B_Descriptive_Stats.Rmd

---
title: "B_Descriptive_Stats"
author: "Emilio Robleda"
date: "`r Sys.Date()`"
output: html_document
---

```{r message=FALSE, warning=FALSE}
library(tidyverse)
library(lubridate)
library(rvg)
library(readxl)

```


```{r load raw data}
interest_time <- read_excel("graph1_interest_in_time.xlsx")
interest_age <- read_excel("graph2_interest_by_age.xlsx")
subreddit_month <- read.csv("subreddit_stats.csv")
interest_age

```


```{r filter time period}
# Assuming "subreddit_month" is the name of your data frame
subreddit_month <- subreddit_month %>%
  mutate(date = parse_date_time(month, "b Y"))

```


```{r filter time period}
# Drop observations before August 2020 and after May 2023
data <- subreddit_month %>%
  filter(date >= as.Date("2020-06-01") & date <= as.Date("2023-05-31"))

```

## Descriptive Stats at a Subreddit (Group Cluster) Level

Plot: Number of Posts

```{r plot: Posts per Subreddit}
# Create specific database for plot
sub_posts_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(posts_sum = sum(posts), .groups = "drop") %>%
  arrange(desc(posts_sum))

# Create plot
sub_posts <- ggplot(sub_posts_data, aes(x = reorder(subreddit, -posts_sum), y = posts_sum, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Number of Posts",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Number of Posts") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts_sum), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_posts

```

Plot: Number of Observations (months in which at least one women's football post was submitted)

```{r plot: observations per subreddit}
# Create specific database for plot
sub_obs_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(posts_count = n(), .groups = "drop") %>%
  arrange(desc(posts_count))

# Create plot
sub_obs <- ggplot(sub_obs_data, aes(x = reorder(subreddit, -posts_count), y = posts_count, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Number of Observations",
       subtitle = "36 month period",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Number of Observations") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts_count), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.11)))

# Display plot
sub_obs

```

Plot: Number of Comments

```{r plot: comments per subreddit}
# Create specific database for plot
sub_com_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(com_sum = sum(comments), .groups = "drop") %>%
  arrange(desc(com_sum))

# Create plot
sub_com <- ggplot(sub_com_data, aes(x = reorder(subreddit, -com_sum), y = com_sum, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Number of Comments",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Number of Comments") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = com_sum), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_com

```

Plot: Average Characters per Comment

```{r plot: avg. chars per subreddit}
# Create specific database for plot
sub_char_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(avg_char = round(mean(avg_chars),0), .groups = "drop") %>%
  arrange(desc(avg_char))

# Create plot
sub_char <- ggplot(sub_char_data, aes(x = reorder(subreddit, -avg_char), y = avg_char, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Average Characters per Comment",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Average Characters per Comment") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_char), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_char

```

Plot: Average Comment Score

```{r plot: avg. comment score per subreddit}
# Create specific database for plot
sub_score_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(avg_score = round(mean(avg_score),0), .groups = "drop") %>%
  arrange(desc(avg_score))

# Create plot
sub_score <- ggplot(sub_score_data, aes(x = reorder(subreddit, -avg_score), y = avg_score, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Average Comment Score",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Average Score per Comment") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_score

```

Plot: Average Post Score

```{r plot: avg. post score per subreddit}
# Create specific database for plot
sub_posts_score_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(avg_posts_score = round(mean(avg_post_score),0), .groups = "drop") %>%
  arrange(desc(avg_posts_score))

# Create plot
sub_posts_score <- ggplot(sub_posts_score_data, aes(x = reorder(subreddit, -avg_posts_score),
                                                    y = avg_posts_score, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Average Post Score",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Average Score per Post") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_posts_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_posts_score

```

Plot: Moderated Comments Proportion

```{r plot: avg. chars per subreddit}
# Create specific database for plot
sub_mod_data <- data %>%
  group_by(subreddit, g) %>%
  summarise(avg_mod_prop = round(mean(mod_prop)*100,0), .groups = "drop") %>%
  arrange(desc(avg_mod_prop))

# Create plot
sub_mod <- ggplot(sub_mod_data, aes(x = reorder(subreddit, -avg_mod_prop), y = avg_mod_prop, fill = factor(g))) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("0" = "gray80", "1" = "gray20"), name = "Subreddit Group") +
  theme_minimal() +
  labs(title = "Moderated Comments Proportion",
       caption = "Period of analysis: June 2020 - May 2023",
       x = "Subreddit",
       y = "Moderated Comments Proportion, in %") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "top",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_mod_prop), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))

# Display plot
sub_mod

