2022-11-01

workflowr

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Last updated: 2022-11-02

Checks: 6 1

Knit directory: myTidyTuesday/

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    Ignored:    data/CNHI_Excel_Chart.xlsx
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    Modified:   analysis/2022_11_01.Rmd
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File	Version	Author	Date	Message
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---

blogdown::shortcode('tweet', '1587245920604897284')

{{% tweet "1587245920604897284" %}}

library(tidyverse, quietly = TRUE)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6      ✔ purrr   0.3.5 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.4.1 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(rtweet)

Attaching package: 'rtweet'

The following object is masked from 'package:purrr':

    flatten

library(lubridate)

Attaching package: 'lubridate'

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

library(ggdist)
library(ggimage)

source(here::here("code","_common.R"))

sysfonts::font_add_google("Creepster", "creepster")
showtext::showtext_auto()

scales::show_col(paletteer::palettes_d$DresdenColor$foolmoon)

base_url <- "https://www.themoviedb.org/t/p/w1280/"

movies_raw <- tidytuesdayR::tt_load("2022-11-01")$horror_movies |> 
  mutate(poster = paste0(base_url, poster_path))

--- Compiling #TidyTuesday Information for 2022-11-01 ----
--- There is 1 file available ---
--- Starting Download ---

    Downloading file 1 of 1: `horror_movies.csv`

--- Download complete ---

skimr::skim(movies_raw)

Data summary

Name	movies_raw
Number of rows	32540
Number of columns	21
_______________________
Column type frequency:
character	11
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	19833	0.39	1	237	0	12515	2
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
poster	0	1.00	39	69	0	28049	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	▇▅▅▅▅

There’s a lot of good material in this dataset. Let’s plot some time series

movies_raw |>
  mutate(original_language = fct_lump(original_language,
    5,
    other_level = "Other"
  )) |>
  group_by(
    release_date = floor_date(release_date,
      unit = "year",
    ),
    original_language
  ) |>
  summarise(
    sum = sum(revenue, na.rm = TRUE),
    .groups = "keep"
  ) |>
  mutate(original_language = fct_reorder(
    original_language,
    sum, max
  )) |>
  ggplot(aes(x = release_date, sum, fill = original_language)) +
  geom_col(show.legend = FALSE) +
  scale_y_continuous(labels = scales::dollar) +
  paletteer::scale_fill_paletteer_d("DresdenColor::foolmoon") +
  labs(
    title = "Global Annual Horror Movie Box Office Revenue",
    subtitle = "A growing genre, in <span style='color:#532026'>English,</span> <span style='color:#BA141E'>German,</span> <span style='color:#E2E3E7'>Spanish,</span> <span style='color:#61829C'>Japanese,</span> <span style='color:#354C6A'>Portuguese,</span> <span style='color:#050505'>and Other</span> languages<br><br>",
    x = NULL, y = NULL, fill = "Language",
    caption = "Plot: @jim_gruman Data: The Movie Database via github.com/tashapiro/horror-movies"
  ) +
  theme(
    panel.background = element_rect(fill = "gray10"),
    legend.text = element_text(color = "gray80"),
    plot.title = element_text(
      color = "gray80",
      size = 60,
      family = "creepster"
    ),
    plot.subtitle = ggtext::element_markdown(
      color = "gray80",
      size = 25
    ),
    plot.caption = element_text(color = "gray80"),
    axis.text = element_text(color = "gray80"),
    panel.grid = element_line(color = "gray5"),
    plot.background = element_rect(fill = "gray10")
  )

