imdb_assignment

Packages

Firstly, we have to install our packages.

# install.packages("tidyverse")
# install.packages("rvest")
# install.packages("stringr")
# install.packages("ggplot2")

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Secondly, we need to active these packages.

 library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

 library(rvest)

Attaching package: 'rvest'

The following object is masked from 'package:readr':

    guess_encoding

 library(stringr)
 library(ggplot2)

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Datas

Links which we will pull data

https://m.imdb.com/search/title/?title_type=feature&release_date=2010-01-01,2023-12-25&num_votes=2499,&country_of_origin=TR&count=250

https://m.imdb.com/search/title/?title_type=feature&release_date=,2009-12-31&num_votes=2499,&country_of_origin=TR&count=250

Assigning links from which we will pull data

link1 <- "https://m.imdb.com/search/title/?title_type=feature&release_date=2010-01-01,2023-12-25&num_votes=2499,&country_of_origin=TR&count=250"
link2 <- "https://m.imdb.com/search/title/?title_type=feature&release_date=,2009-12-31&num_votes=2499,&country_of_origin=TR&count=250"

page1 <- read_html(link1)
page2 <- read_html(link2)

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Web Wrangling

We must obtain a “data frame” by parsing the characteristic features of the films. Thus, we can perform our exploratory data analysis with the data set we created.

(Since we have two separate links, after performing separate operations for both of them, we put them into a single vector with the c() function.)

For Titles :

titles1 <- page1 %>% html_nodes(".dli-title") %>% html_text()
titles2 <- page2 %>% html_nodes(".dli-title") %>% html_text()

combined_titles <- c(titles1, titles2)
# print(combined_titles)

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For Years :

years1 <- page1 %>% html_nodes(".dli-title-metadata-item:nth-child(1)") %>% html_text()
years2 <- page2 %>% html_nodes(".dli-title-metadata-item:nth-child(1)") %>% html_text()

combined_years <- c(years1, years2)
# print(combined_years)

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For Durations :

durations1 <- page1 %>% html_nodes(".dli-title-metadata-item:nth-child(2)") %>% html_text()
durations2 <- page2 %>% html_nodes(".dli-title-metadata-item:nth-child(2)") %>% html_text()

combined_durations <- c(durations1, durations2)
# print(combined_durations)

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For Ratings :

ratings1 <- page1 %>% html_nodes(".ratingGroup--imdb-rating") %>% html_text()
ratings2 <- page2 %>% html_nodes(".ratingGroup--imdb-rating") %>% html_text()

combined_ratings <- c(ratings1, ratings2)
# print(combined_ratings)

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For Votes :

votes1 <- page1 %>% html_nodes(".ipc-rating-star--voteCount") %>% html_text()
votes2 <- page2 %>% html_nodes(".ipc-rating-star--voteCount") %>% html_text()

combined_votes <- c(votes1, votes2)
# print(combined_votes)

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String Processing

We pulled our data from the web. Now we need to make our data suitable for the data frame with string processing.

For Titles :

title_names1 <- page1 %>% html_nodes('.ipc-title__text')
title_names1 <- html_text(title_names1)
title_names1 <- tail(head(title_names1,-1),-1)
title_names1 <- str_split(title_names1, " ", n=2)
title_names1 <- unlist(lapply(title_names1, function(x) {x[2]}))

title_names2 <- page2 %>% html_nodes('.ipc-title__text')
title_names2 <- html_text(title_names2)
title_names2 <- tail(head(title_names2,-1),-1)
title_names2 <- str_split(title_names2, " ", n=2)
title_names2 <- unlist(lapply(title_names2, function(x) {x[2]}))

combined_title_names <- c(title_names1, title_names2)
# print(combined_title_names)

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For Years :

Since the year data is available, we do not need to do any processing on them.

---

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For Durations :

# For Durations - 1
# Saat ve daikaları ayırıp tanımlıyorum :
hours1 <- as.integer(str_extract(durations1, "\\d+(?=h)"))  # For Hours
minutes1 <- as.integer(str_extract(durations1, "\\d+(?=m)"))  # For Minutes

hours1[is.na(hours1)] <- as.integer("0")     # sadece dakika olan var ise
minutes1[is.na(minutes1)] <- as.integer("0") # sadece saat olan var ise

# Süreleri dakika cinsine dönüştürmek için :
total_duration_minutes1 <- (hours1 * 60) + minutes1



# For Durations - 2
hours2 <- as.integer(str_extract(durations2, "\\d+(?=h)"))
minutes2 <- as.integer(str_extract(durations2, "\\d+(?=m)"))

hours2[is.na(hours2)] <- as.integer("0")
minutes2[is.na(minutes2)] <- as.integer("0")

total_duration_minutes2 <- (hours2 * 60) + minutes2


combined_durations <- c(total_duration_minutes1 , total_duration_minutes2)
# print(combined_durations)

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For Votes :

votes1 <- page1 %>% html_nodes(".ipc-rating-star--voteCount") %>% html_text()
votes2 <- page2 %>% html_nodes(".ipc-rating-star--voteCount") %>% html_text()

# From ChatGPT :
votes1 <- str_replace_all(votes1, "[()]", "")
votes2 <- str_replace_all(votes2, "[()]", "")

# From ChatGPT :
votes1 <- str_remove_all(votes1, "[^0-9.K]")
votes2 <- str_remove_all(votes2, "[^0-9.K]")


votes_numeric1 <- str_remove_all(votes1, "K") %>% as.numeric() * 1000
votes_numeric2 <- str_remove_all(votes2, "K") %>% as.numeric() * 1000

combined_votes <- c(votes_numeric1 , votes_numeric2)
# print(combined_votes)