```

## Descriptive Stats per Subreddit: pre and post Euro 2022 

### Gunners

```{r}
# Gunners
Gunners <- data %>%
  filter(subreddit == "Gunners")

# Convert "month" to a factor with the desired order
Gunners$month <- factor(Gunners$month, levels = unique(Gunners$month))

# Create the bar plot for posts per month
Gunners_posts <- ggplot(data = Gunners, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Gunners Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Gunners_posts

# Create the bar plot for comments per month
Gunners_com <- ggplot(data = Gunners, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Gunners Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Gunners_com

# Create the bar plot for average comments per post per month
Gunners_avgcom <- ggplot(data = Gunners, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Gunners Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Gunners_avgcom

# Create the bar plot for average post score
Gunners_pscore <- ggplot(data = Gunners, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Gunners Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Gunners_pscore

```

### reddevils

```{r}
# reddevils
reddevils <- data %>%
  filter(subreddit == "reddevils")

# Convert "month" to a factor with the desired order
reddevils$month <- factor(reddevils$month, levels = unique(reddevils$month))

# Create the bar plot for posts per month
reddevils_posts <- ggplot(data = reddevils, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "reddevils Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
reddevils_posts

# Create the bar plot for comments per month
reddevils_com <- ggplot(data = reddevils, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "reddevils Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
reddevils_com

# Create the bar plot for average comments per post per month
reddevils_avgcom <- ggplot(data = reddevils, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "reddevils Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
reddevils_avgcom

# Create the bar plot for average post score
reddevils_pscore <- ggplot(data = reddevils, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "reddevils Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
reddevils_pscore

```

### Barca

```{r}
# Barca
Barca <- data %>%
  filter(subreddit == "Barca")

# Convert "month" to a factor with the desired order
Barca$month <- factor(Barca$month, levels = unique(Barca$month))

# Create the bar plot for posts per month
Barca_posts <- ggplot(data = Barca, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Barca Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Barca_posts

# Create the bar plot for comments per month
Barca_com <- ggplot(data = Barca, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Barca Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Barca_com

# Create the bar plot for average comments per post per month
Barca_avgcom <- ggplot(data = Barca, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Barca Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Barca_avgcom

# Create the bar plot for average post score
Barca_pscore <- ggplot(data = Barca, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Barca Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Barca_pscore

```

### coys

```{r}
# coys
coys <- data %>%
  filter(subreddit == "coys")

# Convert "month" to a factor with the desired order
coys$month <- factor(coys$month, levels = unique(coys$month))

# Create the bar plot for posts per month
coys_posts <- ggplot(data = coys, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "coys Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
coys_posts

# Create the bar plot for comments per month
coys_com <- ggplot(data = coys, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "coys Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
coys_com

# Create the bar plot for average comments per post per month
coys_avgcom <- ggplot(data = coys, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "coys Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
coys_avgcom

# Create the bar plot for average post score
coys_pscore <- ggplot(data = coys, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "coys Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
coys_pscore

```

### chelseafc

```{r}
# chelseafc
chelseafc <- data %>%
  filter(subreddit == "chelseafc")

# Convert "month" to a factor with the desired order
chelseafc$month <- factor(chelseafc$month, levels = unique(chelseafc$month))

# Create the bar plot for posts per month
chelseafc_posts <- ggplot(data = chelseafc, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "chelseafc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
chelseafc_posts

# Create the bar plot for comments per month
chelseafc_com <- ggplot(data = chelseafc, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "chelseafc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
chelseafc_com

# Create the bar plot for average comments per post per month
chelseafc_avgcom <- ggplot(data = chelseafc, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "chelseafc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
chelseafc_avgcom

# Create the bar plot for average post score
chelseafc_pscore <- ggplot(data = chelseafc, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "chelseafc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
chelseafc_pscore

```

### LiverpoolFC

```{r}
# LiverpoolFC
LiverpoolFC <- data %>%
  filter(subreddit == "LiverpoolFC")

# Convert "month" to a factor with the desired order
LiverpoolFC$month <- factor(LiverpoolFC$month, levels = unique(LiverpoolFC$month))

# Create the bar plot for posts per month
LiverpoolFC_posts <- ggplot(data = LiverpoolFC, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "LiverpoolFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
LiverpoolFC_posts

# Create the bar plot for comments per month
LiverpoolFC_com <- ggplot(data = LiverpoolFC, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "LiverpoolFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
LiverpoolFC_com

# Create the bar plot for average comments per post per month
LiverpoolFC_avgcom <- ggplot(data = LiverpoolFC, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "LiverpoolFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
LiverpoolFC_avgcom

# Create the bar plot for average post score
LiverpoolFC_pscore <- ggplot(data = LiverpoolFC, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "LiverpoolFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
LiverpoolFC_pscore