movies_raw |>
  filter(budget > 1e6) |>
  mutate(
    image = case_when(
      budget > 100000000 ~ poster,
      release_date < as.Date("1960-01-01") &
        budget > 10000000 ~ poster,
      TRUE ~ NA_character_
    ),
    profitable = if_else(
      revenue > budget,
      TRUE, FALSE
    )
  ) |>
  ggplot(aes(release_date, budget / 1e6)) +
  geom_point(aes(color = profitable),
    show.legend = FALSE,
    size = 2,
    shape = 21
  ) +
  geom_image(aes(
    x = release_date + years(3),
    image = image
  )) +
  geom_text(
    data = count(movies_raw, release_date = floor_date(release_date, unit = "year")),
    aes(y = if_else(year(release_date) %% 2 == 0,
      -2, -6
    ), label = n),
    color = "gray80"
  ) +
  scale_y_continuous(
    labels = scales::dollar,
    position = "right"
  ) +
  scale_x_date(expand = expansion(mult = c(0, 0))) +
  scale_color_manual(values = c(
    paletteer::palettes_d$DresdenColor$foolmoon[[5]],
    paletteer::palettes_d$DresdenColor$foolmoon[[2]]
  )) +
  labs(
    title = "Horror Movie Budgets",
    subtitle = "There have been more and more massive productions since the 1980s.  <span style='color:#354C6A'>Revenue > Budget</span> and <span style='color:#BA141E'>Revenue < Budget</span>",
    x = NULL, y = NULL, fill = "Language",
    caption = "Numbers are the annual counts of releases with budgets over $1M by year. Budgets in Millions $US. Plot: @jim_gruman Data: The Movie Database via github.com/tashapiro/horror-movies"
  ) +
  theme(
    panel.background = element_rect(fill = "gray7"),
    plot.title = element_text(
      color = "#532026",
      size = 100,
      vjust = -50,
      hjust = 0.1,
      family = "creepster"
    ),
    plot.subtitle = ggtext::element_markdown(
      color = "gray80",
      size = 20
    ),
    plot.caption = element_text(color = "gray80"),
    axis.ticks = element_blank(),
    axis.ticks.length = unit(c(0, 0, 0, 0), "cm"),
    axis.text = element_text(
      color = "gray80",
      size = 20
    ),
    axis.line = element_blank(),
    panel.grid = element_line(color = "gray5"),
    plot.background = element_rect(
      fill = "gray7",
      color = "gray7"
    ),
    plot.margin = unit(c(0, 0.2, 0.1, 0), "cm"),
  )

ggsave(here::here("data", "2022_11_01.png"),
  width = 6, height = 5, dpi = 300, bg = "black",
  device = "png"
)

post_tweet(
  status = "#TidyTuesday on Horror Movie Budgets. Special thanks to @tanya_shapiro for the dataset. #rstats #r4ds",
  media = here::here("data", "2022_11_01.png"),
  token = NULL,
  in_reply_to_status_id = NULL,
  destroy_id = NULL,
  retweet_id = NULL,
  auto_populate_reply_metadata = FALSE,
  media_alt_text = "The Horror Movie Budgets by Year in points, with movie posters for the largest",
  lat = NULL,
  long = NULL,
  display_coordinates = FALSE
)

blogdown::shortcode("tweet", "1587245920604897284")

{{% tweet "1587245920604897284" %}}

Session information

sessionInfo()

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggimage_0.3.1   ggdist_3.2.0    lubridate_1.8.0 rtweet_1.0.2   
 [5] forcats_0.5.2   stringr_1.4.1   dplyr_1.0.10    purrr_0.3.5    
 [9] readr_2.1.3     tidyr_1.2.1     tibble_3.1.8    ggplot2_3.3.6  
[13] tidyverse_1.3.2 workflowr_1.7.0

