Manipulación de datos usando R base y dplyr (dataframe)

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# Manipulación de datos usando R base y dplyr (dataframe)
### ⚔<br/> VieRnes de Bioinformatica en el LIIGH

####Dra. Evelia Coss 
#### 2026/03/27
]

---

### Paquetes necesarios

``` r
install.packages("tidyverse")
install.packages("reshape2")
```

---

## Cargar paquetes

``` r
library(tidyverse) # Manipulación de datos
library(reshape2)  # Transformación de datos
```
---

background-color: var(--Purple)
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---

## Existen 5 estructuras de datos en R

Las estructuras de datos son objetos que contienen datos. Cuando trabajamos con R, lo que estamos haciendo es manipular estas estructuras.

| Tipo        | Declaración de la variable       | Conversión       | Dimensiones  | Contenido               |
|-------------|----------------------------------|------------------|--------------|-------------------------|
| Vector      | c(), seq(), rep(), valorI:valorN; seq_len() | as.vector()      |  1           | homogénea               |
| Lists       | list()                           | as.list()        |  1*          | homogénea               |
| Matrix      | matrix(valores, nrow=x, ncol=y)  | as.matrix()      |  2           | heterogénea             |
| Dataframe   | data.frame()                      | as.dataframe()   |  2           | heterogénea             |

Tambien estan los `array` pero son poco usados en R.

Para conocer la estructura del archivo debes usar `str()`.

Una lista puede contener matrices en su interior, vectores, funciones, etc.

---

---

## Recordatorio: Clases de variables

| Clases    | Declaración de la variable    | Conversión de formato  |                   Reglas                                     |
|-----------|------------------------------|-------------------------|--------------------------------------------------------------|
| numeric   | `numeric()`                  | `as.numeric()`          | FALSE -> 0, TRUE -> 1; "1", "2", ... -> 1,2, ...; "A" -> NA  |
| integer   | `integer()`                  | `as.integer()`          | FALSE -> 0, TRUE -> 1; "1", "2", ... -> 1,2, ...; "A" -> NA  |
| double    | `double()`                   | `as.double()`           |                                                              |
| character | `character()`                | `as.character()`        |  1,2, ... -> "1", "2", ...; FALSE -> "FALSE"; TRUE -> "TRUE"|
| logical   | `logical()`                  | `as.logical()`          | 0 -> FALSE, other numbers -> TRUE; ("FALSE", "F") -> FALSE; ("TRUE", "T") -> TRUE, other character -> NA|
| factor    | `factor()`                   | `as.factor()`           |                                                             |

Si queremos saber la clase de la variable debemos usar `class()`, aunque tambien `str()` te dice esta información.

> NOTA: `as.integer()` convierte los numeros a enteros. Ejemplo: as.integer(2.5) = 2. En cambio, `as.double()` permite que los numeros contengan decimales.

---

## Recordatorio: Operadores

| Aritméticos              | Comparación               | Argumentos lógicos (Logical Operators)   |
|--------------------------|---------------------------|------------------------------------------|
| `+`  Adición / suma      | `<`  Menor que            | `!x`  - Not x (logical NOT)              |  
| `-`  Sustracción / resta | `>`  Mayor que            | `x & y` OR `x AND y` -  (logical AND)    |                
| `*`  Multiplicación      | `<=` Menor o igual que    | `x && y`  - identico                     |
| `/` OR %% División       | `>=`  Mayor o igual que   | `xor(x,y)`  Funcion OR                   |
| `^` OR ** Exponencial    | `==`  Igual a             | `%in%` pertenece a                       | 
| `%/%` División integral  | `!=`  Diferente de        |                                          |

Otra forma de escrir OR es `x | y` OR `x || y`.

---
## Jerarquía de operaciones

En R, al igual que en matemáticas, las operaciones tienen un orden de evaluación definido.

Cuanto tenemos varias operaciones ocurriendo al mismo tiempo, en realidad, algunas de ellas son realizadas antes que otras y el resultado de ellas dependerá de este orden.

| Orden |  Operadores             | 
|-------|-------------------------|
| 1     |  `^`                    |
| 2     | `* /`                   |
| 3	    | `+ -`                   |
| 4	    | `< > <= >= == !=`       |
| 5     | `!`                     |
| 6	    | `&`                     |
| 7	    | OR                      |

---

name: en-vivo
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---

## Vector: Formas de declarar un vector

``` r
# Declarar el vector con 5 posiciones (del 1 al 5)
# Opcion A - c()
x <- c(1,2,3,4,5)

# Opcion B - valorI:valorN
x <- 1:5 
x
# [1] 1 2 3 4 5

# Opcion C - seq(inicio, final)
x <- seq(1,5) # 
x
# [1] 1 2 3 4 5

# Opcion D -seq_len(final)
x <- seq_len(5) 
x
# [1] 1 2 3 4 5

# Opcion E - rep(valor, veces)
y <- c(rep(1,5), rep(c(3,5),5))
y
#  [1] 1 1 1 1 1 3 5 3 5 3 5 3 5 3 5
```

---

## Vector: Estructura y clases de variables

Conocer la estructura de un vector mediante la función `str()`. El vector es de tipo `integer` y va de 1 a 5.

``` r
str(x)
#  int [1:5] 1 2 3 4 5
```
Para solo ver el tipo usamos la función `class()`

``` r
class(x)
# [1] "integer"
```

---

## Dataframe: Formas de declarar un vector

``` r
x <- 1:4
n <- 10
M <- c(10, 35)
y <- 2:4
```

Podemos declarar los componentes de un Dataframe en variables separadas y luego juntarlo o en una sola linea.

``` r
x <- 1:4; n <- 10; M <- c(10, 35); y <- 2:4
```

Vamos a crear dataframes apartir de estos vectores.

``` r
data.frame(x, n)
#   x  n
# 1 1 10
# 2 2 10
# 3 3 10
# 4 4 10
data.frame(x, M)
#   x  M
# 1 1 10
# 2 2 35
# 3 3 10
# 4 4 35
```

---

## Dataframe en una sola linea

Crear un dataframe en una sola linea, sin declarar cada variable.

``` r
data.frame(x = 1:4, n = 10)
#   x  n
# 1 1 10
# 2 2 10
# 3 3 10
# 4 4 10
data.frame(x = 1:4, M = c(10, 35))
#   x  M
# 1 1 10
# 2 2 35
# 3 3 10
# 4 4 35
```

---

## Dataframe: Ejercicio

- ¿Porque al intentar hacer esta dataframe nos da error?

``` r
data.frame(x, y)
# Error in data.frame(x, y) : 
#  arguments imply differing number of rows: 4, 3
```

> NOTA: El número de filas y columnas debe ser el mismo número. Existen excepciones al usar paquetes como `dplyr`.

---

## Dataframe: Estructura y clases de variables

Cada columna es una **variable** la cual puede ser de un tipo o clase.

``` r
df <- data.frame(genes = paste0("Gen", seq_len(8)), 
                 expression = c(3.8, 5.5, 6.3, 1.8, 9, rep(3,3)), 
                 treatment =c(rep("Control", 4), rep("Condicion1",4)))
head(df)
#   genes expression  treatment
# 1  Gen1        3.8    Control
# 2  Gen2        5.5    Control
# 3  Gen3        6.3    Control
# 4  Gen4        1.8    Control
# 5  Gen5        9.0 Condicion1
# 6  Gen6        3.0 Condicion1
```

Es un dataframe con 3 variables (`genes`, `expression` y `treatment`) y cada uno tiene su propia clase.

``` r
str(df)
# 'data.frame':	8 obs. of  3 variables:
#  $ genes     : chr  "Gen1" "Gen2" "Gen3" "Gen4" ...
#  $ expression: num  3.8 5.5 6.3 1.8 9 3 3 3
#  $ treatment : chr  "Control" "Control" "Control" "Control" ...
```

---

## Ejercicios:

1) Convierte la columna 3 perteneciente a tratamiento (`treatment`) a `factor`.

> NOTA: En este ejemplo, solo tenemos dos condiciones "Control" y "Condicion1". Por lo que, solo deben hacer dos niveles (levels).

2) Renombra las filas con los nombres de los genes y elimina la columna 1.

> NOTA: Recuerda que para renombrar las filas es `rownames()` y las columnas se usa `colnames()`.

---

## Ejercicios: Respuestas

1) Modifica la columna 3 perteneciente a tratamiento (`treatment`) a `factor`.

``` r
df$treatment <- as.factor(df$treatment) # conversion a factor
str(df)
# 'data.frame':	8 obs. of  3 variables:
#  $ genes     : chr  "Gen1" "Gen2" "Gen3" "Gen4" ...
#  $ expression: num  3.8 5.5 6.3 1.8 9 3 3 3
#  $ treatment : Factor w/ 2 levels "Condicion1","Control": 2 2 2 2 1 1 1 1
```

> NOTA: El signo de dinero `$` nos permite seleccionar una columna (variable) de un dataframe.

2) Renombra las filas con los nombres de los genes y elimina la columna 1.

``` r
rownames(df) <- df$genes # renombrar
df <- df[,-1] # eliminar columna 1
```

---

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---

## Index

- 1) Obtener la información de un dato en específico.
- 2) Modificar un dato en específico.
- 3) Eliminarlo un dato en específico.

Podemos hacer la pregunta de dos maneras, 1) ¿Cuál es el nivel de expresión del Gen2? O 2) ¿Que gen contiene una expresión de 5.5?
]

---

## Ejercicios

1) **¿Cuál es el nivel de expresión del Gen2?**

> NOTA: Recuerda que un dataframe tiene dos dimensiones 1) fila y 2) columna, dataframe[fila, columna]. Estos nos ayudaran como coordenadas en el dataframe.

``` r
# Opcion A
df[2,1]
# [1] 5.5

# Opcion B
df$expression[2]
# [1] 5.5
```
]

``` r
# Opcion C
df["Gen2",]
#      expression treatment
# Gen2        5.5   Control
df["Gen2",1]
# [1] 5.5

# Opcion D
df["Gen2","expression"]
# [1] 5.5
```
]

---

## Ejercicios

2) **¿Qué gen contiene una expresión de 1.8?**

``` r
# Opcion A
df[df[, "expression"] == 1.8,]
#      expression treatment
# Gen4        1.8   Control
df[df[, 1] == 1.8,]
#      expression treatment
# Gen4        1.8   Control

# Opcion B
df[df == 1.8,]
#      expression treatment
# Gen4        1.8   Control
```
]

``` r
# Opcion C
df[df$expression == 1.8,]
#      expression treatment
# Gen4        1.8   Control

# Opcion D
subset(df, expression == 1.8)
#      expression treatment
# Gen4        1.8   Control
```

]

---

## Index usando un vector

Podemos crear una variable *i* que será nuestro index para extraer valores

``` r
x <- 1:5
i <- c(1, 3) # el index es numero y nos permite extraer la posicion 1 y 3 del vector
x[i]
# [1] 1 3
```
Usar el vector `i` para obtener las filas 1 y 3

``` r
df[i,]
#      expression treatment
# Gen1        3.8   Control
# Gen3        6.3   Control
```

> NOTA: Recuerda que una matriz esta compuesta de filas y columnas.

---

background-color: var(--morado-claro)
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---

## Ejercicio 1 - Vector

``` r
# Declarar el vector con 5 posiciones (del 1 al 5)
x <- 1:5
x
# Observar el componente / posicion 3
x[3]
# Observar multiles posiciones
x[c(1,3)]
x[c(1:3,5)] # OR x[-4]
# Sustituir el valor de la posicion 3
x[3] <- 20
x
# Eliminar la posicion 1
x[-1]
```

---

## Ejercicio 2 - Operaciones

``` r
# Ejercicio 1
x <- 1:10 # Declarar un vector
x[x >= 5] <- 20 # todos los numeros que sean mayor o igual a 5 sustituir por 20
x
# A partir del resultado de x realiza lo siguiente
x[x == 1] <- 25 # todos los numeros que sean igual a 1 sustituir por 25
x

# Ejercicio 2
x <- matrix(1:20, 4, 5) #Genera una matriz de tamano 4 x 5 (filas, columnas) (rows, columns) 
x # Visualizar la matriz
# A partir de la matriz generada realiza lo siguiente
x[, 5] <- 51:54 # 1. ¿Qué cambios existieron en la matriz?
x # Visualizar la matriz de salida
# 2. ¿Cuáles son los valores presentes en la columna 3 de la matriz?
x[, 5]
#3. ¿Como cambia el valor de salida cuando coloco drop= FALSE?
x[, 5, drop = FALSE]
```

---

## Ejercicio 3 - Operaciones

``` r
# Ejercicio 3. 
# 1. ¿Como cambia el valor de salida al eliminar la columna 3?
x <- matrix(1:16, 4, 4) #Genera una matriz de tamano 4 x 5 (filas, columnas) (rows, columns) 
x[, -1]
# 2. ¿Como cambia el valor de salida cuando coloco drop= FALSE?
x[, -(1:2), drop = FALSE]
# 3. Renombra las filas y columnas empleando rownames() y colnames(). En fila coloca los nombres: Gen 1, Gen 2, Gen 3 y  Gen 4. Y en columnas coloca los nombres: SRA_1,SRA_2 Y SRA_3

# Ejercicio 4.  Realiza las siguientes operaciones
# 1. Sumatoria
x <- 1:4
y <- 1:2
z <- x + y
z
# 2. Multiplicacion
x <- 1:4
a <- 10
z <- a * x
z
```

---

---

## Paqueterias importantes en la Manipulación de datos en R

- Tidyverse packages
  - tidyr
  - dplyr
  - ggplot2
  - readr
  - purrr
  - tibble
  - stringr
  - forcats
  
Cada paquete contiene sus propias instalaciones, aunque al instalar el paquete `Tidyverse`, algunas funciones podrían compartirse con los demás paquetes.

