#Gráficos interactivos
#Extraido de Interactive charts https://www.r-graph-gallery.com/interactive-charts.html
library(ggplot2)
install.packages("plotly")
library(plotly)
install.packages("gapminder")
library(gapminder)

p <- gapminder %>%
  filter(year==1977) %>%
  ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  geom_point() +
  theme_bw()
p

ggplotly(p)
head(gapminder)


#agregándole texto al popup
p <- gapminder %>%
  filter(year==1977) %>%
  ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent, aux=country, aux2=year)) +
  geom_point() +
  theme_bw()
p
ggplotly(p)


#Otra forma de agregándole texto al popup, esta vez con etiqueta variables
p <- gapminder %>%
  filter(year==1977) %>%
  ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  geom_point(aes(text=sprintf("Año: %i <br> País: %s", year, country))) +
  theme_bw()

ggplotly(p)


#
p <- ggplot(mtcars, aes(x = disp, y= am, color = as.factor(cyl),gear=gear, hp=hp, vsqwe = vs)) + 
  geom_point()
ggplotly(p)
ggplotly(p,tooltip = c( "x", "vsqwe", "gear", "hp"))

#=======
#Gráficos animados
#Extraido de https://www.r-graph-gallery.com/271-ggplot2-animated-gif-chart-with-gganimate.html
  
# Get data:
library(gapminder)

# Charge libraries:
library(ggplot2)
install.packages("gganimate")
library(gganimate)
install.packages('Rcpp')
library(Rcpp)


# Make a ggplot, but add frame=year: one image per year
p<-ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() +
  theme_bw() +
  # gganimate specific bits:
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')
# Save at gif:
animate(p)
anim_save("271-ggplot2-animated-gif-chart-with-gganimate1.gif")


##================= Extraido de https://plotly.com/ggplot2/animations/
#Con slider
library(plotly)
library(gapminder)


p <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
  geom_point(aes(size = pop, frame = year, ids = country)) +
  scale_x_log10()

fig <- ggplotly(p)

fig



#extraido de https://plotly-r.com/improving-ggplotly.html#fig:ggplotly-rangeslider
#install.packages("babynames")
library(babynames)
nms <- filter(babynames, name %in% c("Sam", "Alex")) #las observaciones cuyos nombres sean Sam y Alex
p <- ggplot(nms) + 
  geom_line(aes(year, prop, color = sex, linetype = name))
p

ggplotly(p, dynamicTicks = TRUE) %>%
  rangeslider() %>%
  layout(hovermode = "x unified")
  #layout(hovermode = "x")
  #layout()



#geom_smooth hecho interactivo con ggplotly

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() + geom_smooth()
 ggplotly(p) %>%   layout(hovermode = "x unified") #muestra todos los datos para un x dado

 
 
p %>%
  ggplotly(layerData = 2, originalData = FALSE) %>%
  add_fun(function(p) {
    p %>% slice(which.max(se)) %>%
      add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
      add_annotations("Maximum uncertainty", ax = 60)
  }) %>%
  add_fun(function(p) {
    p %>% slice(which.min(se)) %>%
      add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
      add_annotations("Minimum uncertainty")
  })


##curvas de nivel interactivas con geom_density2d()
p <- ggplot(MASS::geyser, aes(x = waiting, y = duration)) +
  geom_density2d()

ggplotly(p, originalData = FALSE) %>% 
  group_by(piece) %>%
  slice(which.min(y)) %>% 
  add_text(
    text = ~level, size = I(16), color = I("black"), hoverinfo="none"
  )

#===============================
###organizando vistas

##vistas independientes en una misma ventana
library(plotly)
p1 <- plot_ly(economics, x = ~date, y = ~unemploy) %>% 
  add_lines(name = "unemploy")
p2 <- plot_ly(economics, x = ~date, y = ~uempmed) %>% 
  add_lines(name = "uempmed")
subplot(p1, p2)


##vistas dependientes: comparten el eje x pero tienen diferentes ejes y
vars <- setdiff(names(economics), "date")
plots <- lapply(vars, function(var) {
  plot_ly(economics, x = ~date, y = as.formula(paste0("~", var))) %>%
    add_lines(name = var)
})
subplot(plots, nrows = length(plots), shareX = TRUE, titleX = FALSE)


# dibujar valores aleatorios correlacionados por una distribución normal bi-variada
s <- matrix(c(1, 0.3, 0.3, 1), nrow = 2)
m <- mvtnorm::rmvnorm(1e5, sigma = s)
x <- m[, 1]   #genero los x
y <- m[, 2]   #genero los y
s <- subplot(
  plot_ly(x = x, color = I("black")), 
  plotly_empty(), 
  plot_ly(x = x, y = y, color = I("black")) %>%
    add_histogram2dcontour(colorscale = "Viridis"), 
  plot_ly(y = y, color = I("black")),
  nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2), margin = 0,
  shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE
)
layout(s, showlegend = FALSE)



##con mapas interactividad
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  lakecolor = toRGB('white')
)
# crear un mapa de la densidad de población. Los datos vienen del data.frame "state.x77"
density <- state.x77[, "Population"] / state.x77[, "Area"]
map <- plot_geo(
  z = ~density, text = state.name, 
  locations = state.abb, locationmode = 'USA-states'
) %>%
  layout(geo = g)
# create a bunch of horizontal bar charts 
vars <- colnames(state.x77)
barcharts <- lapply(vars, function(var) {
  plot_ly(x = state.x77[, var], y = state.name) %>%
    add_bars(orientation = "h", name = var) %>%
    layout(showlegend = FALSE, hovermode = "y",
           yaxis = list(showticklabels = FALSE))
})
subplot(barcharts, margin = 0.01) %>%
  subplot(map, nrows = 2, heights = c(0.3, 0.7), margin = 0.1) %>%
  layout(legend = list(y = 1)) %>%
  colorbar(y = 0.5)



###=============== Gráficos interactivos 3D ============
# library
library(rgl)

# This is to output a rgl plot in a rmarkdown document.
 setupKnitr()

# Data: the iris data is provided by R
data <- iris

# Add a new column with color
mycolors <- c('royalblue1', 'darkcyan', 'oldlace')
data$color <- mycolors[ as.numeric(data$Species) ]

# Plot
plot3d( 
  x=data$`Sepal.Length`, y=data$`Sepal.Width`, z=data$`Petal.Length`, 
  col = data$color, 
  type = 's', 
  radius = .1,
  xlab="Sepal Length", ylab="Sepal Width", zlab="Petal Length")

head(data)

install.packages('htmlwidgets')
# To display in an R Markdown document:
 rglwidget()

# To save to a file:
htmlwidgets::saveWidget(rglwidget(width = 520, height = 520), 
                        file = "HtmlWidget/3dscatter.html",
                        libdir = "libs",
                        selfcontained = FALSE)

##=======================================================================

###Esquisse    https://cran.r-project.org/web/packages/esquisse/readme/README.html
install.packages("esquisse")
library(esquisse)
esquisse:::esquisser(mpg)
#esquisse:::esquisser() #o sin un data.frame de entrada