```

### MCFC

```{r}
# MCFC
MCFC <- data %>%
  filter(subreddit == "MCFC")

# Convert "month" to a factor with the desired order
MCFC$month <- factor(MCFC$month, levels = unique(MCFC$month))

# Create the bar plot for posts per month
MCFC_posts <- ggplot(data = MCFC, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "MCFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
MCFC_posts

# Create the bar plot for comments per month
MCFC_com <- ggplot(data = MCFC, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "MCFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
MCFC_com

# Create the bar plot for average comments per post per month
MCFC_avgcom <- ggplot(data = MCFC, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "MCFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
MCFC_avgcom

# Create the bar plot for average post score
MCFC_pscore <- ggplot(data = MCFC, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "MCFC Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
MCFC_pscore

```

### fcbayern

```{r}
# fcbayern
fcbayern <- data %>%
  filter(subreddit == "fcbayern")

# Convert "month" to a factor with the desired order
fcbayern$month <- factor(fcbayern$month, levels = unique(fcbayern$month))

# Create the bar plot for posts per month
fcbayern_posts <- ggplot(data = fcbayern, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "fcbayern Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
fcbayern_posts

# Create the bar plot for comments per month
fcbayern_com <- ggplot(data = fcbayern, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "fcbayern Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
fcbayern_com

# Create the bar plot for average comments per post per month
fcbayern_avgcom <- ggplot(data = fcbayern, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "fcbayern Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
fcbayern_avgcom

# Create the bar plot for average post score
fcbayern_pscore <- ggplot(data = fcbayern, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "fcbayern Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
fcbayern_pscore

```

### avfc

```{r}
# avfc
avfc <- data %>%
  filter(subreddit == "avfc")

# Convert "month" to a factor with the desired order
avfc$month <- factor(avfc$month, levels = unique(avfc$month))

# Create the bar plot for posts per month
avfc_posts <- ggplot(data = avfc, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "avfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
avfc_posts

# Create the bar plot for comments per month
avfc_com <- ggplot(data = avfc, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "avfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
avfc_com

# Create the bar plot for average comments per post per month
avfc_avgcom <- ggplot(data = avfc, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "avfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
avfc_avgcom

# Create the bar plot for average post score
avfc_pscore <- ggplot(data = avfc, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "avfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
avfc_pscore

```

### BrightonHoveAlbion

```{r}
# BrightonHoveAlbion
BrightonHoveAlbion <- data %>%
  filter(subreddit == "BrightonHoveAlbion")

# Convert "month" to a factor with the desired order
BrightonHoveAlbion$month <- factor(BrightonHoveAlbion$month, levels = unique(BrightonHoveAlbion$month))

# Create the bar plot for posts per month
BrightonHoveAlbion_posts <- ggplot(data = BrightonHoveAlbion, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "BrightonHoveAlbion Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
BrightonHoveAlbion_posts

# Create the bar plot for comments per month
BrightonHoveAlbion_com <- ggplot(data = BrightonHoveAlbion, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "BrightonHoveAlbion Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
BrightonHoveAlbion_com

# Create the bar plot for average comments per post per month
BrightonHoveAlbion_avgcom <- ggplot(data = BrightonHoveAlbion, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "BrightonHoveAlbion Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
BrightonHoveAlbion_avgcom

# Create the bar plot for average post score
BrightonHoveAlbion_pscore <- ggplot(data = BrightonHoveAlbion, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "BrightonHoveAlbion Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
BrightonHoveAlbion_pscore

```

### Everton

```{r}
# Everton
Everton <- data %>%
  filter(subreddit == "Everton")

# Convert "month" to a factor with the desired order
Everton$month <- factor(Everton$month, levels = unique(Everton$month))

# Create the bar plot for posts per month
Everton_posts <- ggplot(data = Everton, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Everton Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Everton_posts

# Create the bar plot for comments per month
Everton_com <- ggplot(data = Everton, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Everton Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Everton_com

# Create the bar plot for average comments per post per month
Everton_avgcom <- ggplot(data = Everton, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Everton Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Everton_avgcom

# Create the bar plot for average post score
Everton_pscore <- ggplot(data = Everton, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Everton Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Everton_pscore

```

### Hammers

```{r}
# Hammers
Hammers <- data %>%
  filter(subreddit == "Hammers")

# Convert "month" to a factor with the desired order
Hammers$month <- factor(Hammers$month, levels = unique(Hammers$month))

# Create the bar plot for posts per month
Hammers_posts <- ggplot(data = Hammers, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Hammers Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Hammers_posts

# Create the bar plot for comments per month
Hammers_com <- ggplot(data = Hammers, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Hammers Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Hammers_com

# Create the bar plot for average comments per post per month
Hammers_avgcom <- ggplot(data = Hammers, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Hammers Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Hammers_avgcom

# Create the bar plot for average post score
Hammers_pscore <- ggplot(data = Hammers, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Hammers Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Hammers_pscore