> **NOTA:** Cuando queremos evitar chocar entre paquetes indicamos de donde viene la funcion, ejemplo: `dplyr::select()`.

Para más información pueden visitar su [sitio web](https://www.tidyverse.org/packages/).

---

##  %>% pipeline

Empleando el paquete `Tidyverse` y `dplyr` puedes acceder a usar `%>%`, el cual nos permitirá enlazar funciones en la modificación de un dataframe.

``` r
df <- data.frame(genes = paste0("Gen", seq_len(8)), 
                 expression = c(3.8, 5.5, 6.3, 1.8, 9, rep(3,3)), 
                 treatment =c(rep("Control", 4), rep("Condicion1",4)))

df %>% head()
#   genes expression  treatment
# 1  Gen1        3.8    Control
# 2  Gen2        5.5    Control
# 3  Gen3        6.3    Control
# 4  Gen4        1.8    Control
# 5  Gen5        9.0 Condicion1
# 6  Gen6        3.0 Condicion1
```

---

## Archivo tipo `Tibble`

Cuando trabajamos con los paquetes provenientes de `Tidyverse` los archivos de salida por lo regular son tipo `tibble`, que es parecido a un dataframe, pero puede chocar con algunas paqueterias para manipulacion de dataframe.

Por lo que regularmente convertimos esta salida a dataframe usando `as.data.frame()`

Para más información revisa el paquete [tibble](https://tibble.tidyverse.org/).

---

## Funciones que vamos a emplear de `dplyr`

.content-box-gray[
- `select()` : Seleccionar nombres de las columnas. --> select(dataframe, columna1, columna2, ... columnax)
- `filter()` : Filtrar filas por una condicion especifica, apartir de la columna. --> filter(dataframe, columna1 == "condicion")
- `mutate()` : Modificar o agregar columnas. --> mutate(dataframe, columna1 = "condicion")
- `group_by()` : Agrupar informacion de acuerdo a un(as) columna(s) seleccionada(s). 
- `if_else()` : Condicional. --> if_else(dataframe, codicion, si se acepta entonces, si se rechaza entonces)
- `arrange()` : Acomodar los resultados, default de menor a mayor.
- `count()`: Cuenta los valores de acuerdo a una variable.
- `left_join()` : Unir dos dataframe con base en una misma columna en comun. --> left_join(dataframe1, dataframe2, by  = "Columna en comun, mismo nombre")
- `n_distinct()` : Cuenta las filas unicas.
- `distinct()` : Muestra las filas duplicadas.
- `summarise()` : reduce varios valores seleccionados en un resumen.
]

Para más información visita el [Github del paquete dplyr](https://github.com/cran/dplyr).

Para ver todas las funciones existentes del paquete puede ver el [Cheat Sheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/pngs/thumbnails/data-transformation-cheatsheet-thumbs.png).

---

## Funciones que vamos a emplear de `reshape2`

Para más información visita el [Github del paquete reshape2](https://github.com/cran/reshape2). Aunque este paquete se encuentra descontinuado, aun funcionan las funciones. Fue absorbido posteriormente por el paquete `tidyr`.

Se supone que la función `gather()` del paquete `tidyr` realiza lo mismo que `melt()` del paquete `reshape2`. Hoy lo compararemos ambas.

Para más información visita el [Github del paquete tidyr](https://tidyr.tidyverse.org/).

---

## Funciones para unir dataframes con el paquete `dplyr`

---

---

## Importar datos en R

Partiendo del código que ya conocemos, continuamos con el **Script**.

``` r
######
# Script de clase "Intro a R"
# Evelia Coss
# 8 de marzo 2024
#######

# Cargar las variables infoPowers, infoStats, marvelDcInfo
load(file = "Info_data.RData")
```

El archivo [`Info_data.RData`](https://github.com/EveliaCoss/ViernesBioinfo2024/blob/main/Practica_Clase6y7/Info_data.RData) lo creamos en la clase pasada, por ello, solo cargaremos este archivo.

---

## Paso 9. Unir dataframes con base en una columna en común (Join Datasets)

Iniciamos con 3 dataframes (`infoCharacters, infoStats` e `infoPowers`). A partir del dataframe `infoCharacters` seleccionamos solo los datos de **Marvel Comics y DC Comics**, limpiamos los duplicados y almacenamos en la variable `marvelDcInfo`.

> **Primera unión**, empleando la columna `Name` contenida en ambos dataframes. Se unieron las columnas seleccionadas con la información básica de cada personaje con los stats de cada uno.

``` r
marvelDcStatsInfo <- left_join(marvelDcInfo, infoStats, by = "Name")
head(marvelDcStatsInfo)[1:5]
#            Name Gender              Race     Publisher Alignment
# 1        A-Bomb   Male             Human Marvel Comics      good
# 2      Abin Sur   Male           Ungaran     DC Comics      good
# 3   Abomination   Male Human / Radiation Marvel Comics       bad
# 4       Abraxas   Male     Cosmic Entity Marvel Comics       bad
# 5 Absorbing Man   Male             Human Marvel Comics      <NA>
# 6  Adam Strange   Male             Human     DC Comics      good
```

---

## Paso 9. Unir dataframes con base en una columna en común (Join Datasets)

> **Segunda unión**, empleando la columna `Name`. Se el dataframe generado co la informacion de los poderes por cada personaje.

``` r
fullMarvelDc <- left_join(marvelDcStatsInfo, infoPowers, by = "Name")
head(fullMarvelDc)[1:5]
#            Name Gender              Race     Publisher Alignment
# 1        A-Bomb   Male             Human Marvel Comics      good
# 2      Abin Sur   Male           Ungaran     DC Comics      good
# 3   Abomination   Male Human / Radiation Marvel Comics       bad
# 4       Abraxas   Male     Cosmic Entity Marvel Comics       bad
# 5 Absorbing Man   Male             Human Marvel Comics      <NA>
# 6  Adam Strange   Male             Human     DC Comics      good
```

---

## Paso 10. Cambiar formatos en algunas columnas

``` r
fullMarvelDc$Name <- as.factor(fullMarvelDc$Name)
fullMarvelDc$Alignment <- as.factor(fullMarvelDc$Alignment)
```

Si usamos `str()` tendremos desplegado la informacion de todas las 179 columnas presentes en el dataframe, recuerda que puedes usar `dim()` para checar las dimensiones.

``` r
dim(fullMarvelDc)
# [1] 586 179
```

---

## Verificamos los formatos con `class()`

``` r
class(fullMarvelDc$Name)
# [1] "factor"
class(fullMarvelDc$Gender)
# [1] "factor"
class(fullMarvelDc$Race)
# [1] "factor"
class(fullMarvelDc$Publisher)
# [1] "factor"
class(fullMarvelDc$Alignment)
# [1] "factor"
```

---

---

## Ejercicios

1) ¿Cuántos individuos tenemos de cada género (Gender), considerando su raza (Race) y empresa (Publisher)?

2) ¿Cuántos personajes villanos, neutrales y héroes (su bando, Aligment) por cada empresa?

3) ¿Cuántos individuos tenemos de cada género (Gender), considerando su bando (Aligment) y empresa (Publisher)?

---
## Ejercicios: RESPUESTAS

1) ¿Cuántos individuos tenemos de cada género (Gender), considerando su raza (Race) y empresa (Publisher)?

``` r
fullMarvelDc %>% filter(!is.na(Race)) %>%
  group_by(Publisher, Gender) %>%
  dplyr::count(Race) %>%
  select(Publisher, Gender, Race,  Count = n) %>%
  arrange(-Count) # ordenar de max a min
# # A tibble: 72 × 4
# # Groups:   Publisher, Gender [6]
#    Publisher     Gender Race              Count
#    <fct>         <fct>  <fct>             <int>
#  1 DC Comics     Male   Human                65
#  2 Marvel Comics Male   Human                63
#  3 Marvel Comics Male   Mutant               36
#  4 DC Comics     Female Human                21
#  5 Marvel Comics Female Mutant               20
#  6 Marvel Comics Female Human                19
#  7 Marvel Comics Male   Human / Radiation     8
#  8 Marvel Comics Male   Symbiote              8
#  9 DC Comics     Male   God / Eternal         6
# 10 DC Comics     Male   Android               4
# # ℹ 62 more rows
```

]

---
## Ejercicios: RESPUESTAS

2) ¿Cuántos personajes villanos, neutrales y héroes (su bando, Aligment) por cada empresa?

``` r
marvelDcAlignment <- fullMarvelDc %>% filter(!is.na(Alignment)) %>%
  group_by(Publisher) %>%
  dplyr::count(Alignment) %>%
  select(Publisher, Alignment,  Count = n) %>%
  arrange(-Count) # ordenar de max a min

marvelDcAlignment
# # A tibble: 6 × 3
# # Groups:   Publisher [2]
#   Publisher     Alignment Count
#   <fct>         <fct>     <int>
# 1 Marvel Comics good        235
# 2 DC Comics     good        112
# 3 Marvel Comics bad          97
# 4 DC Comics     bad          46
# 5 DC Comics     neutral       6
# 6 Marvel Comics neutral       5
```

---
## Ejercicios: RESPUESTAS

3) ¿Cuántos individuos tenemos de cada género (Gender), considerando su bando (Aligment) y empresa (Publisher)?

``` r
# Y si le agregamos sexo?
fullMarvelDc %>% filter(!is.na(Alignment) & !is.na(Gender)) %>%
  group_by(Publisher, Gender) %>%
  dplyr::count(Alignment) %>%
  select(Publisher, Gender, Alignment,  Count = n) %>%
  arrange(-Count) # ordenar de max a min
# # A tibble: 11 × 4
# # Groups:   Publisher, Gender [4]
#    Publisher     Gender Alignment Count
#    <fct>         <fct>  <fct>     <int>
#  1 Marvel Comics Male   good        135
#  2 DC Comics     Male   good         82
#  3 Marvel Comics Female good         82
#  4 Marvel Comics Male   bad          80
#  5 DC Comics     Male   bad          35
#  6 DC Comics     Female good         30
#  7 Marvel Comics Female bad          13
#  8 DC Comics     Female bad          11
#  9 DC Comics     Male   neutral       5
# 10 Marvel Comics Male   neutral       5
# 11 DC Comics     Female neutral       1
```
]

---
class: center, middle

---

## Esto es lo que realiza `melt()`

---

## Paso 11. Transformar en una sola columna los poderes usando `melt()`

Reacomodar la tabla de acuerdo a las habilidades o poderes.

La función `melt()` te permite acomodar la tabla, cambiando el formato de la misma de acuerdo a las habilidades o poderes.

``` r
marvelDc <- melt(fullMarvelDc, id = c("Name", "Gender", "Race", "Publisher", "Alignment", "Intelligence.x", 
                                         "Strength", "Speed", "Durability.x", "Power", "Combat", "Total"))

str(marvelDc)
# 'data.frame':	97862 obs. of  14 variables:
#  $ Name          : Factor w/ 585 levels "A-Bomb","Abin Sur",..: 1 2 3 4 5 6 7 8 9 10 ...
#  $ Gender        : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 1 2 2 2 ...
#  $ Race          : Factor w/ 48 levels "Alien","Amazon",..: 20 45 26 10 20 20 NA 20 NA NA ...
#  $ Publisher     : Factor w/ 2 levels "DC Comics","Marvel Comics": 2 1 2 2 2 1 2 2 2 2 ...
#  $ Alignment     : Factor w/ 3 levels "bad","good","neutral": 2 2 1 1 NA 2 NA 2 2 1 ...
#  $ Intelligence.x: int  38 50 63 88 NA 1 NA 10 75 50 ...
#  $ Strength      : int  100 90 80 100 NA 1 NA 8 28 85 ...
#  $ Speed         : int  17 53 53 83 NA 1 NA 13 38 100 ...
#  $ Durability.x  : int  80 64 90 99 NA 1 NA 5 80 85 ...
#  $ Power         : int  17 84 55 100 NA 0 NA 5 72 100 ...
#  $ Combat        : int  64 65 95 56 NA 1 NA 20 95 40 ...
#  $ Total         : int  316 406 436 526 NA 5 NA 61 388 460 ...
#  $ variable      : Factor w/ 167 levels "Agility","Accelerated.Healing",..: 1 1 1 1 1 1 1 1 1 1 ...
#  $ value         : chr  "False" "False" "False" "False" ...
```
]

---

## Checamos los cambios en el contenido

Se redujo el numero de columnas y las columnas seleccionados se compactaron en una sola columna. Se agrego la columna `value` con valores de TRUE y FALSE, considerando si el personaje tenia o no la habilidad.

``` r
head(marvelDc, 3)
#          Name Gender              Race     Publisher Alignment Intelligence.x
# 1      A-Bomb   Male             Human Marvel Comics      good             38
# 2    Abin Sur   Male           Ungaran     DC Comics      good             50
# 3 Abomination   Male Human / Radiation Marvel Comics       bad             63
#   Strength Speed Durability.x Power Combat Total variable value
# 1      100    17           80    17     64   316  Agility False
# 2       90    53           64    84     65   406  Agility False
# 3       80    53           90    55     95   436  Agility False
```

---

## Al usar `melt()` se reduce el numero de columnas en 2

``` r
dim(marvelDc)
# [1] 97862    14
```

Se redujeron de 179 columnas a 14 columnas. Creando 2 nuevas columnas:

1) `variable` :  Contiene el nombre de los poderes.

2) `value` : Contiene valores lógicos de acuerdo a cada poder de los personajes.

