Challenge 8 : Joining SNL Data

challenge_8

snl

Saksham Kumar

Joining Data

Author

Saksham Kumar

Published

May 15, 2023

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
join two or more data sets and analyze some aspect of the joined data

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

snl ⭐⭐⭐⭐⭐

snl_actors <- read_csv("_data/snl_actors.csv", show_col_types = FALSE)
snl_casts <- read_csv("_data/snl_casts.csv", show_col_types = FALSE)
snl_seasons <- read_csv("_data/snl_seasons.csv", show_col_types = FALSE)

Briefly describe the data

snl_actors

This data frame is a list of actors who were SNL, if they were guests, crew members or part of the cast, and finally their gender

snl_casts

This data frame contains information about which actor appeared in which season in SNL and other information about the actor-season relationship

snl_seasons

Finally this data frame contains information about each season like air year, no. of episodes and first and last episode id

Join Data

lets joins the actors and casts data frame by aid. Let us also drop url

snl_casts_actors <- snl_casts %>% 
                left_join(snl_actors, by ="aid") %>%
                select(!url)

snl_casts_actors

Let us next join snl_casts_actors and snl_seasons based on sid. Let us also drop episode information

snl_agg_data <- snl_casts_actors %>% 
                left_join(snl_seasons, by ="sid") %>%
                select(-c(first_epid.x, last_epid.x, n_episodes.x, first_epid.y, last_epid.y, n_episodes.y))

snl_agg_data

Let us now try to see the number od male and female actors across the years

snl_agg_data_filtered <- snl_agg_data %>% group_by(sid, year, gender) %>% summarise(count = n(), .groups = 'drop')

snl_agg_data_filtered

Let us try to visualize this

ggplot(snl_agg_data_filtered, aes(year, count, col = gender)) + geom_line()

We can see from the plot that the number of female actors on SNL has increased from the 2010s. Also we can see the actors with gender marked as “unkown” (possibly individuals that identify as non-binary) have appreared on SNL from the 2020s.

--- title: "Challenge 8 : Joining SNL Data" author: "Saksham Kumar" description: "Joining Data" date: "05/15/2023" format: html: df-print: paged toc: true code-copy: true code-tools: true categories: - challenge_8 - snl - Saksham Kumar --- ```{r} #| label: setup #| warning: false #| message: false library(tidyverse) library(ggplot2) knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) ``` ## Challenge Overview Today's challenge is to: 1) read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc) 2) tidy data (as needed, including sanity checks) 3) mutate variables as needed (including sanity checks) 4) join two or more data sets and analyze some aspect of the joined data (be sure to only include the category tags for the data you use!) ## Read in data Read in one (or more) of the following datasets, using the correct R package and command. - snl ⭐⭐⭐⭐⭐ ```{r} snl_actors <- read_csv("_data/snl_actors.csv", show_col_types = FALSE) snl_casts <- read_csv("_data/snl_casts.csv", show_col_types = FALSE) snl_seasons <- read_csv("_data/snl_seasons.csv", show_col_types = FALSE) ``` ### Briefly describe the data ```{r} snl_actors ``` This data frame is a list of actors who were SNL, if they were guests, crew members or part of the cast, and finally their gender ```{r} snl_casts ``` This data frame contains information about which actor appeared in which season in SNL and other information about the actor-season relationship ```{r} snl_seasons ``` Finally this data frame contains information about each season like air year, no. of episodes and first and last episode id ## Join Data lets joins the actors and casts data frame by `aid`. Let us also drop url ```{r} snl_casts_actors <- snl_casts %>% left_join(snl_actors, by ="aid") %>% select(!url) snl_casts_actors ``` Let us next join `snl_casts_actors` and `snl_seasons` based on sid. Let us also drop episode information ```{r} snl_agg_data <- snl_casts_actors %>% left_join(snl_seasons, by ="sid") %>% select(-c(first_epid.x, last_epid.x, n_episodes.x, first_epid.y, last_epid.y, n_episodes.y)) snl_agg_data ``` Let us now try to see the number od male and female actors across the years ```{r} snl_agg_data_filtered <- snl_agg_data %>% group_by(sid, year, gender) %>% summarise(count = n(), .groups = 'drop') snl_agg_data_filtered ``` Let us try to visualize this ```{r} ggplot(snl_agg_data_filtered, aes(year, count, col = gender)) + geom_line() ``` We can see from the plot that the number of female actors on SNL has increased from the 2010s. Also we can see the actors with gender marked as "unkown" (possibly individuals that identify as non-binary) have appreared on SNL from the 2020s.