```

### Juve

```{r}
# Juve
Juve <- data %>%
  filter(subreddit == "Juve")

# Convert "month" to a factor with the desired order
Juve$month <- factor(Juve$month, levels = unique(Juve$month))

# Create the bar plot for posts per month
Juve_posts <- ggplot(data = Juve, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Juve Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Juve_posts

# Create the bar plot for comments per month
Juve_com <- ggplot(data = Juve, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Juve Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Juve_com

# Create the bar plot for average comments per post per month
Juve_avgcom <- ggplot(data = Juve, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Juve Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Juve_avgcom

# Create the bar plot for average post score
Juve_pscore <- ggplot(data = Juve, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Juve Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Juve_pscore

```

### lcfc

```{r}
# lcfc
lcfc <- data %>%
  filter(subreddit == "lcfc")

# Convert "month" to a factor with the desired order
lcfc$month <- factor(lcfc$month, levels = unique(lcfc$month))

# Create the bar plot for posts per month
lcfc_posts <- ggplot(data = lcfc, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "lcfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
lcfc_posts

# Create the bar plot for comments per month
lcfc_com <- ggplot(data = lcfc, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "lcfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
lcfc_com

# Create the bar plot for average comments per post per month
lcfc_avgcom <- ggplot(data = lcfc, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "lcfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
lcfc_avgcom

# Create the bar plot for average post score
lcfc_pscore <- ggplot(data = lcfc, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "lcfc Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
lcfc_pscore

```

### OlympiqueLyonnais

```{r}
# OlympiqueLyonnais
OlympiqueLyonnais <- data %>%
  filter(subreddit == "OlympiqueLyonnais")

# Convert "month" to a factor with the desired order
OlympiqueLyonnais$month <- factor(OlympiqueLyonnais$month, levels = unique(OlympiqueLyonnais$month))

# Create the bar plot for posts per month
OlympiqueLyonnais_posts <- ggplot(data = OlympiqueLyonnais, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "OlympiqueLyonnais Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
OlympiqueLyonnais_posts

# Create the bar plot for comments per month
OlympiqueLyonnais_com <- ggplot(data = OlympiqueLyonnais, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "OlympiqueLyonnais Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
OlympiqueLyonnais_com

# Create the bar plot for average comments per post per month
OlympiqueLyonnais_avgcom <- ggplot(data = OlympiqueLyonnais, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "OlympiqueLyonnais Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
OlympiqueLyonnais_avgcom

# Create the bar plot for average post score
OlympiqueLyonnais_pscore <- ggplot(data = OlympiqueLyonnais, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "OlympiqueLyonnais Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
OlympiqueLyonnais_pscore

```
### realmadrid

```{r}
# realmadrid
realmadrid <- data %>%
  filter(subreddit == "realmadrid")

# Convert "month" to a factor with the desired order
realmadrid$month <- factor(realmadrid$month, levels = unique(realmadrid$month))

# Create the bar plot for posts per month
realmadrid_posts <- ggplot(data = realmadrid, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "realmadrid Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
realmadrid_posts

# Create the bar plot for comments per month
realmadrid_com <- ggplot(data = realmadrid, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "realmadrid Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
realmadrid_com

# Create the bar plot for average comments per post per month
realmadrid_avgcom <- ggplot(data = realmadrid, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "realmadrid Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
realmadrid_avgcom

# Create the bar plot for average post score
realmadrid_pscore <- ggplot(data = realmadrid, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "realmadrid Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
realmadrid_pscore

```

### Urz

```{r}
# Urz
Urz <- data %>%
  filter(subreddit == "Urz")

# Convert "month" to a factor with the desired order
Urz$month <- factor(Urz$month, levels = unique(Urz$month))

# Create the bar plot for posts per month
Urz_posts <- ggplot(data = Urz, aes(x = month, y = posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts",
       subtitle = "Urz Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Urz_posts

# Create the bar plot for comments per month
Urz_com <- ggplot(data = Urz, aes(x = month, y = comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments",
       subtitle = "Urz Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Urz_com

# Create the bar plot for average comments per post per month
Urz_avgcom <- ggplot(data = Urz, aes(x = month, y = avg_com, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post",
       subtitle = "Urz Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Urz_avgcom

# Create the bar plot for average post score
Urz_pscore <- ggplot(data = Urz, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score",
       subtitle = "Urz Subreddit",
       caption = "Analysis of posts and comments rearidng the women's team",
       x = "Month",
       y = "Average Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Urz_pscore

