Visualizing Movies Gross Income
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Shh.. this post is an excuse to test the brand new subtitles and captions in #ggplot2! powered by @hrbrmstr.
“And there came a day unlike any other,when earth's mightiest data sci vis tool added subtitles & captions” https://t.co/eRVPxz52iL #rstats
— boB Rudis (@hrbrmstr) March 15, 2016
The recently (I remeber this movie like it was yesterday) SW7 ($930,901,726 gross income) and the not so standar Deadpool ($329,397,732) are top 1 and top 7 (and climbing) in terms of gross income according to http://www.boxofficemojo.com/ site. Have you ask yourself how much gross income the movies produces? A lot i guess! What movies are the most succesfull in a particular saga? I dont know so write some code to scrap and discover it because http://www.boxofficemojo.com/ have all these data and we’re here visualize it.
Data
We’ll extract the (only US) gross income for the top 200 movies (You can get more if you want to test the visualizations with 1000 movies) and then, for each movie extract the daily chart section which containts for every day since the release date the gross income per day! This is just fantastic. So here we go!!
#### scrap ####
url <- "http://www.boxofficemojo.com/alltime/domestic.htm"
urls <- paste0(url, sprintf("?page=%s&p=.htm", 1:2))
dfmovie <- map_df(urls, function(x){
# x <- sample(size = 1, urls)
urlmovie <- read_html(x) %>%
html_nodes("table table tr a") %>%
html_attr("href") %>%
.[str_detect(., "movies")]
read_html(x) %>%
html_nodes("table table") %>%
html_table(fill = TRUE) %>%
.[[4]] %>%
tbl_df() %>%
.[-1, ] %>%
setNames(c("rank", "title", "studio", "gross", "year")) %>%
mutate(url_movie = urlmovie)
})
dfmovie <- dfmovie %>%
mutate(year = str_extract(year, "\d+"),
year = as.numeric(year),
have_release = str_detect(url_movie, "releases"),
box_id = str_extract(url_movie, "id=.*"),
box_id = str_replace_all(box_id, "^id=|\.htm$", ""))
dfmovie2 <- map_df(dfmovie$box_id, function(x){
# x <- "starwars2"
# x <- sample(dfmovie$box_id, size =1);
message(x)
if (file.exists(sprintf("data/%s-p2.rds", x))) {
# I'm always have conecction issues so for avoid
# loose data I save the data.
dfm <- readRDS(sprintf("data/%s-p2.rds", x))
return(dfm)
}
html <- sprintf("http://www.boxofficemojo.com/movies/?page=main&id=%s.htm", x) %>%
read_html()
img_url <- html %>%
html_nodes("table table table img") %>%
.[[1]] %>%
html_attr("src")
tmp <- tempfile(fileext = ".jpg")
download.file(img_url, tmp, mode = "wb", quiet = TRUE)
img <- jpeg::readJPEG(tmp)
imgpltt <- image_palette(img, n = 1, choice = median)
# par(mfrow = c(1, 2))
# display_image(img)
# show_col(imgpltt)
dfaux <- html %>%
html_nodes("table table table") %>%
.[[2]] %>%
html_table(fill = TRUE) %>%
.[-1, 1:2] %>%
tbl_df()
dfm <- data_frame(
box_id = x,
distributor = str_replace(dfaux[2, 1], "Distributor: ", ""),
genre = str_replace(dfaux[3, 1], "Genre: ", ""),
mpaa_rating = str_replace(dfaux[4, 1], "MPAA Rating: ", ""),
runtime = str_replace(dfaux[3, 2], "Runtime: ", ""),
production_budget = str_extract(dfaux[4, 2], "\d+"),
img_url = img_url,
img_main_color = imgpltt
)
saveRDS(dfm, file = sprintf("data/%s-p2.rds", x))
dfm
})
dfgross <- map_df(dfmovie$box_id, function(x){
# x <- sample(dfmovie$box_id, size =1)
message(x)
if (file.exists(sprintf("data/%s.rds", x))) {
dfgr <- readRDS(sprintf("data/%s.rds", x))
return(dfgr)
}
dfgr <- sprintf("http://www.boxofficemojo.com/movies/?page=daily&view=chart&id=%s.htm", x) %>%
read_html() %>%
html_nodes("table table table") %>%
html_table(fill = TRUE) %>%
last() %>%
tbl_df()
if (nrow(dfgr) == 1) {
dfgr <- data_frame(box_id = x)
} else {
dfgr <- dfgr %>%
.[-1, ] %>%
setNames(c("day", "date", "rank", "gross", "pd","na",
