The IMDB dataset contains information about movies, including their names, release dates, user ratings, genres, overviews, cast and crew members, original titles, production status, original languages, budgets, revenues, and countries of origin. This data can be used for various analyses, such as identifying trends in movie genres, exploring the relationship between budget and revenue, and predicting the success of future movies.
# Load the lubridate package
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(plyr)
library(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following objects are masked from 'package:plyr':
##
## arrange, mutate, rename, summarise## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
movieData <-read.csv('C:/Users/govin/OneDrive/Desktop/RStudio/Data/imdb_movies.csv')
movieData$date_x <- sapply(movieData$date_x, function(x) gsub("/", "-", x))
movieData[c('date_x')] <- lapply(movieData[c('date_x')], function(x) as.Date(x, format="%m-%d-%Y"))
movieData <- type_convert(movieData)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## names = col_character(),
## genre = col_character(),
## overview = col_character(),
## crew = col_character(),
## orig_title = col_character(),
## status = col_character(),
## orig_lang = col_character(),
## country = col_character()
## )head(movieData, 10)## names date_x score
## 1 Creed III 2023-03-02 73
## 2 Avatar: The Way of Water 2022-12-15 78
## 3 The Super Mario Bros. Movie 2023-04-05 76
## 4 Mummies 2023-01-05 70
## 5 Supercell 2023-03-17 61
## 6 Cocaine Bear 2023-02-23 66
## 7 John Wick: Chapter 4 2023-03-23 80
## 8 Puss in Boots: The Last Wish 2022-12-26 83
## 9 Attack on Titan 2022-09-30 59
## 10 The Park 2023-03-02 58
## genre
## 1 Drama, Action
## 2 Science Fiction, Adventure, Action
## 3 Animation, Adventure, Family, Fantasy, Comedy
## 4 Animation, Comedy, Family, Adventure, Fantasy
## 5 Action
## 6 Thriller, Comedy, Crime
## 7 Action, Thriller, Crime
## 8 Animation, Family, Fantasy, Adventure, Comedy
## 9 Action, Science Fiction
## 10 Action, Drama, Horror, Science Fiction, Thriller
## overview
## 1 After dominating the boxing world, Adonis Creed has been thriving in both his career and family life. When a childhood friend and former boxing prodigy, Damien Anderson, resurfaces after serving a long sentence in prison, he is eager to prove that he deserves his shot in the ring. The face-off between former friends is more than just a fight. To settle the score, Adonis must put his future on the line to battle Damien — a fighter who has nothing to lose.
## 2 Set more than a decade after the events of the first film, learn the story of the Sully family (Jake, Neytiri, and their kids), the trouble that follows them, the lengths they go to keep each other safe, the battles they fight to stay alive, and the tragedies they endure.
## 3 While working underground to fix a water main, Brooklyn plumbers—and brothers—Mario and Luigi are transported down a mysterious pipe and wander into a magical new world. But when the brothers are separated, Mario embarks on an epic quest to find Luigi.
## 4 Through a series of unfortunate events, three mummies end up in present-day London and embark on a wacky and hilarious journey in search of an old ring belonging to the Royal Family, stolen by ambitious archaeologist Lord Carnaby.
## 5 Good-hearted teenager William always lived in hope of following in his late father’s footsteps and becoming a storm chaser. His father’s legacy has now been turned into a storm-chasing tourist business, managed by the greedy and reckless Zane Rogers, who is now using William as the main attraction to lead a group of unsuspecting adventurers deep into the eye of the most dangerous supercell ever seen.
## 6 Inspired by a true story, an oddball group of cops, criminals, tourists and teens converge in a Georgia forest where a 500-pound black bear goes on a murderous rampage after unintentionally ingesting cocaine.
## 7 With the price on his head ever increasing, John Wick uncovers a path to defeating The High Table. But before he can earn his freedom, Wick must face off against a new enemy with powerful alliances across the globe and forces that turn old friends into foes.
## 8 Puss in Boots discovers that his passion for adventure has taken its toll: He has burned through eight of his nine lives, leaving him with only one life left. Puss sets out on an epic journey to find the mythical Last Wish and restore his nine lives.
## 9 As viable water is depleted on Earth, a mission is sent to Saturn's moon Titan to retrieve sustainable H2O reserves from its alien inhabitants. But just as the humans acquire the precious resource, they are attacked by Titan rebels, who don't trust that the Earthlings will leave in peace.
## 10 A dystopian coming-of-age movie focused on three kids who find themselves in an abandoned amusement park, aiming to unite whoever remains. With dangers lurking around every corner, they will do whatever it takes to survive their hellish Neverland.
## crew
## 1 Michael B. Jordan, Adonis Creed, Tessa Thompson, Bianca Taylor, Jonathan Majors, Damien Anderson, Wood Harris, Tony 'Little Duke' Evers, Phylicia Rashād, Mary Anne Creed, Mila Davis-Kent, Amara Creed, Florian Munteanu, Viktor Drago, José Benavidez Jr., Felix Chavez, Selenis Leyva, Laura Chavez
## 2 Sam Worthington, Jake Sully, Zoe Saldaña, Neytiri, Sigourney Weaver, Kiri / Dr. Grace Augustine, Stephen Lang, Colonel Miles Quaritch, Kate Winslet, Ronal, Cliff Curtis, Tonowari, Joel David Moore, Norm Spellman, CCH Pounder, Mo'at, Edie Falco, General Frances Ardmore
## 3 Chris Pratt, Mario (voice), Anya Taylor-Joy, Princess Peach (voice), Charlie Day, Luigi (voice), Jack Black, Bowser (voice), Keegan-Michael Key, Toad (voice), Seth Rogen, Donkey Kong (voice), Fred Armisen, Cranky Kong (voice), Kevin Michael Richardson, Kamek (voice), Sebastian Maniscalco, Spike (voice)
## 4 Óscar Barberán, Thut (voice), Ana Esther Alborg, Nefer (voice), Luis Pérez Reina, Carnaby (voice), María Luisa Solá, Madre (voice), Jaume Solà, Sekhem (voice), José Luis Mediavilla, Ed (voice), José Javier Serrano Rodríguez, Danny (voice), Aleix Estadella, Dennis (voice), María Moscardó, Usi (voice)
## 5 Skeet Ulrich, Roy Cameron, Anne Heche, Dr Quinn Brody, Daniel Diemer, William Brody, Jordan Kristine Seamón, Harper Hunter, Alec Baldwin, Zane Rogers, Richard Gunn, Bill Brody, Praya Lundberg, Amy, Johnny Wactor, Martin, Anjul Nigam, Ramesh
## 6 Keri Russell, Sari, Alden Ehrenreich, Eddie, O'Shea Jackson Jr., Daveed, Ray Liotta, Syd, Kristofer Hivju, Olaf (Kristoffer), Margo Martindale, Ranger Liz, Christian Convery, Henry, Isiah Whitlock Jr., Bob, Jesse Tyler Ferguson, Peter
## 7 Keanu Reeves, John Wick, Donnie Yen, Caine, Bill Skarsgård, Marquis de Gramont, Ian McShane, Winston, Laurence Fishburne, Bowery King, Lance Reddick, Charon, Clancy Brown, The Harbinger, Hiroyuki Sanada, Shimazu, Shamier Anderson, Mr Nobody
## 8 Antonio Banderas, Puss in Boots (voice), Salma Hayek, Kitty Softpaws (voice), Harvey Guillén, Perrito (voice), Wagner Moura, Wolf (voice), Florence Pugh, Goldilocks (voice), Olivia Colman, Mama Bear (voice), Ray Winstone, Papa Bear (voice), Samson Kayo, Baby Bear (voice), John Mulaney, Jack Horner (voice)
## 9 Paul Bianchi, Computer (voice), Erin Coker, Allison Quince, Jack Pearson, Max Reece, Anthony Jensen, Jowers, Neli Sabour, Heidi Quince, Karan Sagoo, Adrian Naidu, Natalie Storrs, Saoirse Parker, Justin Tanks, Mark Morales, Jenny Tran, Kim Costa
## 10 Chloe Guidry, Ines, Nhedrick Jabier, Bui, Carmina Garay, Kuan, Billy Slaughter, Martin Parker, Carli McIntyre, Rue, Laura Coover, Reporter, Presley Richardson, Bennett, Sean Papajohn, Jack, Legend Jay Jones, Slingshot Gang
## orig_title status orig_lang budget_x
## 1 Creed III Released English 75000000
## 2 Avatar: The Way of Water Released English 460000000
## 3 The Super Mario Bros. Movie Released English 100000000
## 4 Momias Released Spanish, Castilian 12300000
## 5 Supercell Released English 77000000
## 6 Cocaine Bear Released English 35000000
## 7 John Wick: Chapter 4 Released English 100000000
## 8 Puss in Boots: The Last Wish Released English 90000000
## 9 Attack on Titan Released English 71000000
## 10 The Park Released English 119200000
## revenue country
## 1 271616668 AU
## 2 2316794914 AU
## 3 724459031 AU
## 4 34200000 AU
## 5 340941959 US
## 6 80000000 AU
## 7 351349364 AU
## 8 483480577 AU
## 9 254946484 US
## 10 488962491 USQuestion: A collection of 5-10 random samples of data (with replacement) from at least 6 columns of data Each subsample should be as long as roughly 50% percent of your data. We are simulating the act of collecting data from a population where the “population” is represented by the data set you already have. Store each sample set in a separate data frame (e.g., movieData_i might contain m rows from columns 1-6) These subsamples should include both categorical and continuous (numeric) data?