```

### Control

```{r}
# Control
control <- data %>%
  filter(g == 0)

```


```{r}
# Calculate the sum of posts per month
control_posts <- control %>%
  group_by(month, date, t) %>%
  summarise(total_posts = sum(posts), .groups = "drop") %>%
  arrange(date)

# Convert "month" to a factor with the desired order
control_posts$month <- factor(control_posts$month, levels = unique(control_posts$month))

# Create the bar plot
c_posts <- ggplot(data = control_posts, aes(x = month, y = total_posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts for the Control Group",
       x = "Month",
       y = "Total Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = total_posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
c_posts

```


```{r}
# Calculate the sum of comments per month
control_com <- control %>%
  group_by(month, date, t) %>%
  summarise(total_comments = sum(comments), .groups = "drop") %>%
  arrange(date)

# Convert "month" to a factor with the desired order
control_com$month <- factor(control_com$month, levels = unique(control_com$month))

# Create the bar plot
c_com <- ggplot(data = control_com, aes(x = month, y = total_comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments for the Control Group",
       x = "Month",
       y = "Total Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = total_comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
c_com

```

```{r}
# Calculate the average comments per post per month
control_avgcom <- control %>%
  group_by(month, date, t) %>%
  summarise(total_comments = sum(comments),
            total_posts = sum(posts),
            .groups = "drop") %>%
  mutate(avg_com_pp = round((total_comments / total_posts), 0)) %>%
  arrange(date)

# Convert "month" to a factor with the desired order
control_avgcom$month <- factor(control_avgcom$month, levels = unique(control_avgcom$month))

# Create the bar plot
c_avgcom <- ggplot(data = control_avgcom, aes(x = month, y = avg_com_pp, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post for the Control Group",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com_pp), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
c_avgcom

```

```{r}
# Calculate the average post score per month
control_post_score <- control %>%
  group_by(month, date, t) %>%
  summarise(total_posts = sum(posts),
            total_score = sum(posts*avg_post_score),
            .groups = "drop") %>%
  mutate(avg_post_score = round((total_score / total_posts), 0)) %>%
  arrange(date)

# Convert "month" to a factor with the desired order
control_post_score$month <- factor(control_post_score$month, levels = unique(control_post_score$month))

# Create the bar plot
c_pscore <- ggplot(data = control_post_score, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score for the Control Group",
       x = "Month",
       y = "Avg. Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
c_pscore

```

### Treatment

```{r}
# Treatment
treatment <- data %>%
  filter(g == 1)

```


```{r}
# Calculate the sum of posts per month
treatment_posts <- treatment %>%
  group_by(month, date, t) %>%
  summarise(total_posts = sum(posts), .groups = "drop") %>%
  arrange(date)

# Convert "month" to a factor with the desired order
treatment_posts$month <- factor(treatment_posts$month, levels = unique(treatment_posts$month))

# Create the bar plot
t_posts <- ggplot(data = treatment_posts, aes(x = month, y = total_posts, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Posts for the Treatment Group",
       x = "Month",
       y = "Total Posts",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = total_posts), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
t_posts

```

```{r}
# Calculate the proportion of moderated posts per month
treatment_com <- treatment %>%
  group_by(month, date, t) %>%
  summarise(total_comments = sum(comments), .groups = "drop") %>%
  arrange(date)

# Convert "month" to a factor with the desired order
treatment_com$month <- factor(treatment_com$month, levels = unique(treatment_com$month))

# Create the bar plot
t_com <- ggplot(data = treatment_com, aes(x = month, y = total_comments, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Number of Comments for the Treatment Group",
       x = "Month",
       y = "Total Comments",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = total_comments), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
t_com

```

```{r}
# Calculate the average comments per post per month
treatment_avgcom <- treatment %>%
  group_by(month, date, t) %>%
  summarise(total_comments = sum(comments),
            total_posts = sum(posts),
            .groups = "drop") %>%
  mutate(avg_com_pp = round((total_comments / total_posts), 0)) %>%
  arrange(date)

# Convert "month" to a factor with the desired order
treatment_avgcom$month <- factor(treatment_avgcom$month, levels = unique(treatment_avgcom$month))

# Create the bar plot
t_avgcom <- ggplot(data = treatment_avgcom, aes(x = month, y = avg_com_pp, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Comments per Post for the treatment Group",
       x = "Month",
       y = "Average Comments per Post",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_com_pp), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
t_avgcom

