[BUG] <Missing Rows in load_player_stats()>

Is there an existing issue for this?

[X] I have searched the existing issues

Have you installed the latest development version of the package(s) in question?

[X] I have installed the latest development version of the package.

What version of the package do you have?

1.3.2.9

Describe the bug

load_player_stats() is returning empty rows when a player plays but records no relevant box score statistics.

Reprex

nflreadr::load_player_stats(
+     season = 2021:2022,
+     stat_type = c("defense"))

Expected Behavior

I expect this to return a row for every week a player played football. But this function entirely omits weeks a player plays but records no box score statistics. Some top DT examples in 2022: Jonathan Allen Week 7 Deforest Buckner Week 4 Chris Jones Week 15

I noticed this is also an issue for Offense. Tee Higgins doesn't get a row in Week 5 or Week 14 of 2022 even though he played both weeks.

This causes issues when calculating something like Receptions Per Game across a season (the Games denominator is wrong).

nflverse_sitrep

── System Info ────────────────────────────────────────────────────────────
• R version 4.3.1 (2023-06-16 ucrt) • Running under: Windows 11 x64 (build 22621)
── Package Status ─────────────────────────────────────────────────────────
• nflreadr (1.3.2.09)    • nflseedR (1.2.0)  • nflplotR (1.1.0.9006)  
• nflfastR (4.5.1.9004)  • nfl4th   (1.0.4)  • nflverse (1.0.3)       
── Package Options ────────────────────────────────────────────────────────
No options set for above packages
── Package Dependencies ───────────────────────────────────────────────────
• askpass     (1.1)     • hms        (1.1.3)    • proto        (1.0.0)    
• backports   (1.4.1)   • httr       (1.4.7)    • purrr        (1.0.2)    
• cachem      (1.0.8)   • isoband    (0.2.7)    • R6           (2.5.1)    
• cli         (3.6.1)   • janitor    (2.2.0)    • rappdirs     (0.3.3)    
• codetools   (0.2-19)  • jsonlite   (1.8.7)    • RColorBrewer (1.1-3)    
• colorspace  (2.1-0)   • labeling   (0.4.2)    • Rcpp         (1.0.11)   
• cpp11       (0.4.6)   • lattice    (0.21-8)   • rlang        (1.1.1)    
• crayon      (1.5.2)   • lifecycle  (1.0.3)    • rstudioapi   (0.15.0)   
• curl        (5.0.2)   • listenv    (0.9.0)    • scales       (1.2.1)    
• data.table  (1.14.8)  • lubridate  (1.9.2)    • snakecase    (0.11.1)   
• digest      (0.6.33)  • magick     (2.7.4)    • stringi      (1.7.12)   
• dplyr       (1.1.2)   • magrittr   (2.0.3)    • stringr      (1.5.0)    
• fansi       (1.0.4)   • MASS       (7.3-60)   • sys          (3.4.2)    
• farver      (2.1.1)   • Matrix     (1.6-1)    • tibble       (3.2.1)    
• fastmap     (1.1.1)   • memoise    (2.0.1)    • tidyr        (1.3.0)    
• fastrmodels (1.0.2)   • mgcv       (1.8-42)   • tidyselect   (1.2.0)    
• furrr       (0.3.1)   • mime       (0.12)     • timechange   (0.2.0)    
• future      (1.33.0)  • munsell    (0.5.0)    • utf8         (1.2.3)    
• generics    (0.1.3)   • nlme       (3.1-162)  • vctrs        (0.6.3)    
• ggplot2     (3.4.2)   • openssl    (2.1.0)    • viridisLite  (0.4.2)    
• globals     (0.16.2)  • parallelly (1.36.0)   • withr        (2.5.0)    
• glue        (1.6.2)   • pillar     (1.9.0)    • xgboost      (1.7.5.1)  
• gsubfn      (0.7)     • pkgconfig  (2.0.3)      
• gtable      (0.3.3)   • progressr  (0.13.0)     
───────────────────────────────────────────────────────────────────────────

Screenshots

No response

Additional context

I'm wondering if joining participation data might help on this (include snaps as a variable for offense and defense?) but I'm guessing that creates more dependencies which might not be desirable.

nflverse / nflreadr