# Load packages
library(bayesrules)
library(janitor)
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## chisq.test, fisher.test
library(tidyverse)
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# Importar datos
data(bechdel, package = "bayesrules")
#resumen por año de las películas
bechdel %>%
tabyl(year) %>%
adorn_totals("row")
## year n percent
## 1970 1 0.0005574136
## 1971 5 0.0027870680
## 1972 3 0.0016722408
## 1973 5 0.0027870680
## 1974 7 0.0039018952
## 1975 5 0.0027870680
## 1976 8 0.0044593088
## 1977 7 0.0039018952
## 1978 8 0.0044593088
## 1979 5 0.0027870680
## 1980 14 0.0078037904
## 1981 9 0.0050167224
## 1982 14 0.0078037904
## 1983 5 0.0027870680
## 1984 16 0.0089186176
## 1985 10 0.0055741360
## 1986 10 0.0055741360
## 1987 14 0.0078037904
## 1988 19 0.0105908584
## 1989 14 0.0078037904
## 1990 15 0.0083612040
## 1991 13 0.0072463768
## 1992 20 0.0111482720
## 1993 16 0.0089186176
## 1994 26 0.0144927536
## 1995 36 0.0200668896
## 1996 42 0.0234113712
## 1997 51 0.0284280936
## 1998 62 0.0345596433
## 1999 56 0.0312151616
## 2000 63 0.0351170569
## 2001 64 0.0356744705
## 2002 80 0.0445930881
## 2003 64 0.0356744705
## 2004 81 0.0451505017
## 2005 100 0.0557413601
## 2006 90 0.0501672241
## 2007 73 0.0406911929
## 2008 101 0.0562987737
## 2009 124 0.0691192865
## 2010 129 0.0719063545
## 2011 124 0.0691192865
## 2012 86 0.0479375697
## 2013 99 0.0551839465
## Total 1794 1.0000000000
- John has a flat Beta(1, 1) prior and analyzes movies from the year
1980.
#a)John día 1 (alpha = 1, beta = 1). Películas de 1980
bechdel_1980 <- bechdel %>%
filter(year == 1980)
head(bechdel_1980)
## # A tibble: 6 × 3
## year title binary
## <dbl> <chr> <chr>
## 1 1980 Airplane! FAIL
## 2 1980 Can't Stop the Music PASS
## 3 1980 Fame PASS
## 4 1980 Flash Gordon FAIL
## 5 1980 Friday the 13th FAIL
## 6 1980 Heaven's Gate FAIL
#porcentajes fail - pass
bechdel_1980 %>%
tabyl(binary) %>%
adorn_totals("row")
## binary n percent
## FAIL 10 0.7142857
## PASS 4 0.2857143
## Total 14 1.0000000
# Plot the Beta-Binomial model
plot_beta_binomial(alpha = 1, beta = 1, y = 4, n = 14)
# Summaries of the Beta-Binomial model
summarize_beta_binomial(alpha = 1, beta = 1, y = 4, n = 14)
## model alpha beta mean mode var sd
## 1 prior 1 1 0.5000 NaN 0.08333333 0.2886751
## 2 posterior 5 11 0.3125 0.2857143 0.01263787 0.1124183
- The next day, John analyzes movies from the year 1990, while
building off their analysis from the previous day.