loaded via a namespace (and not attached):
  [1] readxl_1.4.1         backports_1.4.1      systemfonts_1.0.4   
  [4] workflows_1.1.0      selectr_0.4-2        repr_1.1.4          
  [7] tidytuesdayR_1.0.2   splines_4.2.2        listenv_0.8.0       
 [10] usethis_2.1.6        digest_0.6.29        foreach_1.5.2       
 [13] yulab.utils_0.0.5    htmltools_0.5.3      yardstick_1.1.0     
 [16] viridis_0.6.2        magick_2.7.3         parsnip_1.0.2.9001  
 [19] fansi_1.0.3          magrittr_2.0.3       memoise_2.0.1       
 [22] tune_1.0.1           paletteer_1.5.0      googlesheets4_1.0.1 
 [25] tzdb_0.3.0           recipes_1.0.2        globals_0.16.1      
 [28] modelr_0.1.9         gower_1.0.0          R.utils_2.12.0      
 [31] vroom_1.6.0          sysfonts_0.8.8       hardhat_1.2.0       
 [34] rsample_1.1.0        dials_1.0.0          colorspace_2.0-3    
 [37] skimr_2.1.4          rvest_1.0.3          textshaping_0.3.6   
 [40] haven_2.5.1          xfun_0.33            prismatic_1.1.1     
 [43] callr_3.7.2          crayon_1.5.2         jsonlite_1.8.3      
 [46] survival_3.4-0       iterators_1.0.14     glue_1.6.2          
 [49] gtable_0.3.1         gargle_1.2.1         ipred_0.9-13        
 [52] distributional_0.3.1 R.cache_0.16.0       future.apply_1.9.1  
 [55] scales_1.2.1         infer_1.0.3          DBI_1.1.3           
 [58] showtextdb_3.0       Rcpp_1.0.9           gridtext_0.1.5      
 [61] viridisLite_0.4.1    gridGraphics_0.5-1   bit_4.0.4           
 [64] GPfit_1.0-8          lava_1.7.0           prodlim_2019.11.13  
 [67] httr_1.4.4           ellipsis_0.3.2       R.methodsS3_1.8.2   
 [70] pkgconfig_2.0.3      farver_2.1.1         nnet_7.3-18         
 [73] sass_0.4.2           dbplyr_2.2.1         utf8_1.2.2          
 [76] here_1.0.1           labeling_0.4.2       ggplotify_0.1.0     
 [79] tidyselect_1.2.0     rlang_1.0.6          DiceDesign_1.9      
 [82] later_1.3.0          munsell_0.5.0        cellranger_1.1.0    
 [85] tools_4.2.2          cachem_1.0.6         cli_3.4.0           
 [88] generics_0.1.3       broom_1.0.1          evaluate_0.17       
 [91] fastmap_1.1.0        ragg_1.2.4           yaml_2.3.5          
 [94] rematch2_2.1.2       bit64_4.0.5          processx_3.7.0      
 [97] knitr_1.40           fs_1.5.2             workflowsets_1.0.0  
[100] showtext_0.9-5       future_1.28.0        whisker_0.4         
[103] R.oo_1.25.0          xml2_1.3.3           compiler_4.2.2      
[106] rstudioapi_0.14      curl_4.3.3           reprex_2.0.2        
[109] lhs_1.1.5            bslib_0.4.0          stringi_1.7.8       
[112] highr_0.9            ps_1.7.1             blogdown_1.13       
[115] lattice_0.20-45      Matrix_1.5-1         markdown_1.2        
[118] styler_1.8.0         conflicted_1.1.0     vctrs_0.5.0         
[121] tidymodels_1.0.0     pillar_1.8.1         lifecycle_1.0.3     
[124] furrr_0.3.1          jquerylib_0.1.4      httpuv_1.6.6        
[127] R6_2.5.1             promises_1.2.0.1     gridExtra_2.3       
[130] parallelly_1.32.1    codetools_0.2-18     MASS_7.3-58.1       
[133] assertthat_0.2.1     rprojroot_2.0.3      withr_2.5.0         
[136] ggtext_0.1.2         parallel_4.2.2       hms_1.1.2           
[139] grid_4.2.2           rpart_4.1.19         ggfun_0.0.7         
[142] timeDate_4021.106    class_7.3-20         rmarkdown_2.17      
[145] googledrive_2.0.0    git2r_0.30.1         getPass_0.2-2       
[148] base64enc_0.1-3