Answer:
# This object would contain all the dataframes for samples
sample_data <- list()
# 6 subsamples
for (i in (1: 6)){
indexes <- sample(1:nrow(movieData), size = 0.5*nrow(movieData), replace = TRUE)
movieData_i <- movieData[indexes, c( "orig_lang","country", "revenue", "budget_x", "revenue", "score")]
# Appending the sub-sample data frames to the list
sample_data[[paste0("movieData_", i)]] <- movieData_i
}
sample_data## $movieData_1
## orig_lang country revenue budget_x revenue.1 score
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## 2496 English US 249577615.2 130800000.0 249577615.2 54
## 7039 Dutch, Flemish NL 515295729.6 61400000.0 515295729.6 68
## 4830 Turkish TR 175269998.8 193540000.0 175269998.8 0
## 6724 English US 234272533.2 80000000.0 234272533.2 57
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## 4185 Japanese JP 605173999.8 101600000.0 605173999.8 75
## 8170 English AU 53624424.0 40000000.0 53624424.0 67
## 3359 Tagalog PH 251792115.2 98400000.0 251792115.2 61
## 2199 Russian RU 4803565.0 5400000.0 4803565.0 49
## 9622 English CH 455427004.8 88200000.0 455427004.8 53
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## 9707.1 472878915 75
## 3928.1 29855981 71
## 6981 51702929 56
## 3074 173567581 80
## 3742.1 64200321 73
## 2762 141507355 65
## 933.1 101332962 67
## 2125.1 125729635 59
## 173 651717830 61# Assuming you have already created sample_data as described earlier
# Define the continuous column names
continuous_col_names <- c("revenue", "budget_x", "score")
# Initialize an empty list to store statistics data frames
stats_list <- list()
# Loop through each continuous column
for (col_name in continuous_col_names) {
# Initialize a data frame to store statistics for each subsample
statistics_movieData <- data.frame(
Subsample = character(0),
Mean = numeric(0),
Median = numeric(0),
SD = numeric(0),
IQR = numeric(0),
Lower_Range = numeric(0),
Upper_Range = numeric(0)
)
# Loop through each subsample and calculate statistics for the continuous column
for (i in 1:length(sample_data)) {
# Extract the current subsample
current_movieData <- sample_data[[i]]
# Calculate statistics for the continuous column
mean_value <- mean(current_movieData[[col_name]])
sd_value <- sd(current_movieData[[col_name]])
median_value <- median(current_movieData[[col_name]])
# Calculate IQR, lower range, and upper range for outliers
Q1 <- quantile(current_movieData[[col_name]], 0.25)
Q3 <- quantile(current_movieData[[col_name]], 0.75)
iqr_value <- Q3 - Q1
lower_range <- Q1 - 1.5 * iqr_value
upper_range <- Q3 + 1.5 * iqr_value
# Add the statistics to the stats_movieData data frame
statistics_movieData <- rbind(statistics_movieData, data.frame(Subsample = paste0("movieData_", i), Mean = mean_value, Median = median_value, SD = sd_value, IQR = iqr_value, Lower_Range = lower_range, Upper_Range = upper_range))
}
# Add the statistics data frame to the stats_list
stats_list[[col_name]] <- statistics_movieData
}
#Statistics for each column
for (col_name in continuous_col_names) {
print(paste("Statistics for Column:", col_name))
print(stats_list[[col_name]])
}## [1] "Statistics for Column: revenue"
## Subsample Mean Median SD IQR Lower_Range
## 75% movieData_1 254729863 155008909 276514985 397863570 -567428409
## 75%1 movieData_2 263485575 169332978 285929776 398749177 -566441954
## 75%2 movieData_3 254531181 156822020 276659363 398581056 -570720093
## 75%3 movieData_4 253881103 161941727 273676010 385057877 -548231613
## 75%4 movieData_5 255663295 153770052 279910294 387700368 -552959887
## 75%5 movieData_6 249147006 153733800 268975188 383484113 -546523086
## Upper_Range
## 75% 1024025871
## 75%1 1028554753
## 75%2 1023604129
## 75%3 991999896
## 75%4 997841586
## 75%5 987413365
## [1] "Statistics for Column: budget_x"
## Subsample Mean Median SD IQR Lower_Range Upper_Range
## 75% movieData_1 65291049 50000000 56853080 90800000 -121200000 242000000
## 75%1 movieData_2 65549656 52000000 57780177 92000000 -124000000 244000000
## 75%2 movieData_3 65364541 50168000 56954058 91432000 -122580000 243148000
## 75%3 movieData_4 65234914 50610000 56909845 89400000 -119100000 238500000
## 75%4 movieData_5 64912768 50000000 56255037 90200000 -120300000 240500000
## 75%5 movieData_6 64460751 50000000 56873248 91500000 -123750000 242250000
## [1] "Statistics for Column: score"
## Subsample Mean Median SD IQR Lower_Range Upper_Range
## 75% movieData_1 63.53645 65 13.68021 13 39.5 91.5
## 75%1 movieData_2 63.65769 66 13.48750 13 39.5 91.5
## 75%2 movieData_3 63.68717 65 13.21701 13 39.5 91.5
## 75%3 movieData_4 63.62036 65 13.40587 12 41.0 89.0
## 75%4 movieData_5 63.35351 65 13.35416 12 41.0 89.0
## 75%5 movieData_6 63.49008 65 13.55202 12 41.0 89.0Question: How different are they? What would you have called an anomaly in one sub-sample that you wouldn’t in another?
