Goal: to predict total weeks on bestseller list Click here for the data

Need Help with the log transform! Code is not working.

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)
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 ▇▅▃▃▂ 
data <- nyt_titles %>%
    na.omit() %>%
    
    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()

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: 7,427
## Columns: 18
## $ author__Danielle_Steel                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ `author__-OTHER`                                <dbl> 1, 1, 1, 1, 1, 1, 1, 1…
## $ `year__-Inf_1968`                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0…
## $ year__1968_2000                                 <dbl> 1, 1, 1, 0, 0, 0, 1, 1…
## $ year__2000_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_10.5                               <dbl> 0, 0, 1, 0, 0, 1, 0, 1…
## $ best_rank__10.5_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: 18 × 3
##    feature     bin                                correlation
##    <fct>       <chr>                                    <dbl>
##  1 total_weeks 2.30258509299405_Inf                   1      
##  2 total_weeks -Inf_0.693147180559945                -0.397  
##  3 best_rank   -Inf_3                                 0.344  
##  4 total_weeks 1.38629436111989_2.30258509299405     -0.323  
##  5 best_rank   10.5_Inf                              -0.314  
##  6 total_weeks 0.693147180559945_1.38629436111989    -0.256  
##  7 year        1968_2000                              0.242  
##  8 year        2011_Inf                              -0.235  
##  9 year        2000_2011                             -0.230  
## 10 year        -Inf_1968                              0.217  
## 11 best_rank   6_10.5                                -0.130  
## 12 best_rank   3_6                                    0.0800 
## 13 debut_rank  4_8                                   -0.0316 
## 14 debut_rank  8_12                                   0.0201 
## 15 debut_rank  -Inf_4                                 0.0148 
## 16 author      -OTHER                                -0.00390
## 17 author      Danielle_Steel                         0.00390
## 18 debut_rank  12_Inf                                -0.00285
data_corr_tbl %>% 
    plot_correlation_funnel()