---

## Paso 12. Renombrar la columnas

Vamos a renombrar la columna `variable` por `SuperPower`.

``` r
colnames(marvelDc)[colnames(marvelDc) == "variable"] <- "SuperPower" # Renombrar columna
```

A veces el paquete `dplyr` cambia el nombre de algunas columnas solo agregando `.x`, no es preocupante y podemos corregirlo de la siguiente manera.

``` r
# Corregir nombres de columnas
colnames(marvelDc)[colnames(marvelDc) == "Intelligence.x"] <- "Intelligence"  # Renombrar columna
colnames(marvelDc)[colnames(marvelDc) == "Durability.x"] <- "Durability"  # Renombrar columna
```

---

## Cambiamos el formato de las columnas

En caso de que las siguientes columnas no se encuentren convertidas.

``` r
marvelDc$Name <- as.factor(marvelDc$Name)
marvelDc$Gender <- as.factor(marvelDc$Gender)
marvelDc$Race <- as.factor(marvelDc$Race)
marvelDc$Publisher <- as.factor(marvelDc$Publisher)
marvelDc$Alignment <- as.factor(marvelDc$Alignment)
marvelDc$SuperPower <- as.factor(marvelDc$SuperPower)
```

---

## Verificar el formato de las columnas

``` r
class(marvelDc$Name)
# [1] "factor"
class(marvelDc$Gender)
# [1] "factor"
class(marvelDc$Race)
# [1] "factor"
class(marvelDc$Publisher)
# [1] "factor"
class(marvelDc$Alignment)
# [1] "factor"
class(marvelDc$SuperPower)
# [1] "factor"
```

---

## Paso 13. Selección de habilidades con TRUE

``` r
marvelDc <- marvelDc %>%
  filter(value == "True") %>%
  select(-value) #eliminar columna

head(marvelDc)
#         Name Gender    Race     Publisher Alignment Intelligence Strength Speed
# 1       Ajax   Male  Cyborg Marvel Comics      <NA>           NA       NA    NA
# 2      Amazo   Male Android     DC Comics       bad           75      100   100
# 3      Angel   Male    <NA> Marvel Comics      good           63       13    46
# 4 Angel Dust Female  Mutant Marvel Comics      <NA>           NA       NA    NA
# 5  Annihilus   Male    <NA> Marvel Comics       bad           75       80    47
# 6 Ant-Man II   Male   Human Marvel Comics      good           63       10    23
#   Durability Power Combat Total SuperPower
# 1         NA    NA     NA    NA    Agility
# 2        100   100    100   575    Agility
# 3         64    17     42   245    Agility
# 4         NA    NA     NA    NA    Agility
# 5         56    59     64   381    Agility
# 6         28    32     28   184    Agility
```

---

## Ejercicios

1) ¿Quiénes son los personajes con más habilidades/poderes por cada empresa?

``` r
marvelDc %>% group_by(Name, Publisher) %>%
  distinct(SuperPower) %>%
  dplyr::count(Publisher) %>%
  select(Name, Publisher,  Count = n) %>%
  arrange(-Count) %>% # ordenar de max a min
  head(3)
# # A tibble: 3 × 3
# # Groups:   Name, Publisher [3]
#   Name            Publisher     Count
#   <fct>           <fct>         <int>
# 1 Spectre         DC Comics        49
# 2 Amazo           DC Comics        44
# 3 Living Tribunal Marvel Comics    35
```

Spectre es el personaje con más habilidades a comparación de los demás personajes.

> NOTA:  Cuando usamos el simbolo `-` en la funcion `arrange()` para ordenar de mayor a menor los valores.

---

## Ejercicios

1) ¿Quiénes son los personajes con más habilidades/poderes por cada empresa?

Sin embargo, ser el más habilidoso no implica el más poderoso...Si observamos sus stats todos son de 1 o menos.

``` r
marvelDc %>% filter(Name == "Spectre")
#       Name Gender          Race Publisher Alignment Intelligence Strength Speed
# 1  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 2  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 3  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 4  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 5  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 6  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 7  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 8  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 9  Spectre   Male God / Eternal DC Comics      good            1        1     1
# 10 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 11 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 12 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 13 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 14 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 15 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 16 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 17 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 18 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 19 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 20 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 21 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 22 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 23 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 24 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 25 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 26 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 27 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 28 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 29 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 30 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 31 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 32 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 33 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 34 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 35 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 36 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 37 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 38 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 39 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 40 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 41 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 42 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 43 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 44 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 45 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 46 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 47 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 48 Spectre   Male God / Eternal DC Comics      good            1        1     1
# 49 Spectre   Male God / Eternal DC Comics      good            1        1     1
#    Durability Power Combat Total             SuperPower
# 1           1     0      1     5                Agility
# 2           1     0      1     5    Accelerated.Healing
# 3           1     0      1     5  Dimensional.Awareness
# 4           1     0      1     5                Stealth
# 5           1     0      1     5      Energy.Absorption
# 6           1     0      1     5                 Flight
# 7           1     0      1     5           Marksmanship
# 8           1     0      1     5              Longevity
# 9           1     0      1     5         Intelligence.y
# 10          1     0      1     5         Super.Strength
# 11          1     0      1     5              Telepathy
# 12          1     0      1     5          Energy.Blasts
# 13          1     0      1     5            Duplication
# 14          1     0      1     5          Size.Changing
# 15          1     0      1     5        Density.Control
# 16          1     0      1     5                Stamina
# 17          1     0      1     5            Super.Speed
# 18          1     0      1     5             Possession
# 19          1     0      1     5         Electrokinesis
# 20          1     0      1     5 Darkforce.Manipulation
# 21          1     0      1     5            Death.Touch
# 22          1     0      1     5          Teleportation
# 23          1     0      1     5                  Magic
# 24          1     0      1     5            Immortality
# 25          1     0      1     5                Phasing
# 26          1     0      1     5      Astral.Projection
# 27          1     0      1     5           Fire.Control
# 28          1     0      1     5        Invulnerability
# 29          1     0      1     5      Energy.Constructs
# 30          1     0      1     5           Force.Fields
# 31          1     0      1     5         Psionic.Powers
# 32          1     0      1     5             Elasticity
# 33          1     0      1     5              Animation
# 34          1     0      1     5           Precognition
# 35          1     0      1     5           Hypnokinesis
# 36          1     0      1     5         Animal.Control
# 37          1     0      1     5        Heat.Generation
# 38          1     0      1     5        Gravity.Control
# 39          1     0      1     5          Light.Control
# 40          1     0      1     5             Levitation
# 41          1     0      1     5            Time.Travel
# 42          1     0      1     5              Illusions
# 43          1     0      1     5          Water.Control
# 44          1     0      1     5         Omnilingualism
# 45          1     0      1     5      Time.Manipulation
# 46          1     0      1     5           Invisibility
# 47          1     0      1     5            Sub.Mariner
# 48          1     0      1     5          Vision...Heat
# 49          1     0      1     5        Reality.Warping
```
]

Es el personaje con más habilidades, pero tiene sus stats muy bajos.

---
## Ejercicios

2) ¿Quiénes son los personajes con los stats más altos por cada empresa?

``` r
marvelDc %>% arrange(-Intelligence, -Strength, -Speed, -Durability, -Power, -Combat) %>%
  select(-SuperPower) %>%
  distinct() %>% head(5)
#                Name Gender          Race     Publisher Alignment Intelligence
# 1   Mister Mxyzptlk   Male God / Eternal     DC Comics       bad          113
# 2          Superman   Male    Kryptonian     DC Comics      good          100
# 3 Martian Manhunter   Male       Martian     DC Comics      good          100
# 4          Galactus   Male Cosmic Entity Marvel Comics   neutral          100
# 5    Captain Marvel Female    Human-Kree Marvel Comics      good          100
#   Strength Speed Durability Power Combat Total
# 1       10    12         14   100     28   277
# 2      100   100        100    94     85   579
# 3      100    96        100   100     85   581
# 4      100    83        100   100     70   553
# 5      100    67         95    62     56   480
```

> NOTA:  Cuando usamos el simbolo `-` en la funcion `select()` estamos eliminando la columna SuperPower.

---

## Ejercicios

3) ¿Existe algún personaje que tenga todos sus stats en 100?

Hay 6 columnas de stats evaluados que sumados nos da la columna Total, osea que los valores maximos se encuentran cercanos a 600 (columnas 6:11).

``` r
# Opcion B
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Intelligence >= 100 & Strength >= 100 & Speed >= 100 & Durability >= 100 & Power >= 100 & Combat >= 100)
#  [1] Name         Gender       Race         Publisher    Alignment   
#  [6] Intelligence Strength     Speed        Durability   Power       
# [11] Combat       Total       
# <0 rows> (or 0-length row.names)
```

La salida da NINGUNO, ya que no existe un personaje que tenga los 6 Stats con valores de 100.

---

## Ejercicios

Vamos a disminuir el filtro en valores cercanos o iguales a 90.

``` r
# Personaje con stats superiores a 90 en cada uno
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Intelligence >= 90 & Strength >= 90 & Speed >= 90 & Durability >= 90 & Power >= 90 & Combat >= 90)
#          Name Gender       Race Publisher Alignment Intelligence Strength Speed
# 1 General Zod   Male Kryptonian DC Comics       bad           94      100    96
#   Durability Power Combat Total
# 1        100    94     95   579
```

---

## Ejercicios

Y si filtramos por el valor total ...

``` r
# Y si filtramos 
# Personas con valores totales superiores a 570
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Total >= 570)
#                Name Gender       Race Publisher Alignment Intelligence Strength
# 1             Amazo   Male    Android DC Comics       bad           75      100
# 2 Martian Manhunter   Male    Martian DC Comics      good          100      100
# 3          Superman   Male Kryptonian DC Comics      good          100      100
# 4       General Zod   Male Kryptonian DC Comics       bad           94      100
# 5    Superboy-Prime   Male Kryptonian DC Comics       bad           94      100
#   Speed Durability Power Combat Total
# 1   100        100   100    100   575
# 2    96        100   100     85   581
# 3   100        100    94     85   579
# 4    96        100    94     95   579
# 5   100        100   100     85   579
```

---

## Paso 14. Renombrar el formato de Alignment

Cuando usamos la función `if_else()` la empleamos para asignar valores de acuerdo a una conducional.

- Usage: if_else(condición, TRUE, FALSE)
  - Condición: Selecciona una columna y verifica que debe cumplir la misma.
  - TRUE: En caso de que la condición sea VERDADERA, que acción debe hacer.
  - FALSE: En caso de que la condición sea FALSA, que acción debe hacer

Vamos agregar una nueva columna y vamos a cambiar los valores a usar el bando (Aligment)

``` r
# Opcion A - Función `if_else()`
marvelDc_edited <- marvelDc %>% mutate(Group = 
                    if_else(Alignment == "good", "hero", # Primer if_else
                          if_else(Alignment == "bad","villain", "neutral"))) # Segundo if_else
```

Tambien podemos usar `case_when()`

``` r
# Opcion B - Funcion `case_when()`
marvelDc_edited <- marvelDc %>% mutate(Group = 
                   case_when(Alignment == "good" ~ "hero", 
                             Alignment == "bad" ~ "villain", 
                             TRUE ~ "neutral"))
```