Here we can observe for same variables, the sample statistics i.e the mean, median, standard deviation, outliers and range how it changes.
We can see here the values for upper as well as the lower range are different for a column in different samples which also suggests that the outliers marked would be different in these according to the lower and upper range.
for instance an anomaly in sample 1 might not be an anomaly in sample 2 since it falls inside of upper and lower whiskers.
Question: Are there aspects of the data that are consistent among all sub-samples?
There are no aspects of data which are consistent but still mean is something which is somewhat in similar range. This could mean either there aren’t too many outliers or similar outliers are present in all the samples.
# Finding out Categorical columns
categorical_col_names <- setdiff(colnames(sample_data[["df_1"]]), continuous_col_names)
# Results for each combination of column and sample would be contained in this list
aggregate_results <- list()
for (col in categorical_col_names) {
for (sample in 1:length(sample_data)) {
# Filtering the data frame to include only the current categorical column
filtered_data <- sample_data[[sample]][, col]
# Performing a group-by and storing the results
result <- aggregate(. ~ filtered_data, data = sample_data[[sample]], FUN = length)
result <- result[, categorical_col_names]
result_name <- paste("Column:", col, "Sample:", sample)
aggregate_results[[result_name]] <- result
}
}
# Printing the results for each combination of column and sample
for (result_name in names(aggregate_results)) {
cat("Distribution for", result_name, "\n")
print(aggregate_results[[result_name]])
}It can be observed column no. of unique values for each column in each sample is different for the categorical columns showing the distribution is different. Not performing any form of hypothesis testing here to find the relationship between categorical columns to show that different type of relationsip can exist between them in different samples.
Question: How different are they? What would you have called an anomaly in one sub-sample that you wouldn’t in another?
Here we can observe for same variables, the sample statistics i.e the mean, median, standard deviation, outliers and range how it changes.
We can see here the values for upper as well as the lower range are different for a column in different samples which also suggests that the outliers marked would be different in these according to the lower and upper range.
for instance an anomaly in sample 1 might not be an anomaly in sample 2 since it falls inside of upper and lower whiskers.
Question: Are there aspects of the data that are consistent among all sub-samples?
There are no aspects of data which are consistent but still mean is something which is somewhat in similar range. This could mean either there aren’t too many outliers or similar outliers are present in all the samples.
# Finding out Categorical columns
categorical_col_names <- setdiff(colnames(sample_data[["movieData_1"]]), continuous_col_names)
# Results for each combination of column and sample would be contained in this list
aggregate_results <- list()
for (col in categorical_col_names) {
for (sample in 1:length(sample_data)) {
# Filtering the data frame to include only the current categorical column
filtered_data <- sample_data[[sample]][, col]
# Performing a group-by and storing the results
result <- aggregate(. ~ filtered_data, data = sample_data[[sample]], FUN = length)
result <- result[, categorical_col_names]
result_name <- paste("Column:", col, "Sample:", sample)
aggregate_results[[result_name]] <- result
}
}
# Printing the results for each combination of column and sample
for (result_name in names(aggregate_results)) {
cat("Distribution for", result_name, "\n")
print(aggregate_results[[result_name]])
}## Distribution for Column: orig_lang Sample: 1
## orig_lang country revenue.1
## 1 3 3 3
## 2 2 2 2
## 3 74 74 74
## 4 1 1 1
## 5 1 1 1
## 6 77 77 77
## 7 1 1 1
## 8 11 11 11
## 9 14 14 14
## 10 1 1 1
## 11 3692 3692 3692
## 12 3 3 3
## 13 155 155 155
## 14 44 44 44
## 15 2 2 2
## 16 10 10 10
## 17 2 2 2
## 18 4 4 4
## 19 61 61 61
## 20 340 340 340
## 21 3 3 3
## 22 215 215 215
## 23 2 2 2
## 24 1 1 1
## 25 2 2 2
## 26 5 5 5
## 27 1 1 1
## 28 20 20 20
## 29 5 5 5
## 30 8 8 8
## 31 13 13 13
## 32 2 2 2
## 33 39 39 39
## 34 213 213 213
## 35 9 9 9
## 36 15 15 15
## 37 2 2 2
## 38 1 1 1
## 39 14 14 14
## 40 16 16 16
## 41 5 5 5
## Distribution for Column: orig_lang Sample: 2
## orig_lang country revenue.1
## 1 2 2 2
## 2 1 1 1
## 3 2 2 2
## 4 72 72 72
## 5 74 74 74
## 6 11 11 11
## 7 15 15 15
## 8 1 1 1
## 9 3619 3619 3619
## 10 6 6 6
## 11 164 164 164
## 12 1 1 1
## 13 43 43 43
## 14 2 2 2
## 15 12 12 12
## 16 2 2 2
## 17 4 4 4
## 18 81 81 81
## 19 401 401 401
## 20 198 198 198
## 21 2 2 2
## 22 4 4 4
## 23 2 2 2
## 24 21 21 21
## 25 1 1 1
## 26 17 17 17
## 27 10 10 10
## 28 3 3 3
## 29 33 33 33
## 30 218 218 218
## 31 9 9 9
## 32 20 20 20
## 33 7 7 7
## 34 4 4 4
## 35 15 15 15
## 36 10 10 10
## 37 1 1 1
## 38 1 1 1
## Distribution for Column: orig_lang Sample: 3
## orig_lang country revenue.1
## 1 1 1 1
## 2 1 1 1
## 3 75 75 75
## 4 80 80 80
## 5 12 12 12
## 6 5 5 5
## 7 3705 3705 3705
## 8 1 1 1
## 9 137 137 137
## 10 51 51 51
## 11 2 2 2
## 12 11 11 