Quarto enables you to weave together content and executable code into a finished presentation. To learn more about Quarto presentations see https://quarto.org/docs/presentations/.
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When you click the Render button a document will be generated that includes:
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Output from executable code
Code
When you click the Render button a presentation will be generated that includes both content and the output of embedded code. You can embed code like this:
# =========================================# Netflix Dataset Full Analysis (Single Code)# =========================================# Install packages (run once if needed)# install.packages(c("tidyverse","lubridate"))library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.1 ✔ readr 2.2.0
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.2 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.2.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(lubridate)# ------------------------------# Load Dataset (file upload)# ------------------------------df <-read.csv(file.choose(), stringsAsFactors =FALSE)# ------------------------------# Data Cleaning# ------------------------------df$date_added <-mdy(df$date_added)df$year_added <-year(df$date_added)# ------------------------------# 1. Bar Chart: Content Type# ------------------------------ggplot(df, aes(x = type)) +geom_bar(fill ="steelblue") +labs(title ="Content Type Distribution",x ="Type", y ="Count")
# ------------------------------# 2. Time-Series: Content Added Per Year# ------------------------------df %>%count(year_added) %>%arrange(year_added) %>%ggplot(aes(x = year_added, y = n)) +geom_line(color ="darkgreen") +geom_point() +labs(title ="Content Added Per Year",x ="Year", y ="Number of Titles")
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_line()`).
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
# ------------------------------# 3. Heatmap: Genre vs Country# ------------------------------df2 <- df %>%drop_na(country, listed_in) %>%separate_rows(country, sep =", ") %>%separate_rows(listed_in, sep =", ")heatmap_data <- df2 %>%count(country, listed_in)# Select top 10 countries and genrestop_countries <- heatmap_data %>%group_by(country) %>%summarise(total =sum(n)) %>%slice_max(total, n =10)top_genres <- heatmap_data %>%group_by(listed_in) %>%summarise(total =sum(n)) %>%slice_max(total, n =10)heatmap_filtered <- heatmap_data %>%filter(country %in% top_countries$country, listed_in %in% top_genres$listed_in)ggplot(heatmap_filtered, aes(x = listed_in, y = country, fill = n)) +geom_tile() +scale_fill_gradient(low ="white", high ="red") +labs(title ="Genre vs Country Heatmap",x ="Genre", y ="Country") +theme(axis.text.x =element_text(angle =45, hjust =1))
# ------------------------------# (Optional) Pie Chart for Ratings# ------------------------------rating_counts <- df %>%count(rating)ggplot(rating_counts, aes(x ="", y = n, fill = rating)) +geom_bar(stat ="identity", width =1) +coord_polar("y") +labs(title ="Ratings Distribution (Pie Chart)")
