compound_scatterplot_group <- function(dataset, compound, timepoints){
  if(max(dataset[,compound],na.rm=TRUE)==0){
    print(
      dataset |> 
        filter(!is.na(time_from_start)) |>
        ggplot(aes_string(x="time_from_start", 
                          y=compound,
                          color="group")) + 
        geom_point() +
        geom_vline(data=timepoints, aes(xintercept=as.numeric(stop)), 
                   linetype="dashed", 
                   color="gray28") +
        scale_color_manual(values=c("#19831C", "#A27FC9")) +
        scale_y_continuous(limits=c(0,3)) +
        theme_classic() +
        theme(legend.position="bottom",
              legend.title=element_blank()) +
        labs(x='Time From Start (min)',
             y=gsub('GLUC', 'gluc',gsub("_", "-", toupper(compound))))
    )}else{
      print(
        dataset |> 
          filter(!is.na(time_from_start)) |>
          ggplot(aes_string(x="time_from_start", 
                            y=compound,
                            color="group")) + 
          geom_point() +
          geom_vline(data=timepoints, aes(xintercept=as.numeric(stop)), 
                     linetype="dashed", 
                     color="gray28")  +
          scale_color_manual(values=c("#19831C", "#A27FC9")) +
          theme_classic() +
          theme(legend.position="bottom",
                legend.title=element_blank()) +
          labs(x='Time From Start (min)',
               y=gsub('GLUC', 'gluc', gsub("_", "-", toupper(compound))))
      )
    }
}

scatter_wb <- map(compounds_WB, ~ compound_scatterplot_group( 
    dataset=WB_dups, 
    compound=.x, 
    timepoints=timepoints_WB))

scatter_of <- map(compounds_OF, ~ compound_scatterplot_group( 
    dataset=OF_dups, 
    compound=.x, 
    timepoints=timepoints_OF))

scatter_br <- map(compounds_BR, ~ compound_scatterplot_group( 
    dataset=BR_dups, 
    compound=.x, 
    timepoints=timepoints_BR))

pairs(WB[,unlist(compounds_WB)], 
      pch=19, 
      cex=0.3, 
      cex.labels=0.6,
      labels=gsub('GLUC','gluc',gsub("_","-",toupper(colnames(WB[,unlist(compounds_WB)])))))

pairs(OF[,unlist(compounds_OF)], 
      pch=19, 
      cex=0.4, 
      cex.labels=0.6,
      labels=gsub('GLUC','gluc',gsub("_","-",toupper(colnames(OF[,unlist(compounds_OF)])))))

compound_boxplot_group_only <- function(dataset, compounds, tissue, legend=TRUE, y_lab=TRUE){
  timepoint_to_use=levels(dataset$timepoint_use)[1]
  df <- dataset |> 
    filter(timepoint_use == timepoint_to_use) |>
    select(group, all_of(compounds)) 
  df <- df |> 
    gather(compound, value, -group) |> 
    clean_gluc() |> 
    group_by(compound) |> 
    mutate(y_max=1.2*max(value)) |> 
    group_by(compound, group) |> 
    mutate(n=n(),
           my_label=paste0(group, ' N=', n),
           my_label= gsub(" ", "\n", my_label))
  
  if(tissue == "Blood"){
    df$compound=factor(df$compound, levels=c("THC","11-OH-THC","THCCOOH","THCCOOH-gluc")) 
  }
  
  y_pos <- df |> 
    group_by(compound) |> 
    summarize(y.position=mean(y_max))
  
  stat.test <- df |>
    group_by(compound) |>
    t_test(value ~ my_label) |>
    adjust_pvalue(method="bonferroni") |>
    add_significance()
  test <- stat.test |>
    left_join(y_pos) |>
    mutate(p.adj.signif=ifelse(p.adj.signif=='?', 'ns', p.adj.signif),
           p.adj=ifelse(p.adj < 0.001, "<0.001", p.adj))

