Replication of Figures (Thesis)
Preparation
Packages to Load.
library(dplyr) # For Data Manipulation
library(lme4) # For Linear Mixed-effects Modeling
library(lmerTest) # For Type III Anova
library(ggplot2) # For Creating Customized Figures
library(gridExtra) # For Two ggplots Next to Each Other
library(corrplot) # Correlation plotEncoding and Data.
encoding <- "UTF-8"
csv_file <- "sen_youtube_data.csv"
df <- read.csv(csv_file, fileEncoding = encoding) # Load in the necessary file. Make sure its in the working directory.Figure 5.1 Histogram of View Count
figure_5_1 <- ggplot(df, aes(x = log_viewCount)) +
geom_histogram(binwidth = 1, fill = "gray70", color = "black", alpha = 0.7, boundary = -1) +
labs(x = "View Count (Log)", y = "Frequency") +
theme_minimal() + theme(plot.title = element_text(hjust = 0.5)) +
theme(
text = element_text(size = 12),
axis.title.x = element_text(size = 12, margin = margin(t = 10)),
axis.title.y = element_text(size = 12, margin = margin(r = 10)),
axis.text = element_text(size = 12, face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
figure_5_1
Function that customized each of the plots – instead of doing it twice
plot_sentiment_histogram <- function(data, sentiment_column, binwidth = 1/10, title = "", x_label = "Sentiment Score") {
ggplot(data, aes(x = !!sym(sentiment_column))) +
geom_histogram(binwidth = binwidth, fill = "gray90", color = "black", alpha = 0.7, boundary = 0) +
labs(title = title, x = x_label, y = "Frequency") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.title.x = element_text(size = 16, margin = margin(t = 10)),
axis.title.y = element_text(size = 16, margin = margin(r = 10)),
axis.text = element_text(size = 16, margin = margin(t = 10), face = "bold"),
axis.text.y.left = element_text(size = 16, margin = margin(r = 10)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
}Figure 5.2 Histogram of Individual Sentiments
pos_sent <- plot_sentiment_histogram(df, "pos_prob", title= "Positive")
neg_sent <- plot_sentiment_histogram(df, "neg_prob", title= "Negative")
grid.arrange(pos_sent, neg_sent, ncol = 2) # gridExtra package
Figure 5.3 Histogram of Composite Score
figure_5_3 <- ggplot(df, aes(x = compound_score)) +
geom_histogram(binwidth = 0.2, fill = "gray70", color = "black", alpha = 0.7, boundary = -1) +
labs(x = "Sentiment Score", y = "Frequency") +
theme_minimal() + theme(plot.title = element_text(hjust = 0.5)) +
theme(
text = element_text(size = 12),
axis.title.x = element_text(size = 12, margin = margin(t = 10)),
axis.title.y = element_text(size = 12, margin = margin(r = 10)),
axis.text = element_text(size = 12, face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
figure_5_3
Figure 5.4 Correlation Matrix (Video Level)
video_df <- df[c("neg_prob", "pos_prob","compound_score", "log_viewCount",
"durationMin")]
corr_matrix <- cor(video_df)# Creating a matrix
custom_labels1 <- c("Negative Prob.", "Positive Prob.", "Composite Score", "View Count (Log)",
"Duration (Min)") # Custom Labels
rownames(corr_matrix) <- colnames(corr_matrix) <- custom_labels1
corrplot(corr_matrix, method = "number", col = gray.colors(50),
tl.col = "black", tl.srt = 45, tl.cex = 0.8)
Figure 6.1 Coefficient Plot (Model 8)
negative_model3 <- lmer(log_viewCount ~ neg_prob + abs_nom + durationMin +
seniority + gender + log_subscriber +
(1 + neg_prob| channelId ), data = df)
label_vector <- c("(Intercept)", "Ideology", "Duration", "Gender (M)", "Subscribers (Log)", "Negativity", "Seniority")
coefficients <- as.data.frame(summary(negative_model3)$coefficients)
figure_6_1 <- ggplot(data = coefficients, aes(x = rownames(coefficients), y = Estimate)) +
geom_point() +
geom_errorbar(aes(ymin = Estimate - `Std. Error`, ymax = Estimate + `Std. Error`)) +
labs(x = "Coefficients", y = "Estimate") + theme_minimal() +
theme(plot.title = element_text(hjust = 0.5),
text = element_text(size = 12),
axis.title.x = element_text(size = 12, margin = margin(t = 10)),
axis.title.y = element_text(size = 12, margin = margin(r = 10)),
axis.text = element_text(size = 12, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank()) +
scale_x_discrete(labels = label_vector)
print(figure_6_1) # Display plot
Figure 6.2 Histograms of Random Components
par(mar = c(5, 4, 1, 2) + 0.1) # Adjust the margins
par(mfrow=c(1,2)) # 1 by 2
# Model 4 load in
negative_model3 <- lmer(log_viewCount ~ neg_prob + abs_nom + durationMin +
seniority + gender + log_subscriber +
(1 + neg_prob| channelId ), data = df)
# Random components of Model 4
random_components <- ranef(negative_model3)
# Intercept Histogram
hist(random_components$channelId[, "(Intercept)"],
xlab = "Intercept Random Effect",
main = "Distribution of Random Intercepts",
breaks = seq(-2, 3, by = 0.5))
# Slope Histogram
hist(random_components$channelId[,"neg_prob"], xlab= "Negative Sentiment Random Effect",
main = "Distribution of Random Slopes",
breaks = seq(-2, 3, by = 0.5))
Figure 6.3 Random Components for Each Senator
negative_model3 <- lmer(log_viewCount ~ neg_prob + abs_nom + durationMin +
seniority + gender + log_subscriber +
(1 + neg_prob| channelId ), data = df)
random_components <- ranef(negative_model3)
par(mar = c(5, 4, 1, 2) + 0.1) # Adjust the margins
par(mfrow=c(1,1)) # 1 histogram
plot(random_components$channelId[, "(Intercept)"],
random_components$channelId[, "neg_prob"],
xlab = "Intercept",
ylab = "Negative Slope",
xlim = range(random_components$channelId[, "(Intercept)"]),
ylim = range(random_components$channelId[, "neg_prob"]),
pch = 16,
col = "black",
cex = 1.2,
grid()
)
Figure A.1 Histogram of Video Duration (Minutes)
par(mfrow=c(1,1))
par(mar = c(5, 4, 1, 2) + 0.1) # Adjusts the margins
# Video Duration
hist(df$durationMin,
xlab = "Duration (in Minutes)",
main = "",
breaks = seq(0, 60, length.out = 31))