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YouTube Streamer Analysis

Examining the dataset of the top 1000 YouTube streamers offers valuable insights into the platform's dynamics. The exploration commences with a thorough examination of the dataset, delving into its structure, variables, and identification of outliers. Through trend analysis, we unveil prevalent content categories and delve into the relationships between subscriber numbers and engagement metrics. The distribution of the audience by country sheds light on regional preferences. This analysis goes beyond mere statistics, incorporating performance metrics such as average subscribers and engagement rates to highlight variations across different content categories. Additionally, it delves into the realm of brand collaborations, identifying noteworthy partnerships. By pinpointing the top-performing content creators, this analysis provides a benchmark for success within the YouTube streaming landscape. In summary, it delivers a succinct yet all-encompassing overview of trends, audience behaviors, and success factors in the realm of YouTube streaming

# Load necessary libraries
library(tidyverse)
library(ggplot2)

# Load the dataset
youtube_data <- read.csv("your_dataset.csv")

# Explore the structure and summary of the dataset
str(youtube_data)
summary(youtube_data)

# Check for missing values
missing_values <- colSums(is.na(youtube_data))
print(missing_values)

# Identify outliers
# You can use different methods like boxplots, scatter plots, or statistical methods to identify outliers.

# Trend analysis
# Identify popular content categories
content_categories <- youtube_data %>% 
  group_by(ContentCategory) %>%
  summarise(average_subscribers = mean(Subscribers),
            average_engagement = mean(EngagementMetrics))

# Plot the trends
ggplot(content_categories, aes(x = ContentCategory, y = average_subscribers, fill = ContentCategory)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Subscribers by Content Category",
       x = "Content Category",
       y = "Average Subscribers") +
  theme_minimal()

ggplot(content_categories, aes(x = ContentCategory, y = average_engagement, fill = ContentCategory)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Engagement by Content Category",
       x = "Content Category",
       y = "Average Engagement") +
  theme_minimal()

# Correlation analysis
correlation_matrix <- cor(youtube_data[c("Subscribers", "EngagementMetrics")])
print(correlation_matrix)

# Audience distribution by country
audience_distribution <- youtube_data %>% 
  group_by(Country) %>%
  summarise(total_streamers = n())

# Plot audience distribution
ggplot(audience_distribution, aes(x = reorder(Country, -total_streamers), y = total_streamers)) +
  geom_bar(stat = "identity") +
  labs(title = "Audience Distribution by Country",
       x = "Country",
       y = "Total Streamers") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Performance metrics
performance_metrics <- youtube_data %>% 
  group_by(ContentCategory) %>%
  summarise(avg_subscribers = mean(Subscribers),
            avg_engagement = mean(EngagementMetrics),
            num_collaborations = sum(BrandCollaborations))

# Identify top-performing content creators
top_creators <- youtube_data %>% 
  arrange(desc(EngagementMetrics)) %>%
  slice(1:10)

# Print the top-performing content creators
print(top_creators)

 # Load necessary libraries
library(tidyverse)
library(ggplot2)
library(htmltools)

# Load the dataset
youtube_data <- read.csv("your_dataset.csv")

# ... (Your previous code for data analysis and visualization)

# Save plots as HTML files
subscribers_plot <- ggplot(content_categories, aes(x = ContentCategory, y = average_subscribers, fill = ContentCategory)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Subscribers by Content Category",
       x = "Content Category",
       y = "Average Subscribers") +
  theme_minimal()

engagement_plot <- ggplot(content_categories, aes(x = ContentCategory, y = average_engagement, fill = ContentCategory)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Engagement by Content Category",
       x = "Content Category",
       y = "Average Engagement") +
  theme_minimal()

audience_plot <- ggplot(audience_distribution, aes(x = reorder(Country, -total_streamers), y = total_streamers)) +
  geom_bar(stat = "identity") +
  labs(title = "Audience Distribution by Country",
       x = "Country",
       y = "Total Streamers") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Save plots as HTML files
subscribers_html <- html_print(subscribers_plot, background = "white")
engagement_html <- html_print(engagement_plot, background = "white")
audience_html <- html_print(audience_plot, background = "white")

# Save HTML files
writeLines(subscribers_html, "subscribers_plot.html")
writeLines(engagement_html, "engagement_plot.html")
writeLines(audience_html, "audience_plot.html")

   

YouTube Analysis Pie Chart

This work is licensed under a Creative Commons Attribution 4.0 International License.