---

## Ejercicio: Función `if_else()`

¿Quiénes son los hombres más poderosos y malvados?

``` r
marvelDc_edited %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Gender == "Male" & Group == "villain") %>%
  arrange(-Total)
#                  Name Gender              Race     Publisher Alignment
# 1         General Zod   Male        Kryptonian     DC Comics       bad
# 2      Superboy-Prime   Male        Kryptonian     DC Comics       bad
# 3               Amazo   Male           Android     DC Comics       bad
# 4            Dormammu   Male              <NA> Marvel Comics       bad
# 5            Doomsday   Male             Alien     DC Comics       bad
# 6     Cyborg Superman   Male            Cyborg     DC Comics       bad
# 7               Magus   Male              <NA> Marvel Comics       bad
# 8             Abraxas   Male     Cosmic Entity Marvel Comics       bad
# 9          Black Adam   Male              <NA>     DC Comics       bad
# 10      Fallen One II   Male              <NA> Marvel Comics       bad
# 11           Darkseid   Male           New God     DC Comics       bad
# 12          Onslaught   Male            Mutant Marvel Comics       bad
# 13              Match   Male              <NA>     DC Comics       bad
# 14       Anti-Monitor   Male     God / Eternal     DC Comics       bad
# 15         Apocalypse   Male            Mutant Marvel Comics       bad
# 16               Lobo   Male          Czarnian     DC Comics       bad
# 17             Thanos   Male           Eternal Marvel Comics       bad
# 18             Ultron   Male           Android Marvel Comics       bad
# 19          Destroyer   Male              <NA> Marvel Comics       bad
# 20         Air-Walker   Male              <NA> Marvel Comics       bad
# 21            Carnage   Male          Symbiote Marvel Comics       bad
# 22            Magneto   Male            Mutant Marvel Comics       bad
# 23           Mephisto   Male              <NA> Marvel Comics       bad
# 24              Venom   Male          Symbiote Marvel Comics       bad
# 25        Abomination   Male Human / Radiation Marvel Comics       bad
# 26        Doctor Doom   Male             Human Marvel Comics       bad
# 27           Sinestro   Male         Korugaran     DC Comics       bad
# 28               Loki   Male         Asgardian Marvel Comics       bad
# 29               Kang   Male              <NA> Marvel Comics       bad
# 30           Brainiac   Male           Android     DC Comics       bad
# 31        Swamp Thing   Male     God / Eternal     DC Comics       bad
# 32            Metallo   Male           Android     DC Comics       bad
# 33          Venom III   Male          Symbiote Marvel Comics       bad
# 34          Annihilus   Male              <NA> Marvel Comics       bad
# 35        Iron Monger   Male              <NA> Marvel Comics       bad
# 36           Scorpion   Male             Human Marvel Comics       bad
# 37        Utgard-Loki   Male       Frost Giant Marvel Comics       bad
# 38           Mandarin   Male             Human Marvel Comics       bad
# 39       Doppelganger   Male              <NA> Marvel Comics       bad
# 40            Mach-IV   Male              <NA> Marvel Comics       bad
# 41             Azazel   Male          Neyaphem Marvel Comics       bad
# 42             Exodus   Male            Mutant Marvel Comics       bad
# 43               Bane   Male             Human     DC Comics       bad
# 44              Rhino   Male Human / Radiation Marvel Comics       bad
# 45               Klaw   Male             Human Marvel Comics       bad
# 46         Sabretooth   Male            Mutant Marvel Comics       bad
# 47         Molten Man   Male              <NA> Marvel Comics       bad
# 48           Venom II   Male              <NA> Marvel Comics       bad
# 49       Ra's Al Ghul   Male             Human     DC Comics       bad
# 50           Evilhawk   Male             Alien Marvel Comics       bad
# 51           Blackout   Male             Demon Marvel Comics       bad
# 52     Solomon Grundy   Male            Zombie     DC Comics       bad
# 53            Electro   Male             Human Marvel Comics       bad
# 54             Morlun   Male              <NA> Marvel Comics       bad
# 55               Blob   Male              <NA> Marvel Comics       bad
# 56                Gog   Male              <NA>     DC Comics       bad
# 57           Junkpile   Male            Mutant Marvel Comics       bad
# 58              Joker   Male             Human     DC Comics       bad
# 59         Demogoblin   Male             Demon Marvel Comics       bad
# 60               Zoom   Male              <NA>     DC Comics       bad
# 61        Killer Croc   Male         Metahuman     DC Comics       bad
# 62            Shocker   Male             Human Marvel Comics       bad
# 63           Vanisher   Male              <NA> Marvel Comics       bad
# 64            Vulture   Male             Human Marvel Comics       bad
# 65  Kraven the Hunter   Male             Human Marvel Comics       bad
# 66             Lizard   Male             Human Marvel Comics       bad
# 67    Green Goblin II   Male              <NA> Marvel Comics       bad
# 68    Mister Mxyzptlk   Male     God / Eternal     DC Comics       bad
# 69       Green Goblin   Male             Human Marvel Comics       bad
# 70          Hydro-Man   Male              <NA> Marvel Comics       bad
# 71        Tiger Shark   Male             Human Marvel Comics       bad
# 72       Moses Magnum   Male              <NA> Marvel Comics       bad
# 73         Changeling   Male              <NA> Marvel Comics       bad
# 74          Rick Flag   Male              <NA>     DC Comics       bad
# 75          Kraven II   Male             Human Marvel Comics       bad
# 76      Mister Freeze   Male             Human     DC Comics       bad
# 77           Bullseye   Male             Human Marvel Comics       bad
# 78           Deadshot   Male             Human     DC Comics       bad
# 79             Leader   Male              <NA> Marvel Comics       bad
# 80     Lightning Lord   Male              <NA>     DC Comics       bad
# 81            Big Man   Male              <NA> Marvel Comics       bad
# 82       Proto-Goblin   Male              <NA> Marvel Comics       bad
# 83        Blizzard II   Male              <NA> Marvel Comics       bad
# 84          Scarecrow   Male             Human     DC Comics       bad
# 85               Warp   Male              <NA>     DC Comics       bad
# 86          Red Skull   Male              <NA> Marvel Comics       bad
# 87            Penguin   Male             Human     DC Comics       bad
# 88           Kevin 11   Male             Human     DC Comics       bad
# 89          Chameleon   Male              <NA>     DC Comics       bad
# 90          Blackwing   Male              <NA> Marvel Comics       bad
# 91         Lex Luthor   Male             Human     DC Comics       bad
# 92           Tinkerer   Male              <NA> Marvel Comics       bad
# 93             Walrus   Male             Human Marvel Comics       bad
# 94            Riddler   Male              <NA>     DC Comics       bad
# 95               Pyro   Male              <NA> Marvel Comics       bad
# 96     Doctor Doom II   Male              <NA> Marvel Comics       bad
# 97     Doctor Octopus   Male             Human Marvel Comics       bad
# 98          Weapon XI   Male              <NA> Marvel Comics       bad
# 99    Mister Sinister   Male   Human / Altered Marvel Comics       bad
# 100       Bloodwraith   Male              <NA> Marvel Comics       bad
# 101          Bird-Man   Male             Human Marvel Comics       bad
# 102       Bird-Man II   Male             Human Marvel Comics       bad
# 103              Ammo   Male             Human Marvel Comics       bad
# 104         Hobgoblin   Male              <NA> Marvel Comics       bad
# 105          Blizzard   Male              <NA> Marvel Comics       bad
# 106      Black Abbott   Male              <NA> Marvel Comics       bad
# 107     Razor-Fist II   Male              <NA> Marvel Comics       bad
#     Intelligence Strength Speed Durability Power Combat Total   Group
# 1             94      100    96        100    94     95   579 villain
# 2             94      100   100        100   100     85   579 villain
# 3             75      100   100        100   100    100   575 villain
# 4             88       95    83        100   100     80   546 villain
# 5             88       80    67        120   100     90   545 villain
# 6             75       93    92        100   100     80   540 villain
# 7             88      100    70         99   100     74   531 villain
# 8             88      100    83         99   100     56   526 villain
# 9             88      100    92        100    89     56   525 villain
# 10            88       85    83        100   100     56   512 villain
# 11            88      100    23        100   100     95   506 villain
# 12            88       85    47        100   100     85   505 villain
# 13            75       95    83         85    90     70   498 villain
# 14            88       90    38         90   100     90   496 villain
# 15           100      100    33        100   100     60   493 villain
# 16            88       83    35        100    95     85   486 villain
# 17            88      100    17        100   100     80   485 villain
# 18            88       83    42        100   100     64   477 villain
# 19            50       95    58         98    90     70   461 villain
# 20            50       85   100         85   100     40   460 villain
# 21            63       63    70         84    81     90   451 villain
# 22            88       80    27         84    91     80   450 villain
# 23            88       85    35         95   100     42   445 villain
# 24            75       57    65         84    79     84   444 villain
# 25            63       80    53         90    55     95   436 villain
# 26           100       32    20        100    93     84   429 villain
# 27            75       80    53         64   100     56   428 villain
# 28            88       57    47         85    85     56   418 villain
# 29           100       48    58         70    71     70   417 villain
# 30           100       28    63         90    60     75   416 villain
# 31            88       80    23        100    66     56   413 villain
# 32            75       53    23         95    74     64   384 villain
# 33            63       73    35         90    66     56   383 villain
# 34            75       80    47         56    59     64   381 villain
# 35            88       63    25         90    57     56   379 villain
# 36            50       52    60         85    49     80   376 villain
# 37            50       80    23         84    72     64   373 villain
# 38           100       28    23         28    99     95   373 villain
# 39             8       63    60         95    62     84   372 villain
# 40            75       36    60         84    60     56   371 villain
# 41            50       11    47         95    87     80   370 villain
# 42            63       81    28         28   100     70   370 villain
# 43            88       53    23         56    51     95   366 villain
# 44            25       80    43         90    37     85   360 villain
# 45            63       38    33        100    62     60   356 villain
# 46            55       53    25         90    28    100   351 villain
# 47            50       73    23         84    53     56   339 villain
# 48            50       57    47         70    54     56   334 villain
# 49           100       28    32         42    27    100   329 villain
# 50            50       32    33         70    78     64   327 villain
# 51            63       32    45         80    38     65   323 villain
# 52             9       93    13        100    78     30   323 villain
# 53            69       10    50         56    67     64   316 villain
# 54            63       60    35         42    59     56   315 villain
# 55            10       83    23         95    26     72   309 villain
# 56            50       34    47         72    39     56   298 villain
# 57            50       38    17         90    71     30   296 villain
# 58           100       10    12         56    22     90   290 villain
# 59            50       48    42         35    54     60   289 villain
# 60            50       10   100         28    72     28   288 villain
# 61            25       48    33         85    36     60   287 villain
# 62            63       10    23         70    63     56   285 villain
# 63            63       10    75         56    39     42   285 villain
# 64            63       22    47         56    40     56   284 villain
# 65            63       32    35         42    26     85   283 villain
# 66            38       51    27         70    40     56   282 villain
# 67            75       55    37         50    38     26   281 villain
# 68           113       10    12         14   100     28   277 villain
# 69            75       48    35         48    38     28   272 villain
# 70            38       13    25         80    66     50   272 villain
# 71            38       72    42         70    21     28   271 villain
# 72            75       28    12         42    55     56   268 villain
# 73            63       10    23         42    64     64   266 villain
# 74            88       11    23         28    19     95   264 villain
# 75            50       34    23         28    36     85   256 villain
# 76            75       32    12         70    37     28   254 villain
# 77            50       11    25         70    20     70   246 villain
# 78            50       10    23         28    47     80   238 villain
# 79           100       10    12         14    58     42   236 villain
# 80            44       10    23         42    66     42   227 villain
# 81            75       12    23         28    19     70   227 villain
# 82            63       38    23         48    22     28   222 villain
# 83            38       10    27         42    47     56   220 villain
# 84            81       10    12         14    48     50   215 villain
# 85            38       10    23         28    63     50   212 villain
# 86            75       10    12         14    19     80   210 villain
# 87            75       10    12         28    30     45   200 villain
# 88            25        7    12         14   100     40   198 villain
# 89            88       10    12         28    28     28   194 villain
# 90            38       10    35         32    20     56   191 villain
# 91           100       10    12         14    10     28   174 villain
# 92           100       10    23         14    10     14   171 villain
# 93            50       28     8         50    11     20   167 villain
# 94           100       10    12         14    10     14   160 villain
# 95            38       10    12         14    50     28   152 villain
# 96             1       32     1          1     0      1    36 villain
# 97             1        1     1          1     0      1     5 villain
# 98             1        1     1          1     0      1     5 villain
# 99             1        1     1          1     0      1     5 villain
# 100            1        1     1          1     0      1     5 villain
# 101            1        1     1          1     0      1     5 villain
# 102            1        1     1          1     0      1     5 villain
# 103            1        1     1          1     0      1     5 villain
# 104            1        1     1          1     0      1     5 villain
# 105            1        1     1          1     0      1     5 villain
# 106            1        1     1          1     0      1     5 villain
# 107            1        1     1          1     0      1     5 villain
```

---

# Guardar el output

Vamos a almacenar la variable `marvelDc_edited` la cual ya se encuentra limpia de duplicados y mantiene todas las ediciones que le hemos hecho.

``` r
save(marvelDc_edited, file = "Presentaciones/data/marvelDc_edited.RData")
```

Todos estos pasos se encuentran en el Script [MarvelvsDCComics_limpieza_script.R](https://github.com/EveliaCoss/ViernesBioinfo2024/blob/main/Practica_Clase6y7/MarvelvsDCComics_limpieza_script.R).

---

---

## Ejercicios

1) ¿Quiénes son los personajes más fuertes de cada empresa?

2) ¿Quiénes son los personajes más inteligentes de cada empresa?

3) ¿Quiénes son las mujeres más poderosas y malvadas?

4) ¿Quiénes son los hombres más poderosos y malvados?

---

## Ejercicios: RESPUESTAS

1) ¿Quiénes son los personajes más fuertes de cada empresa?

``` r
marvelDc %>% filter(!is.na(Strength)) %>%  # filtrar NA
  group_by(Name, Publisher) %>%  # Agrupar por nombre del heroe y empresa
  distinct(Strength) %>% # Eliminar duplicados en la columna, para tener solo un valor por personaje
  select(Name, Publisher, Strength) %>% # seleccionar columnas
  arrange(-Strength) %>% # Ordenar de mayor a menor
  head(5)
# # A tibble: 5 × 3
# # Groups:   Name, Publisher [5]
#   Name           Publisher     Strength
#   <fct>          <fct>            <int>
# 1 Amazo          DC Comics          100
# 2 Apocalypse     Marvel Comics      100
# 3 Captain Marvel Marvel Comics      100
# 4 Darkseid       DC Comics          100
# 5 Hulk           Marvel Comics      100
```

---
## Ejercicios: RESPUESTAS

2) ¿Quiénes son los personajes más inteligentes de cada empresa?

``` r
marvelDc %>% filter(!is.na(Intelligence)) %>%  # filtrar NA
  group_by(Name, Publisher) %>%
  distinct(Intelligence) %>% # eliminar duplicados
  select(Name, Publisher, Intelligence) %>%
  arrange(-Intelligence) %>% # Ordenar de mayor a menor
  head(5)
# # A tibble: 5 × 3
# # Groups:   Name, Publisher [5]
#   Name              Publisher     Intelligence
#   <fct>             <fct>                <int>
# 1 Mister Mxyzptlk   DC Comics              113
# 2 Apocalypse        Marvel Comics          100
# 3 Batman            DC Comics              100
# 4 Captain Marvel    Marvel Comics          100
# 5 Martian Manhunter DC Comics              100
```