11
## 13 1 1 1
## 14 5 5 5
## 15 2 2 2
## 16 75 75 75
## 17 381 381 381
## 18 2 2 2
## 19 183 183 183
## 20 2 2 2
## 21 2 2 2
## 22 2 2 2
## 23 3 3 3
## 24 1 1 1
## 25 2 2 2
## 26 19 19 19
## 27 1 1 1
## 28 13 13 13
## 29 13 13 13
## 30 3 3 3
## 31 47 47 47
## 32 1 1 1
## 33 176 176 176
## 34 12 12 12
## 35 22 22 22
## 36 1 1 1
## 37 3 3 3
## 38 18 18 18
## 39 13 13 13
## 40 2 2 2
## 41 3 3 3
## Distribution for Column: orig_lang Sample: 4
## orig_lang country revenue.1
## 1 3 3 3
## 2 1 1 1
## 3 1 1 1
## 4 77 77 77
## 5 76 76 76
## 6 1 1 1
## 7 22 22 22
## 8 12 12 12
## 9 1 1 1
## 10 3698 3698 3698
## 11 4 4 4
## 12 159 159 159
## 13 44 44 44
## 14 4 4 4
## 15 11 11 11
## 16 1 1 1
## 17 2 2 2
## 18 5 5 5
## 19 66 66 66
## 20 353 353 353
## 21 1 1 1
## 22 204 204 204
## 23 1 1 1
## 24 2 2 2
## 25 2 2 2
## 26 2 2 2
## 27 11 11 11
## 28 1 1 1
## 29 3 3 3
## 30 12 12 12
## 31 19 19 19
## 32 26 26 26
## 33 1 1 1
## 34 2 2 2
## 35 190 190 190
## 36 6 6 6
## 37 23 23 23
## 38 4 4 4
## 39 1 1 1
## 40 23 23 23
## 41 10 10 10
## 42 3 3 3
## 43 1 1 1
## Distribution for Column: orig_lang Sample: 5
## orig_lang country revenue.1
## 1 1 1 1
## 2 52 52 52
## 3 1 1 1
## 4 2 2 2
## 5 68 68 68
## 6 16 16 16
## 7 8 8 8
## 8 3779 3779 3779
## 9 3 3 3
## 10 139 139 139
## 11 1 1 1
## 12 44 44 44
## 13 2 2 2
## 14 2 2 2
## 15 11 11 11
## 16 5 5 5
## 17 4 4 4
## 18 69 69 69
## 19 334 334 334
## 20 183 183 183
## 21 2 2 2
## 22 5 5 5
## 23 1 1 1
## 24 11 11 11
## 25 1 1 1
## 26 4 4 4
## 27 15 15 15
## 28 11 11 11
## 29 1 1 1
## 30 33 33 33
## 31 1 1 1
## 32 1 1 1
## 33 206 206 206
## 34 14 14 14
## 35 25 25 25
## 36 2 2 2
## 37 4 4 4
## 38 17 17 17
## 39 8 8 8
## 40 2 2 2
## 41 1 1 1
## Distribution for Column: orig_lang Sample: 6
## orig_lang country revenue.1
## 1 2 2 2
## 2 1 1 1
## 3 67 67 67
## 4 3 3 3
## 5 1 1 1
## 6 68 68 68
## 7 9 9 9
## 8 15 15 15
## 9 3765 3765 3765
## 10 1 1 1
## 11 147 147 147
## 12 33 33 33
## 13 6 6 6
## 14 8 8 8
## 15 4 4 4
## 16 7 7 7
## 17 1 1 1
## 18 61 61 61
## 19 349 349 349
## 20 201 201 201
## 21 2 2 2
## 22 1 1 1
## 23 1 1 1
## 24 4 4 4
## 25 2 2 2
## 26 13 13 13
## 27 4 4 4
## 28 12 12 12
## 29 18 18 18
## 30 2 2 2
## 31 34 34 34
## 32 1 1 1
## 33 182 182 182
## 34 9 9 9
## 35 16 16 16
## 36 4 4 4
## 37 3 3 3
## 38 20 20 20
## 39 10 10 10
## 40 2 2 2
## Distribution for Column: country Sample: 1
## orig_lang country revenue.1
## 1 27 27 27
## 2 2 2 2
## 3 2438 2438 2438
## 4 3 3 3
## 5 15 15 15
## 6 33 33 33
## 7 4 4 4
## 8 6 6 6
## 9 46 46 46
## 10 5 5 5
## 11 1 1 1
## 12 54 54 54
## 13 15 15 15
## 14 1 1 1
## 15 79 79 79
## 16 6 6 6
## 17 109 109 109
## 18 101 101 101
## 19 3 3 3
## 20 62 62 62
## 21 2 2 2
## 22 5 5 5
## 23 7 7 7
## 24 1 1 1
## 25 19 19 19
## 26 53 53 53
## 27 254 254 254
## 28 1 1 1
## 29 197 197 197
## 30 1 1 1
## 31 2 2 2
## 32 51 51 51
## 33 2 2 2
## 34 9 9 9
## 35 14 14 14
## 36 3 3 3
## 37 15 15 15
## 38 10 10 10
## 39 2 2 2
## 40 1 1 1
## 41 28 28 28
## 42 2 2 2
## 43 1 1 1
## 44 4 4 4
## 45 11 11 11
## 46 15 15 15
## 47 10 10 10
## 48 4 4 4
## 49 1351 1351 1351
## 50 1 1 1
## 51 3 3 3
## Distribution for Column: country Sample: 2
## orig_lang country revenue.1
## 1 21 21 21
## 2 1 1 1
## 3 2392 2392 2392
## 4 6 6 6
## 5 1 1 1
## 6 8 8 8
## 7 1 1 1
## 8 37 37 37
## 9 4 4 4
## 10 3 3 3
## 11 45 45 45
## 12 6 6 6
## 13 46 46 46
## 14 8 8 8
## 15 1 1 1
## 16 90 90 90
## 17 6 6 6
## 18 117 117 117
## 19 79 79 79
## 20 4 4 4
## 21 3 3 3
## 22 64 64 64
## 23 2 2 2
## 24 3 3 3
## 25 22 22 22
## 26 2 2 2
## 27 1 1 1
## 28 71 71 71
## 29 304 304 304
## 30 190 190 190
## 31 1 1 1
## 32 1 1 1
## 33 51 51 51
## 34 13 13 13
## 35 12 12 12
## 36 4 4 4
## 37 20 20 20
## 38 13 13 13
## 39 2 2 2
## 40 1 1 1
## 41 28 28 28
## 42 5 5 5
## 43 1 1 1
## 44 4 4 4
## 45 12 12 12
## 46 10 10 10
## 47 8 8 8
## 48 1 1 1
## 49 1360 1360 1360
## 50 3 3 3
## 51 1 1 1
## Distribution for Column: country Sample: 3
## orig_lang country revenue.1
## 1 15 15 15
## 2 1 1 1
## 3 2448 2448 2448
## 4 3 3 3
## 5 13 13 13
## 6 2 2 2
## 7 45 45 45
## 8 2 2 2
## 9 3 3 3
## 10 55 55 55
## 11 4 4 4
## 12 43 43 43
## 13 11 11 11
## 14 69 69 69
## 15 3 3 3
## 16 115 115 115
## 17 92 92 92
## 18 5 5 5
## 19 2 2 2
## 20 59 59 59
## 21 1 1 1
## 22 4 4 4
## 23 6 6 6
## 24 19 19 19
## 25 1 1 1
## 26 63 63 63
## 27 297 297 297
## 28 165 165 165
## 29 1 1 1
## 30 1 1 1
## 31 51 51 51
## 32 2 2 2
## 33 4 4 4
## 34 11 11 11
## 35 22 22 22
## 36 12 12 12
## 37 40 40 40
## 38 3 3 3
## 39 3 3 3
## 40 3 3 3
## 41 15 15 15
## 42 13 13 13
## 43 5 5 5
## 44 3 3 3
## 45 1350 1350 1350
## 46 2 2 2
## 47 2 2 2
## Distribution for Column: country Sample: 4
## orig_lang country revenue.1
## 1 22 22 22
## 2 1 1 1
## 3 2463 2463 2463
## 4 5 5 5
## 5 19 19 19
## 6 2 2 2
## 7 38 38 38
## 8 2 2 2
## 9 4 4 4
## 10 45 45 45
## 11 6 6 6
## 12 1 1 1
## 13 34 34 34
## 14 23 23 23
## 15 1 1 1
## 16 69 69 69
## 17 3 3 3
## 18 120 120 120
## 19 77 77 77
## 20 4 4 4
## 21 68 68 68
## 22 3 3 3
## 23 4 4 4
## 24 6 6 6
## 25 21 21 21
## 26 1 1 1
## 27 1 1 1
## 28 56 56 56
## 29 250 250 250
## 30 186 186 186
## 31 50 50 50
## 32 2 2 2
## 33 6 6 6
## 34 7 7 7
## 35 3 3 3
## 36 23 23 23
## 37 8 8 8
## 38 17 17 17
## 39 1 1 1
## 40 3 3 3
## 41 2 2 2
## 42 4 4 4
## 43 22 22 22
## 44 9 9 9
## 45 7 7 7
## 46 1384 1384 1384
## 47 2 2 2
## 48 1 1 1
## 49 1 1 1
## 50 2 2 2
## Distribution for