```

```{r}
# Calculate the average post score per month
treatment_post_score <- treatment %>%
  group_by(month, date, t) %>%
  summarise(total_posts = sum(posts),
            total_score = sum(posts*avg_post_score),
            .groups = "drop") %>%
  mutate(avg_post_score = round((total_score / total_posts), 0)) %>%
  arrange(date)

# Convert "month" to a factor with the desired order
treatment_post_score$month <- factor(treatment_post_score$month, levels = unique(treatment_post_score$month))

# Create the bar plot
t_pscore <- ggplot(data = treatment_post_score, aes(x = month, y = avg_post_score, fill = t)) +
  geom_bar(stat = "identity") +
  labs(title = "Average Post Score for the Treatment Group",
       x = "Month",
       y = "Avg. Post Score",
       fill = "T") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  geom_text(aes(label = avg_post_score), vjust = -0.5) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
t_pscore

```

### OLS Per Subreddit

```{r}
# Convert the 'date' column to POSIXct class
data$date <- as.POSIXct(data$date)

# Get unique subreddits
subreddits <- unique(data$subreddit)
```


```{r}
# Iterate over each subreddit
for (subreddit in subreddits) {
  # Subset the data for the current subreddit and date <= 2022-07-01
  pscore_subset_data1 <- data[data$subreddit == subreddit & data$date <= as.Date("2022-07-01"), ]
  
  # Subset the data for the current subreddit and date > 2022-07-01
  pscore_subset_data2 <- data[data$subreddit == subreddit & data$date > as.Date("2022-07-01"), ]
  
  # Fit the regression model for subset_data1
  pscore_regression_model1 <- lm(avg_post_score ~ date, data = pscore_subset_data1)
  
  # Fit the regression model for subset_data2
  pscore_regression_model2 <- lm(avg_post_score ~ date, data = pscore_subset_data2)
  
  # Create a data frame for predictions
  prediction_data1 <- data.frame(date = pscore_subset_data1$date)
  prediction_data2 <- data.frame(date = pscore_subset_data2$date)
  
  # Add predictions to the data frame
  prediction_data1$prediction1 <- predict(pscore_regression_model1, newdata = prediction_data1)
  prediction_data2$prediction2 <- predict(pscore_regression_model2, newdata = prediction_data2)
  
  # Calculate the maximum avg_post_score within each subreddit subset
  pscore_max_score <- max(c(max(pscore_subset_data1$avg_post_score),
                            max(pscore_subset_data2$avg_post_score)))
  
  # Create a graph for both regressions
  pscore_p <- ggplot() +
    geom_point(data = pscore_subset_data1,
               aes(x = date, y = avg_post_score), color = "red") +
    geom_point(data = pscore_subset_data2,
               aes(x = date, y = avg_post_score), color = "blue") +
    geom_line(data = prediction_data1,
              aes(x = date, y = prediction1), color = "red") +
    geom_line(data = prediction_data2,
              aes(x = date, y = prediction2), color = "blue") +
    labs(title = paste("Regressions for Subreddit", subreddit),
         subtitle = "Outcome Variable: Average Post Score",
         x = "Date",
         y = "Average Post Score") +
    scale_color_manual(values = c("red", "blue"),
                       labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
    theme_minimal() +
    geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
    annotate("text", x = as.POSIXct("2022-07-01"), y = pscore_max_score, 
             label = "Women Euros 2022", vjust = -0.5, hjust = 0, color = "black", fontface = "bold")
  
  # Print the plot
  print(pscore_p)
  
  # Assign the plot object to a variable with a name corresponding to the subreddit
  assign(paste0("pscore_", subreddit), pscore_p)
}