  if(legend){
    leg_position='right'
  }else{
    leg_position='none'
  }
  
  if(y_lab){
    y_text="Concentration (ng/mL)"
  }else{
    y_text=''
  }
  
  medianFunction <- function(x){
    return(data.frame(y=round(median(x),1),label=round(median(x,na.rm=T),1)))}
  
  p2 <- ggplot(df, aes(x=my_label, y=value, fill=my_label)) + 
    geom_jitter(position=position_jitter(width=.3, height=0), size=0.8, color="gray65")  +
    geom_boxplot(outlier.shape=NA, alpha=0.6) +
    stat_summary(fun="median", geom="point", shape=19, size=3, fill="black") + 
    stat_summary(fun.data=medianFunction, geom ="text", color="black", size=3.5, vjust=-0.65) +
    facet_wrap(~compound, scales="free_y", ncol=4) +
    geom_blank(aes(y=y_max)) + 
    scale_y_continuous(limits=c(0, NA), expand=expansion(mult=c(0, 0.1))) +
    scale_fill_manual(values=c("#19831C", "#A27FC9")) +
    theme_classic() +
    theme(text=element_text(size=14),
          legend.position=leg_position,
          legend.title=element_blank(),
          panel.grid=element_blank(),
          strip.background=element_blank()) +
    labs(title=tissue,
         x='',
         y=y_text) 
  
  ann_text <- test |> 
    select(compound, p.adj, value=y.position, my_label=group1) |>
    filter(p.adj < 0.05) |> 
    mutate(x1=1, x2=2)
  
  if(tissue == "Whole Blood"){
    ann_text$compound=factor(ann_text$compound, 
                               levels=c("THC","11-OH-THC","THCCOOH","THCCOOH-gluc")) 
  }
  
  p2 + geom_text(data=ann_text, label=ann_text$p.adj, nudge_x=0.5) +
    geom_segment(data=ann_text, aes(x=x1, xend=x2,
                                    y=value - (0.04 * value), 
                                    yend=value - (0.04*value)))
  
}

compound_boxplot_group_only(WB_dups, compounds=unlist(compounds_WB), tissue="Whole Blood")

compound_boxplot_group_only(OF_dups, compounds=unlist(compounds_OF), tissue="Oral Fluid")

compound_boxplot_group_only(BR_dups, compounds=unlist(compounds_BR), tissue="Breath")

compound_boxplot_treatment <- function(dataset, compounds, tissue){
  timepoint_to_use=levels(dataset$timepoint_use)[2]
  df <- dataset |> 
    filter(timepoint_use == timepoint_to_use) |>
    select(treatment, group, compounds)
  df <- df |> 
    gather(compound, value, -treatment, -group) |> 
    clean_gluc()
  
  df |> 
    ggplot(aes(x=treatment, y=value, fill=group)) + 
    # geom_jitter(color="gray36") +
    geom_boxplot(outlier.size=0.1) +
    scale_fill_manual(values=c("#19831C", "#A27FC9")) +
    facet_wrap(~compound, scales="free_y", ncol=4) +
    scale_x_discrete(labels=function(x) str_wrap(x, width=11)) +
    theme_classic(base_size=10) +
    theme(legend.position="bottom",
          legend.title=element_blank(),
          panel.grid=element_blank(),
          strip.background=element_blank()) +
    labs(title=tissue,
         x="Treatment",
         y="Measurement (ng/mL)")
  
}

compound_boxplot_treatment(WB_dups, compounds=unlist(compounds_WB), tissue="Whole Blood")

compound_boxplot_treatment(OF_dups, compounds=unlist(compounds_OF), tissue="Oral Fluid")

compound_boxplot_treatment(BR_dups, compounds=unlist(compounds_BR), tissue="Breath")

