse, center, middle
### *El problema clásico de juntar diccionarios*

---
class: inverse, center
# Left join
&lt;img src="figures/joins/left_join.svg"&gt;

---
class: inverse, center
# Left join


```r
dplyr::left_join(x = spiderman_raimi,
                 y = spiderman_amazing,
                 suffix = c("_raimi", "_amazing"),
                 by = c("character" = "character")
                 )
```

```
## # A tibble: 11 x 3
##    character        cast_raimi          cast_amazing   
##    &lt;chr&gt;            &lt;chr&gt;               &lt;chr&gt;          
##  1 Spider-man       Tobey Maguire       Andrew Garfield
##  2 Mary Jane Watson Kirsten Dunst       &lt;NA&gt;           
##  3 Harry Osborn     James Franco        Dane DeHaan    
##  4 Tía May          Rosemary Harris     Sally Field    
##  5 J. Jonah Jameson J.K. Simmons        &lt;NA&gt;           
##  6 Norman Osborn    Willem Dafoe        Chris Cooper   
##  7 Doctor Octopus   Alfred Molina       &lt;NA&gt;           
##  8 Venom            Topher Grace        &lt;NA&gt;           
##  9 Sandman          Thomas Haden Church &lt;NA&gt;           
## 10 Gwen Stacy       Bryce Dallas Howard Emma Stone     
## 11 Dr. Curt Connors Dylan Baker         Rhys Ifans
```

---
class: inverse, center
# Right join
&lt;img src="figures/joins/right_join.svg"&gt;

---
class: inverse, center
# Right join


```r
dplyr::right_join(x = spiderman_raimi,
                 y = spiderman_amazing,
                 suffix = c("_raimi", "_amazing"),
                 by = c("character" = "character")
                 )
```

```
## # A tibble: 8 x 3
##   character        cast_raimi          cast_amazing   
##   &lt;chr&gt;            &lt;chr&gt;               &lt;chr&gt;          
## 1 Spider-man       Tobey Maguire       Andrew Garfield
## 2 Harry Osborn     James Franco        Dane DeHaan    
## 3 Tía May          Rosemary Harris     Sally Field    
## 4 Norman Osborn    Willem Dafoe        Chris Cooper   
## 5 Gwen Stacy       Bryce Dallas Howard Emma Stone     
## 6 Dr. Curt Connors Dylan Baker         Rhys Ifans     
## 7 Electro          &lt;NA&gt;                Jamie Foxx     
## 8 Rhino            &lt;NA&gt;                Paul Giamatti
```
---
class: inverse, center
# semi join
&lt;img src="figures/joins/inner_join.svg"&gt;

---
class: inverse, center
# semi join


```r
dplyr::semi_join(x = spiderman_raimi,
                 y = spiderman_amazing,
                 suffix = c("_raimi", "_amazing"),
                 by = c("character" = "character")
                 )
```

```
## # A tibble: 6 x 2
##   character        cast               
##   &lt;chr&gt;            &lt;chr&gt;              
## 1 Spider-man       Tobey Maguire      
## 2 Harry Osborn     James Franco       
## 3 Tía May          Rosemary Harris    
## 4 Norman Osborn    Willem Dafoe       
## 5 Gwen Stacy       Bryce Dallas Howard
## 6 Dr. Curt Connors Dylan Baker
```
---
class: inverse, center
# full join
&lt;img src="figures/joins/full_join.svg"&gt;

---
class: inverse, center
# full join

```r
dplyr::full_join(x = spiderman_raimi,
                 y = spiderman_amazing,
                 suffix = c("_raimi", "_amazing"),
                 by = c("character" = "character")
                 )
```

```
## # A tibble: 13 x 3
##    character        cast_raimi          cast_amazing   
##    &lt;chr&gt;            &lt;chr&gt;               &lt;chr&gt;          
##  1 Spider-man       Tobey Maguire       Andrew Garfield
##  2 Mary Jane Watson Kirsten Dunst       &lt;NA&gt;           
##  3 Harry Osborn     James Franco        Dane DeHaan    
##  4 Tía May          Rosemary Harris     Sally Field    
##  5 J. Jonah Jameson J.K. Simmons        &lt;NA&gt;           
##  6 Norman Osborn    Willem Dafoe        Chris Cooper   
##  7 Doctor Octopus   Alfred Molina       &lt;NA&gt;           
##  8 Venom            Topher Grace        &lt;NA&gt;           
##  9 Sandman          Thomas Haden Church &lt;NA&gt;           
## 10 Gwen Stacy       Bryce Dallas Howard Emma Stone     
## 11 Dr. Curt Connors Dylan Baker         Rhys Ifans     
## 12 Electro          &lt;NA&gt;                Jamie Foxx     
## 13 Rhino            &lt;NA&gt;                Paul Giamatti
```
---
class: inverse, center
# anti join
&lt;img src="figures/joins/anti_join.svg"&gt;