```

```{r}
# Iterate over each subreddit
for (subreddit in subreddits) {
  # Subset the data for the current subreddit and date <= 2022-07-01
  avgcom_subset_data1 <- data[data$subreddit == subreddit & data$date <= as.Date("2022-07-01"), ]
  
  # Subset the data for the current subreddit and date > 2022-07-01
  avgcom_subset_data2 <- data[data$subreddit == subreddit & data$date > as.Date("2022-07-01"), ]
  
  # Fit the regression model for subset_data1
  avgcom_regression_model1 <- lm(avg_com ~ date, data = avgcom_subset_data1)
  
  # Fit the regression model for subset_data2
  avgcom_regression_model2 <- lm(avg_com ~ date, data = avgcom_subset_data2)
  
  # Create a data frame for predictions
  prediction_data1 <- data.frame(date = avgcom_subset_data1$date)
  prediction_data2 <- data.frame(date = avgcom_subset_data2$date)
  
  # Add predictions to the data frame
  prediction_data1$prediction1 <- predict(avgcom_regression_model1, newdata = prediction_data1)
  prediction_data2$prediction2 <- predict(avgcom_regression_model2, newdata = prediction_data2)
  
  # Calculate the maximum avg_com within each subreddit subset
  avgcom_max_score <- max(c(max(avgcom_subset_data1$avg_com),
                            max(avgcom_subset_data2$avg_com)))
  
  # Create a graph for both regressions
  avgcom_p <- ggplot() +
    geom_point(data = avgcom_subset_data1,
               aes(x = date, y = avg_com), color = "red") +
    geom_point(data = avgcom_subset_data2,
               aes(x = date, y = avg_com), color = "blue") +
    geom_line(data = prediction_data1,
              aes(x = date, y = prediction1), color = "red") +
    geom_line(data = prediction_data2,
              aes(x = date, y = prediction2), color = "blue") +
    labs(title = paste("Regressions for Subreddit", subreddit),
         subtitle = "Outcome Variable: Average Comments per Post",
         x = "Date",
         y = "Average Comments per Post") +
    scale_color_manual(values = c("red", "blue"),
                       labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
    theme_minimal() +
    geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
    annotate("text", x = as.POSIXct("2022-07-01"), y = avgcom_max_score, 
             label = "Women Euros 2022", vjust = -0.5, hjust = 0, color = "black", fontface = "bold")
  
  # Print the plot
  print(avgcom_p)
  
  # Assign the plot object to a variable with a name corresponding to the subreddit
  assign(paste0("avgcom_", subreddit), avgcom_p)
}

```
### Control and Treatment OLS Analysis

Average Post Score

```{r}
# Convert the 'date' column to POSIXct class
control$date <- as.POSIXct(control$date)

# Subset the control for date <= 2022-07-01
pscore_subset_c1 <- control[control$date <= as.Date("2022-07-01"), ]
# Subset the control for date > 2022-07-01
pscore_subset_c2 <- control[control$date > as.Date("2022-07-01"), ]
# Fit the regression model for subset_control1
pscore_regression1_c <- lm(avg_post_score ~ date, data = pscore_subset_c1)
# Fit the regression model for subset_control2
pscore_regression2_c <- lm(avg_post_score ~ date, data = pscore_subset_c2)
# Calculate the maximum avg_post_score for each subset
pscore_max_score_c <- max(control$avg_post_score)

# Create a graph for both regressions
pscore_p_c <- ggplot() +
  geom_point(data = pscore_subset_c1,
             aes(x = date, y = avg_post_score), color = "red") +
  geom_point(data = pscore_subset_c2,
             aes(x = date, y = avg_post_score), color = "blue") +
  geom_line(data = pscore_subset_c1,
            aes(x = date, y = predict(pscore_regression1_c)), color = "red") +
  geom_line(data = pscore_subset_c2,
            aes(x = date, y = predict(pscore_regression2_c)), color = "blue") +
  labs(title = "Regressions for the Control Group",
       subtitle = "Outcome Variable: Average Post Score",
       caption = "First OLS: Jun20-Jul22; Second OLS: Jul22-May23",
       x = "Date",
       y = "Average Post Score") +
  scale_color_manual(values = c("red", "blue"),
                     labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
  theme_minimal() +
  geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
  annotate("text", x = as.POSIXct("2022-07-01"), y = pscore_max_score_c, 
           label = "July 2022: Women Euros", vjust = 1, hjust = 0, color = "black", fontface = "bold")

print(pscore_p_c)

```

```{r}
# Convert the 'date' column to POSIXct class
treatment$date <- as.POSIXct(treatment$date)

# Subset the treatment for date <= 2022-07-01
pscore_subset_t1 <- treatment[treatment$date <= as.Date("2022-07-01"), ]
# Subset the treatment for date > 2022-07-01
pscore_subset_t2 <- treatment[treatment$date > as.Date("2022-07-01"), ]
# Fit the regression model for subset_treatment1
pscore_regression1_t <- lm(avg_post_score ~ date, data = pscore_subset_t1)
# Fit the regression model for subset_treatment2
pscore_regression2_t <- lm(avg_post_score ~ date, data = pscore_subset_t2)
# Calculate the maximum avg_post_score for each subset
pscore_max_score_t <- max(treatment$avg_post_score)

# Create a graph for both regressions
pscore_p_t <- ggplot() +
  geom_point(data = pscore_subset_t1,
             aes(x = date, y = avg_post_score), color = "red") +
  geom_point(data = pscore_subset_t2,
             aes(x = date, y = avg_post_score), color = "blue") +
  geom_line(data = pscore_subset_t1,
            aes(x = date, y = predict(pscore_regression1_t)), color = "red") +
  geom_line(data = pscore_subset_t2,
            aes(x = date, y = predict(pscore_regression2_t)), color = "blue") +
  labs(title = "Regressions for the Treatment Group",
       subtitle = "Outcome Variable: Average Post Score",
       caption = "First OLS: Jun20-Jul22; Second OLS: Jul22-May23",
       x = "Date",
       y = "Average Post Score") +
  scale_color_manual(values = c("red", "blue"),
                     labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
  theme_minimal() +
  geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
  annotate("text", x = as.POSIXct("2022-07-01"), y = pscore_max_score_c, 
           label = "July 2022: Women Euros", vjust = 1, hjust = 0, color = "black", fontface = "bold")

print(pscore_p_t)