---

## Ejercicios: RESPUESTAS

3) ¿Quiénes son las mujeres más poderosas y malvadas?

``` r
# con filtro
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Total >= 500  & Gender == "Female" & Alignment == "bad") 
#    Name Gender       Race Publisher Alignment Intelligence Strength Speed
# 1 Faora Female Kryptonian DC Comics       bad           88       95    75
#   Durability Power Combat Total
# 1        100    87     90   535

# sin filtro
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Gender == "Female" & Alignment == "bad") %>%
  arrange(-Total)
#                Name Gender       Race     Publisher Alignment Intelligence
# 1             Faora Female Kryptonian     DC Comics       bad           88
# 2          Bloodaxe Female      Human Marvel Comics       bad           63
# 3            Maxima Female       <NA>     DC Comics       bad           75
# 4           Giganta Female       <NA>     DC Comics       bad           81
# 5            Frenzy Female       <NA> Marvel Comics       bad           63
# 6          Callisto Female       <NA> Marvel Comics       bad           63
# 7          Mystique Female     Mutant Marvel Comics       bad           75
# 8       Black Mamba Female       <NA> Marvel Comics       bad           75
# 9          Arclight Female       <NA> Marvel Comics       bad           38
# 10     Harley Quinn Female      Human     DC Comics       bad           81
# 11    Scarlet Witch Female     Mutant Marvel Comics       bad           88
# 12     Goblin Queen Female       <NA> Marvel Comics       bad           75
# 13       Poison Ivy Female      Human     DC Comics       bad           69
# 14            Siryn Female       <NA> Marvel Comics       bad           38
# 15        Plastique Female       <NA>     DC Comics       bad           50
# 16          Scorpia Female       <NA> Marvel Comics       bad           38
# 17            Siren Female  Atlantean     DC Comics       bad           38
# 18       Cheetah II Female      Human     DC Comics       bad            1
# 19      Cheetah III Female      Human     DC Comics       bad            1
# 20 Lady Deathstrike Female     Cyborg Marvel Comics       bad            1
# 21  Spider-Woman IV Female       <NA> Marvel Comics       bad            1
# 22  Granny Goodness Female       <NA>     DC Comics       bad            1
# 23         Siren II Female       <NA>     DC Comics       bad            1
# 24    Lady Bullseye Female       <NA> Marvel Comics       bad            1
#    Strength Speed Durability Power Combat Total
# 1        95    75        100    87     90   535
# 2        80    33         80    84     84   424
# 3        90    35         56    67     75   398
# 4        89    23         85    32     42   352
# 5        53    35        100    38     52   341
# 6        53    23         42    45     85   311
# 7        12    23         64    58     74   306
# 8        10    35         42    78     65   305
# 9        63    23         42    52     70   288
# 10       12    23         42    30     80   268
# 11       10    23         42    67     28   258
# 12       10    23         28    65     56   257
# 13       14    23         14    71     36   227
# 14        8    47         28    50     42   213
# 15       10    23         28    60     42   213
# 16       28    42         28    34     28   198
# 17       10    12         14    55     28   157
# 18        1     1          1     0      1     5
# 19        1     1          1     0      1     5
# 20        1     1          1     0      1     5
# 21        1     1          1     0      1     5
# 22        1     1          1     0      1     5
# 23        1     1          1     0      1     5
# 24        1     1          1     0      1     5
```
]