#b)John día 2 (alpha = 5, beta = 11. Películas de 1990
bechdel_1990 <- bechdel %>%
filter(year == 1990)
head(bechdel_1990)
## # A tibble: 6 × 3
## year title binary
## <dbl> <chr> <chr>
## 1 1990 Back to the Future Part III FAIL
## 2 1990 Child's Play 2 PASS
## 3 1990 Dark Angel (I Come in Peace) FAIL
## 4 1990 Die Hard 2 FAIL
## 5 1990 Edward Scissorhands PASS
## 6 1990 Flatliners PASS
#porcentajes fail - pass
bechdel_1990 %>%
tabyl(binary) %>%
adorn_totals("row")
## binary n percent
## FAIL 9 0.6
## PASS 6 0.4
## Total 15 1.0
# Plot the Beta-Binomial model
plot_beta_binomial(alpha = 5, beta = 11, y = 6, n = 15)
# Summaries of the Beta-Binomial model
summarize_beta_binomial(alpha = 5, beta = 11, y = 6, n = 15)
## model alpha beta mean mode var sd
## 1 prior 5 11 0.3125000 0.2857143 0.012637868 0.11241827
## 2 posterior 11 20 0.3548387 0.3448276 0.007154006 0.08458136
- The third day, John analyzes movies from the year 2000, while again
building off of their analyses from the previous two days.
#c)John día 3 (alpha = 11, beta = 20). Películas de 2000
bechdel_2000 <- bechdel %>%
filter(year == 2000)
head(bechdel_2000)
## # A tibble: 6 × 3
## year title binary
## <dbl> <chr> <chr>
## 1 2000 28 Days PASS
## 2 2000 Almost Famous PASS
## 3 2000 American Psycho FAIL
## 4 2000 Beautiful Creatures PASS
## 5 2000 Best in Show PASS
## 6 2000 Billy Elliot PASS
#porcentajes fail - pass
bechdel_2000 %>%
tabyl(binary) %>%
adorn_totals("row")
## binary n percent
## FAIL 34 0.5396825
## PASS 29 0.4603175
## Total 63 1.0000000
# Plot the Beta-Binomial model
plot_beta_binomial(alpha = 11, beta = 20, y = 29, n = 63)
# Summaries of the Beta-Binomial model
summarize_beta_binomial(alpha = 11, beta = 20, y = 29, n = 63)
## model alpha beta mean mode var sd
## 1 prior 11 20 0.3548387 0.3448276 0.007154006 0.08458136
## 2 posterior 40 54 0.4255319 0.4239130 0.002573205 0.05072677
- Jenna also starts her analysis with a \(Beta(1, 1)\) prior, but analyzes movies
from 1980, 1990, 2000 all on day one.
#d)Jenna día 1 (alpha = 1, beta = 1). Películas de 1980, 1990, 2000
bechdel_80_90_20 <- bechdel %>%
filter(year %in% c(1980 , 1990 , 2000))
bechdel_80_90_20
## # A tibble: 92 × 3
## year title binary
## <dbl> <chr> <chr>
## 1 2000 28 Days PASS
## 2 2000 Almost Famous PASS
## 3 2000 American Psycho FAIL
## 4 2000 Beautiful Creatures PASS
## 5 2000 Best in Show PASS
## 6 2000 Billy Elliot PASS
## 7 2000 Boiler Room FAIL
## 8 2000 Bring It On PASS
## 9 2000 Cast Away FAIL
## 10 2000 Cecil B. Demented PASS
## # ℹ 82 more rows
#resumen por año de la película
bechdel_80_90_20 %>%
tabyl(year) %>%
adorn_totals("row")
## year n percent
## 1980 14 0.1521739
## 1990 15 0.1630435
## 2000 63 0.6847826
## Total 92 1.0000000
#porcentajes fail - pass
bechdel_80_90_20 %>%
tabyl(binary) %>%
adorn_totals("row")
## binary n percent
## FAIL 53 0.576087
## PASS 39 0.423913
## Total 92 1.000000
# Plot the Beta-Binomial model
plot_beta_binomial(alpha = 1, beta = 1, y = 39, n = 92)
# Summaries of the Beta-Binomial model
summarize_beta_binomial(alpha = 1, beta = 1, y = 39, n = 92)
## model alpha beta mean mode var sd
## 1 prior 1 1 0.5000000 NaN 0.083333333 0.28867513
## 2 posterior 40 54 0.4255319 0.423913 0.002573205 0.05072677