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)
Data summary
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 ▇▅▃▃▂ 
data1 <- nyt_titles %>% 
  
  #treat missing values
  select(-id, -year, -first_week) %>%
  na.omit() %>%

  #log transform variables with pos-skewed distribution
  mutate(total_weeks = log(total_weeks))
data_binarized_tbl1 <- data1 %>%
  select(-author) %>%
  binarize()

#step 2 correlate
data_corr_tbl1 <- data_binarized_tbl1 %>%
  correlate(total_weeks__0.693147180559945_1.38629436111989)

data_corr_tbl1 %>%
  plot_correlation_funnel()
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
##   Please report the issue at
##   <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the correlationfunnel package.
##   Please report the issue at
##   <https://github.com/business-science/correlationfunnel/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Split data

#data <- sample_n(data, 100)

#split into train and test dataset
set.seed(1234)
data_split <- rsample::initial_split(data1)
data_train <- training(data_split)
data_test <- testing(data_split)

#further split training dataset 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 [5013/557]> Fold01
##  2 <split [5013/557]> Fold02
##  3 <split [5013/557]> Fold03
##  4 <split [5013/557]> Fold04
##  5 <split [5013/557]> Fold05
##  6 <split [5013/557]> Fold06
##  7 <split [5013/557]> Fold07
##  8 <split [5013/557]> Fold08
##  9 <split [5013/557]> Fold09
## 10 <split [5013/557]> Fold10
library(usemodels)
usemodels::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(41811)
## 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) %>% 
  step_tokenize(title) %>%
  step_tokenfilter(title, max_tokens = 100) %>%
  step_tf(title) %>%
  step_other(author, threshold = 0.003) %>%
  step_dummy(author) %>%
  step_zv(all_predictors()) 

xgboost_recipe %>% prep() %>% juice() %>% glimpse
## Rows: 5,570
## Columns: 144
## $ debut_rank                               <dbl> 8, 4, 14, 4, 12, 12, 10, 6, 7…
## $ best_rank                                <dbl> 10, 6, 13, 1, 6, 4, 7, 5, 8, …
## $ total_weeks                              <dbl> 2.1972246, 0.6931472, 0.00000…
## $ tf_title_a                               <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_after                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_all                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_an                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_and                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_are                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_as                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_at                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_big                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_black                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_blood                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_blue                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_bones                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_book                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_cat                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_christmas                       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_city                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_cold                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_dark                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_day                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_days                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_dead                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_death                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_die                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_down                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_edge                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_fall                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_fire                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_first                           <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
## $ tf_title_for                             <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_from                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_game                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_girl                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_girls                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_god                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_golden                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_gone                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_good                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_heart                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_heaven                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_high                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_home                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_honor                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_house                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_i                               <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_in                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_is                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_island                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_lady                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_last                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_life                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_light                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_little                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_long                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_lost                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_love                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_man                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_me                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_men                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_midnight                        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_moon                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_mr                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_murder                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_my                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_new                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_night                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_no                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_not                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_of                              <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ tf_title_on                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_one                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_prey                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_red                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_river                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_road                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_sea                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_second                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_secret                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_shadow                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_son                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_star                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_storm                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_street                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_summer                          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_the                             <int> 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,…
## $ tf_title_this                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_three                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_time                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_to                              <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ tf_title_tree                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_two                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_we                              <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_white                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_who                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_wife                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_wind                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_with                            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_woman                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_world                           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tf_title_you                             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Anne.Rice                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Barbara.Taylor.Bradford           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Catherine.Coulter                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Danielle.Steel                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_David.Baldacci                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Dean.Koontz                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Debbie.Macomber                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Dick.Francis                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Douglas.Preston.and.Lincoln.Child <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Harlan.Coben                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Iris.Johansen                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_J..A..Jance                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_J..D..Robb                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Jack.Higgins                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_James.Lee.Burke                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_James.Patterson                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_James.Patterson.and.Maxine.Paetro <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Janet.Evanovich                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Jeffrey.Archer                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_John.Grisham                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_John.le.Carré                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_John.Sandford                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Jonathan.Kellerman                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Ken.Follett                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Lisa.Scottoline                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Mary.Higgins.Clark                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Michael.Connelly                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Nora.Roberts                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Patricia.Cornwell                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Richard.Paul.Evans                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Robert.B..Parker                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Robert.Ludlum                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Robin.Cook                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Sandra.Brown                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Stephen.King                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Stuart.Woods                      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Sue.Grafton                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Taylor.Caldwell                   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_Terry.Brooks                      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ author_W..E..B..Griffin                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ author_other                             <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
rf_spec <-
  rand_forest(trees = 500) %>%
  set_mode("regression")

rf_spec
## Random Forest Model Specification (regression)
## 
## Main Arguments:
##   trees = 500
## 
## Computational engine: ranger
xgboost_spec <- 
  boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune())%>% 
  set_mode("regression") %>% 
  set_engine("xgboost") 

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

rf_wf <- workflow(xgboost_recipe, rf_spec)


set.seed(12193) 
xgboost_tune <-
  tune_grid(xgboost_workflow, 
            resamples = data_cv,
            grid = 5)

#evaluate models

tune::show_best(xgboost_tune, metric = "rmse")
## # A tibble: 5 × 10
##   trees min_n tree_depth learn_rate .metric .estimator  mean     n std_err
##   <int> <int>      <int>      <dbl> <chr>   <chr>      <dbl> <int>   <dbl>
## 1  1000     2          1    0.0750  rmse    standard   0.828    10 0.00614
## 2  2000    40          8    0.0178  rmse    standard   0.834    10 0.00653
## 3  1500    11         11    0.001   rmse    standard   0.845    10 0.00478
## 4   500    21         15    0.316   rmse    standard   0.899    10 0.00691
## 5     1    30          4    0.00422 rmse    standard   1.11     10 0.00741
## # ℹ 1 more variable: .config <chr>
#update the model by selecting best hyperparameters.
xgboost_fw <- tune::finalize_workflow(xgboost_workflow,
                        tune::select_best(xgboost_tune, metric = "rmse"))

#fit the model on the entire training data and test it on the test data.
data_fit <- tune::last_fit(xgboost_fw, data_split)

contrl_preds <- control_resamples(save_pred = TRUE)


ranger_rs <- fit_resamples(
  rf_wf,
  resamples = data_cv,
  control = contrl_preds)
collect_metrics(ranger_rs)
## # A tibble: 2 × 6
##   .metric .estimator  mean     n std_err .config        
##   <chr>   <chr>      <dbl> <int>   <dbl> <chr>          
## 1 rmse    standard   0.828    10 0.00462 pre0_mod0_post0
## 2 rsq     standard   0.446    10 0.00814 pre0_mod0_post0
bind_rows(collect_predictions(ranger_rs) %>%
  mutate(mod = "ranger")) %>%
  ggplot(aes(total_weeks, .pred)) +
  geom_point(alpha = 0.3, fill = "midnightblue") +
  geom_abline(lty = 2, color = "gray50") +
  facet_wrap(vars(mod)) +
  coord_fixed()

tune::collect_metrics(data_fit)
## # A tibble: 2 × 4
##   .metric .estimator .estimate .config        
##   <chr>   <chr>          <dbl> <chr>          
## 1 rmse    standard       0.816 pre0_mod0_post0
## 2 rsq     standard       0.452 pre0_mod0_post0
tune::collect_predictions(data_fit) %>%
  ggplot(aes(total_weeks, .pred)) +
  geom_point(alpha = 0.3, fill = "midnightblue") +
  geom_abline(lty = 2, color = "gray50") +
  coord_fixed()