```

Comments per Post

```{r}
# Convert the 'date' column to POSIXct class
control$date <- as.POSIXct(control$date)

# Subset the control for date <= 2022-07-01
avgcom_subset_c1 <- control[control$date <= as.Date("2022-07-01"), ]
# Subset the control for date > 2022-07-01
avgcom_subset_c2 <- control[control$date > as.Date("2022-07-01"), ]
# Fit the regression model for subset_control1
avgcom_regression1_c <- lm(avg_com ~ date, data = avgcom_subset_c1)
# Fit the regression model for subset_control2
avgcom_regression2_c <- lm(avg_com ~ date, data = avgcom_subset_c2)
# Calculate the maximum avg_com for each subset
avgcom_max_score_c <- max(control$avg_com)

# Create a graph for both regressions
avgcom_p_c <- ggplot() +
  geom_point(data = avgcom_subset_c1,
             aes(x = date, y = avg_com), color = "red") +
  geom_point(data = avgcom_subset_c2,
             aes(x = date, y = avg_com), color = "blue") +
  geom_line(data = avgcom_subset_c1,
            aes(x = date, y = predict(avgcom_regression1_c)), color = "red") +
  geom_line(data = avgcom_subset_c2,
            aes(x = date, y = predict(avgcom_regression2_c)), color = "blue") +
  labs(title = "Regressions for the Control Group",
       subtitle = "Outcome Variable: Average Comments per Post",
       caption = "First OLS: Jun20-Jul22; Second OLS: Jul22-May23",
       x = "Date",
       y = "Average Comments per Post") +
  scale_color_manual(values = c("red", "blue"),
                     labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
  theme_minimal() +
  geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
  annotate("text", x = as.POSIXct("2022-07-01"), y = avgcom_max_score_c, 
           label = "July 2022: Women Euros", vjust = 1, hjust = 0, color = "black", fontface = "bold")

print(avgcom_p_c)

```

```{r}
# Convert the 'date' column to POSIXct class
treatment$date <- as.POSIXct(treatment$date)

# Subset the treatment for date <= 2022-07-01
avgcom_subset_t1 <- treatment[treatment$date <= as.Date("2022-07-01"), ]
# Subset the treatment for date > 2022-07-01
avgcom_subset_t2 <- treatment[treatment$date > as.Date("2022-07-01"), ]
# Fit the regression model for subset_treatment1
avgcom_regression1_t <- lm(avg_com ~ date, data = avgcom_subset_t1)
# Fit the regression model for subset_treatment2
avgcom_regression2_t <- lm(avg_com ~ date, data = avgcom_subset_t2)
# Calculate the maximum avg_com for each subset
avgcom_max_score_t <- max(treatment$avg_com)

# Create a graph for both regressions
avgcom_p_t <- ggplot() +
  geom_point(data = avgcom_subset_t1,
             aes(x = date, y = avg_com), color = "red") +
  geom_point(data = avgcom_subset_t2,
             aes(x = date, y = avg_com), color = "blue") +
  geom_line(data = avgcom_subset_t1,
            aes(x = date, y = predict(avgcom_regression1_t)), color = "red") +
  geom_line(data = avgcom_subset_t2,
            aes(x = date, y = predict(avgcom_regression2_t)), color = "blue") +
  labs(title = "Regressions for the Treatment Group",
       subtitle = "Outcome Variable: Average Comments per Post",
       caption = "First OLS: Jun20-Jul22; Second OLS: Jul22-May23",
       x = "Date",
       y = "Average Comments per Post") +
  scale_color_manual(values = c("red", "blue"),
                     labels = c("Date <= 2022-07-01", "Date > 2022-07-01")) +
  theme_minimal() +
  geom_vline(xintercept = as.numeric(as.POSIXct("2022-07-01")), linetype = "dashed", color = "gray") +
  annotate("text", x = as.POSIXct("2022-07-01"), y = avgcom_max_score_t, 
           label = "July 2022: Women Euros", vjust = 1, hjust = 0, color = "black", fontface = "bold")

print(avgcom_p_t)

```