T2A_plot <- function(dataset, compound, timepoint_use=2){
  timepoint_to_use=levels(factor(dataset$timepoint_use))[timepoint_use]
  if(max(dataset[,compound],na.rm=TRUE)==0){
    print(
      ggplot(subset(dataset, timepoint_use==timepoint_to_use), 
             aes_string(x="group", 
                        y=compound, 
                        fill="group")) + 
        geom_boxplot(outlier.size=0.1) +
        scale_fill_manual(values=c("#19831C", "#A27FC9")) +
        scale_x_discrete(labels=function(x) str_wrap(x, width=10)) +
        scale_y_continuous(limits=c(0,3)) +
        facet_grid(~treatment) + 
        theme_classic() +
        theme(legend.position="none",
              panel.grid=element_blank(),
              strip.background=element_blank(),
              plot.title.position="plot") +
        labs(title=paste0('Timepoint: ',
                          levels(dataset$timepoint_use)[timepoint_use],
                          ' post-smoking'),
             x='Group',
             y=gsub('GLUC', 'gluc',gsub("_", "-", toupper(compound))))
    )}else{
      print(
        ggplot(subset(dataset, timepoint_use==timepoint_to_use), 
               aes_string(x="group", 
                          y=compound, 
                          fill="group")) + 
          geom_boxplot(outlier.shape=NA, alpha=0.8) +
          scale_fill_manual(values=c("#19831C", "#A27FC9")) +
          scale_x_discrete(labels=function(x) str_wrap(x, width=10)) +
          facet_grid(~treatment) + 
          theme_classic() +
          theme(legend.position="none",
                panel.grid=element_blank(),
                strip.background=element_blank(),
                plot.title.position="plot") +
          labs(title=paste0('Timepoint: ',
                            levels(dataset$timepoint_use)[timepoint_use],
                            ' post-smoking'),
               x='Group',
               y=gsub('GLUC', 'gluc',gsub("_","-",toupper(compound))))
      )
    }
}

post_wb <- map(compounds_WB, ~ T2A_plot( 
    dataset=WB_dups, 
    compound=.x))

post_of <- map(compounds_OF, ~ T2A_plot( 
    dataset=OF_dups, 
    compound=.x))

post_br <- map(compounds_BR, ~ T2A_plot( 
    dataset=BR_dups, 
    compound=.x))

COVID_approval <- read_csv(url("https://raw.githubusercontent.com/fivethirtyeight/covid-19-polls/master/covid_approval_polls.csv"))


chess <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/chess-transfers/transfers.csv")

chess_trans <- chess |>
  count(Federation) |>
  arrange(desc(n)) |>
  slice_head(n = 10)

chess_trans <- chess_trans |>
  mutate(rank = row_number()) |>
  mutate(Federation = case_when(
    Federation == "USA" ~ "United States",
    Federation == "GER" ~ "Germany",
    Federation == "CAN" ~ "Canada",
    Federation == "ESP" ~ "Spain",
    Federation == "RUS" ~ "Russia",
    Federation == "FRA" ~ "France",
    Federation == "BIH" ~ "Bosnia and Herzegovina",
    Federation == "CRO" ~ "Croatia",
    Federation == "TUR" ~ "Turkey",
    Federation == "AUT" ~ "Austria",
    
    TRUE ~ Federation  # Keep the name as-is for other cases
  ))

chess_trans <- chess |>
  count(Federation) |>
  arrange(desc(n)) |>
  slice_head(n = 10)

chess_trans <- chess_trans |>
  mutate(rank = row_number()) |>
  mutate(Federation = case_when(
    Federation == "USA" ~ "United States",
    Federation == "GER" ~ "Germany",
    Federation == "CAN" ~ "Canada",
    Federation == "ESP" ~ "Spain",
    Federation == "RUS" ~ "Russia",
    Federation == "FRA" ~ "France",
    Federation == "BIH" ~ "Bosnia and Herzegovina",
    Federation == "CRO" ~ "Croatia",
    Federation == "TUR" ~ "Turkey",
    Federation == "AUT" ~ "Austria",
    
