font_add_google("IBM Plex Sans Condensed", "ibm")
showtext_auto()

light_blue <- "#B5C2E2"
blue <- "#09689B"
  
annos <- c(2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022)
lab <- "*Name:* <span style = 'color:black'>George FLoyd</span><br>
*Age:* <span style = 'color:black'>25</span><br>
*A Father*"
anno_labs <- c(" **2013**",
               " **2014**",
               " **2015**",
               " **2016**",
               " **2017**",
               " **2017**",
               " **2019**",
               " **2020**",
               " **2021**",
               " **2022**")
lab_hjust = c(0, 0,0,0,0,0,0,0,0,0)

labs_df <- tibble(annos = annos, 
                  anno_labs = glue("<span style='color:black'>{anno_labs}</span>"),
                  lab_hjust = lab_hjust)


df <- read.csv("data_police.csv")

df$date <- as.Date(df$date, format = "%Y-%m-%d")

# Extract the year from the date column
df$year <- as.integer(format(df$date, "%Y"))

# Group the data by year and count the number of rows for each year
yearly_counts <- group_by(df, year) %>% 
  summarise(count = n())

# Plot the timeline plot using ggplot
ggplot(yearly_counts, aes(x=year, y=count)) +
  geom_line() +
   geom_text(
    mapping = aes(label = paste0( count)), 
    nudge_x = -0.3
  ) + 
  geom_segment(data = labs_df, aes(x = annos, xend = annos, y = 900, yend = 1200),
             linetype = 2, alpha = .1) +
  geom_curve(x = 2016.5, y = 980,
             xend = 2017, yend =1090,
             arrow = arrow(length = unit(.5, "cm")),
           color = "grey40")+
  #geom_segment(x = 2020, y = 1050,
      #       xend = 2021, yend =1156,
     #        arrow = arrow(length = unit(.1, "cm")),
    #       color = "orange")+
   #geom_richtext(aes(x = 2020, y = 1050,
   #                 label = lab), col="orange",hjust = 0)+
  
  
  #geom_segment(x = 2019, y = 1000,
     #        xend = 2020, yend =1142,
           #  arrow = arrow(length = unit(.1, "cm")),
    #       color = "orange")+
   #geom_richtext(aes(x = 2019, y = 1000,
     #               label = lab), col="orange",hjust = 0)+
  
  
 # geom_segment(x = 2018, y = 950,
    #         xend = 2019, yend =1097,
     #        arrow = arrow(length = unit(.1, "cm")),
      #     color = "orange")+
   #geom_richtext(aes(x = 2018, y = 950,
    #                label = lab), col="orange",hjust = 0)+
  
  
  annotate(geom = "text", x = 2016.05, y = 970, size = 2.5,
           label = "The highest consecutive\nincrements", hjust = 0, vjust = 0,
           lineheight = .9, family = "ibm", color = "grey40") +
  annotate("rect", xmin = 2021, xmax = 2022, ymin = 900, ymax = 1200,
           alpha = .1, fill="black")+
  annotate(geom = "text", x = 2021.5, y = 1164, size = 3.5,
           label = "Incomplete Report", hjust = 0, vjust = 0,
           lineheight = .9, family = "ibm", color = "darkblue", angle=90) +
  geom_point(color = light_blue) +
  ggtitle("Timeline Plot of Number of Events per Year") +
  scale_y_continuous(limits = c(900, 1200), breaks = c(900, 500, 1200), 
                     position = "right") +
  theme_minimal() +
  theme(text = element_text(family = "ibm"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),
        plot.title = element_text(face = "bold", hjust = .5),
        plot.caption = element_textbox_simple(width = unit(8.5, "in"), hjust = 0,
                                       lineheight = 1.25, size = 9,
                                       color = "grey40")) +
  labs(title = "Police fatal shooting ",
       y = "Total deaths", x = "Year (2013 - 2022)",
       caption = glue("<br>Data shown spans from 2013 and 2022 and it shows 
                      deaths after encounter with a police. 
                      As it is shown in the figure the first order is suggestive of an increasing fatal encounters with a police over the years. 
                      Comparing the percentage difference in total number of deaths by police, from 2013 to 2021 there is an increament of 6.5% (76 deaths). 
                      2021 shows the highest death numbers (1159) compared to the previous years. 
                      From these deaths xx% are attributed to Black community. 
                      Adjusting for the population difference the Black community faces xx times more risk of fatal police shooting as opposed white community. "))