---
class: inverse, center
# anti join

```r
dplyr::anti_join(x = spiderman_raimi,
                 y = spiderman_amazing,
                 suffix = c("_raimi", "_amazing"),
                 by = c("character" = "character")
                 )
```

```
## # A tibble: 5 x 2
##   character        cast               
##   &lt;chr&gt;            &lt;chr&gt;              
## 1 Mary Jane Watson Kirsten Dunst      
## 2 J. Jonah Jameson J.K. Simmons       
## 3 Doctor Octopus   Alfred Molina      
## 4 Venom            Topher Grace       
## 5 Sandman          Thomas Haden Church
```

---
class: inverse, center

# Ejercicio:

.left[
# Haga el join adecuado para los datos en **data/flights.txt** y **data/airlines.txt**
]

---
class: inverse, center, middle

# Más tidying

---
class: inverse, center, middle

# Veamos un plot de calificaciones 
![](slides_03_files/figure-html/unnamed-chunk-51-1.png)&lt;!-- --&gt;
---
class: inverse, center, middle

### Es un plot feo... no es la mejor forma de presentar la información

### ... pero digamos que queremos reproducirlo 

-- 

### La próxima semana veremos como hacerlo mejor
---
class: inverse, center, middle

### Leamos los datos

```r
my_data &lt;- vroom::vroom("data/calificaciones_untidy.txt")
my_data
```

```
## # A tibble: 10 x 5
##    student    fisica quimica matematicas biologia
##    &lt;chr&gt;       &lt;dbl&gt;   &lt;dbl&gt;       &lt;dbl&gt;    &lt;dbl&gt;
##  1 STUDENT_01      6      10           5        7
##  2 STUDENT_02      5      10           8       10
##  3 STUDENT_03      6      10           6       10
##  4 STUDENT_04      6       5           5        6
##  5 STUDENT_05      6       9           8        6
##  6 STUDENT_06      5      10          10        7
##  7 STUDENT_07      7       7          10        6
##  8 STUDENT_08      6      10           9        8
##  9 STUDENT_09      7       5           7        7
## 10 STUDENT_10      6       6           9        8
```
---
class: inverse, center, middle

### Hagamos el plot... 


```r
my_data %&gt;% 
  ggplot() %&gt;% 
  geom_point(mapping = aes(x = estudiante?????, 
                           y = calificaciones?????
                           donde pongo las materias???
                          )
             )
```

-- 

### Los datos no están organizados para esta aplicación 

--

Tenemos que cambiar la *forma* de los datos = &gt; *pivotear*

---
class: inverse, center, middle

# tidyr::gather


```r
my_data_gather &lt;-
my_data %&gt;% 
  gather(-"student", key = "materia", value = "calificacion")

my_data_gather
```

```
## # A tibble: 40 x 3
##    student    materia calificacion
##    &lt;chr&gt;      &lt;chr&gt;          &lt;dbl&gt;
##  1 STUDENT_01 fisica             6
##  2 STUDENT_02 fisica             5
##  3 STUDENT_03 fisica             6
##  4 STUDENT_04 fisica             6
##  5 STUDENT_05 fisica             6
##  6 STUDENT_06 fisica             5
##  7 STUDENT_07 fisica             7
##  8 STUDENT_08 fisica             6
##  9 STUDENT_09 fisica             7
## 10 STUDENT_10 fisica             6
## # … with 30 more rows
```
---
class: inverse, center, middle

# tidyr::gather


```r
my_data_gather %&gt;% 
  ggplot(mapping = aes(x = as_factor(student),
                       y = calificacion,
                       colour = as_factor(materia),
                       )
         ) +
  geom_point()
```
---
class: inverse, center, middle

# tidyr::gather

![](slides_03_files/figure-html/unnamed-chunk-56-1.png)&lt;!-- --&gt;
---
class: inverse, center, middle

### tidyr::gather no es la única manera de *pivotear* los datos

### reshape2::melt

### tidyr::pivot_long **en tidyr 1.0**

---
class: inverse, center, middle

### reshape2::melt


```r
my_data_melt &lt;-
  my_data %&gt;% 
  reshape2::melt(variable.name = "materia", 
                 value.name = "calificacion", 
                 id.vars = "student")

head(my_data_melt)
```

```
##      student materia calificacion
## 1 STUDENT_01  fisica            6
## 2 STUDENT_02  fisica            5
## 3 STUDENT_03  fisica            6
## 4 STUDENT_04  fisica            6
## 5 STUDENT_05  fisica            6
## 6 STUDENT_06  fisica            5
```