    TRUE ~ Federation  # Keep the name as-is for other cases
  ))

ggplot(chess_trans, aes(y = reorder(Federation, n), x = n)) +
  geom_bar(stat = "identity",fill = "#1c9099") +
  geom_text(aes(x = -3, y = Federation, label = n), size = 4) + #add count at the left side of the bars
  labs(title = bquote(bold("More players transfer to the U.S. than to any other country")),
       subtitle = "Nations that received the highest number of player transfers, 2000-17",
       x = "NUMBER OF TRANSFERS",
       y = "COUNTRY") +
  scale_fill_identity() +
  theme_minimal() + 
  theme(plot.title.position = "plot", 
        panel.grid.major.y = element_blank()) + 
  theme(axis.text.y = element_text(size = 10, angle = 0, hjust = 0), #align to the left
        plot.title = element_text(size=18)) #change font size

# Kushi also got font working and stars on x-axis that I'd have to spend more time to get working 
fandango_score_comparison <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/fandango/fandango_score_comparison.csv")

all_scores_comp <- fandango_score_comparison |>
  select(FILM, RT_norm_round, RT_user_norm_round, Metacritic_norm_round, Metacritic_user_norm_round, IMDB_norm_round, Fandango_Stars) |>
  pivot_longer(c(RT_norm_round, RT_user_norm_round, Metacritic_norm_round, Metacritic_user_norm_round, IMDB_norm_round, Fandango_Stars), names_to = "Site", values_to = "Score")

counts_of_scores <- all_scores_comp |>
  group_by(Site, Score) |>
  summarize(Count = n())

counts_of_scores <- counts_of_scores |>
  group_by(Site) |>
  mutate(Total = sum(Count))

scores_with_percents <- counts_of_scores |>
  mutate(Percent = Count / Total * 100)

# making percents 0 for any scores that don't have any values

sites <- tibble(Site = c("RT_norm_round", "RT_user_norm_round", "Metacritic_norm_round", "Metacritic_user_norm_round", "IMDB_norm_round", "Fandango_Stars"))

ratings <- tibble(Score = seq(0, 5, by = 0.5))

all_scores <- expand.grid(Site = sites$Site, Score = ratings$Score)

all_scores <- all_scores |>
  full_join(scores_with_percents, by = c("Site", "Score")) |>
  mutate(Percent = case_when(is.na(Count) ~ 0,
                             TRUE ~ Percent))

scores <- ggplot(all_scores, aes(x = Score, y = Percent, color = Site)) +
  geom_line()  +
  geom_hline(yintercept = 0, size = 0.7, color = "black") +
  labs(x = NULL, y = NULL,
       title = "Fandango LOVES Movies",
       subtitle = "Normalized ratings distrubution of 146 films in theaters in 2015 that\n had 30+ reviews on Fandango.com") +
  scale_y_continuous(labels = c("0", "10", "20", "30", "40%")) +
  scale_x_continuous(labels = c("☆", "★", "★★", "★★★", "★★★★", "★★★★★")) +
  scale_color_manual(values = c("Fandango_Stars" = "#fa6d54",
                                "IMDB_norm_round" = "#e5c66a",
                                "Metacritic_user_norm_round" = "#aeca91",
                                "RT_user_norm_round" = "#76bde0",
                                "Metacritic_norm_round" = "#b87eb5",
                                "RT_norm_round" = "#a3a3a3")) +
  geom_ribbon(data = filter(all_scores, Site != "Fandango_Stars"), aes(ymin = 0, ymax = Percent), alpha = 0.1) +
  geom_ribbon(data = filter(all_scores, Site == "Fandango_Stars"), aes(ymin = 0, ymax = Percent, fill = "#fa6d54", alpha = 0.24)) +
  guides(color = "none", fill = "none", alpha = "none")