"theatres_avg", "na2", "gross_to_date", "day_number")) %>%
mutate(box_id = x) %>%
filter(!is.na(day_number))
}
saveRDS(dfgr, file = sprintf("data/%s.rds", x))
dfgr
})
# This is only necessary if you have a non english R version
try(x <- Sys.setlocale("LC_TIME", "en_US.UTF-8"))
try(x <- Sys.setlocale("LC_TIME", "English"))
dfgross <- dfgross %>%
mutate(gross = as.numeric(str_replace_all(gross, "\$|\,", "")),
gross_to_date = as.numeric(str_replace_all(gross_to_date, "\$|\,", "")),
day_number = as.numeric(day_number),
date2 = str_replace_all(date, "\t|\.", ""),
date2 = as.Date(date2, "%b %d, %Y"),
decade = year(date2)/100,
movieserie = str_extract(box_id, "^[A-Za-z]+|\d{2,3}"),
serienumber = str_extract(box_id, "\d{1,2}$"),
serienumber = ifelse(is.na(serienumber), 1, serienumber)) %>%
filter(!is.na(date))
dfmovie <- dfmovie %>%
left_join(dfmovie2, by = "box_id") %>%
left_join(dfgross %>%
group_by(box_id) %>%
summarise(max_day = max(day_number)),
by = "box_id")
dfmovie <- dfmovie %>%
mutate(rank = as.numeric(rank),
gross = as.numeric(str_replace_all(gross, "\$|\,", "")),
studio = str_replace_all(studio, "\.", ""),
production_budget = 1e6 * as.numeric(production_budget)
)
rm(dfmovie2)
Finally we have the movies data with some interesting colums like
production_budget, total life time gross income gross and the
max_day column which count the days in theatres. Here are
the top 10 movies
dfmovie %>%
select(rank, title, year, gross, genre) %>%
mutate(gross = dollar(gross)) %>%
head(10)
| rank | title | year | gross | genre |
|---|---|---|---|---|
| 1 | Star Wars: The Force Awakens | 2015 | $931,216,133 | Sci-Fi Fantasy |
| 2 | Avatar | 2009 | $760,507,625 | Sci-Fi Adventure |
| 3 | Titanic | 1997 | $658,672,302 | Romance |
| 4 | Jurassic World | 2015 | $652,270,625 | Sci-Fi Horror |
| 5 | Marvel’s The Avengers | 2012 | $623,357,910 | Action / Adventure |
| 6 | The Dark Knight | 2008 | $534,858,444 | Action / Adventure |
| 7 | Star Wars: Episode I – The Phantom Menace | 1999 | $474,544,677 | Sci-Fi Fantasy |
| 8 | Star Wars | 1977 | $460,998,007 | Sci-Fi Fantasy |
| 9 | Avengers: Age of Ultron | 2015 | $459,005,868 | Action / Adventure |
| 10 | The Dark Knight Rises | 2012 | $448,139,099 | Action Thriller |
Phantom Menace ad Jurassic World top 10?
We have the incomes by day for every movie too. So we can plot time series and compare! The data is just telling us what to do. Here’s a sample of the detailed data by day.
dfgross %>%
filter(box_id == "starwars7") %>%
mutate(gross = dollar(gross),
gross_to_date = dollar(gross_to_date)) %>%
select(box_id, date, day_number, gross, gross_to_date) %>%
head(10)
| box_id | date | day_number | gross | gross_to_date |
|---|---|---|---|---|
| starwars7 | Dec. 18, 2015 | 1 | $119,119,282 | $119,119,282 |
| starwars7 | Dec. 19, 2015 | 2 | $68,294,204 | $187,413,486 |
| starwars7 | Dec. 20, 2015 | 3 | $60,553,189 | $247,966,675 |
| starwars7 | Dec. 21, 2015 | 4 | $40,109,742 | $288,076,417 |
| starwars7 | Dec. 22, 2015 | 5 | $37,361,729 | $325,438,146 |
| starwars7 | Dec. 23, 2015 | 6 | $38,022,183 | $363,460,329 |
| starwars7 | Dec. 24, 2015 | 7 | $27,395,725 | $390,856,054 |
| starwars7 | Dec. 25, 2015 | 8 | $49,325,663 | $440,181,717 |
| starwars7 | Dec. 26, 2015 | 9 | $56,731,532 | $496,913,249 |
| starwars7 | Dec. 27, 2015 | 10 | $43,145,665 | $540,058,914 |
Plot
Okey, here we take a breath. A lot of ideas and only one order to code all of them. Just start considering the release date for every movie and its gross income evolution.
First well use the color for every movie extracted using the
nice RImagePalette package and the select the top movies and
the movies with more days in theatres to annotate them in the plot.