Column: country Sample: 5
## orig_lang country revenue.1
## 1 20 20 20
## 2 2478 2478 2478
## 3 5 5 5
## 4 15 15 15
## 5 31 31 31
## 6 3 3 3
## 7 3 3 3
## 8 46 46 46
## 9 8 8 8
## 10 44 44 44
## 11 12 12 12
## 12 80 80 80
## 13 3 3 3
## 14 114 114 114
## 15 96 96 96
## 16 2 2 2
## 17 43 43 43
## 18 5 5 5
## 19 4 4 4
## 20 8 8 8
## 21 18 18 18
## 22 56 56 56
## 23 248 248 248
## 24 2 2 2
## 25 175 175 175
## 26 1 1 1
## 27 56 56 56
## 28 2 2 2
## 29 8 8 8
## 30 3 3 3
## 31 2 2 2
## 32 25 25 25
## 33 12 12 12
## 34 1 1 1
## 35 1 1 1
## 36 24 24 24
## 37 8 8 8
## 38 1 1 1
## 39 1 1 1
## 40 5 5 5
## 41 16 16 16
## 42 7 7 7
## 43 4 4 4
## 44 2 2 2
## 45 1385 1385 1385
## 46 1 1 1
## 47 1 1 1
## 48 4 4 4
## Distribution for Column: country Sample: 6
## orig_lang country revenue.1
## 1 20 20 20
## 2 2436 2436 2436
## 3 4 4 4
## 4 1 1 1
## 5 16 16 16
## 6 36 36 36
## 7 2 2 2
## 8 4 4 4
## 9 42 42 42
## 10 8 8 8
## 11 32 32 32
## 12 17 17 17
## 13 1 1 1
## 14 65 65 65
## 15 1 1 1
## 16 106 106 106
## 17 100 100 100
## 18 6 6 6
## 19 3 3 3
## 20 58 58 58
## 21 1 1 1
## 22 5 5 5
## 23 5 5 5
## 24 2 2 2
## 25 15 15 15
## 26 3 3 3
## 27 3 3 3
## 28 53 53 53
## 29 271 271 271
## 30 1 1 1
## 31 192 192 192
## 32 1 1 1
## 33 1 1 1
## 34 50 50 50
## 35 1 1 1
## 36 10 10 10
## 37 6 6 6
## 38 6 6 6
## 39 16 16 16
## 40 11 11 11
## 41 1 1 1
## 42 25 25 25
## 43 2 2 2
## 44 2 2 2
## 45 2 2 2
## 46 2 2 2
## 47 19 19 19
## 48 8 8 8
## 49 5 5 5
## 50 4 4 4
## 51 1405 1405 1405
## 52 1 1 1
## 53 2 2 2
## Distribution for Column: revenue.1 Sample: 1
## orig_lang country revenue.1
## 1 35 35 35
## 2 1 1 1
## 3 1 1 1
## 4 1 1 1
## 5 1 1 1
## 6 1 1 1
## 7 1 1 1
## 8 1 1 1
## 9 1 1 1
## 10 1 1 1
## 11 1 1 1
## 12 2 2 2
## 13 2 2 2
## 14 2 2 2
## 15 1 1 1
## 16 1 1 1
## 17 1 1 1
## 18 1 1 1
## 19 1 1 1
## 20 1 1 1
## 21 2 2 2
## 22 1 1 1
## 23 1 1 1
## 24 1 1 1
## 25 1 1 1
## 26 4 4 4
## 27 1 1 1
## 28 1 1 1
## 29 1 1 1
## 30 1 1 1
## 31 1 1 1
## 32 1 1 1
## 33 1 1 1
## 34 1 1 1
## 35 1 1 1
## 36 1 1 1
## 37 1 1 1
## 38 1 1 1
## 39 2 2 2
## 40 1 1 1
## 41 1 1 1
## 42 1 1 1
## 43 2 2 2
## 44 1 1 1
## 45 1 1 1
## 46 1 1 1
## 47 2 2 2
## 48 1 1 1
## 49 1 1 1
## 50 1 1 1
## 51 3 3 3
## 52 1 1 1
## 53 1 1 1
## 54 2 2 2
## 55 1 1 1
## 56 1 1 1
## 57 1 1 1
## 58 2 2 2
## 59 2 2 2
## 60 1 1 1
## 61 1 1 1
## 62 1 1 1
## 63 1 1 1
## 64 1 1 1
## 65 1 1 1
## 66 1 1 1
## 67 1 1 1
## 68 1 1 1
## 69 1 1 1
## 70 2 2 2
## 71 1 1 1
## 72 1 1 1
## 73 2 2 2
## 74 2 2 2
## 75 2 2 2
## 76 1 1 1
## 77 1 1 1
## 78 1 1 1
## 79 1 1 1
## 80 1 1 1
## 81 1 1 1
## 82 1 1 1
## 83 1 1 1
## 84 1 1 1
## 85 1 1 1
## 86 1 1 1
## 87 1 1 1
## 88 1 1 1
## 89 1 1 1
## 90 2 2 2
## 91 1 1 1
## 92 1 1 1
## 93 1 1 1
## 94 1 1 1
## 95 1 1 1
## 96 1 1 1
## 97 1 1 1
## 98 1 1 1
## 99 1 1 1
## 100 1 1 1
## 101 2 2 2
## 102 1 1 1
## 103 1 1 1
## 104 1 1 1
## 105 3 3 3
## 106 1 1 1
## 107 1 1 1
## 108 2 2 2
## 109 2 2 2
## 110 2 2 2
## 111 2 2 2
## 112 2 2 2
## 113 1 1 1
## 114 1 1 1
## 115 1 1 1
## 116 1 1 1
## 117 1 1 1
## 118 1 1 1
## 119 1 1 1
## 120 1 1 1
## 121 1 1 1
## 122 3 3 3
## 123 1 1 1
## 124 3 3 3
## 125 1 1 1
## 126 2 2 2
## 127 1 1 1
## 128 1 1 1
## 129 1 1 1
## 130 1 1 1
## 131 4 4 4
## 132 1 1 1
## 133 1 1 1
## 134 1 1 1
## 135 1 1 1
## 136 1 1 1
## 137 1 1 1
## 138 1 1 1
## 139 1 1 1
## 140 1 1 1
## 141 1 1 1
## 142 2 2 2
## 143 1 1 1
## 144 1 1 1
## 145 1 1 1
## 146 1 1 1
## 147 1 1 1
## 148 1 1 1
## 149 1 1 1
## 150 1 1 1
## 151 3 3 3
## 152 1 1 1
## 153 1 1 1
## 154 1 1 1
## 155 1 1 1
## 156 1 1 1
## 157 1 1 1
## 158 2 2 2
## 159 1 1 1
## 160 1 1 1
## 161 2 2 2
## 162 3 3 3
## 163 3 3 3
## 164 1 1 1
## 165 3 3 3
## 166 1 1 1
## 167 1 1 1
## 168 1 1 1
## 169 2 2 2
## 170 1 1 1
## 171 1 1 1
## 172 1 1 1
## 173 3 3 3
## 174 2 2 2
## 175 1 1 1
## 176 2 2 2
## 177 3 3 3
## 178 1 1 1
## 179 1 1 1
## 180 1 1 1
## 181 1 1 1
## 182 3 3 3
## 183 2 2 2
## 184 1 1 1
## 185 1 1 1
## 186 1 1 1
## 187 1 1 1
## 188 2 2 2
## 189 1 1 1
## 190 2 2 2
## 191 1 1 1
## 192 1 1 1
## 193 19 19 19
## 194 2 2 2
## 195 1 1 1
## 196 1 1 1
## 197 1 1 1
## 198 2 2 2
## 199 2 2 2
## 200 1 1 1
## 201 1 1 1
## 202 1 1 1
## 203 1 1 1
## 204 1 1 1
## 205 2 2 2
## 206 1 1 1
## 207 1 1 1
## 208 1 1 1
## 209 2 2 2
## 210 1 1 1
## 211 1 1 1
## 212 1 1 1
## 213 1 1 1
## 214 1 1 1
## 215 1 1 1
## 216 1 1 1
## 217 1 1 1
## 218 1 1 1
## 219 2 2 2
## 220 1 1 1
## 221 1 1 1
## 222 3 3 3
## 223 1 1 1
## 224 1 1 1
## 225 1 1 1
## 226 1 1 1
## 227 1 1 1
## 228 1 1 1
## 229 1 1 1
## 230 1 1 1
## 231 2 2 2
## 232 1 1 1
## 233 1 1 1
## 234 1 1 1
## 235 1 1 1
## 236 1 1 1
## 237 1 1 1
## 238 2 2 2
## 239 1 1 1
## 240 1 1 1
## 241 2 2 2
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## Distribution for Column: revenue.1 Sample: 2
## orig_lang country revenue.1
## 1 33 33 33
## 2 1 1 1
## 3 1 1 1
## 4 1 1 1
## 5 1 1 1
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## 7 1 1 1
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## 3028 1 1 1
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## 3030 1 1 1
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## 3032 3 3 3
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## 3034 1 1 1
## 3035 1 1 1
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## 3221 3 3 3
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## 3422 1 1 1
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## 3424 1 1 1