scores + 
  annotate("text", x = 4.9, y = 35, label = "Fandango", size = 5, color = "#fa6d54", fontface = "bold") +
  annotate("text", x = 2.9, y = 37, label = "IMDb users", size = 5, color = "#e5c66a") +
  annotate("text", x = 2.7, y = 27, label = "Metacritic\nusers", size = 5, color = "#aeca91") +
  annotate("text", x = 2.2, y = 20, label = "Rotten\nTomatoes\nusers", size = 5, color = "#76bde0") +
  annotate("text", x = 1.5, y = 16, label = "Metacritic", size = 5, color = "#b87eb5") +
  annotate("text", x = 0.7, y = 13, label = "Rotten\nTomatoes", size = 5, color = "#a3a3a3") +
  theme(#text = element_text(family = "NimbusSan"), 
        plot.title = element_text(face = "bold", size = 25, hjust = -0.1), 
        plot.subtitle = element_text(size = 15, hjust = -0.1),
        plot.background = element_rect(fill = "#f0f0f0"),
        panel.background = element_rect(fill = "#f0f0f0"),
        panel.grid.major = element_line(color = "gray75", size = 0.2),
        panel.grid.minor = element_blank(),
        axis.text = element_text(size = 14)
        )

congress_data <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/congress-demographics/data_aging_congress.csv")

congress_ages =
  congress_data |>
  select(congress, chamber, age_years) |>
  mutate(congress_year = case_when(TRUE ~ 1787 + (2 * as.integer(congress)))) |>
  group_by(congress_year, chamber) |>
  mutate(mean_age = case_when(TRUE ~ mean(age_years))) |>
  mutate(chamber = fct_recode(chamber,
                              SENATE = "Senate",
                              HOUSE = "House",))

ggplot(data = congress_ages,
       mapping = aes(y = mean_age,
                     x = congress_year,
                     color = fct_rev(chamber),
                     )) +
  geom_step(size = 1) + 
  guides() +
  labs(title = "The House and Senate are older than ever before",
       subtitle = "Median age of the U.S. Senate and U.S. House by Congress, 1919 to 2023",
       caption = "Data is based on all members who served in either the Senate or House in each Congress, which is notated\nby the year in which it was seated. Any member who served in bothchambers in the same Congress was\nassigned to the chamber in which they cast more votes.\n FiveThirtyEight\nSOURCES: BIOGRAPHICAL DIRECTORY OF THE U.S. CONGRESS, U.S. HOUSE OF REPRESENTATIVES,\nU.S. SENATE, UNITEDSTATES GITHUB, VOTEVIEW.COM",
       y = NULL,
       x = NULL
  ) +
  scale_color_manual(values=c("#6b4ddd","#29ae53")) +
  theme_minimal(base_size = 13) + 
  theme(plot.title.position = "plot", 
        plot.title = element_text(face = "bold"),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank(),
        legend.title=element_blank(),
        legend.position = "top")

library(ggpubr)

voters = read.csv("data/nonvoters_data.csv")


#creating subplots
race = ggplot(data = voters, mapping = aes(y = factor(race), fill = factor(voter_category))) +
  geom_bar(position = "fill") +
  ggtitle("Race") +
  scale_fill_manual(values = c("always" = "#ffbc00","sporadic"="#e082ad", "rarely/never" ="#ae4dff")) +
  theme_minimal() +
  theme(axis.title = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), line = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank())+
    guides(fill = guide_legend(override.aes = list(shape = 16, key_width = 1, key_height = 1))) #trying to change the shape of the legend key

income = ggplot(data = voters, mapping = aes(y = factor(income_cat), fill = factor(voter_category))) +
  geom_bar(position = "fill") +
  ggtitle("Income") +
  scale_fill_manual(values = c("always" = "#ffbc00","sporadic"="#e082ad", "rarely/never" ="#ae4dff")) +
  theme_minimal() +
  theme(axis.title = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), line = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank())

