Goal: Split our data and build a model Click [here for the data] https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-11-01

Import Data

horror_movies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-01/horror_movies.csv')

skimr:: skim(horror_movies)
Data summary
Name horror_movies
Number of rows 32540
Number of columns 20
_______________________
Column type frequency:
character 10
Date 1
logical 1
numeric 8
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
original_title 0 1.00 1 191 0 30296 0
title 0 1.00 1 191 0 29563 0
original_language 0 1.00 2 2 0 97 0
overview 1286 0.96 1 1000 0 31020 0
tagline 19835 0.39 1 237 0 12513 0
poster_path 4474 0.86 30 32 0 28048 0
status 0 1.00 7 15 0 4 0
backdrop_path 18995 0.42 29 32 0 13536 0
genre_names 0 1.00 6 144 0 772 0
collection_name 30234 0.07 4 56 0 815 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
release_date 0 1 1950-01-01 2022-12-31 2012-12-09 10999

Variable type: logical

skim_variable n_missing complete_rate mean count
adult 0 1 0 FAL: 32540

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
id 0 1.00 445910.83 305744.67 17 146494.8 426521.00 707534.00 1033095.00 ▇▆▆▅▅
popularity 0 1.00 4.01 37.51 0 0.6 0.84 2.24 5088.58 ▇▁▁▁▁
vote_count 0 1.00 62.69 420.89 0 0.0 2.00 11.00 16900.00 ▇▁▁▁▁
vote_average 0 1.00 3.34 2.88 0 0.0 4.00 5.70 10.00 ▇▂▆▃▁
budget 0 1.00 543126.59 4542667.81 0 0.0 0.00 0.00 200000000.00 ▇▁▁▁▁
revenue 0 1.00 1349746.73 14430479.15 0 0.0 0.00 0.00 701842551.00 ▇▁▁▁▁
runtime 0 1.00 62.14 41.00 0 14.0 80.00 91.00 683.00 ▇▁▁▁▁
collection 30234 0.07 481534.88 324498.16 656 155421.0 471259.00 759067.25 1033032.00 ▇▅▅▅▅

Clean Data

data <- horror_movies %>% 
  
  # Remove unnecessary variables
  
select(-id, -title, -original_language, -overview, -tagline, -release_date, -poster_path, -budget, -revenue, -runtime, -status, -adult, -status, -backdrop_path, -genre_names, -collection, -collection_name) %>%
  
  na.omit() 
  
  skimr:: skim(data)
Data summary
Name data
Number of rows 32540
Number of columns 4
_______________________
Column type frequency:
character 1
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
original_title 0 1 1 191 0 30296 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
popularity 0 1 4.01 37.51 0 0.6 0.84 2.24 5088.58 ▇▁▁▁▁
vote_count 0 1 62.69 420.89 0 0.0 2.00 11.00 16900.00 ▇▁▁▁▁
vote_average 0 1 3.34 2.88 0 0.0 4.00 5.70 10.00 ▇▂▆▃▁

Explore Data

data %>%
  
  ggplot(aes(vote_count, vote_average)) +
  geom_point()

data %>%
  group_by(vote_count, vote_average) %>%
  summarise(mean_group = mean(vote_average)) -> data2

data2 %>%
  ggplot(aes(x= vote_count, y= mean_group,
             color= vote_count, shape= vote_average,
             group = vote_count,
             label = round(mean_group, 2))) +
  scale_shape_binned() +
  geom_point()

Prepare Data

data_binarized_tbl <- data %>%
  select(-original_title, -popularity) %>%
  binarize()

data_binarized_tbl %>% glimpse()
## Rows: 32,540
## Columns: 6
## $ `vote_count__-Inf_2`   <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ vote_count__2_11       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ vote_count__11_Inf     <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `vote_average__-Inf_4` <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ vote_average__4_5.7    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ vote_average__5.7_Inf  <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…

Correlate

data_corr_tbl <- data_binarized_tbl %>%
  correlate( `vote_average__-Inf_4`)

data_corr_tbl
## # A tibble: 6 × 3
##   feature      bin     correlation
##   <fct>        <chr>         <dbl>
## 1 vote_average -Inf_4        1    
## 2 vote_average 4_5.7        -0.588
## 3 vote_average 5.7_Inf      -0.579
## 4 vote_count   -Inf_2        0.575
## 5 vote_count   11_Inf       -0.467
## 6 vote_count   2_11         -0.214

Plot

data_corr_tbl %>%
  plot_correlation_funnel()

Build Models

Split Data

data <- sample_n(data, 100)

# Split into train and test data set
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)


# Further split training data set for cross-validation
set.seed(2345)
data_cv <- rsample::vfold_cv(data_train)
data_cv
## #  10-fold cross-validation 
## # A tibble: 10 × 2
##    splits         id    
##    <list>         <chr> 
##  1 <split [67/8]> Fold01
##  2 <split [67/8]> Fold02
##  3 <split [67/8]> Fold03
##  4 <split [67/8]> Fold04
##  5 <split [67/8]> Fold05
##  6 <split [68/7]> Fold06
##  7 <split [68/7]> Fold07
##  8 <split [68/7]> Fold08
##  9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10
library(usemodels)
## Warning: package 'usemodels' was built under R version 4.4.1
usemodels::use_xgboost(vote_average ~., data = data_train)
## xgboost_recipe <- 
##   recipe(formula = vote_average ~ ., data = data_train) %>% 
##   step_zv(all_predictors()) 
## 
## xgboost_spec <- 
##   boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
##     loss_reduction = tune(), sample_size = tune()) %>% 
##   set_mode("classification") %>% 
##   set_engine("xgboost") 
## 
## xgboost_workflow <- 
##   workflow() %>% 
##   add_recipe(xgboost_recipe) %>% 
##   add_model(xgboost_spec) 
## 
## set.seed(6804)
## xgboost_tune <-
##   tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))
xgboost_recipe <- 
  recipe(formula = vote_average ~ ., data = data_train) %>% 
  step_zv(all_predictors()) 

xgboost_spec <- 
  boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
    loss_reduction = tune(), sample_size = tune()) %>% 
  set_mode("classification") %>% 
  set_engine("xgboost") 

xgboost_workflow <- 
  workflow() %>% 
  add_recipe(xgboost_recipe) %>% 
  add_model(xgboost_spec) 

set.seed(24097)
xgboost_tune <-
  tune_grid(xgboost_workflow, resamples = data_cv,
            grid = 5)
## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.