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For Ratings :

# Parantezleri tamamen kaldırma
rating_no_parant1 <- str_replace_all(combined_ratings, "\\(.*\\)", "")
rating_no_parant_no_space <- str_trim(str_extract(rating_no_parant1, "^\\d+\\.?\\d*"))

# Simdi sayıları double tipine çevirelim : 
combined_ratings <- as.double(rating_no_parant_no_space)

class(combined_ratings)

[1] "numeric"

# print(combined_ratings)

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Creating a Data Frame :

We have completed String Processing. All the data we have obtained now is of a simple and appropriate type suitable for adding to the data frame.

imdb_movies_data <- data.frame(Title = combined_title_names, Year = combined_years, Duration = combined_durations, Rate = combined_ratings, Vote = combined_votes)

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---

Upgrade Data Frame

Let’s sort our data set in decreasing order of vote rate.

imdb_movies_data_rate_decreasing <- imdb_movies_data %>%
  arrange(desc(Rate))

Let’s add a column to our data set showing the average rate by year.

updated_imdb_movies_data <- imdb_movies_data_rate_decreasing %>% 
  group_by(Year) %>%
  mutate(Mean_Rate = mean(Rate))

Let’s add a column to our data set showing its rank according to vote rate s:

updated_imdb_movies_data <- imdb_movies_data_rate_decreasing %>%
  mutate(Order = row_number())

Let’s examine the differences :

head(imdb_movies_data)

                     Title Year Duration Rate  Vote
1        Kuru Otlar Üstüne 2023      197  8.0  7700
2 Yedinci Kogustaki Mucize 2019      132  8.2 55000
3  Istanbul Için Son Çagri 2023       91  5.3  8400
4               Kis Uykusu 2014      196  8.0 55000
5                   Baskin 2015       97  5.8 12000
6                   Bihter 2023      113  3.6  3600

head(updated_imdb_movies_data)

                         Title Year Duration Rate  Vote Order
1               Hababam Sinifi 1975       87  9.2 43000     1
2       CM101MMXI Fundamentals 2013      139  9.1 47000     2
3                   Tosun Pasa 1976       90  8.9 24000     3
4 Hababam Sinifi Sinifta Kaldi 1975       95  8.9 25000     4
5                Süt Kardesler 1976       80  8.8 21000     5
6                      Eflâtun 2022      103  8.7  3000     6

Adding Mean Rate :

updated_imdb_movies_data <- imdb_movies_data %>% 
  group_by(Year) %>%
  mutate(Mean_Rate = mean(Rate), Total_Film_Numbers = n())

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Extras

Let’s find the 5 movies with the highest and lowest scores based on IMDb scores.

imdb_movies_data_rate_decreasing <- imdb_movies_data %>%
  arrange(desc(Rate))

# En iyi ve en kotu 5 film :
print("En iyi oy oranına sahip 5 film:")

[1] "En iyi oy oranına sahip 5 film:"

print(head(imdb_movies_data_rate_decreasing, 5))

                         Title Year Duration Rate  Vote
1               Hababam Sinifi 1975       87  9.2 43000
2       CM101MMXI Fundamentals 2013      139  9.1 47000
3                   Tosun Pasa 1976       90  8.9 24000
4 Hababam Sinifi Sinifta Kaldi 1975       95  8.9 25000
5                Süt Kardesler 1976       80  8.8 21000

# En alttaki 5 satırı yazdır
print("En kotu oy oranına sahip 5 film:")

[1] "En kotu oy oranına sahip 5 film:"

print(tail(imdb_movies_data_rate_decreasing, 5))

                             Title Year Duration Rate  Vote
470                 Cumali Ceber 2 2018      100  1.2 10000
471                          Müjde 2022       48  1.2  9900
472              15/07 Safak Vakti 2021       95  1.2 21000
473                           Reis 2017      108  1.0 74000
474 Cumali Ceber: Allah Seni Alsin 2017      100  1.0 39000

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Let’s examine the imdb data of 4 movies I like.

search_film <- function(name_of_the_film) {
  result <- imdb_movies_data_rate_decreasing[imdb_movies_data_rate_decreasing$Title == name_of_the_film, ]
  return(result)
}

search_film("Senden Bana Kalan")

                Title Year Duration Rate Vote
264 Senden Bana Kalan 2015      117  6.5 3400

search_film("Aykut Eniste")

           Title Year Duration Rate Vote
194 Aykut Eniste 2019      115    7 7500

search_film("Kolonya Cumhuriyeti")

                  Title Year Duration Rate Vote
353 Kolonya Cumhuriyeti 2017      110  5.8 5500

search_film("Recep Ivedik 2")

             Title Year Duration Rate  Vote
421 Recep Ivedik 2 2009      107  4.5 23000

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PLOTS

MEAN RATE

plot_mean_rate_per_year_col <- updated_imdb_movies_data %>% 
  ggplot(aes(x = Year, y = Mean_Rate)) +
  geom_col()

print(plot_mean_rate_per_year_col)

plot_mean_rate_per_year_point <- updated_imdb_movies_data %>% 
  ggplot(aes(x = Year, y = Mean_Rate)) +
  geom_point()

print(plot_mean_rate_per_year_point)

RATE

plot_rate_per_year_box <- updated_imdb_movies_data %>% 
  ggplot(aes(x = Year, y = Rate)) +
  geom_boxplot()

print(plot_rate_per_year_box)

FILM NUMBER

plot_total_film_number_per_year_point <- updated_imdb_movies_data %>% 
  ggplot(aes(x = Year, y = Total_Film_Numbers)) +
  geom_point()

print(plot_total_film_number_per_year_point)