age = ggplot(data = voters, mapping = aes(y = factor(race), fill = factor(voter_category))) +
  geom_bar(position = "fill", show.legend = FALSE) +
  ggtitle("Age") +
  scale_fill_manual(values = c("always" = "#ffbc00","sporadic"="#e082ad", "rarely/never" ="#ae4dff")) +
  theme_minimal() +
  theme(axis.title = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), line = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank())

education = ggplot(data = voters, mapping = aes(y = factor(educ), fill = factor(voter_category))) +
  geom_bar(position = "fill", show.legend = FALSE) +
  ggtitle("Education") +
  scale_fill_manual(values = c("always" = "#ffbc00","sporadic"="#e082ad", "rarely/never" ="#ae4dff")) +
  theme_minimal() +
  theme(axis.title = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), line = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank())

partyID = ggplot(data = voters, mapping = aes(y = factor(race), fill = factor(voter_category))) +
  geom_bar(position = "fill", show.legend = FALSE) +
  ggtitle("party ID") +
  scale_fill_manual(values = c("always" = "#ffbc00","sporadic"="#e082ad", "rarely/never" ="#ae4dff")) +
  theme_minimal() +
  theme(axis.title = element_blank(), axis.text.x = element_blank(), axis.ticks = element_blank(), line = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank())

#combining subplots
ria = ggarrange(race, income, age, ncol = 3, common.legend = TRUE, legend = "top") +
  theme(plot.margin = margin(0.5, 0,-2, -1,"cm")) 

ep = ggarrange(education, partyID) +
  theme(plot.margin = margin(2, 4, -2.5, 1, "cm"))

voterplot = ggarrange(ria, ep, nrow = 3) 


#titles
voterplot = annotate_figure(annotate_figure(voterplot, 
  top = text_grob("Demographic information of survey respondants, by voting history")),
  top = text_grob("Those who always vote and those who sometimes vote aren't that different", face = "bold")
)


voterplot

raw_csv_file <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/foul-balls/foul-balls.csv"
foulballs <- read.csv(url(raw_csv_file))

foulballs <- foulballs |>
  mutate(over90 = case_when(
    exit_velocity < 90 ~ "no",
    exit_velocity >= 90 ~ "yes"
  ))

ggplot(foulballs,
       aes (y = used_zone)) +
  geom_bar(aes(fill = over90),
           position = position_stack(reverse = TRUE),
           show.legend = FALSE) +
  scale_fill_manual(labels = c("< 90 mph", "≥ 90 mph", "Unknown exit velocity"),
                    values = c("#97c16d", "#63abb0"), na.value = "#d3d3d3") +
  scale_y_discrete(limits = rev(unique(foulballs$used_zone)))+
  scale_x_continuous(expand = expansion(mult = c(0, 0))) +
  theme(panel.grid.minor.y = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.major.x = element_line(color = "#ececec"),
        panel.background = element_rect(fill = "white"),
        axis.text.x = element_text(color = "#9e9e9e"),
        plot.caption = element_text(color = "#b5b5b5"),
        axis.line.y = element_line(colour = "#343434"),
        axis.title.y = element_text(angle = 0, vjust = 0.12, color = "#343434", size = 9),
        axis.ticks = element_blank(),
        legend.title = element_blank()) +
  labs(title = "The hardest-hit fouls seem to land in unprotected areas",
       subtitle = str_wrap("Foul balls by the stadium zone they landed in and their exit velocity, among 906 fouls hit this season in the most foul-heavy day at the 10 MLB stadiums that produced the most fouls as of June 5", 85),
       x = "", y = "Zone",
       caption = "SOURCE: BASEBALL SAVANT") +
  annotate("text", x = 75, y = 1, label = "< 90 mph", col = "white") +
  annotate("text", x = 140, y = 3.3, label = "≥ 90 mph", col = "#63abb0") +
  annotate("text", x = 215, y = 1, label = "Unknown exit velocity", col = "white")