## 3425 1 1 1
## 3426 1 1 1
## 3427 1 1 1
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## 3441 1 1 1
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## 3443 1 1 1
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## 3474 1 1 1
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## 3476 1 1 1
## 3477 3 3 3
## 3478 2 2 2
## 3479 1 1 1
## Distribution for Column: revenue.1 Sample: 3
## orig_lang country revenue.1
## 1 40 40 40
## 2 1 1 1
## 3 2 2 2
## 4 1 1 1
## 5 1 1 1
## 6 1 1 1
## 7 1 1 1
## 8 1 1 1
## 9 1 1 1
## 10 1 1 1
## 11 1 1 1
## 12 1 1 1
## 13 1 1 1
## 14 1 1 1
## 15 1 1 1
## 16 1 1 1
## 17 1 1 1
## 18 1 1 1
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## 20 1 1 1
## 21 2 2 2
## 22 2 2 2
## 23 2 2 2
## 24 1 1 1
## 25 1 1 1
## 26 1 1 1
## 27 1 1 1
## 28 1 1 1
## 29 1 1 1
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## 32 1 1 1
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## 34 1 1 1
## 35 1 1 1
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## 40 1 1 1
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## 43 1 1 1
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## 46 1 1 1
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## 48 1 1 1
## 49 1 1 1
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## 57 1 1 1
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## 59 1 1 1
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## 185 1 1 1
## 186 1 1 1
## 187 1 1 1
## 188 1 1 1
## 189 1 1 1
## 190 1 1 1
## 191 16 16 16
## 192 1 1 1
## 193 1 1 1
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## 201 1 1 1
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## 2747 1 1 1
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## 3252 3 3 3
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## 3375 5 5 5
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## 3390 1 1 1
## 3391 1 1 1
## 3392 5 5 5
## 3393 1 1 1
## 3394 1 1 1
## 3395 1 1 1
## 3396 1 1 1
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## 3399 4 4 4
## 3400 1 1 1
## 3401 1 1 1
## 3402 2 2 2
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## 3405 1 1 1
## 3406 3 3 3
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## 3410 1 1 1
## 3411 2 2 2
## 3412 5 5 5
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## 3415 1 1 1
## 3416 1 1 1
## 3417 1 1 1
## 3418 1 1 1
## 3419 2 2 2
## 3420 1 1 1
## 3421 5 5 5
## 3422 1 1 1
## 3423 7 7 7
## 3424 3 3 3
## 3425 1 1 1
## 3426 2 2 2
## 3427 3 3 3
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## 3429 1 1 1
## 3430 1 1 1
## 3431 2 2 2
## 3432 1 1 1
## 3433 1 1 1
## 3434 1 1 1
## 3435 1 1 1
## 3436 1 1 1
## 3437 1 1 1
## 3438 2 2 2
## 3439 2 2 2
## 3440 1 1 1
## 3441 4 4 4
## 3442 1 1 1
## 3443 1 1 1
## 3444 1 1 1
## 3445 1 1 1
## 3446 1 1 1
## 3447 1 1 1
## 3448 2 2 2
## 3449 1 1 1
## 3450 3 3 3
## 3451 4 4 4
## 3452 1 1 1
## 3453 2 2 2
## 3454 1 1 1
## 3455 1 1 1
## 3456 1 1 1
## 3457 2 2 2
## 3458 1 1 1
## 3459 1 1 1
## 3460 1 1 1
## 3461 1 1 1
## 3462 2 2 2
## 3463 1 1 1
## 3464 1 1 1
## 3465 2 2 2
## 3466 2 2 2
## 3467 1 1 1
## 3468 2 2 2
## 3469 5 5 5
## 3470 2 2 2
## 3471 2 2 2
## 3472 2 2 2
## 3473 1 1 1
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## 3481 1 1 1
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## 3484 1 1 1
## 3485 1 1 1
## 3486 1 1 1
## 3487 1 1 1
## 3488 2 2 2
## 3489 1 1 1
## 3490 1 1 1
## Distribution for Column: revenue.1 Sample: 4
## orig_lang country revenue.1
## 1 33 33 33
## 2 1 1 1
## 3 1 1 1
## 4 1 1 1
## 5 2 2 2
## 6 2 2 2
## 7 1 1 1
## 8 1 1 1
## 9 1 1 1
## 10 1 1 1
## 11 1 1 1
## 12 1 1 1
## 13 1 1 1
## 14 1 1 1
## 15 1 1 1
## 16 1 1 1
## 17 1 1 1
## 18 2 2 2
## 19 1 1 1
## 20 1 1 1
## 21 1 1 1
## 22 1 1 1
## 23 1 1 1
## 24 1 1 1
## 25 1 1 1
## 26 1 1 1
## 27 1 1 1
## 28 1 1 1
## 29 1 1 1
## 30 1 1 1
## 31 1 1 1
## 32 2 2 2
## 33 1 1 1
## 34 1 1 1
## 35 2 2 2
## 36 1 1 1
## 37 1 1 1
## 38 2 2 2
## 39 1 1 1
## 40 1 1 1
## 41 1 1 1
## 42 1 1 1
## 43 1 1 1
## 44 1 1 1
## 45 1 1 1
## 46 1 1 1
## 47 1 1 1
## 48 1 1 1
## 49 2 2 2
## 50 1 1 1
## 51 1 1 1
## 52 2 2 2
## 53 1 1 1
## 54 1 1 1
## 55 2 2 2
## 56 3 3 3
## 57 1 1 1
## 58 1 1 1
## 59 1 1 1
## 60 2 2 2
## 61 2 2 2
## 62 1 1 1
## 63 1 1 1
## 64 1 1 1
## 65 1 1 1
## 66 1 1 1
## 67 2 2 2
## 68 1 1 1
## 69 1 1 1
## 70 1 1 1
## 71 2 2 2
## 72 1 1 1
## 73 1 1 1
## 74 1 1 1
## 75 1 1 1
## 76 1 1 1
## 77 1 1 1
## 78 1 1 1
## 79 1 1 1
## 80 1 1 1
## 81 1 1 1
## 82 1 1 1
## 83 1 1 1
## 84 1 1 1
## 85 1 1 1
## 86 1 1 1
## 87 1 1 1
## 88 1 1 1
## 89 5 5 5
## 90 2 2 2
## 91 1 1 1
## 92 1 1 1
## 93 2 2 2
## 94 1 1 1
## 95 1 1 1
## 96 1 1 1
## 97 1 1 1
## 98 1 1 1
## 99 1 1 1
## 100 1 1 1
## 101 1 1 1
## 102 1 1 1
## 103 1 1 1
## 104 3 3 3
## 105 1 1 1
## 106 1 1 1
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## 108 1 1 1
## 109 1 1 1
## 110 1 1 1
## 111 2 2 2
## 112 1 1 1
## 113 1 1 1
## 114 1 1 1
## 115 1 1 1
## 116 1 1 1
## 117 1 1 1
## 118 1 1 1
## 119 1 1 1
## 120 1 1 1
## 121 1 1 1
## 122 1 1 1
## 123 1 1 1
## 124 2 2 2
## 125 1 1 1
## 126 1 1 1
## 127 1 1 1
## 128 2 2 2
## 129 1 1 1
## 130 1 1 1
## 131 2 2 2
## 132 1 1 1
## 133 1 1 1
## 134 1 1 1
## 135 1 1 1
## 136 1 1 1
## 137 1 1 1
## 138 1 1 1
## 139 1 1 1
## 140 1 1 1
## 141 1 1 1
## 142 2 2 2
## 143 2 2 2
## 144 1 1 1
## 145 2 2 2
## 146 1 1 1
## 147 3 3 3
## 148 1 1 1
## 149 1 1 1
## 150 1 1 1
## 151 1 1 1
## 152 1 1 1
## 153 1 1 1
## 154 1 1 1
## 155 1 1 1
## 156 1 1 1
## 157 1 1 1
## 158 1 1 1
## 159 1 1 1
## 160 2 2 2
## 161 1 1 1
## 162 2 2 2
## 163 1 1 1
## 164 1 1 1
## 165 1 1 1
## 166 1 1 1
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## 168 2 2 2
