data dive week-5.Rmd

data <- read.csv ("C:\\Users\\91630\\OneDrive\\Desktop\\statistics\\age_gaps.CSV")

age_gaps_df <- read.csv("age_gaps.CSV")


str(age_gaps_df)

## 'data.frame':    1155 obs. of  13 variables:
##  $ movie_name        : chr  "Harold and Maude" "Venus" "The Quiet American" "The Big Lebowski" ...
##  $ release_year      : int  1971 2006 2002 1998 2010 1992 2009 1999 1992 1999 ...
##  $ director          : chr  "Hal Ashby" "Roger Michell" "Phillip Noyce" "Joel Coen" ...
##  $ age_difference    : int  52 50 49 45 43 42 40 39 38 38 ...
##  $ couple_number     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ actor_1_name      : chr  "Ruth Gordon" "Peter O'Toole" "Michael Caine" "David Huddleston" ...
##  $ actor_2_name      : chr  "Bud Cort" "Jodie Whittaker" "Do Thi Hai Yen" "Tara Reid" ...
##  $ character_1_gender: chr  "woman" "man" "man" "man" ...
##  $ character_2_gender: chr  "man" "woman" "woman" "woman" ...
##  $ actor_1_birthdate : chr  "1896-10-30" "1932-08-02" "1933-03-14" "1930-09-17" ...
##  $ actor_2_birthdate : chr  "1948-03-29" "1982-06-03" "1982-10-01" "1975-11-08" ...
##  $ actor_1_age       : int  75 74 69 68 81 59 62 69 57 77 ...
##  $ actor_2_age       : int  23 24 20 23 38 17 22 30 19 39 ...

Three column that are unclear

• “character_1_gender” and “character_2_gender” : It’s not made apparent in these columns what the values “man” and “woman” stand for unless you read the documentation. Whether these values reflect the gender of the actors portraying those roles or the gender of the characters themselves is unclear.

• “age_difference”: Without context or documentation, it’s unclear what this value represents. Is it the age difference between the characters, the actors, or some other factor?

• “couple_number”: It’s unclear what this column represents without further explanation. Is it indicating the number of couples involved in the movie’s plot, the order in which the couples appear, or some other factor? Without clear documentation or context, it’s challenging to interpret the significance of this column.

Reasons for Data Encoding:

• The data might have been encoded this way to represent the genders of the characters in the movie. “man” and “woman” are straightforward categories that could be used to denote the gender of the characters or the actors portraying them.

• The “age_difference” column could be encoding the age difference between the characters in the movie. This information could be relevant for analyzing relationships or character dynamics.

Unclear Element After Reading Documentation:

• Despite consulting the provided documentation, the nature of the “age_difference” column remains ambiguous.

• It’s challenging to ascertain whether this column denotes the age difference between characters depicted in the film, the actors portraying those characters, or potentially another aspect altogether.

• This lack of clarity introduces uncertainty into any analysis relying on this column and may impede accurate interpretation and comparison of results.

• Clarifying the intended meaning of this column within the documentation or through direct communication with data providers would greatly enhance the utility and reliability of the dataset.

Documentation Gaps:

• The documentation may not explain the specific encoding conventions used for the “character_1_gender” and “character_2_gender” columns. Clarification on whether these values represent the gender of the characters or the actors would be helpful for accurate data interpretation.

• Additionally, if the documentation does not explicitly define the “age_difference” column, it could lead to misinterpretation or inconsistency in analysis.

Visualization Highlighting the Issue:

library(ggplot2)
# Plotting histogram
ggplot(age_gaps_df, aes(x = age_difference)) +
  geom_histogram(binwidth = 5, fill = "skyblue", color = "black") +
  labs(title = "Distribution of Age Differences Between Characters",
       x = "Age Difference",
       y = "Frequency") +
  theme_minimal()

This visualization provides insights into the range and distribution of age differences between characters across the movies in the dataset. It helps identify common age disparities and outliers, allowing for a better understanding of how age dynamics are represented in cinema.

Significant Risks and Mitigation:

• One significant risk is misinterpretation of the gender and age difference data, which could lead to biased or inaccurate analyses.

• To mitigate this risk, it’s essential to clarify the encoding conventions for gender and provide clear definitions for columns such as “age_difference” in the documentation.

• Furthermore, sensitivity analysis and cross-referencing with additional sources can be used to confirm the data’s accuracy and spot inconsistencies.

Insight:

• The analysis reveals potential ambiguity in the encoding of gender and age difference data, highlighting the importance of clear documentation and consistent encoding conventions.

• Further investigation is needed to clarify the meaning of the “age_difference” column and determine whether it represents the age difference between characters, actors, or some other factor.

• Understanding these nuances is crucial for accurate analysis and interpretation of the data, particularly in studies focusing on relationship dynamics or character portrayal in movies.

Further questions

• How do age differences between characters influence their relationships and interactions within the movies?

• What patterns or trends can be observed in the portrayal of gender roles and dynamics among the characters?

• How do directors’ casting decisions and storytelling choices contribute to the representation of age and gender dynamics in the films?