ball_data <- foulballs |>
  mutate(category = case_when(
    exit_velocity < 90 ~ "<90",
    exit_velocity >= 90 ~ ">=90",
    is.na(exit_velocity) ~ "Unknown"
                           ))
my_plot <- ggplot(ball_data, aes(y = fct_rev(as.character(used_zone)), fill = category)) +
  geom_bar(position = position_stack(reverse = TRUE)) +
  labs(title = "The hardest-hit fouls seem \nto land in unprotected areas",
       subtitle = "The 906 foul balls hit this season from \nthe most foul-heavy day at each of the \n10 MLB stadiums that produced the \nthe most fouls as of June 5, by zone where \nthe balls landed and their exit velocities",
       y = "Zone", x = NULL) +
  scale_fill_manual(values = c("Unknown" = "#DEDEDE", "<90" = "#9ECE88", ">=90" = "#17AFAD")) +
  scale_x_continuous(breaks = c(0, 50, 100, 150, 200, 250),position = "top") +
  theme(
    panel.background = element_rect(fill = "white"),
    panel.grid.major.x = element_line(size = 0.5, linetype="solid", color="#CECECE"),
    panel.grid.minor = element_blank(),
    axis.text.y = element_text(color = "black", face = "bold"),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(size = 10, margin = margin(b = 20))
)

my_plot

library(plotly)
raptor <- read.csv("data/latest_RAPTOR_by_player.csv")

raptor_rounded <- raptor |> 
  filter(mp >= 1137) |>
  mutate(across(where(is.numeric), round, 1))

raptor_plot <- raptor_rounded |>
  ggplot(aes(x=raptor_offense, y = raptor_defense)) + 
  
  # Annotations
    # The colored rectangles for the 1 & 3 quadrants
    annotate("rect", xmin=0, xmax=10, ymin=0, ymax=10, fill = '#c5ecee', alpha = .85) + 
    annotate("rect", xmin=-10, xmax=0, ymin=-10, ymax=0, fill = '#fecada', alpha = .85) +
    
    # The 3rd quadrant text rectangles
    annotate("rect", xmin=-9.8, xmax=-5.5, ymin = -7.8, ymax = -6.8, fill = '#fd97b6') +
    annotate("rect", xmin=-9.8, xmax=-5.5, ymin = -9.4, ymax = -8.4, fill = '#fd97b6') +
    annotate("text", x = -7.8, y = -7.4, label = " -  offense") +
    annotate("text", x = -7.8, y = -9, label = " -  defense") +
    
    # The 1st quadrant text rectangles
    annotate("rect", xmin=5.5, xmax=9.8, ymin = 6.8, ymax = 7.8, fill = '#8cdadf') +
    annotate("rect", xmin=5.5, xmax=9.8, ymin = 8.4, ymax = 9.4, fill = '#8cdadf') +
    annotate("text", x = 7.5, y = 8.8, label = " +  offense") +
    annotate("text", x = 7.55, y = 7.1, label = " +  defense") +
  
    # The 2nd quadrant text rectangles
    annotate("rect", xmin=-9.8, xmax=-5.5, ymin = 6.8, ymax = 7.8, fill = '#8cdadf') +
    annotate("rect", xmin=-9.8, xmax=-5.5, ymin = 8.4, ymax = 9.4, fill = '#fd97b6') +
    annotate("text", x = -7.8, y = 8.8, label = " -  offense") +
    annotate("text", x = -7.8, y = 7.1, label = " +  defense") +
  
    # The 4th quadrant text rectangles
    annotate("rect", xmin=5.5, xmax=9.8, ymin = -7.8, ymax = -6.8, fill = '#8cdadf') +
    annotate("rect", xmin=5.5, xmax=9.8, ymin = -9.4, ymax = -8.4, fill = '#fd97b6') +
    annotate("text", x = 7.55, y = -7.4, label = " +  offense") +
    annotate("text", x = 7.5, y = -9, label = " -  defense") +
  
  geom_point(shape= 21, colour = "black", fill = "white", size = 4) + 
  
  labs(
    x = "Offensive RAPTOR rating", 
    y = "Defensive RAPTOR rating",
    title = paste0('Nikola Jokic is the Best NBA Player Based on Overall RAPTOR Rating',
            '<br>',
            '<sup>',
            'An Analytical Approach to the 2022 - 2023 NBA Season','</sup>')
    ) + 
    