---

## Ejercicios: RESPUESTAS

4) ¿Quiénes son los hombres más poderosos y malvados?

``` r
# sin filtro
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Gender == "Male" & Alignment == "bad") %>%
  arrange(-Total)
#                  Name Gender              Race     Publisher Alignment
# 1         General Zod   Male        Kryptonian     DC Comics       bad
# 2      Superboy-Prime   Male        Kryptonian     DC Comics       bad
# 3               Amazo   Male           Android     DC Comics       bad
# 4            Dormammu   Male              <NA> Marvel Comics       bad
# 5            Doomsday   Male             Alien     DC Comics       bad
# 6     Cyborg Superman   Male            Cyborg     DC Comics       bad
# 7               Magus   Male              <NA> Marvel Comics       bad
# 8             Abraxas   Male     Cosmic Entity Marvel Comics       bad
# 9          Black Adam   Male              <NA>     DC Comics       bad
# 10      Fallen One II   Male              <NA> Marvel Comics       bad
# 11           Darkseid   Male           New God     DC Comics       bad
# 12          Onslaught   Male            Mutant Marvel Comics       bad
# 13              Match   Male              <NA>     DC Comics       bad
# 14       Anti-Monitor   Male     God / Eternal     DC Comics       bad
# 15         Apocalypse   Male            Mutant Marvel Comics       bad
# 16               Lobo   Male          Czarnian     DC Comics       bad
# 17             Thanos   Male           Eternal Marvel Comics       bad
# 18             Ultron   Male           Android Marvel Comics       bad
# 19          Destroyer   Male              <NA> Marvel Comics       bad
# 20         Air-Walker   Male              <NA> Marvel Comics       bad
# 21            Carnage   Male          Symbiote Marvel Comics       bad
# 22            Magneto   Male            Mutant Marvel Comics       bad
# 23           Mephisto   Male              <NA> Marvel Comics       bad
# 24              Venom   Male          Symbiote Marvel Comics       bad
# 25        Abomination   Male Human / Radiation Marvel Comics       bad
# 26        Doctor Doom   Male             Human Marvel Comics       bad
# 27           Sinestro   Male         Korugaran     DC Comics       bad
# 28               Loki   Male         Asgardian Marvel Comics       bad
# 29               Kang   Male              <NA> Marvel Comics       bad
# 30           Brainiac   Male           Android     DC Comics       bad
# 31        Swamp Thing   Male     God / Eternal     DC Comics       bad
# 32            Metallo   Male           Android     DC Comics       bad
# 33          Venom III   Male          Symbiote Marvel Comics       bad
# 34          Annihilus   Male              <NA> Marvel Comics       bad
# 35        Iron Monger   Male              <NA> Marvel Comics       bad
# 36           Scorpion   Male             Human Marvel Comics       bad
# 37        Utgard-Loki   Male       Frost Giant Marvel Comics       bad
# 38           Mandarin   Male             Human Marvel Comics       bad
# 39       Doppelganger   Male              <NA> Marvel Comics       bad
# 40            Mach-IV   Male              <NA> Marvel Comics       bad
# 41             Azazel   Male          Neyaphem Marvel Comics       bad
# 42             Exodus   Male            Mutant Marvel Comics       bad
# 43               Bane   Male             Human     DC Comics       bad
# 44              Rhino   Male Human / Radiation Marvel Comics       bad
# 45               Klaw   Male             Human Marvel Comics       bad
# 46         Sabretooth   Male            Mutant Marvel Comics       bad
# 47         Molten Man   Male              <NA> Marvel Comics       bad
# 48           Venom II   Male              <NA> Marvel Comics       bad
# 49       Ra's Al Ghul   Male             Human     DC Comics       bad
# 50           Evilhawk   Male             Alien Marvel Comics       bad
# 51           Blackout   Male             Demon Marvel Comics       bad
# 52     Solomon Grundy   Male            Zombie     DC Comics       bad
# 53            Electro   Male             Human Marvel Comics       bad
# 54             Morlun   Male              <NA> Marvel Comics       bad
# 55               Blob   Male              <NA> Marvel Comics       bad
# 56                Gog   Male              <NA>     DC Comics       bad
# 57           Junkpile   Male            Mutant Marvel Comics       bad
# 58              Joker   Male             Human     DC Comics       bad
# 59         Demogoblin   Male             Demon Marvel Comics       bad
# 60               Zoom   Male              <NA>     DC Comics       bad
# 61        Killer Croc   Male         Metahuman     DC Comics       bad
# 62            Shocker   Male             Human Marvel Comics       bad
# 63           Vanisher   Male              <NA> Marvel Comics       bad
# 64            Vulture   Male             Human Marvel Comics       bad
# 65  Kraven the Hunter   Male             Human Marvel Comics       bad
# 66             Lizard   Male             Human Marvel Comics       bad
# 67    Green Goblin II   Male              <NA> Marvel Comics       bad
# 68    Mister Mxyzptlk   Male     God / Eternal     DC Comics       bad
# 69       Green Goblin   Male             Human Marvel Comics       bad
# 70          Hydro-Man   Male              <NA> Marvel Comics       bad
# 71        Tiger Shark   Male             Human Marvel Comics       bad
# 72       Moses Magnum   Male              <NA> Marvel Comics       bad
# 73         Changeling   Male              <NA> Marvel Comics       bad
# 74          Rick Flag   Male              <NA>     DC Comics       bad
# 75          Kraven II   Male             Human Marvel Comics       bad
# 76      Mister Freeze   Male             Human     DC Comics       bad
# 77           Bullseye   Male             Human Marvel Comics       bad
# 78           Deadshot   Male             Human     DC Comics       bad
# 79             Leader   Male              <NA> Marvel Comics       bad
# 80     Lightning Lord   Male              <NA>     DC Comics       bad
# 81            Big Man   Male              <NA> Marvel Comics       bad
# 82       Proto-Goblin   Male              <NA> Marvel Comics       bad
# 83        Blizzard II   Male              <NA> Marvel Comics       bad
# 84          Scarecrow   Male             Human     DC Comics       bad
# 85               Warp   Male              <NA>     DC Comics       bad
# 86          Red Skull   Male              <NA> Marvel Comics       bad
# 87            Penguin   Male             Human     DC Comics       bad
# 88           Kevin 11   Male             Human     DC Comics       bad
# 89          Chameleon   Male              <NA>     DC Comics       bad
# 90          Blackwing   Male              <NA> Marvel Comics       bad
# 91         Lex Luthor   Male             Human     DC Comics       bad
# 92           Tinkerer   Male              <NA> Marvel Comics       bad
# 93             Walrus   Male             Human Marvel Comics       bad
# 94            Riddler   Male              <NA>     DC Comics       bad
# 95               Pyro   Male              <NA> Marvel Comics       bad
# 96     Doctor Doom II   Male              <NA> Marvel Comics       bad
# 97     Doctor Octopus   Male             Human Marvel Comics       bad
# 98          Weapon XI   Male              <NA> Marvel Comics       bad
# 99    Mister Sinister   Male   Human / Altered Marvel Comics       bad
# 100       Bloodwraith   Male              <NA> Marvel Comics       bad
# 101          Bird-Man   Male             Human Marvel Comics       bad
# 102       Bird-Man II   Male             Human Marvel Comics       bad
# 103              Ammo   Male             Human Marvel Comics       bad
# 104         Hobgoblin   Male              <NA> Marvel Comics       bad
# 105          Blizzard   Male              <NA> Marvel Comics       bad
# 106      Black Abbott   Male              <NA> Marvel Comics       bad
# 107     Razor-Fist II   Male              <NA> Marvel Comics       bad
#     Intelligence Strength Speed Durability Power Combat Total
# 1             94      100    96        100    94     95   579
# 2             94      100   100        100   100     85   579
# 3             75      100   100        100   100    100   575
# 4             88       95    83        100   100     80   546
# 5             88       80    67        120   100     90   545
# 6             75       93    92        100   100     80   540
# 7             88      100    70         99   100     74   531
# 8             88      100    83         99   100     56   526
# 9             88      100    92        100    89     56   525
# 10            88       85    83        100   100     56   512
# 11            88      100    23        100   100     95   506
# 12            88       85    47        100   100     85   505
# 13            75       95    83         85    90     70   498
# 14            88       90    38         90   100     90   496
# 15           100      100    33        100   100     60   493
# 16            88       83    35        100    95     85   486
# 17            88      100    17        100   100     80   485
# 18            88       83    42        100   100     64   477
# 19            50       95    58         98    90     70   461
# 20            50       85   100         85   100     40   460
# 21            63       63    70         84    81     90   451
# 22            88       80    27         84    91     80   450
# 23            88       85    35         95   100     42   445
# 24            75       57    65         84    79     84   444
# 25            63       80    53         90    55     95   436
# 26           100       32    20        100    93     84   429
# 27            75       80    53         64   100     56   428
# 28            88       57    47         85    85     56   418
# 29           100       48    58         70    71     70   417
# 30           100       28    63         90    60     75   416
# 31            88       80    23        100    66     56   413
# 32            75       53    23         95    74     64   384
# 33            63       73    35         90    66     56   383
# 34            75       80    47         56    59     64   381
# 35            88       63    25         90    57     56   379
# 36            50       52    60         85    49     80   376
# 37            50       80    23         84    72     64   373
# 38           100       28    23         28    99     95   373
# 39             8       63    60         95    62     84   372
# 40            75       36    60         84    60     56   371
# 41            50       11    47         95    87     80   370
# 42            63       81    28         28   100     70   370
# 43            88       53    23         56    51     95   366
# 44            25       80    43         90    37     85   360
# 45            63       38    33        100    62     60   356
# 46            55       53    25         90    28    100   351
# 47            50       73    23         84    53     56   339
# 48            50       57    47         70    54     56   334
# 49           100       28    32         42    27    100   329
# 50            50       32    33         70    78     64   327
# 51            63       32    45         80    38     65   323
# 52             9       93    13        100    78     30   323
# 53            69       10    50         56    67     64   316
# 54            63       60    35         42    59     56   315
# 55            10       83    23         95    26     72   309
# 56            50       34    47         72    39     56   298
# 57            50       38    17         90    71     30   296
# 58           100       10    12         56    22     90   290
# 59            50       48    42         35    54     60   289
# 60            50       10   100         28    72     28   288
# 61            25       48    33         85    36     60   287
# 62            63       10    23         70    63     56   285
# 63            63       10    75         56    39     42   285
# 64            63       22    47         56    40     56   284
# 65            63       32    35         42    26     85   283
# 66            38       51    27         70    40     56   282
# 67            75       55    37         50    38     26   281
# 68           113       10    12         14   100     28   277
# 69            75       48    35         48    38     28   272
# 70            38       13    25         80    66     50   272
# 71            38       72    42         70    21     28   271
# 72            75       28    12         42    55     56   268
# 73            63       10    23         42    64     64   266
# 74            88       11    23         28    19     95   264
# 75            50       34    23         28    36     85   256
# 76            75       32    12         70    37     28   254
# 77            50       11    25         70    20     70   246
# 78            50       10    23         28    47     80   238
# 79           100       10    12         14    58     42   236
# 80            44       10    23         42    66     42   227
# 81            75       12    23         28    19     70   227
# 82            63       38    23         48    22     28   222
# 83            38       10    27         42    47     56   220
# 84            81       10    12         14    48     50   215
# 85            38       10    23         28    63     50   212
# 86            75       10    12         14    19     80   210
# 87            75       10    12         28    30     45   200
# 88            25        7    12         14   100     40   198
# 89            88       10    12         28    28     28   194
# 90            38       10    35         32    20     56   191
# 91           100       10    12         14    10     28   174
# 92           100       10    23         14    10     14   171
# 93            50       28     8         50    11     20   167
# 94           100       10    12         14    10     14   160
# 95            38       10    12         14    50     28   152
# 96             1       32     1          1     0      1    36
# 97             1        1     1          1     0      1     5
# 98             1        1     1          1     0      1     5
# 99             1        1     1          1     0      1     5
# 100            1        1     1          1     0      1     5
# 101            1        1     1          1     0      1     5
# 102            1        1     1          1     0      1     5
# 103            1        1     1          1     0      1     5
# 104            1        1     1          1     0      1     5
# 105            1        1     1          1     0      1     5
# 106            1        1     1          1     0      1     5
# 107            1        1     1          1     0      1     5

# Y bueno ?
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Gender == "Male" & Alignment == "good") %>%
  arrange(-Total)
#                  Name Gender              Race     Publisher Alignment
# 1   Martian Manhunter   Male           Martian     DC Comics      good
# 2            Superman   Male        Kryptonian     DC Comics      good
# 3            Stardust   Male              <NA> Marvel Comics      good
# 4                Thor   Male         Asgardian Marvel Comics      good
# 5                Nova   Male             Human Marvel Comics      good
# 6            Iron Man   Male             Human Marvel Comics      good
# 7             Watcher   Male              <NA> Marvel Comics      good
# 8         War Machine   Male             Human Marvel Comics      good
# 9            Superboy   Male              <NA>     DC Comics      good
# 10         Black Bolt   Male           Inhuman Marvel Comics      good
# 11      Silver Surfer   Male             Alien Marvel Comics      good
# 12           Beyonder   Male     God / Eternal Marvel Comics      good
# 13             Vision   Male           Android Marvel Comics      good
# 14              X-Man   Male              <NA> Marvel Comics      good
# 15            Warlock   Male              <NA> Marvel Comics      good
# 16               Hulk   Male Human / Radiation Marvel Comics      good
# 17           Hercules   Male          Demi-God Marvel Comics      good
# 18            Century   Male             Alien Marvel Comics      good
# 19           Hyperion   Male           Eternal Marvel Comics      good
# 20      Beta Ray Bill   Male              <NA> Marvel Comics      good
# 21             Sentry   Male            Mutant Marvel Comics      good
# 22            Kilowog   Male        Bolovaxian     DC Comics      good
# 23         Wonder Man   Male              <NA> Marvel Comics      good
# 24               Ares   Male              <NA> Marvel Comics      good
# 25              Steel   Male              <NA>     DC Comics      good
# 26              Toxin   Male          Symbiote Marvel Comics      good
# 27         Hal Jordan   Male             Human     DC Comics      good
# 28               Nova   Male             Human Marvel Comics      good
# 29             Osiris   Male              <NA>     DC Comics      good
# 30         Spider-Man   Male             Human Marvel Comics      good
# 31              Groot   Male    Flora Colossus Marvel Comics      good
# 32            Aquaman   Male         Atlantean     DC Comics      good
# 33           Red Hulk   Male Human / Radiation Marvel Comics      good
# 34             Iceman   Male            Mutant Marvel Comics      good
# 35           Abin Sur   Male           Ungaran     DC Comics      good
# 36           Colossus   Male            Mutant Marvel Comics      good
# 37      Thunderstrike   Male              <NA> Marvel Comics      good
# 38       Dr Manhattan   Male    Human / Cosmic     DC Comics      good
# 39              Skaar   Male              <NA> Marvel Comics      good
# 40              Cable   Male            Mutant Marvel Comics      good
# 41        Plastic Man   Male             Human     DC Comics      good
# 42      Fin Fang Foom   Male   Kakarantharaian Marvel Comics      good
# 43             Krypto   Male        Kryptonian     DC Comics      good
# 44             Cyborg   Male            Cyborg     DC Comics      good
# 45         Animal Man   Male             Human     DC Comics      good
# 46               Ymir   Male       Frost Giant Marvel Comics      good
# 47         Agent Zero   Male              <NA> Marvel Comics      good
# 48         Alan Scott   Male              <NA>     DC Comics      good
# 49         Cannonball   Male              <NA> Marvel Comics      good
# 50              Thing   Male Human / Radiation Marvel Comics      good
# 51         Doc Samson   Male Human / Radiation Marvel Comics      good
# 52    Captain Britain   Male             Human Marvel Comics      good
# 53        Red Tornado   Male           Android     DC Comics      good
# 54        Guy Gardner   Male   Human-Vuldarian     DC Comics      good
# 55          Wolverine   Male            Mutant Marvel Comics      good
# 56       John Stewart   Male             Human     DC Comics      good
# 57     Doctor Strange   Male             Human Marvel Comics      good
# 58        Quicksilver   Male            Mutant Marvel Comics      good
# 59              Atlas   Male            Mutant Marvel Comics      good
# 60  Captain Marvel II   Male             Human     DC Comics      good
# 61        Ghost Rider   Male             Demon Marvel Comics      good
# 62            Aqualad   Male         Atlantean     DC Comics      good
# 63          Firestorm   Male              <NA>     DC Comics      good
# 64     Scarlet Spider   Male             Human Marvel Comics      good
# 65       Booster Gold   Male             Human     DC Comics      good
# 66              Mimic   Male              <NA> Marvel Comics      good
# 67        Doctor Fate   Male             Human     DC Comics      good
# 68          Offspring   Male              <NA>     DC Comics      good
# 69    Blue Beetle III   Male             Human     DC Comics      good
# 70          Flash III   Male             Human     DC Comics      good
# 71     Jack of Hearts   Male             Human Marvel Comics      good
# 72          Northstar   Male              <NA> Marvel Comics      good
# 73              Beast   Male            Mutant Marvel Comics      good
# 74        Shatterstar   Male              <NA> Marvel Comics      good
# 75            Impulse   Male             Human     DC Comics      good
# 76            Warpath   Male            Mutant Marvel Comics      good
# 77      Black Panther   Male             Human Marvel Comics      good
# 78            Deadman   Male             Human     DC Comics      good
# 79            Sunspot   Male            Mutant Marvel Comics      good
# 80         Battlestar   Male              <NA> Marvel Comics      good
# 81     Winter Soldier   Male             Human Marvel Comics      good
# 82           Flash II   Male             Human     DC Comics      good
# 83  Scarlet Spider II   Male             Clone Marvel Comics      good
# 84          Man-Thing   Male              <NA> Marvel Comics      good
# 85             Bishop   Male            Mutant Marvel Comics      good
# 86             Batman   Male             Human     DC Comics      good
# 87           Wildfire   Male              <NA>     DC Comics      good
# 88           Darkhawk   Male             Human Marvel