#### plot ####
cols <- setNames(dfmovie$img_main_color, dfmovie$box_id)
ntoplabel <- 10 + 1 # rm starwars
nmostlong <- 10
moviestop <- dfmovie %>%
arrange(rank) %>%
head(ntoplabel) %>%
.$box_id
movieslng <- dfmovie %>%
arrange(desc(max_day)) %>%
select(max_day, box_id) %>%
head(ntoplabel) %>%
.$box_id
movieslbl <- unique(c(moviestop, movieslng))
movieslbl <- setdiff(movieslbl, c("starwars4"))
fmt_dllr_mm <- function(x) {
x %>%
{./1000000} %>%
dollar()
}
tt1 <- "Cumulative Gross Income"
stt1 <- "Titanic (1997), Avatar (2009) and Star Wars VII (2016) are the movies with most gross income in the film history."
cptn <- "jkunst.com | Data from boxofficemojo.com"
dfgross %>%
ggplot(aes(date2, gross_to_date,
color = box_id, label = str_to_title(box_id))) +
geom_line(alpha = 0.25) +
scale_color_manual(values = cols) +
geom_label(data = dfgross %>%
filter(box_id %in% movieslbl) %>%
arrange(desc(day_number)) %>%
distinct(box_id)) +
theme(legend.position = "none") +
xlim(as.Date(min(dfgross$date2)), as.Date(ymd(20170101))) +
scale_y_continuous(labels = fmt_dllr_mm) +
labs(title = tt1, subtitle = stt1, caption = cptn,
x = "Date", y = "Cumulative Gross (millions)")
Mmm the first conclusion I get from this:
You don’t need only network data to get a spaghetti-like plot.
Mmm I think this is a nice result for the first try (this is a lie, I did more tries
before this plot XD). Clearly we can observe the date of release and compare the
gross income between the movies. Nice to see and remeber old classics like ET and
Back to the Future. Well the time scale is so big we can’t differentiate how long
each movie had been in theatres. To get a more fair comparision we plot every movie
considering x the day since release. I’m not sure if gross is comparable due
time of release but well keep data as is.
tt2 <- "Cumulative Gross Income by Days"
stt2 <- "Only 3 movies: Jurassic Park (497 days) ET, Gladiator and were more than a year in theaters."
dfgross %>%
ggplot(aes(day_number, gross_to_date,
color = box_id, label = str_to_title(box_id))) +
geom_line(alpha = 0.25) +
geom_label(data = dfgross %>%
filter(box_id %in% movieslbl) %>%
arrange(desc(day_number)) %>%
distinct(box_id)) +
scale_color_manual(values = cols) +
theme(legend.position = "none") +
xlim(NA, 550) +
scale_y_continuous(labels = fmt_dllr_mm) +
labs(title = tt2, subtitle = stt2, caption = cptn,
x = "Days since release", y = "Cumulative Gross (millions)") +
annotate("segment", x = 365, xend = 365, y = 0, yend = 925000000, colour = "gray") +
geom_text(label = "One Year", x = 365, y = 950000000)
Jurassic Park and ET were more than a year! The plot still like spaghetti but a info-tasty spagehtti.
Now, we can compare movies between other movies in their saga to show what part number is in general most successful in terms of income.
moviessaga <- dfgross %>%
distinct(movieserie, serienumber) %>%
count(movieserie) %>%
arrange(desc(n)) %>%
filter(n >= 4) %>%
.$movieserie
tt3 <- "Comparing Gross Income between Sagas"
stt3 <- "Interesting pattern and order is showed in Pirates of the Carrbbean,
Shrek and Transformes where the second movie have the greatest income"
st <- gsub("n", " ", stt3)
dfgross %>%
filter(movieserie %in% moviessaga) %>%
mutate(movieserie = factor(movieserie, levels = moviessaga)) %>%
ggplot(aes(day_number, gross_to_date, label = serienumber)) +
geom_line(aes(color = box_id), alpha = 0.5) +
geom_label(data = dfgross %>%
filter(movieserie %in% moviessaga) %>%
mutate(serienumber = ifelse(box_id == "transformers06", 1, serienumber),
movieserie = factor(movieserie, levels = moviessaga)) %>%
arrange(desc(day_number)) %>%
distinct(box_id)) +
facet_wrap(~movieserie, scales = "free_y") +
scale_y_continuous(labels = fmt_dllr_mm) +
labs(title = tt3, subtitle = stt3, caption = cptn,
x = "Days since release", y = "Gross (millions)") +
theme(legend.position = "none")
Aha! Nice pattern 2-3-1-4 in the Pirates of the caribbean, Shrek and Transformers we got: The first movie have a long time in theatres but they arent more popular than the second one (and the 3rd) in the saga and the 4th is the movie with less gross imcome.
