# Load libraries
library(lattice)        # Very versatile graphics package
library(latticeExtra)   # Addition to lattice that makes layering graphs a breathe

# This starts the IPython Notebook pylab module, useful for plotting and
# interactive scientific computing
%pylab inline
from pandas import *

# Load data into a data frame
body.count.data <- read.csv("http://files.figshare.com/1332945/film_death_counts.csv")

# Read the data into a pandas DataFrame
body_count_data = read_csv("http://files.figshare.com/1332945/film_death_counts.csv")

# Compute on screen deaths per minute for each movie.
body.count.data <- within(body.count.data, {
  Deaths_Per_Minute <- Body_Count / Length_Minutes
  ord <- order(Deaths_Per_Minute, decreasing = TRUE)  # useful later
})

# Divide the body counts by the length of the film
body_count_data["Deaths_Per_Minute"] = (body_count_data["Body_Count"].apply(float).values /
                                          body_count_data["Length_Minutes"].values)

# Reorder "body.count.data" by (descending) number of on screen deaths per minute
body.count.data <- body.count.data[body.count.data$ord, ]

# Select top 25 most violent movies by number of on screen deaths per minute
body.count.data <- body.count.data[1:25,]

# Only keep the top 25 highest kills per minute films
body_count_data = body_count_data.sort("Deaths_Per_Minute", ascending=False)[:25]

# Change the order of the data so highest kills per minute films are on top in the plot
body_count_data = body_count_data.sort("Deaths_Per_Minute", ascending=True)

# Combine film title and release date into a new factor column with levels
# ordered by ascending violence
body.count.data <- within(body.count.data, {
  Full_Title <- paste0(Film, " (", Year, ")")
  ord <- order(Deaths_Per_Minute, decreasing = TRUE)
  Full_Title <- ordered(Full_Title, levels = rev(unique(Full_Title[ord])))  # some films are duplicated! Bad Randy!
})

# Generate the full titles for the movies: movie name (year)
full_title = []

for film, year in zip(body_count_data["Film"].values, body_count_data["Year"].values):
  full_title.append(film + " (" + str(year) + ")")

body_count_ y-axis ticks on the left and x-axis ticks on the bottom
ax.yaxis.tick_left()
ax.xaxis.tick_bottom()data["Full_Title"] = array(full_title)

# Generate base graph
graph <- barchart(Full_Title ~ Deaths_Per_Minute, data = body.count.data)
print(graph)

# plot the bars
fig = plt.figure(figsize=(8,12))

# Plot the red horizontal bars
rects = plt.barh(range(len(body_count_data["Deaths_Per_Minute"])),
                 body_count_data["Deaths_Per_Minute"],
                 height=0.8,
                 align="center",
                 color="#8A0707",
                 edgecolor="none")

# Add the film labels to left of the bars (y-axis)
yticks(range(len(body_count_data["Full_Title"])), body_count_data["Full_Title"].values, fontsize=14)xticks(arange(0, 5, 1), [""])

# Create theme
my.bloody.theme <- within(trellis.par.get(), {    # Initialize theme with default value
  axis.line$col <- NA                             # Remove axes
  plot.polygon <- within(plot.polygon, {
    col <- "#8A0606"                              # Set bar colors to a nice bloody red
    border <- NA                                  # Remove bars' outline
  })
  axis.text$cex <- 1                              # Default axis text size is a bit small. Make it bigger
  layout.heights <- within(layout.heights, {
    bottom.padding <- 0                           # Remove bottom padding
    axis.bottom <- 0                              # Remove axis padding at the bottom of the graph
    axis.top <- 0                                 # Remove axis padding at the top of the graph
  })
})

# Update figure with new theme + other improvements (like a title for instance)
graph <- update(
  graph,
  main='25 most violence packed films by deaths per minute',    # Title of the barchart
  par.settings = my.bloody.theme,                               # Use custom theme
  xlab = NULL,                                                  # Remove label of x axis
  scales=list(x=list(at=NULL)),                                 # Remove rest of x axis
  xlim = c(0, 6.7),                                             # Set graph limits along x axis to accomodate the additional text (requires some trial and error)
  box.width=0.75)                                               # Default bar width is a bit small. Make it bigger)

print(graph)

# Don't have any x tick labels
xticks(arange(0, 5, 1), [""])

# Plot styling

# Remove the plot frame lines
ax = axes()
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)

# Color the y-axis ticks the same dark red color, and the x-axis ticks white
ax.tick_params(axis="y", color="#8A0707")
ax.tick_params(axis="x", color="white")

ax.xaxis.grid(color="white", linestyle="-")

# Combine number of on screen death per minute and duration of the movies into a new character string column
body.count.data <- within(body.count.data, {
  Deaths_Per_Minute_With_Length = paste0(round(body.count.data$Deaths_Per_Minute, digits=2), " (", body.count.data$Length_Minutes, " mins)")
})

# Add number of on screen deaths per minute and duration of movies at the end of each bar
graph <- graph + layer(with(body.count.data,
  panel.text(
    Deaths_Per_Minute,                  # x position of the text
    25:1,                               # y position of the text
    pos = 4,                            # Position of the text relative to the x and y position (4 = to the right)
    Deaths_Per_Minute_With_Length)))    # Text to display                                     

# Print graph
print(graph)

# This function adds the deaths per minute label to the right of the bars
def autolabel(rects):
  for i, rect in enumerate(rects):
  width = rect.get_width()
label_text = (str(round(float(width), 2)) +
                " (" + str(body_count_data["Length_Minutes"].values[i]) +
                " mins)")

plt.text(width + 0.25,
         rect.get_y() + rect.get_height() / 2.,
         label_text,
         ha="left",
         va="center",
         fontsize=14)

autolabel(rects)

# Load additional libraries
library(jpeg)  # To read JPG images
library(grid)  # Graphics library with better image plotting capabilities

# Download a pretty background image; mode is set to "wb" because it seems that
# Windows needs it. I don't use Windows, I can't confirm
download.file(url = "http://www.theswarmlab.com/wp-content/uploads/2014/01/bloody_gun.jpg",
              destfile = "bloody_gun.jpg", quiet = TRUE, mode = "wb")

# Load gun image using "readJPEG" from the "jpeg" package
img <- readJPEG("bloody_gun.jpg")

# Add image to graph using "grid.raster" from the "grid" package
graph <- graph + layer_(
  grid.raster(
    as.raster(img),                 # Image as a raster
    x = 1,                          # x location of image "Normalised Parent Coordinates"
    y = 0,                          # y location of image "Normalised Parent Coordinates"
    height = 0.7,                   # Height of the image. 1 indicates that the image height is equal to the graph height
    just = c("right", "bottom")))   # Justification of the image relative to its x and y locations

# Print graph
print(graph)