Comics      good
# 89              Magog   Male              <NA>     DC Comics      good
# 90              Synch   Male              <NA> Marvel Comics      good
# 91          Bloodhawk   Male            Mutant Marvel Comics      good
# 92    Captain America   Male             Human Marvel Comics      good
# 93          Iron Fist   Male             Human Marvel Comics      good
# 94          Beast Boy   Male             Human     DC Comics      good
# 95             A-Bomb   Male             Human Marvel Comics      good
# 96          Luke Cage   Male             Human Marvel Comics      good
# 97        Human Torch   Male Human / Radiation Marvel Comics      good
# 98              Cloak   Male              <NA> Marvel Comics      good
# 99           Flash IV   Male             Human     DC Comics      good
# 100           Cyclops   Male            Mutant Marvel Comics      good
# 101             Blade   Male           Vampire Marvel Comics      good
# 102          Deathlok   Male            Cyborg Marvel Comics      good
# 103       Space Ghost   Male             Human     DC Comics      good
# 104            Metron   Male              <NA>     DC Comics      good
# 105         Batman II   Male             Human     DC Comics      good
# 106         Nightwing   Male             Human     DC Comics      good
# 107         Daredevil   Male             Human Marvel Comics      good
# 108       John Wraith   Male              <NA> Marvel Comics      good
# 109  Mister Fantastic   Male Human / Radiation Marvel Comics      good
# 110          Man-Wolf   Male              <NA> Marvel Comics      good
# 111         Sasquatch   Male              <NA> Marvel Comics      good
# 112           Banshee   Male             Human Marvel Comics      good
# 113   Black Lightning   Male              <NA>     DC Comics      good
# 114              Hawk   Male              <NA>     DC Comics      good
# 115           Chamber   Male            Mutant Marvel Comics      good
# 116         Shang-Chi   Male             Human Marvel Comics      good
# 117         Nick Fury   Male             Human Marvel Comics      good
# 118             Havok   Male            Mutant Marvel Comics      good
# 119          Maverick   Male              <NA> Marvel Comics      good
# 120         Archangel   Male            Mutant Marvel Comics      good
# 121       Green Arrow   Male             Human     DC Comics      good
# 122  Black Knight III   Male             Human Marvel Comics      good
# 123            Gambit   Male            Mutant Marvel Comics      good
# 124         Robin III   Male             Human     DC Comics      good
# 125         Red Robin   Male             Human     DC Comics      good
# 126          Question   Male             Human     DC Comics      good
# 127           Gravity   Male             Human Marvel Comics      good
# 128      Multiple Man   Male              <NA> Marvel Comics      good
# 129       Professor X   Male            Mutant Marvel Comics      good
# 130       Moon Knight   Male             Human Marvel Comics      good
# 131      Nightcrawler   Male              <NA> Marvel Comics      good
# 132        Brainiac 5   Male              <NA>     DC Comics      good
# 133     Elongated Man   Male              <NA>     DC Comics      good
# 134           Justice   Male             Human Marvel Comics      good
# 135             Angel   Male              <NA> Marvel Comics      good
# 136          Punisher   Male             Human Marvel Comics      good
# 137               Ink   Male            Mutant Marvel Comics      good
# 138          Longshot   Male             Human Marvel Comics      good
# 139         Rorschach   Male             Human     DC Comics      good
# 140            Azrael   Male             Human     DC Comics      good
# 141            Bantam   Male              <NA> Marvel Comics      good
# 142             Sobek   Male              <NA>     DC Comics      good
# 143         Red Arrow   Male             Human     DC Comics      good
# 144       Thunderbird   Male              <NA> Marvel Comics      good
# 145           Ant-Man   Male             Human Marvel Comics      good
# 146           Robin V   Male             Human     DC Comics      good
# 147            Falcon   Male             Human Marvel Comics      good
# 148             Spyke   Male            Mutant Marvel Comics      good
# 149         Kid Flash   Male             Human     DC Comics      good
# 150           Hawkeye   Male             Human Marvel Comics      good
# 151     Lightning Lad   Male              <NA>     DC Comics      good
# 152    Rocket Raccoon   Male            Animal Marvel Comics      good
# 153         Micro Lad   Male              <NA>     DC Comics      good
# 154        Ant-Man II   Male             Human Marvel Comics      good
# 155            Ben 10   Male              <NA>     DC Comics      good
# 156      Yellowjacket   Male             Human Marvel Comics      good
# 157           Bushido   Male             Human     DC Comics      good
# 158             Leech   Male              <NA> Marvel Comics      good
# 159             Quill   Male              <NA> Marvel Comics      good
# 160          Aquababy   Male              <NA>     DC Comics      good
# 161         Agent Bob   Male             Human Marvel Comics      good
# 162      Captain Atom   Male Human / Radiation     DC Comics      good
# 163 Franklin Richards   Male            Mutant Marvel Comics      good
# 164             Namor   Male              <NA> Marvel Comics      good
# 165          Robin II   Male             Human     DC Comics      good
# 166           Spectre   Male     God / Eternal     DC Comics      good
# 167       Garbage Man   Male            Mutant     DC Comics      good
# 168         Hellstorm   Male              <NA> Marvel Comics      good
# 169      Adam Strange   Male             Human     DC Comics      good
# 170  Green Goblin III   Male              <NA> Marvel Comics      good
# 171   Green Goblin IV   Male              <NA> Marvel Comics      good
# 172            Speedy   Male             Human     DC Comics      good
# 173         Speedball   Male              <NA> Marvel Comics      good
# 174   Thunderbird III   Male              <NA> Marvel Comics      good
# 175           Atom II   Male             Human     DC Comics      good
# 176             Aztar   Male              <NA>     DC Comics      good
# 177              Beak   Male              <NA> Marvel Comics      good
# 178       Blue Beetle   Male              <NA>     DC Comics      good
# 179         Bumbleboy   Male              <NA> Marvel Comics      good
# 180    Captain Planet   Male     God / Eternal Marvel Comics      good
# 181  Crimson Crusader   Male              <NA> Marvel Comics      good
# 182           Monarch   Male              <NA>     DC Comics      good
# 183           Phantom   Male              <NA>     DC Comics      good
# 184              Atom   Male              <NA>     DC Comics      good
# 185           Atom IV   Male              <NA>     DC Comics      good
# 186    Blue Beetle II   Male              <NA>     DC Comics      good
# 187        Goliath IV   Male              <NA> Marvel Comics      good
# 188        Penance II   Male              <NA> Marvel Comics      good
# 189          Atom III   Male              <NA>     DC Comics      good
# 190           Hawkman   Male              <NA>     DC Comics      good
# 191    Brother Voodoo   Male             Human Marvel Comics      good
# 192        Metamorpho   Male              <NA>     DC Comics      good
# 193           Genesis   Male              <NA> Marvel Comics      good
#     Intelligence Strength Speed Durability Power Combat Total
# 1            100      100    96        100   100     85   581
# 2            100      100   100        100    94     85   579
# 3             88       85   100        110   100     85   568
# 4             69      100    92        100   100     85   546
# 5            100       85    67        101   100     85   538
# 6            100       85    58         85   100     64   492
# 7            100       80    67         89   100     56   492
# 8             63       80    63        100   100     85   491
# 9             75       95    83         90    84     60   487
# 10            75       67   100         84   100     56   482
# 11            63      100    84        101   100     32   480
# 12            88      100    23        100   100     56   467
# 13           100       72    54         95    76     70   467
# 14            88       53    53         95    92     84   465
# 15            88       36    79         95    71     95   464
# 16            88      100    47        100    41     85   461
# 17            63      100    46         85    67    100   461
# 18            88       80    53         64    74    100   459
# 19            63       95    75         95    58     72   458
# 20            63       80    35         95   100     84   457
# 21            75       80    58         84    97     54   448
# 22            81       90    53         42   100     80   446
# 23            75      100    53         90    64     64   446
# 24            75       82    35         80    62    101   435
# 25            81       82    53         90    64     64   434
# 26            56       73    70         84    75     70   428
# 27            63       90    53         64   100     56   426
# 28            38       60   100        100   100     25   423
# 29            75       85    60         95    65     42   422
# 30            88       55    60         74    58     85   420
# 31            75       85    33         70    92     64   419
# 32            63       80    50         80    65     80   418
# 33            50       88    47         95    59     75   414
# 34            63       32    53        100   100     64   412
# 35            50       90    53         64    84     65   406
# 36            63       83    33        100    46     80   405
# 37            63       80    70         84    31     72   400
# 38            88       32    42         95   100     42   399
# 39            50       85    27         90    62     85   399
# 40            88       48    23         56   100     80   395
# 41            50       63    23        100   100     56   392
# 42            50       81    23        100    68     70   392
# 43             9       80   100         90    72     40   391
# 44            75       53    42         85    72     64   391
# 45            56       48    47         85    73     80   389
# 46            50      100    27        100    83     28   388
# 47            75       28    38         80    72     95   388
# 48            63       80    23         90    98     32   386
# 49            50       28    67         99    79     56   379
# 50            75       84    21         90    26     80   376
# 51            75       80    33         80    36     70   374
# 52            75       77    50         60    31     80   373
# 53            75       38    67         60    92     40   372
# 54            38       80    27         64    97     64   370
# 55            55       32    38        100    44    100   369
# 56            50       80    27         64    94     54   369
# 57           100       10    12         84   100     60   366
# 58            63       28   100         60    57     56   364
# 59            50       80    23         99    69     42   363
# 60            75       81    27         90    34     56   363
# 61            50       55    25        100    70     60   360
# 62            63       44    42         75    76     60   360
# 63            50       53    58         56   100     42   359
# 64            75       53    60         74    39     56   357
# 65            56       85    53         50    72     40   356
# 66            63       67    47         56    79     42   354
# 67            81       16    25         80   100     50   352
# 68            50       10    35         99   100     56   350
# 69            50       34    58         80    97     30   349
# 70            63       10   100         60    83     32   348
# 71            63       55   100         30    70     30   348
# 72            50       18    83         56    70     70   347
# 73            88       48    35         56    35     84   346
# 74            63       48    45         64    42     84   346
# 75            50       10   100         60    63     60   343
# 76            38       72    47         70    26     84   337
# 77            88       16    30         60    41    100   335
# 78            50       10    33        100   100     42   335
# 79            63       63    35         25    79     70   335
# 80            50       53    35         74    48     74   334
# 81            56       32    35         65    60     84   332
# 82            63       10   100         60    66     32   331
# 83            88       55    60         40    30     56   329
# 84            50       32     8        100    98     40   328
# 85            63       14    23         75    85     65   325
# 86           100       18    27         42    37    100   324
# 87            50       32    23        100    77     42   324
# 88            50       32    33         70    74     64   323
# 89            50       48    23         64    66     72   323
# 90            75       67    23         28    74     56   323
# 91            50       10    50         80    66     64   320
# 92            63       19    35         56    46    100   319
# 93            75       23    35         42    43    100   318
# 94            50       28    50         70    79     40   317
# 95            38      100    17         80    17     64   316
# 96            44       55    23         95    35     64   316
# 97            56       10    63         70    74     42   315
# 98            63       10    47         64    74     56   314
# 99            63       10   100         32    59     48   312
# 100           75       10    23         42    76     80   306
# 101           63       28    38         50    33     90   302
# 102           69       32    30         70    40     60   301
# 103           38       18    33         40    90     80   299
# 104           88       10    47         56    69     28   298
# 105           88       11    33         28    36    100   296
# 106           88       11    33         28    36    100   296
# 107           75       13    27         28    57     95   295
# 108           75       12    35         28    65     80   295
# 109          100       10    18         70    32     64   294
# 110           63       44    35         42    38     70   292
# 111           75       80    23         56    15     42   291
# 112           50       10    58         42    60     70   290
# 113           50       11    53         28    70     75   287
# 114           38       38    35         95    36     42   284
# 115           50       10    20         80    57     64   281
# 116           63       12    30         50    25    100   280
# 117           75       11    23         42    25    100   276
# 118           63       10    25         60    71     45   274
# 119           63       10    12         42    60     85   272
# 120           63       13    58         64    28     42   268
# 121           75       12    35         28    26     90   266
# 122           63       10     8         70    43     70   264
# 123           63       10    23         28    55     84   263
# 124           81       11    27         28    30     85   262
# 125           81       11    27         32    29     80   260
# 126           81       14    27         35    20     80   257
# 127           50       32    33         40    72     30   257
# 128           63       11    23         70    28     62   257
# 129          100        8    12         14    90     32   256
# 130           50       36    23         42    28     75   254
# 131           50       10    47         14    52     80   253
# 132          100       10    23         28    60     32   253
# 133           38       10    27         90    41     42   248
# 134           50       10    33         70    55     30   248
# 135           63       13    46         64    17     42   245
# 136           50       16    23         28    22    100   239
# 137           38       28    17         40    66     50   239
# 138           50       10    27         10    71     70   238
# 139           75       10    29         20    23     80   237
# 140           63       18    17         20    35     80   233
# 141           25       53    23         54    21     56   232
# 142           50       34    23         46    20     56   229
# 143           63       16    25         20    24     80   228
# 144           50       32    27         32    14     70   225
# 145          100       10    23         28    32     32   225
# 146           69        8    33         16    29     65   220
# 147           38       13    50         28    22     64   215
# 148           50       12    17         60    41     30   210
# 149           25        4    92         42    13     30   206
# 150           50       12    23         14    26     80   205
# 151           38       10    23         28    60     42   201
# 152           50        5    23         28    28     64   198
# 153           38       28    27         28    44     32   197
# 154           63       10    23         28    32     28   184
# 155           10        7     8         10    90     40   165
# 156           88       10    12         28    12     14   164
# 157           38       10     8         10    19     70   155
# 158           25        5    12         14    62     14   132
# 159           38       10    12         14    23     14   111
# 160           10       16    12         14    37     14   103
# 161           10        8    13          5     5     20    61
# 162            1        1     1          1     0      1     5
# 163            1        1     1          1     0      1     5
# 164            1        1     1          1     0      1     5
# 165            1        1     1          1     0      1     5
# 166            1        1     1          1     0      1     5
# 167            1        1     1          1     0      1     5
# 168            1        1     1          1     0      1     5
# 169            1        1     1          1     0      1     5
# 170            1        1     1          1     0      1     5
# 171            1        1     1          1     0      1     5
# 172            1        1     1          1     0      1     5
# 173            1        1     1          1     0      1     5
# 174            1        1     1          1     0      1     5
# 175            1        1     1          1     0      1     5
# 176            1        1     1          1     0      1     5
# 177            1        1     1          1     0      1     5
# 178            1        1     1          1     0      1     5
# 179            1        1     1          1     0      1     5
# 180            1        1     1          1     0      1     5
# 181            1        1     1          1     0      1     5
# 182            1        1     1          1     0      1     5
# 183            1        1     1          1     0      1     5
# 184            1        1     1          1     0      1     5
# 185            1        1     1          1     0      1     5
# 186            1        1     1          1     0      1     5
# 187            1        1     1          1     0      1     5
# 188            1        1     1          1     0      1     5
# 189            1        1     1          1     0      1     5
# 190            1        1     1          1     0      1     5
# 191            1        1     1          1     0      1     5
# 192            1        1     1          1     0      1     5
# 193            1        1     1          1     0      1     5

# Ambos ?
marvelDc %>% select(-SuperPower) %>% distinct() %>% # eliminas duplicados
  filter(Gender == "Male" & Alignment == c("good", "bad")) %>%
  arrange(-Total)