## 169 1 1 1
## 170 1 1 1
## 171 2 2 2
## 172 2 2 2
## 173 1 1 1
## 174 1 1 1
## 175 1 1 1
## 176 2 2 2
## 177 1 1 1
## 178 1 1 1
## 179 16 16 16
## 180 1 1 1
## 181 1 1 1
## 182 1 1 1
## 183 2 2 2
## 184 1 1 1
## 185 1 1 1
## 186 1 1 1
## 187 2 2 2
## 188 2 2 2
## 189 1 1 1
## 190 1 1 1
## 191 1 1 1
## 192 1 1 1
## 193 3 3 3
## 194 2 2 2
## 195 2 2 2
## 196 1 1 1
## 197 1 1 1
## 198 1 1 1
## 199 1 1 1
## 200 1 1 1
## 201 1 1 1
## 202 1 1 1
## 203 1 1 1
## 204 1 1 1
## 205 1 1 1
## 206 1 1 1
## 207 1 1 1
## 208 1 1 1
## 209 1 1 1
## 210 1 1 1
## 211 1 1 1
## 212 1 1 1
## 213 1 1 1
## 214 1 1 1
## 215 1 1 1
## 216 1 1 1
## 217 1 1 1
## 218 1 1 1
## 219 1 1 1
## 220 1 1 1
## 221 1 1 1
## 222 1 1 1
## 223 2 2 2
## 224 1 1 1
## 225 1 1 1
## 226 2 2 2
## 227 1 1 1
## 228 2 2 2
## 229 2 2 2
## 230 4 4 4
## 231 1 1 1
## 232 1 1 1
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## 237 2 2 2
## 238 3 3 3
## 239 1 1 1
## 240 1 1 1
## 241 4 4 4
## 242 1 1 1
## 243 1 1 1
## 244 1 1 1
## 245 1 1 1
## 246 1 1 1
## 247 1 1 1
## 248 2 2 2
## 249 1 1 1
## 250 1 1 1
## 251 1 1 1
## 252 1 1 1
## 253 3 3 3
## 254 1 1 1
## 255 1 1 1
## 256 2 2 2
## 257 2 2 2
## 258 1 1 1
## 259 1 1 1
## 260 1 1 1
## 261 1 1 1
## 262 1 1 1
## 263 1 1 1
## 264 1 1 1
## 265 1 1 1
## 266 1 1 1
## 267 1 1 1
## 268 1 1 1
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## 3491 2 2 2
## Distribution for Column: revenue.1 Sample: 5
## orig_lang country revenue.1
## 1 39 39 39
## 2 2 2 2
## 3 1 1 1
## 4 1 1 1
## 5 1 1 1
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## 3089 1 1 1
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## 3103 1 1 1
## 3104 1 1 1
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## 3117 3 3 3
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## 3119 1 1 1
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## 3150 1 1 1
## 3151 1 1 1
## 3152 1 1 1
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## 3165 1 1 1
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## 3183 3 3 3
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## 3208 1 1 1
## 3209 4 4 4
## 3210 2 2 2
## 3211 5 5 5
## 3212 2 2 2
## 3213 1 1 1
## 3214 2 2 2
## 3215 2 2 2
## 3216 1 1 1
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## 3221 2 2 2
## 3222 1 1 1
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## 3226 1 1 1
## 3227 1 1 1
## 3228 1 1 1
## 3229 1 1 1
## 3230 2 2 2
## 3231 1 1 1
## 3232 3 3 3
## 3233 1 1 1
## 3234 1 1 1
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## 3238 7 7 7
## 3239 2 2 2
## 3240 1 1 1
## 3241 1 1 1
## 3242 1 1 1
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## 3245 1 1 1
## 3246 1 1 1
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## 3365 1 1 1
## 3366 1 1 1
## 3367 1 1 1
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## 3369 1 1 1
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## 3373 1 1 1
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## 3410 1 1 1
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## 3412 8 8 8
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## 3422 1 1 1
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## 3429 1 1 1
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## 3431 1 1 1
## 3432 1 1 1
## 3433 1 1 1
## 3434 1 1 1
## 3435 1 1 1
## 3436 6 6 6
## 3437 1 1 1
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## 3439 1 1 1
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## 3441 1 1 1
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## 3443 1 1 1
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## 3446 1 1 1
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## 3448 1 1 1
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## 3450 1 1 1
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## 3453 1 1 1
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## 3456 1 1 1
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## 3481 1 1 1
## 3482 1 1 1
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## 3484 2 2 2
## 3485 1 1 1
## 3486 1 1 1
## Distribution for Column: revenue.1 Sample: 6
## orig_lang country revenue.1
## 1 37 37 37
## 2 1 1 1
## 3 1 1 1
## 4 1 1 1
## 5 3 3 3
## 6 1 1 1
## 7 2 2 2
## 8 2 2 2
## 9 2 2 2
## 10 1 1 1
## 11 1 1 1
## 12 1 1 1
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## 14 1 1 1
## 15 1 1 1
## 16 1 1 1
## 17 1 1 1
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## 19 1 1 1
## 20 1 1 1
## 21 1 1 1
## 22 3 3 3
## 23 2 2 2
## 24 1 1 1
## 25 1 1 1
## 26 1 1 1
## 27 1 1 1
## 28 1 1 1
## 29 1 1 1
## 30 1 1 1
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## 35 1 1 1
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## 37 1 1 1
## 38 1 1 1
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## 40 2 2 2
## 41 1 1 1
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## 43 1 1 1
## 44 1 1 1
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## 46 1 1 1
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## 48 1 1 1
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## 51 1 1 1
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## 53 1 1 1