---
class: inverse, center, middle

### tidyr::pivot_long (*SOLO DE LA VERSIÓN 1.0 DE TIDYR EN ADELANTE*)


```r
my_data_pivot_longer &lt;-
  my_data %&gt;% 
  tidyr::pivot_longer(cols = -"student", 
                      names_to = "materia", 
                      values_to = "calificacion")

my_data_pivot_longer
```

```
## # A tibble: 40 x 3
##    student    materia     calificacion
##    &lt;chr&gt;      &lt;chr&gt;              &lt;dbl&gt;
##  1 STUDENT_01 fisica                 6
##  2 STUDENT_01 quimica               10
##  3 STUDENT_01 matematicas            5
##  4 STUDENT_01 biologia               7
##  5 STUDENT_02 fisica                 5
##  6 STUDENT_02 quimica               10
##  7 STUDENT_02 matematicas            8
##  8 STUDENT_02 biologia              10
##  9 STUDENT_03 fisica                 6
## 10 STUDENT_03 quimica               10
## # … with 30 more rows
```

---
class: inverse, center

# Tareas...

### - Volver a la versión original de los datos ... 

### Pista: 

### Usar tidyr::spread, tidyr::pivot_wide, reshape2::dcast 
    </textarea>
<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
<script>var slideshow = remark.create({
"highlightStyle": "github",
"highlightLines": true,
"countIncrementalSlides": false
});
if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
  window.dispatchEvent(new Event('resize'));
});
(function(d) {
  var s = d.createElement("style"), r = d.querySelector(".remark-slide-scaler");
  if (!r) return;
  s.type = "text/css"; s.innerHTML = "@page {size: " + r.style.width + " " + r.style.height +"; }";
  d.head.appendChild(s);
})(document);

(function(d) {
  var el = d.getElementsByClassName("remark-slides-area");
  if (!el) return;
  var slide, slides = slideshow.getSlides(), els = el[0].children;
  for (var i = 1; i < slides.length; i++) {
    slide = slides[i];
    if (slide.properties.continued === "true" || slide.properties.count === "false") {
      els[i - 1].className += ' has-continuation';
    }
  }
  var s = d.createElement("style");
  s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
  d.head.appendChild(s);
})(document);
// delete the temporary CSS (for displaying all slides initially) when the user
// starts to view slides
(function() {
  var deleted = false;
  slideshow.on('beforeShowSlide', function(slide) {
    if (deleted) return;
    var sheets = document.styleSheets, node;
    for (var i = 0; i < sheets.length; i++) {
      node = sheets[i].ownerNode;
      if (node.dataset["target"] !== "print-only") continue;
      node.parentNode.removeChild(node);
    }
    deleted = true;
  });
})();
(function() {
  "use strict"
  // Replace <script> tags in slides area to make them executable
  var scripts = document.querySelectorAll(
    '.remark-slides-area .remark-slide-container script'
  );
  if (!scripts.length) return;
  for (var i = 0; i < scripts.length; i++) {
    var s = document.createElement('script');
    var code = document.createTextNode(scripts[i].textContent);
    s.appendChild(code);
    var scriptAttrs = scripts[i].attributes;
    for (var j = 0; j < scriptAttrs.length; j++) {
      s.setAttribute(scriptAttrs[j].name, scriptAttrs[j].value);
    }
    scripts[i].parentElement.replaceChild(s, scripts[i]);
  }
})();
(function() {
  var links = document.getElementsByTagName('a');
  for (var i = 0; i < links.length; i++) {
    if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
      links[i].target = '_blank';
    }
  }
})();
// adds .remark-code-has-line-highlighted class to <pre> parent elements
// of code chunks containing highlighted lines with class .remark-code-line-highlighted
(function(d) {
  const hlines = d.querySelectorAll('.remark-code-line-highlighted');
  const preParents = [];
  const findPreParent = function(line, p = 0) {
    if (p > 1) return null; // traverse up no further than grandparent
    const el = line.parentElement;
    return el.tagName === "PRE" ? el : findPreParent(el, ++p);
  };

  for (let line of hlines) {
    let pre = findPreParent(line);
    if (pre && !preParents.includes(pre)) preParents.push(pre);
  }
  preParents.forEach(p => p.classList.add("remark-code-has-line-highlighted"));
})(document);</script>

<script>
slideshow._releaseMath = function(el) {
  var i, text, code, codes = el.getElementsByTagName('code');
  for (i = 0; i < codes.length;) {
    code = codes[i];
    if (code.parentNode.tagName !== 'PRE' && code.childElementCount === 0) {
      text = code.textContent;
      if (/^\\\((.|\s)+\\\)$/.test(text) || /^\\\[(.|\s)+\\\]$/.test(text) ||
          /^\$\$(.|\s)+\$\$$/.test(text) ||
          /^\\begin\{([^}]+)\}(.|\s)+\\end\{[^}]+\}$/.test(text)) {
        code.outerHTML = code.innerHTML;  // remove <code></code>
        continue;
      }
    }
    i++;
  }
};
slideshow._releaseMath(document);
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
  var script = document.createElement('script');
  script.type = 'text/javascript';
  script.src  = 'https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML';
  if (location.protocol !== 'file:' && /^https?:/.test(script.src))
    script.src  = script.src.replace(/^https?:/, '');
  document.getElementsByTagName('head')[0].appendChild(script);
})();
</script>
  </body>
</html>