#                  Name Gender              Race     Publisher Alignment
# 1            Superman   Male        Kryptonian     DC Comics      good
# 2      Superboy-Prime   Male        Kryptonian     DC Comics       bad
# 3               Amazo   Male           Android     DC Comics       bad
# 4            Stardust   Male              <NA> Marvel Comics      good
# 5            Dormammu   Male              <NA> Marvel Comics       bad
# 6            Doomsday   Male             Alien     DC Comics       bad
# 7                Nova   Male             Human Marvel Comics      good
# 8             Abraxas   Male     Cosmic Entity Marvel Comics       bad
# 9          Black Adam   Male              <NA>     DC Comics       bad
# 10              Match   Male              <NA>     DC Comics       bad
# 11           Iron Man   Male             Human Marvel Comics      good
# 12        War Machine   Male             Human Marvel Comics      good
# 13           Superboy   Male              <NA>     DC Comics      good
# 14      Silver Surfer   Male             Alien Marvel Comics      good
# 15             Vision   Male           Android Marvel Comics      good
# 16            Warlock   Male              <NA> Marvel Comics      good
# 17          Destroyer   Male              <NA> Marvel Comics       bad
# 18         Air-Walker   Male              <NA> Marvel Comics       bad
# 19            Century   Male             Alien Marvel Comics      good
# 20           Hyperion   Male           Eternal Marvel Comics      good
# 21      Beta Ray Bill   Male              <NA> Marvel Comics      good
# 22            Carnage   Male          Symbiote Marvel Comics       bad
# 23            Magneto   Male            Mutant Marvel Comics       bad
# 24             Sentry   Male            Mutant Marvel Comics      good
# 25            Kilowog   Male        Bolovaxian     DC Comics      good
# 26        Abomination   Male Human / Radiation Marvel Comics       bad
# 27              Steel   Male              <NA>     DC Comics      good
# 28        Doctor Doom   Male             Human Marvel Comics       bad
# 29           Sinestro   Male         Korugaran     DC Comics       bad
# 30              Toxin   Male          Symbiote Marvel Comics      good
# 31         Hal Jordan   Male             Human     DC Comics      good
# 32             Osiris   Male              <NA>     DC Comics      good
# 33              Groot   Male    Flora Colossus Marvel Comics      good
# 34               Kang   Male              <NA> Marvel Comics       bad
# 35           Red Hulk   Male Human / Radiation Marvel Comics      good
# 36        Swamp Thing   Male     God / Eternal     DC Comics       bad
# 37           Abin Sur   Male           Ungaran     DC Comics      good
# 38           Colossus   Male            Mutant Marvel Comics      good
# 39      Thunderstrike   Male              <NA> Marvel Comics      good
# 40              Skaar   Male              <NA> Marvel Comics      good
# 41        Plastic Man   Male             Human     DC Comics      good
# 42      Fin Fang Foom   Male   Kakarantharaian Marvel Comics      good
# 43         Animal Man   Male             Human     DC Comics      good
# 44         Agent Zero   Male              <NA> Marvel Comics      good
# 45         Alan Scott   Male              <NA>     DC Comics      good
# 46        Iron Monger   Male              <NA> Marvel Comics       bad
# 47              Thing   Male Human / Radiation Marvel Comics      good
# 48         Doc Samson   Male Human / Radiation Marvel Comics      good
# 49        Utgard-Loki   Male       Frost Giant Marvel Comics       bad
# 50            Mach-IV   Male              <NA> Marvel Comics       bad
# 51             Azazel   Male          Neyaphem Marvel Comics       bad
# 52             Exodus   Male            Mutant Marvel Comics       bad
# 53        Quicksilver   Male            Mutant Marvel Comics      good
# 54  Captain Marvel II   Male             Human     DC Comics      good
# 55        Ghost Rider   Male             Demon Marvel Comics      good
# 56              Rhino   Male Human / Radiation Marvel Comics       bad
# 57          Firestorm   Male              <NA>     DC Comics      good
# 58        Doctor Fate   Male             Human     DC Comics      good
# 59          Offspring   Male              <NA>     DC Comics      good
# 60          Flash III   Male             Human     DC Comics      good
# 61     Jack of Hearts   Male             Human Marvel Comics      good
# 62            Impulse   Male             Human     DC Comics      good
# 63         Molten Man   Male              <NA> Marvel Comics       bad
# 64      Black Panther   Male             Human Marvel Comics      good
# 65            Deadman   Male             Human     DC Comics      good
# 66         Battlestar   Male              <NA> Marvel Comics      good
# 67           Venom II   Male              <NA> Marvel Comics       bad
# 68     Winter Soldier   Male             Human Marvel Comics      good
# 69           Flash II   Male             Human     DC Comics      good
# 70       Ra's Al Ghul   Male             Human     DC Comics       bad
# 71  Scarlet Spider II   Male             Clone Marvel Comics      good
# 72             Bishop   Male            Mutant Marvel Comics      good
# 73             Batman   Male             Human     DC Comics      good
# 74           Wildfire   Male              <NA>     DC Comics      good
# 75              Magog   Male              <NA>     DC Comics      good
# 76          Bloodhawk   Male            Mutant Marvel Comics      good
# 77          Iron Fist   Male             Human Marvel Comics      good
# 78          Beast Boy   Male             Human     DC Comics      good
# 79             A-Bomb   Male             Human Marvel Comics      good
# 80            Electro   Male             Human Marvel Comics       bad
# 81        Human Torch   Male Human / Radiation Marvel Comics      good
# 82              Cloak   Male              <NA> Marvel Comics      good
# 83               Blob   Male              <NA> Marvel Comics       bad
# 84            Cyclops   Male            Mutant Marvel Comics      good
# 85           Deathlok   Male            Cyborg Marvel Comics      good
# 86   Mister Fantastic   Male Human / Radiation Marvel Comics      good
# 87           Man-Wolf   Male              <NA> Marvel Comics      good
# 88          Sasquatch   Male              <NA> Marvel Comics      good
# 89              Joker   Male             Human     DC Comics       bad
# 90            Banshee   Male             Human Marvel Comics      good
# 91            Shocker   Male             Human Marvel Comics       bad
# 92           Vanisher   Male              <NA> Marvel Comics       bad
# 93            Vulture   Male             Human Marvel Comics       bad
# 94               Hawk   Male              <NA>     DC Comics      good
# 95             Lizard   Male             Human Marvel Comics       bad
# 96    Green Goblin II   Male              <NA> Marvel Comics       bad
# 97          Shang-Chi   Male             Human Marvel Comics      good
# 98    Mister Mxyzptlk   Male     God / Eternal     DC Comics       bad
# 99       Green Goblin   Male             Human Marvel Comics       bad
# 100       Tiger Shark   Male             Human Marvel Comics       bad
# 101       Green Arrow   Male             Human     DC Comics      good
# 102  Black Knight III   Male             Human Marvel Comics      good
# 103            Gambit   Male            Mutant Marvel Comics      good
# 104         Red Robin   Male             Human     DC Comics      good
# 105          Question   Male             Human     DC Comics      good
# 106           Gravity   Male             Human Marvel Comics      good
# 107      Multiple Man   Male              <NA> Marvel Comics      good
# 108         Kraven II   Male             Human Marvel Comics       bad
# 109     Mister Freeze   Male             Human     DC Comics       bad
# 110       Moon Knight   Male             Human Marvel Comics      good
# 111      Nightcrawler   Male              <NA> Marvel Comics      good
# 112          Bullseye   Male             Human Marvel Comics       bad
# 113             Angel   Male              <NA> Marvel Comics      good
# 114          Longshot   Male             Human Marvel Comics      good
# 115          Deadshot   Male             Human     DC Comics       bad
# 116            Azrael   Male             Human     DC Comics      good
# 117         Red Arrow   Male             Human     DC Comics      good
# 118    Lightning Lord   Male              <NA>     DC Comics       bad
# 119           Ant-Man   Male             Human Marvel Comics      good
# 120      Proto-Goblin   Male              <NA> Marvel Comics       bad
# 121           Robin V   Male             Human     DC Comics      good
# 122            Falcon   Male             Human Marvel Comics      good
# 123             Spyke   Male            Mutant Marvel Comics      good
# 124         Red Skull   Male              <NA> Marvel Comics       bad
# 125           Hawkeye   Male             Human Marvel Comics      good
# 126     Lightning Lad   Male              <NA>     DC Comics      good
# 127          Kevin 11   Male             Human     DC Comics       bad
# 128    Rocket Raccoon   Male            Animal Marvel Comics      good
# 129         Blackwing   Male              <NA> Marvel Comics       bad
# 130        Lex Luthor   Male             Human     DC Comics       bad
# 131          Tinkerer   Male              <NA> Marvel Comics       bad
# 132            Walrus   Male             Human Marvel Comics       bad
# 133            Ben 10   Male              <NA>     DC Comics      good
# 134      Yellowjacket   Male             Human Marvel Comics      good
# 135           Bushido   Male             Human     DC Comics      good
# 136              Pyro   Male              <NA> Marvel Comics       bad
# 137             Leech   Male              <NA> Marvel Comics      good
# 138             Quill   Male              <NA> Marvel Comics      good
# 139         Agent Bob   Male             Human Marvel Comics      good
# 140      Captain Atom   Male Human / Radiation     DC Comics      good
# 141    Doctor Octopus   Male             Human Marvel Comics       bad
# 142             Namor   Male              <NA> Marvel Comics      good
# 143          Robin II   Male             Human     DC Comics      good
# 144           Spectre   Male     God / Eternal     DC Comics      good
# 145         Weapon XI   Male              <NA> Marvel Comics       bad
# 146         Hellstorm   Male              <NA> Marvel Comics      good
# 147   Mister Sinister   Male   Human / Altered Marvel Comics       bad
# 148      Adam Strange   Male             Human     DC Comics      good
# 149  Green Goblin III   Male              <NA> Marvel Comics      good
# 150            Speedy   Male             Human     DC Comics      good
# 151         Speedball   Male              <NA> Marvel Comics      good
# 152           Atom II   Male             Human     DC Comics      good
# 153          Bird-Man   Male             Human Marvel Comics       bad
# 154         Bumbleboy   Male              <NA> Marvel Comics      good
# 155  Crimson Crusader   Male              <NA> Marvel Comics      good
# 156           Monarch   Male              <NA>     DC Comics      good
# 157              Ammo   Male             Human Marvel Comics       bad
# 158              Atom   Male              <NA>     DC Comics      good
# 159    Blue Beetle II   Male              <NA>     DC Comics      good
# 160          Blizzard   Male              <NA> Marvel Comics       bad
# 161        Penance II   Male              <NA> Marvel Comics      good
# 162          Atom III   Male              <NA>     DC Comics      good
# 163     Razor-Fist II   Male              <NA> Marvel Comics       bad
# 164           Genesis   Male              <NA> Marvel Comics      good
#     Intelligence Strength Speed Durability Power Combat Total
# 1            100      100   100        100    94     85   579
# 2             94      100   100        100   100     85   579
# 3             75      100   100        100   100    100   575
# 4             88       85   100        110   100     85   568
# 5             88       95    83        100   100     80   546
# 6             88       80    67        120   100     90   545
# 7            100       85    67        101   100     85   538
# 8             88      100    83         99   100     56   526
# 9             88      100    92        100    89     56   525
# 10            75       95    83         85    90     70   498
# 11           100       85    58         85   100     64   492
# 12            63       80    63        100   100     85   491
# 13            75       95    83         90    84     60   487
# 14            63      100    84        101   100     32   480
# 15           100       72    54         95    76     70   467
# 16            88       36    79         95    71     95   464
# 17            50       95    58         98    90     70   461
# 18            50       85   100         85   100     40   460
# 19            88       80    53         64    74    100   459
# 20            63       95    75         95    58     72   458
# 21            63       80    35         95   100     84   457
# 22            63       63    70         84    81     90   451
# 23            88       80    27         84    91     80   450
# 24            75       80    58         84    97     54   448
# 25            81       90    53         42   100     80   446
# 26            63       80    53         90    55     95   436
# 27            81       82    53         90    64     64   434
# 28           100       32    20        100    93     84   429
# 29            75       80    53         64   100     56   428
# 30            56       73    70         84    75     70   428
# 31            63       90    53         64   100     56   426
# 32            75       85    60         95    65     42   422
# 33            75       85    33         70    92     64   419
# 34           100       48    58         70    71     70   417
# 35            50       88    47         95    59     75   414
# 36            88       80    23        100    66     56   413
# 37            50       90    53         64    84     65   406
# 38            63       83    33        100    46     80   405
# 39            63       80    70         84    31     72   400
# 40            50       85    27         90    62     85   399
# 41            50       63    23        100   100     56   392
# 42            50       81    23        100    68     70   392
# 43            56       48    47         85    73     80   389
# 44            75       28    38         80    72     95   388
# 45            63       80    23         90    98     32   386
# 46            88       63    25         90    57     56   379
# 47            75       84    21         90    26     80   376
# 48            75       80    33         80    36     70   374
# 49            50       80    23         84    72     64   373
# 50            75       36    60         84    60     56   371
# 51            50       11    47         95    87     80   370
# 52            63       81    28         28   100     70   370
# 53            63       28   100         60    57     56   364
# 54            75       81    27         90    34     56   363
# 55            50       55    25        100    70     60   360
# 56            25       80    43         90    37     85   360
# 57            50       53    58         56   100     42   359
# 58            81       16    25         80   100     50   352
# 59            50       10    35         99   100     56   350
# 60            63       10   100         60    83     32   348
# 61            63       55   100         30    70     30   348
# 62            50       10   100         60    63     60   343
# 63            50       73    23         84    53     56   339
# 64            88       16    30         60    41    100   335
# 65            50       10    33        100   100     42   335
# 66            50       53    35         74    48     74   334
# 67            50       57    47         70    54     56   334
# 68            56       32    35         65    60     84   332
# 69            63       10   100         60    66     32   331
# 70           100       28    32         42    27    100   329
# 71            88       55    60         40    30     56   329
# 72            63       14    23         75    85     65   325
# 73           100       18    27         42    37    100   324
# 74            50       32    23        100    77     42   324
# 75            50       48    23         64    66     72   323
# 76            50       10    50         80    66     64   320
# 77            75       23    35         42    43    100   318
# 78            50       28    50         70    79     40   317
# 79            38      100    17         80    17     64   316
# 80            69       10    50         56    67     64   316
# 81            56       10    63         70    74     42   315
# 82            63       10    47         64    74     56   314
# 83            10       83    23         95    26     72   309
# 84            75       10    23         42    76     80   306
# 85            69       32    30         70    40     60   301
# 86           100       10    18         70    32     64   294
# 87            63       44    35         42    38     70   292
# 88            75       80    23         56    15     42   291
# 89           100       10    12         56    22     90   290
# 90            50       10    58         42    60     70   290
# 91            63       10    23         70    63     56   285
# 92            63       10    75         56    39     42   285
# 93            63       22    47         56    40     56   284
# 94            38       38    35         95    36     42   284
# 95            38       51    27         70    40     56   282
# 96            75       55    37         50    38     26   281
# 97            63       12    30         50    25    100   280
# 98           113       10    12         14   100     28   277
# 99            75       48    35         48    38     28   272
# 100           38       72    42         70    21     28   271
# 101           75       12    35         28    26     90   266
# 102           63       10     8         70    43     70   264
# 103           63       10    23         28    55     84   263
# 104           81       11    27         32    29     80   260
# 105           81       14    27         35    20     80   257
# 106           50       32    33         40    72     30   257
# 107           63       11    23         70    28     62   257
# 108           50       34    23         28    36     85   256
# 109           75       32    12         70    37     28   254
# 110           50       36    23         42    28     75   254
# 111           50       10    47         14    52     80   253
# 112           50       11    25         70    20     70   246
# 113           63       13    46         64    17     42   245
# 114           50       10    27         10    71     70   238
# 115           50       10    23         28    47     80   238
# 116           63       18    17         20    35     80   233
# 117           63       16    25         20    24     80   228
# 118           44       10    23         42    66     42   227
# 119          100       10    23         28    32     32   225
# 120           63       38    23         48    22     28   222
# 121           69        8    33         16    29     65   220
# 122           38       13    50         28    22     64   215
# 123           50       12    17         60    41     30   210
# 124           75       10    12         14    19     80   210
# 125           50       12    23         14    26     80   205
# 126           38       10    23         28    60     42   201
# 127           25        7    12         14   100     40   198
# 128           50        5    23         28    28     64   198
# 129           38       10    35         32    20     56   191
# 130          100       10    12         14    10     28   174
# 131          100       10    23         14    10     14   171
# 132           50       28     8         50    11     20   167
# 133           10        7     8         10    90     40   165
# 134           88       10    12         28    12     14   164
# 135           38       10     8         10    19     70   155
# 136           38       10    12         14    50     28   152
# 137           25        5    12         14    62     14   132
# 138           38       10    12         14    23     14   111
# 139           10        8    13          5     5     20    61
# 140            1        1     1          1     0      1     5
# 141            1        1     1          1     0      1     5
# 142            1        1     1          1     0      1     5
# 143            1        1     1          1     0      1     5
# 144            1        1     1          1     0      1     5
# 145            1        1     1          1     0      1     5
# 146            1        1     1          1     0      1     5
# 147            1        1     1          1     0      1     5
# 148            1        1     1          1     0      1     5
# 149            1        1     1          1     0      1     5
# 150            1        1     1          1     0      1     5
# 151            1        1     1          1     0      1     5
# 152            1        1     1          1     0      1     5
# 153            1        1     1          1     0      1     5
# 154            1        1     1          1     0      1     5
# 155            1        1     1          1     0      1     5
# 156            1        1     1          1     0      1     5
# 157            1        1     1          1     0      1     5
# 158            1        1     1          1     0      1     5
# 159            1        1     1          1     0      1     5
# 160            1        1     1          1     0      1     5
# 161            1        1     1          1     0      1     5
# 162            1        1     1          1     0      1     5
# 163            1        1     1          1     0      1     5
# 164            1        1     1          1     0      1     5
```
]

---

## Deben practicar mucho cuando aprenden un lenguaje

### Cursos

- Cursos de [Software Carpentry](https://software-carpentry.org/lessons/)

---

## Referencias

- [Tidyverse: dplyr y tidyr](http://gauss.inf.um.es:8080/tabular-tidyverse/)
- [Mutating joins](https://dplyr.tidyverse.org/reference/mutate-joins.html)

---

Gracias por tu atención, respira y coméntame tus dudas.