Gilgamath

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IMDb Rating by Actor Age
Wed 13 July 2016 by Steven E. Pav

I recently looked at IMDb ratings for Robert De Niro movies, finding slight evidence for a dip in ratings in his third act. I noted then that the data were subject to all kinds of selection biases, and that even in a perfect world would only reflect the ratings of movies that De Niro was in, not of his individual performance. I speculated that older actors might no longer be offered parts in good movies. This is something that can be explored via the IMDb mirror at my disposal, but only very weakly: if actors 'stopped caring' after a certain age, or declined in abilities, or even if IMDb raters simply liked movies with more young people, one might see the same patterns in the data. Despite these caveats, let us press on.

That struts and frets his hour upon the stage

First, I collect all movies which are not marked as Documentary in the data, and which have a production year between 1965 and 2015, and have at least 250 votes on IMDb. This does present a selection bias towards better movies in the earlier period we will have to correct for. I then collect actors and actresses with a known date of birth who have featured in at least 30 of these films. I bring them into R via dplyr, and then subselect to observations where the actor was between 18 and 90 in the production year of the film. This should look like a lot of blah blah blah, but you can follow along at home if you have the mirror, which you can install yourself.

library(RMySQL)
library(dplyr)
library(knitr)
# get the connection and set to UTF-8 (probably not necessary here)
dbcon <- src_mysql(host='0.0.0.0',user='moe',password='movies4me',dbname='IMDB',port …
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Analyze This: Robert De Niro and IMDb Average Ratings
Sat 09 July 2016 by Steven E. Pav

I recently saw a plot purporting to show the Rotten Tomatoes' 'freshness' rating of Robert De Niro movies over the years, with some chart fluff suggesting 'Bobby' has essentially been phoning it in since 2002, at age 59. Somehow I wrote and maintain a mirror of IMDb which would be well suited to explore questions of this kind. Since I am inherently a skeptical person, I decided to look for myself.

You talkin' to me?

First, we grab the 'acts in' table from the MariaDB via dplyr. I found that working with dplyr allowed me to very quickly switch between in-database processing and 'real' analysis in R, and I highly recommend it. Then we get information about De Niro, and join with information about his movies, and the votes for the same:

library(RMySQL)
library(dplyr)
library(knitr)
# get the connection and set to UTF-8 (probably not necessary here)
dbcon <- src_mysql(host='0.0.0.0',user='moe',password='movies4me',dbname='IMDB',port=23306)
capt <- dbGetQuery(dbcon$con,'SET NAMES utf8')
# acts in relation
acts_in <- tbl(dbcon,'cast_info') %>%
    inner_join(tbl(dbcon,'role_type') %>% 
        filter(role %regexp% 'actor|actress'),
        by='role_id')
# Robert De Niro, as a person:
bobby <- tbl(dbcon,'name') %>%
    filter(name %regexp% 'De Niro, Robert$') %>%
    select(name,gender,dob,person_id)
# all movies:
titles <- tbl(dbcon,'title') 
# his movies:
all_bobby_movies <- acts_in %>%
    inner_join(bobby,by='person_id') %>%
    left_join(titles,by='movie_id')
# genre information
movie_genres <- tbl(dbcon,'movie_info') %>%
    inner_join(tbl(dbcon,'info_type') %>% 
        filter(info %regexp% 'genres') %>%
        select(info_type_id),
        by='info_type_id') 
# get rid of _documentaries_ :
bobby_movies <- all_bobby_movies %>% 
    anti_join(movie_genres %>% 
        filter(info %regexp% 'Documentary'),by='movie_id')
# get votes for all movies:
vote_info <- tbl(dbcon,'movie_votes') %>% 
    select(movie_id,votes,vote_mean,vote_sd,vote_se)
# votes for De Niro movies:
bobby_votes <- bobby_movies %>%
    inner_join(vote_info,by='movie_id')
# now collect them:
bv <- bobby_votes %>% collect() 
# sort it
bv <- bv %>% 
    distinct(movie_id,.keep_all=TRUE …
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