Movie Data

The following is a data export brought into R through a csv file transfer from a SQL Select Statement that brought together a person and a movie preferences tables.

movie_data <- read.table('/Users/Michele/Desktop/Movie_Data.csv', header = TRUE, sep = ",")
is.na(movie_data) <- movie_data == "NULL"
movie_data

##   ID Age Favorite_Genre Get_Out Interstellar Mad_Max_Fury_Road  Her X13th
## 1  1  22         Sci-Fi       5            5                 3    3     5
## 2  2  21    Documentary       5            3                 5 <NA>     5
## 3  3  22          Anime       5            3                 5    4     5
## 4  4  22          Drama       3            5              <NA> <NA>     3
## 5  5  34         Comedy       3            4                 5    5  <NA>
##   Ex_Machina
## 1       <NA>
## 2       <NA>
## 3       <NA>
## 4          4
## 5          3

Do Correlations Exist?

Age_Get_Out <- cor.test(movie_data$Age, movie_data$Get_Out, method = "pearson")
Age_Get_Out

## 
##  Pearson's product-moment correlation
## 
## data:  movie_data$Age and movie_data$Get_Out
## t = -1.4097, df = 3, p-value = 0.2534
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9721139  0.5665594
## sample estimates:
##        cor 
## -0.6312327

Age_Interstellar <- cor.test(movie_data$Age, movie_data$Interstellar, method = "pearson")
Age_Interstellar

## 
##  Pearson's product-moment correlation
## 
## data:  movie_data$Age and movie_data$Interstellar
## t = 0.078876, df = 3, p-value = 0.9421
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8717635  0.8919587
## sample estimates:
##        cor 
## 0.04549216

Would Person #5 like the documentary X13th?

Person5 <- lm(X13th ~ Age + Get_Out, data=movie_data)

## Warning in model.response(mf, "numeric"): using type = "numeric" with a
## factor response will be ignored

## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors

Person5

## 
## Call:
## lm(formula = X13th ~ Age + Get_Out, data = movie_data)
## 
## Coefficients:
## (Intercept)          Age      Get_Out  
##  -5.000e-01   -2.564e-16    5.000e-01

Person_5_13th_Rating = -.5 + (-.00000000000000256 * 34) + (.5 * 3)
Person_5_13th_Rating

## [1] 1

Using linear regression, person 5 would rate the documentary “13th” with a value of 1. Again, we take all of this analysis with caution, since there is not enough data in the dataset.