Goal: To predict total weeks on best sellers list

Click[here for the data] (‘https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-05-10/nyt_titles.tsv’)

nyt_titles <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-05-10/nyt_titles.tsv')

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 %>%
  
  # Treat missing values 
    select(-id) %>%
    filter(!is.na(author)) %>%
    filter(total_weeks < 100) %>%
    mutate(total_weeks = log(total_weeks)) %>%
    mutate(decade = year %/% 10 * 10)

#Explore Data

Identify Good Predictors

best_rank

data %>%
    ggplot(aes(as.factor(best_rank), total_weeks)) +
    geom_boxplot()

debut_rank

data %>%
  ggplot(aes(as.factor(total_weeks), debut_rank)) +
  scale_y_log10() +
  geom_boxplot() +
  scale_x_discrete(breaks = unique(data$total_weeks)[c(TRUE, FALSE)])

author

data %>%
    
    group_by(author) %>%
    summarise(total_weeks_avg = mean(total_weeks)) %>% ungroup() %>%
    
    slice_max(order_by = total_weeks_avg, n = 20) %>%

    ggplot(aes(total_weeks_avg, fct_reorder(author, total_weeks_avg))) +
    geom_col() +

labs(title = "Best Author by Total Weeks", y = NULL)

Title

data %>%
    
    #tokenize title
    unnest_tokens(output = word, input = title) %>%
    
    #calculate avg rent per word
    group_by(word) %>%
    summarise(total_weeks = mean(total_weeks),
              n     = n()) %>%
    ungroup() %>%
    
    filter(n > 10, !str_detect(word, "\\a")) %>%
    slice_max(order_by = total_weeks, n = 20) %>%
    
    #plot
    ggplot(aes(total_weeks, fct_reorder(word, total_weeks))) +
    geom_point() +
    
    labs(y = "Words in Title")