Clase teórica Semana 11

library(rio)
library(dplyr)
library(scales)
data=import("data/MDb Top Rated Titles.xlsx")

data=data %>% 
  filter(!is.na(type)) # removemos valores perdidos en la variable type

tabla_porcentajes= data %>%
  count(type) %>%
 mutate(porcentaje = n / sum(n) * 100)  # Calculamos el porcentaje
tabla_porcentajes

library(ggplot2)
ggplot(tabla_porcentajes, aes(x=type,y=porcentaje, fill=type))+
  geom_bar(stat = "identity")+
    coord_flip()+
  labs(title = "Gráfico de barras de porcentaje de peliculas por tipo",
       x = "tipo",
       y = "Porcentaje") +
  geom_text(aes(label=round(porcentaje,1)), vjust=-0.3) +
  theme_minimal() +  # Tema minimalista
  scale_fill_brewer(palette = "Set3") 

names(data)

[1] "id"            "title"         "type"          "genres"       
[5] "averageRating" "numVotes"      "releaseYear"  

tabla_edescriptivos=data%>%
  summarise(promedio=mean(averageRating,na.rm=T),
            mediana=median(averageRating,na.rm=T),
            minimo=min(averageRating,na.rm=T),
            maximo=max(averageRating,na.rm=T),
            desviacion=sd(averageRating,na.rm=T))

tabla_edescriptivos

tabla_edescriptivos=data%>%
  group_by(type)%>%
  summarise(promedio=mean(averageRating,na.rm=T),
            mediana=median(averageRating,na.rm=T),
            minimo=min(averageRating,na.rm=T),
            maximo=max(averageRating,na.rm=T),
            desviacion=sd(averageRating,na.rm=T))

tabla_edescriptivos

library(ggplot2)
ggplot(data,aes(y=averageRating,x=type))+
  geom_boxplot()

library(ggplot2)
ggplot(data,aes(y=numVotes,x=type))+
  geom_boxplot()

table(data$type)

       movie tvMiniSeries     tvSeries 
        4105          278         1650 

ggplot(data, aes(x = averageRating, fill = type)) +
  geom_histogram() +
  facet_wrap(~ type, scales = "free") +  # Divide en facetas por grupo
  labs(title = "Histogramas de Variable Numérica por Grupo",
       x = "Valor", y = "Frecuencia") 

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data, aes(x = numVotes, fill = type)) +
  geom_histogram() +
  facet_wrap(~ type, scales = "free") +  # Divide en facetas por grupo
  scale_x_continuous(n.breaks = 3, labels = label_number()) + # especificar 2 cortes en eje x y evitar numeros en notacion cientifica
  labs(title = "Histogramas de Variable Numérica por Grupo",
       x = "Valor", y = "Frecuencia") 

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data, aes(x = averageRating, fill = type)) +
  geom_histogram(binwidth = 2, alpha = 0.6, position = "identity", color = "black") +
  labs(title = "Histograma de Variable Numérica por Grupo",
       x = "Valor", y = "Frecuencia") +
  theme_minimal() +
  scale_fill_manual(values = c("movie" = "red", "tvMiniSeries" = "blue", "tvSeries" = "green"))  # Colores por grupo