## 54 1 1 1
## 55 1 1 1
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## 59 1 1 1
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## 62 1 1 1
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## 172 1 1 1
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## 183 1 1 1
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## 191 1 1 1
## 192 1 1 1
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## 194 15 15 15
## 195 1 1 1
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## 199 1 1 1
## 200 2 2 2
## 201 1 1 1
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## 203 1 1 1
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## 433 1 1 1
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## 681 1 1 1
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## 2976 1 1 1
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## 2990 2 2 2
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## 3019 1 1 1
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## 3023 1 1 1
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## 3029 1 1 1
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## 3033 1 1 1
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## 3050 1 1 1
## 3051 1 1 1
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## 3159 1 1 1
## 3160 1 1 1
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## 3166 2 2 2
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## 3173 1 1 1
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## 3177 1 1 1
## 3178 1 1 1
## 3179 1 1 1
## 3180 5 5 5
## 3181 1 1 1
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## 3184 1 1 1
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## 3188 2 2 2
## 3189 1 1 1
## 3190 1 1 1
## 3191 1 1 1
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## 3208 3 3 3
## 3209 2 2 2
## 3210 1 1 1
## 3211 1 1 1
## 3212 6 6 6
## 3213 1 1 1
## 3214 3 3 3
## 3215 5 5 5
## 3216 4 4 4
## 3217 2 2 2
## 3218 1 1 1
## 3219 2 2 2
## 3220 1 1 1
## 3221 1 1 1
## 3222 2 2 2
## 3223 2 2 2
## 3224 1 1 1
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## 3226 1 1 1
## 3227 1 1 1
## 3228 1 1 1
## 3229 1 1 1
## 3230 2 2 2
## 3231 2 2 2
## 3232 1 1 1
## 3233 1 1 1
## 3234 2 2 2
## 3235 2 2 2
## 3236 2 2 2
## 3237 1 1 1
## 3238 1 1 1
## 3239 1 1 1
## 3240 4 4 4
## 3241 1 1 1
## 3242 1 1 1
## 3243 1 1 1
## 3244 1 1 1
## 3245 1 1 1
## 3246 1 1 1
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## 3248 2 2 2
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## 3250 1 1 1
## 3251 2 2 2
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## 3253 1 1 1
## 3254 4 4 4
## 3255 1 1 1
## 3256 1 1 1
## 3257 3 3 3
## 3258 1 1 1
## 3259 1 1 1
## 3260 1 1 1
## 3261 1 1 1
## 3262 1 1 1
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## 3264 1 1 1
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## 3284 4 4 4
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## 3287 1 1 1
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## 3319 1 1 1
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## 3331 1 1 1
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## 3333 1 1 1
## 3334 1 1 1
## 3335 6 6 6
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## 3373 1 1 1
## 3374 1 1 1
## 3375 1 1 1
## 3376 5 5 5
## 3377 1 1 1
## 3378 1 1 1
## 3379 7 7 7
## 3380 1 1 1
## 3381 1 1 1
## 3382 1 1 1
## 3383 2 2 2
## 3384 2 2 2
## 3385 1 1 1
## 3386 1 1 1
## 3387 2 2 2
## 3388 6 6 6
## 3389 1 1 1
## 3390 4 4 4
## 3391 1 1 1
## 3392 1 1 1
## 3393 1 1 1
## 3394 1 1 1
## 3395 1 1 1
## 3396 2 2 2
## 3397 2 2 2
## 3398 1 1 1
## 3399 1 1 1
## 3400 1 1 1
## 3401 1 1 1
## 3402 3 3 3
## 3403 1 1 1
## 3404 3 3 3
## 3405 1 1 1
## 3406 1 1 1
## 3407 1 1 1
## 3408 1 1 1
## 3409 1 1 1
## 3410 2 2 2
## 3411 2 2 2
## 3412 1 1 1
## 3413 1 1 1
## 3414 1 1 1
## 3415 1 1 1
## 3416 1 1 1
## 3417 1 1 1
## 3418 1 1 1
## 3419 1 1 1
## 3420 3 3 3
## 3421 4 4 4
## 3422 1 1 1
## 3423 1 1 1
## 3424 2 2 2
## 3425 1 1 1
## 3426 1 1 1
## 3427 1 1 1
## 3428 1 1 1
## 3429 1 1 1
## 3430 1 1 1
## 3431 2 2 2
## 3432 1 1 1
## 3433 1 1 1
## 3434 1 1 1
## 3435 1 1 1
## 3436 1 1 1
## 3437 1 1 1
## 3438 2 2 2
## 3439 1 1 1
## 3440 5 5 5
## 3441 1 1 1
## 3442 1 1 1
## 3443 1 1 1
## 3444 7 7 7
## 3445 1 1 1
## 3446 1 1 1
## 3447 2 2 2
## 3448 2 2 2
## 3449 1 1 1
## 3450 1 1 1
## 3451 4 4 4
## 3452 1 1 1
## 3453 1 1 1
## 3454 1 1 1
## 3455 1 1 1
## 3456 1 1 1
## 3457 1 1 1
## 3458 1 1 1
## 3459 2 2 2
## 3460 1 1 1
## 3461 1 1 1
## 3462 1 1 1
## 3463 1 1 1
## 3464 2 2 2
## 3465 1 1 1
## 3466 1 1 1
## 3467 1 1 1
## 3468 2 2 2
## 3469 2 2 2
## 3470 2 2 2
## 3471 2 2 2
## 3472 1 1 1
## 3473 2 2 2
## 3474 1 1 1
## 3475 1 1 1
## 3476 1 1 1
## 3477 1 1 1
## 3478 1 1 1
## 3479 1 1 1
## 3480 2 2 2
## 3481 1 1 1
## 3482 1 1 1
## 3483 1 1 1
## 3484 2 2 2
## 3485 1 1 1
## 3486 1 1 1
## 3487 1 1 1
## 3488 1 1 1
## 3489 3 3 3
## 3490 2 2 2
## 3491 3 3 3
## 3492 1 1 1
## 3493 1 1 1
## 3494 4 4 4
## 3495 1 1 1
## 3496 1 1 1
## 3497 1 1 1
## 3498 1 1 1
## 3499 1 1 1It is observed column no. of unique values for each column in each sample is different for the categorical columns showing the distribution is different. Not performing any form of hypothesis testing here to find the relationship between categorical columns to show that different type of relationship can exist between them in different samples.