Apply1: NYT Bestsellers
Paige Biester
2025-03-01
Goal: to predict total weeks on bestseller list Click here for the data
Import Data
nyt_titles <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-05-10/nyt_titles.tsv')## Rows: 7431 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): title, author
## dbl (5): id, year, total_weeks, debut_rank, best_rank
## date (1): first_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.skimr::skim(nyt_titles)| Name | nyt_titles |
| Number of rows | 7431 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| Date | 1 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| title | 0 | 1 | 1 | 74 | 0 | 7172 | 0 |
| author | 4 | 1 | 4 | 73 | 0 | 2205 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| first_week | 0 | 1 | 1931-10-12 | 2020-12-06 | 2000-06-25 | 3348 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 3715.00 | 2145.29 | 0 | 1857.5 | 3715 | 5572.5 | 7430 | ▇▇▇▇▇ |
| year | 0 | 1 | 1989.61 | 26.23 | 1931 | 1968.0 | 2000 | 2011.0 | 2020 | ▂▂▂▃▇ |
| total_weeks | 0 | 1 | 8.13 | 11.21 | 1 | 2.0 | 4 | 10.0 | 178 | ▇▁▁▁▁ |
| debut_rank | 0 | 1 | 7.90 | 4.57 | 1 | 4.0 | 8 | 12.0 | 17 | ▇▆▅▅▅ |
| best_rank | 0 | 1 | 6.91 | 4.57 | 1 | 3.0 | 6 | 10.0 | 17 | ▇▅▃▃▂ |
data <- nyt_titles %>%
na.omit() %>%
separate_rows(author, sep = "and | with")%>%
mutate(author = as.factor(author)) %>%
# Remove outliers
filter(total_weeks < 100) %>%
mutate(total_weeks = log(total_weeks))Explore Data
Debut Rank
data %>%
ggplot(aes(total_weeks, as.factor(debut_rank))) +
geom_boxplot()
Best Rank
data %>%
ggplot(aes(total_weeks, as.factor(best_rank))) +
geom_boxplot()
data %>%
ggplot(aes(year, total_weeks)) +
geom_col()
data %>%
unnest_tokens(output = word, input = title) %>%
group_by(word) %>%
summarise(total_weeks = mean(total_weeks),
n = n ()) %>%
ungroup () %>%
filter(n > 20) %>%
slice_max(order_by = total_weeks, n = 20) %>%
ggplot(aes(total_weeks, fct_reorder(word, total_weeks))) +
geom_point() +
labs(y = "Words in Title")
EDA Shortcut
cols.dont.want <- "first_week"
data <- data[, ! names(data) %in% cols.dont.want, drop = F]
data_binarized_tbl <- data %>%
select(-id, -title) %>%
binarize()
data_binarized_tbl %>% glimpse()## Rows: 7,823
## Columns: 19
## $ author__Danielle_Steel <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ author__James_Patterson <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `author__-OTHER` <dbl> 1, 1, 1, 1, 1, 1, 1, 1…
## $ `year__-Inf_1971` <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ year__1971_2001 <dbl> 1, 1, 1, 0, 0, 0, 1, 1…
## $ year__2001_2011 <dbl> 0, 0, 0, 0, 1, 0, 0, 0…
## $ year__2011_Inf <dbl> 0, 0, 0, 1, 0, 1, 0, 0…
## $ `total_weeks__-Inf_0.693147180559945` <dbl> 0, 0, 0, 1, 1, 0, 0, 0…
## $ total_weeks__0.693147180559945_1.38629436111989 <dbl> 0, 0, 0, 0, 0, 1, 0, 0…
## $ total_weeks__1.38629436111989_2.30258509299405 <dbl> 0, 0, 1, 0, 0, 0, 0, 1…
## $ total_weeks__2.30258509299405_Inf <dbl> 1, 1, 0, 0, 0, 0, 1, 0…
## $ `debut_rank__-Inf_4` <dbl> 1, 0, 1, 1, 0, 1, 0, 0…
## $ debut_rank__4_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 1…
## $ debut_rank__8_12 <dbl> 0, 0, 0, 0, 1, 0, 1, 0…
## $ debut_rank__12_Inf <dbl> 0, 1, 0, 0, 0, 0, 0, 0…
## $ `best_rank__-Inf_3` <dbl> 1, 1, 0, 0, 0, 0, 1, 0…
## $ best_rank__3_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ best_rank__6_10 <dbl> 0, 0, 1, 0, 0, 1, 0, 1…
## $ best_rank__10_Inf <dbl> 0, 0, 0, 1, 1, 0, 0, 0…data_corr_tbl <- data_binarized_tbl %>%
correlate(total_weeks__2.30258509299405_Inf)