  # Theme settings
  theme_light() + 
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(), 
    panel.grid.major = element_line(color = "#cdcdcd", linewidth = 0.5), 
    plot.margin = margin(l = 100, r = 100, b = 20, t = 10),
    plot.title = element_text(hjust = 0.5),
    axis.text = element_text(color = "#cdcdcd", size = 12),
    axis.title.x = element_text(margin = margin(t=3)),
    axis.title.y = element_text(margin = margin(r=3)),
    ) + 
  coord_fixed(ratio = 1) 

# interactive
# ggplotly(raptor_plot, text=player_name, hoverinfo='text') 

raptor_plot

COVID_approval <- read_csv(url("https://raw.githubusercontent.com/fivethirtyeight/covid-19-polls/master/covid_approval_polls.csv"))

#getting only data from beginning of COVID to Jan 19, 2021 and getting rid of the "all" category in the party column 
year_20 <- 
  COVID_approval |>
  separate(end_date, into = c("end_year", "end_month", "end_day"),convert = TRUE) |>
  filter(party != "all", 
         end_year <= 2020)|>
  unite(end_date, end_year, end_month, end_day, sep = "-")

year_21 <-
  COVID_approval |>
  separate(end_date, into = c("end_year", "end_month", "end_day"),convert = TRUE) |>
  filter(party != "all", 
         end_year == 2021 & end_month == 1 & end_day <= 19) |>
  unite(end_date, end_year, end_month, end_day, sep = "-")
       #  if (end_year == 2021 & end_month == 1 & end_day <= 19))

select_COVID_approval <-
  full_join(year_20, year_21)

ggplot(select_COVID_approval, aes(x = as.Date(end_date),
                           y = approve,
                           color = party)) +
  geom_smooth(aes(group = party), span = 0.05, se = FALSE) + 
  scale_color_manual(values = c("D" = "#008fd5", 
                               "R" = "#ff2700",
                               "I" = "#a55330")) +
  
  #Important Dates
  geom_vline(xintercept = as.Date("2020-02-29"), linetype=3) + 
    annotate("text", x= as.Date("2020-02-29"), y = 95, label="First U.S. \n Death \n Reported", size=3, color="black") +
  geom_vline(xintercept = as.Date("2020-05-20"), linetype=3) + 
    annotate("text", x= as.Date("2020-05-20"), y = 95, label="U.S. Deaths \n surpass \n 100,000", size=3, color="black") +
  geom_vline(xintercept = as.Date("2020-10-02"), linetype=3) + #Trump Diagnosed with COVID-19
    annotate("text", x= as.Date("2020-10-02"), y = 95, label="Trump \n Diagnosed \n with \n COVID-19", size=3, color="black") +
  geom_vline(xintercept = as.Date("2020-11-07"), linetype=3) + 
    annotate("text", x= as.Date("2020-11-07"), y = 95, label="Biden \n declared \n election \n winner", size=3, color="black") +
  geom_vline(xintercept = as.Date("2021-01-19"), linetype=3) + 
    annotate("text", x= as.Date("2021-01-19"), y = 95, label="Biden \n sworn \n into \n office", size=3, color="black") +
  
  labs(title = "Approval of Trump’s response varies widely by party",
       subtitle = "A calculation of the share of Democrats, Republicans and independents who approve of the president’s \n handling of the coronavirus outbreak") +
  scale_y_continuous(limits = c(0, 100)) +
  theme(plot.title = element_text(face = "bold", hjust = 0.5 ), 
        plot.subtitle = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        legend.position="none",
        panel.border = element_blank(),
        panel.background = element_blank(),
        panel.grid.minor = element_line(colour="gray")
        )