Now well try to implement the scatter version of gross vs production_budget.
#### chart ####
dsmovie <- dfmovie %>%
filter(!is.na(production_budget)) %>%
mutate(x = gross,
y = production_budget,
gross_budget_ratio = percent(gross/production_budget),
production_budget = fmt_dllr_mm(production_budget),
gross = fmt_dllr_mm(gross),
name = title,
color = img_main_color) %>%
list.parse3()
t <- c("gross_budget_ratio", "production_budget", "gross", "distributor", "mpaa_rating")
x <- t %>% str_to_title() %>% gsub("_", " ", .)
y <- sprintf("{point.%s}", t)
tooltip <- tooltip_table(
x, y,
img = tags$img(src = "{point.img_url}", width = 150, height = 222,
style = "display: block;margin-left: auto;margin-right:auto"),
`min-heigth` = 300
)
hcscttr <- highchart() %>%
hc_chart(zoomType = "xy") %>%
hc_title(text = "Gross Income versus Production Budget") %>%
hc_add_series(data = dsmovie, type = "scatter", showInLegend = FALSE) %>%
hc_xAxis(title = list(text = "Gross income")) %>%
hc_yAxis(title = list(text = "Production Budget")) %>%
hc_tooltip(useHTML = TRUE,
headerFormat = as.character(tags$small("{point.key}")),
pointFormat = tooltip) %>%
hc_add_theme(hc_theme_smpl())
hcscttr
Mmm, not sure if we see a interesting pattern but the chart is good for an exploratoy process: For example we can see Superman Returns have a ~1 gross budget ratio.
Now we’ll replicate the previous plots using highcharter to have tooltips with more information ;D. Remember, you can zoom the chart to view with more detail
x <- c("Income:", "Genre", "Runtime")
y <- c("$ {point.y}", "{point.series.options.extra.genre}", "{point.series.options.extra.runtime}")
tooltip <- tooltip_table(
x, y,
tags$img(src = "{point.series.options.extra.img_url}", width = 150, height = 222,
style = "display: block;margin-left: auto;margin-right:auto")
)
# This function is a little tricky. We put the
# title (not the value) only if the point is
# the LAST point in the data
fmtrr <- "function() {
if (this.point.x == this.series.data[this.series.data.length-1].x &
this.series.options.showlabel) {
return this.series.options.extra.title;
} else {
return null;
}
}"
hcgross <- highchart() %>%
hc_chart(zoomType = "x") %>%
hc_tooltip(followPointer = FALSE) %>%
hc_yAxis(title = list(text = "Gross income")) %>%
hc_tooltip(
useHTML = TRUE,
pointFormat = tooltip
) %>%
hc_plotOptions(
series = list(
dataLabels = list(
enabled = TRUE,
align = "left",
verticalAlign = "middle",
formatter = JS(fmtrr),
crop = FALSE,
overflow = FALSE
)
)
) %>%
hc_add_theme(hc_theme_smpl())
hcgross1 <- hcgross %>%
hc_title(text = tt1) %>%
hc_subtitle(text = stt1) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_xAxis(type = "datetime")
hcgross2 <- hcgross %>%
hc_title(text = tt1) %>%
hc_subtitle(text = stt2) %>%
hc_xAxis(title = list(text = "Days since release")) %>%
hc_tooltip(headerFormat = as.character(tags$small("{point.key} days sinsce release")))
for (id in unique(dfgross$box_id)) {
# for (id in head(unique(dfgross$box_id), 10)) {
message(id)
dfaux <- dfgross %>% filter(box_id == id)
dsmov <- dfmovie %>%
filter(box_id == id) %>%
as.list()
showlabel <- id %in% movieslbl
hcgross1 <- hcgross1 %>%
hc_add_serie_times_values(dfaux$date2, dfaux$gross_to_date,
name = id, showInLegend = FALSE,
extra = dsmov, showlabel = showlabel,
color = hex_to_rgba(dsmov$img_main_color, 0.52))
hcgross2 <- hcgross2 %>%
hc_add_serie(data = dfaux %>% select(day_number, gross_to_date) %>% list.parse2(),
name = id, showInLegend = FALSE, marker = list(enabled = FALSE),
extra = dsmov, showlabel = showlabel,
color = hex_to_rgba(dsmov$img_main_color, 0.25))
}
hcgross1
hcgross2
What do you think? I love see how R can make (almost!) (with ggplot2 and highcharter) ready-to-publish plots and charts!
See you next time!
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.