data_corr_tbl## # A tibble: 19 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 total_weeks 2.30258509299405_Inf 1
## 2 total_weeks -Inf_0.693147180559945 -0.388
## 3 best_rank -Inf_3 0.332
## 4 total_weeks 1.38629436111989_2.30258509299405 -0.319
## 5 best_rank 10_Inf -0.305
## 6 total_weeks 0.693147180559945_1.38629436111989 -0.256
## 7 year -Inf_1971 0.245
## 8 year 2011_Inf -0.241
## 9 year 2001_2011 -0.220
## 10 year 1971_2001 0.209
## 11 best_rank 6_10 -0.127
## 12 best_rank 3_6 0.0768
## 13 author James_Patterson -0.0415
## 14 debut_rank 4_8 -0.0321
## 15 author -OTHER 0.0236
## 16 debut_rank 8_12 0.0179
## 17 debut_rank -Inf_4 0.0165
## 18 author Danielle_Steel 0.00603
## 19 debut_rank 12_Inf -0.00195data_corr_tbl %>%
plot_correlation_funnel()
Split Data
# data <- sample_n(data, 100)
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)
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 [5280/587]> Fold01
## 2 <split [5280/587]> Fold02
## 3 <split [5280/587]> Fold03
## 4 <split [5280/587]> Fold04
## 5 <split [5280/587]> Fold05
## 6 <split [5280/587]> Fold06
## 7 <split [5280/587]> Fold07
## 8 <split [5281/586]> Fold08
## 9 <split [5281/586]> Fold09
## 10 <split [5281/586]> Fold10library(usemodels)## Warning: package 'usemodels' was built under R version 4.4.2usemodels::use_xgboost(total_weeks ~ ., data = data_train)## xgboost_recipe <-
## recipe(formula = total_weeks ~ ., 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(33967)
## xgboost_tune <-
## tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))xgboost_recipe <-
recipe(formula = total_weeks ~ ., data = data_train) %>%
recipes::update_role(id, new_role = "id variable") %>%
step_other(author, threshold = 3) %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tf(title) %>%
step_dummy(author, one_hot = TRUE) %>%
step_YeoJohnson(year, debut_rank)
xgboost_recipe %>% prep() %>% juice() %>% glimpse() ## Rows: 5,867
## Columns: 655
## $ id <dbl> 7371, 712, 7212, 1849, 1528, 7025, 33…
## $ year <dbl> 1999, 2012, 2012, 2004, 2006, 2017, 2…
## $ debut_rank <dbl> 6.6293175, 1.5708250, 5.0839439, 0.86…
## $ best_rank <dbl> 12, 4, 7, 9, 16, 8, 1, 15, 5, 15, 14,…
## $ total_weeks <dbl> 1.0986123, 1.7917595, 0.6931472, 0.00…
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## $ tf_title_midnight <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tf_title_moon <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
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## $ tf_title_prey <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tf_title_private <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tf_title_red <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tf_title_river <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tf_title_road <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
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## $ author_X.Grant.Blackwood <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
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## $ author_other <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0…xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune(), learn_rate = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
xgboost_workflow <-
workflow() %>%
add_recipe(xgboost_recipe) %>%
add_model(xgboost_spec)
set.seed(344)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = data_cv,
grid = 10)## i Creating pre-processing data to finalize unknown parameter: mtry## Warning: package 'xgboost' was built under R version 4.4.2Evaluate Models
tune::show_best(xgboost_tune, metric = "rmse")## # A tibble: 5 × 10
## mtry trees min_n learn_rate .metric .estimator mean n std_err .config
## <int> <int> <int> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 103 1074 3 0.100 rmse standard 0.515 10 0.00625 Preproces…
## 2 257 1820 16 0.0223 rmse standard 0.518 10 0.00687 Preproces…
## 3 590 1385 11 0.0491 rmse standard 0.521 10 0.00614 Preproces…
## 4 423 928 24 0.0171 rmse standard 0.523 10 0.00674 Preproces…
## 5 390 255 19 0.211 rmse standard 0.527 10 0.00715 Preproces…xgboost_fw <- tune::finalize_workflow(xgboost_workflow,
tune::select_best(xgboost_tune, metric = "rmse"))
data_fit <- tune::last_fit(xgboost_fw, data_split)tune::collect_metrics(data_fit)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 0.517 Preprocessor1_Model1
## 2 rsq standard 0.782 Preprocessor1_Model1tune::collect_predictions(data_fit) %>%
ggplot(aes(total_weeks, .pred)) +
geom_point(alpha = 0.3, fill = "hotpink") +
geom_abline(lty = 2, color = "gray50") +
coord_fixed()
Random Forest Attempt
library(textrecipes)
rf_data <-
recipe(best_rank ~ title, data = data_train) %>%
step_tokenize(title) %>%
step_tokenfilter(title, max_tokens = 100) %>%
step_tf(title)ranger_spec <-
rand_forest(trees = 500) %>%
set_engine("ranger") %>%
set_mode("regression")
ranger_spec## Random Forest Model Specification (regression)
##
## Main Arguments:
## trees = 500
##
## Computational engine: rangersvm_spec <-
svm_linear() %>%
set_engine("LiblineaR") %>%
set_mode("regression")
svm_spec## Linear Support Vector Machine Model Specification (regression)
##
## Computational engine: LiblineaRranger_wf <- workflow(rf_data, ranger_spec)
svm_wf <- workflow(rf_data, svm_spec)doParallel::registerDoParallel()
contrl_preds <- control_resamples(save_pred = TRUE)
svm_rs <- fit_resamples(
svm_wf,
resamples = data_cv,
control = contrl_preds
)
ranger_rs <- fit_resamples(
ranger_wf,
resamples = data_cv,
control = contrl_preds
)bind_rows(
collect_predictions(svm_rs) %>%
mutate(mod = "SVM"),
collect_predictions(ranger_rs) %>%
mutate(mod = "ranger")
) %>%
ggplot(aes(best_rank, .pred, color = id)) +
geom_abline(lty = 2, color = "grey50", linewidth = 1.2) +
geom_jitter(width = 0.5, alpha = 0.5) +
facet_wrap(vars(mod)) +
coord_fixed()
final_fitted <- last_fit(svm_wf, data_split)## Warning: package 'LiblineaR' was built under R version 4.4.2collect_metrics(final_fitted) ## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 4.55 Preprocessor1_Model1
## 2 rsq standard 0.00484 Preprocessor1_Model1final_wf <- extract_workflow(final_fitted)
predict(final_wf, data_test[55,])## # A tibble: 1 × 1
## .pred
## <dbl>
## 1 7.29extract_workflow(final_fitted) %>%
tidy() %>%
filter(term != "Bias") %>%
group_by(estimate > 0) %>%
slice_max(abs(estimate), n = 10) %>%
ungroup() %>% mutate(term = str_remove(term, "tfidf_title_")) %>%
ggplot(aes(estimate, fct_reorder(term, estimate), fill = estimate > 0)) +
geom_col(alpha = 0.8) +
scale_fill_discrete(labels = c("low rank", "high rank")) +
labs(y = NULL, fill = "More from...")
Changes
I switched out step_tfidf with step_tf, which not only changed the words in title being used, but seemed to change which authors were being chosen. Apparently it really likes John Updike.I also changed the grid.
Then I attempted to use the Random Forest algorithm because I wanted to see if the words changed. If focuses on the more common words, obviously being filler words such as the, of, and a. I do find it interesting to look at words used in lower ranks, like prey and girl.