PS 171 Research Paper Visualizations

Evictions in San Francisco From 2015 to 2024

library(ggplot2)

eviction_numbers <-c(2376,1881,1657,1592,1442,733,1048,1197,797,1033)

years <- c(2015, 2016,2017,2018, 2019,
           2020, 2021, 2022, 2023, 2024)

evictions <- data.frame(years,eviction_numbers)

ggplot(evictions, aes(x = years, y = eviction_numbers)) +
  geom_line() +
  labs(
    title = "Number of Evictions in SF",
    x = "Year",
    y = "Number of Evictions"
  ) +
  scale_x_continuous(breaks = seq(2015, 2024)) +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5)) +
  
  geom_vline(aes(xintercept = 2020, linetype = "Stay-at-home order enacted")) +
  geom_vline(aes(xintercept = 2021, linetype = "Stay-at-home order lifted")) +
  geom_vline(aes(xintercept = 2023, linetype = "All pandemic orders lifted")) +
  
  scale_linetype_manual(
    name = "COVID Events",
    values = c(
      "Stay-at-home order enacted" = "solid",
      "Stay-at-home order lifted" = "dashed",
      "All pandemic orders lifted" = "dotted"
  ))

Estimates of Homeless Population in San Francisco From 2015 to 2024

yearsx2 <- c(2015, 2016,2017,2018, 2019,
           2020, 2021, 2022, 2023, 2024,
           2015, 2016,2017,2018, 2019,
           2020, 2021, 2022, 2023, 2024)
race <- c("white","white","white","white","white","white","white","white","white","white",
          "black","black","black","black","black","black","black","black","black","black")
homelessness_numbers <- c(133,158,125,124,200,153,96,227,479,363,
                          2232,2457,2353,2698,2978,3083,1677,2957,471,373)

homeless_number <- data.frame(yearsx2, race, homelessness_numbers)

ggplot(homeless_number, aes(x = yearsx2, y = homelessness_numbers, color = race)) + geom_line() + 
         labs(title="Estimates of Homeless Population in SF", x="Year", y="Estimates of homeless population") +
  scale_x_continuous(breaks = seq(from = 2015, to = 2024)) + 
  theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
  
  geom_vline(aes(xintercept = 2020, linetype = "Stay-at-home order enacted")) +
  geom_vline(aes(xintercept = 2021, linetype = "Stay-at-home order lifted")) +
  geom_vline(aes(xintercept = 2023, linetype = "All pandemic orders lifted")) +
  
  scale_linetype_manual(
    name = "COVID Events",
    values = c(
      "Stay-at-home order enacted" = "solid",
      "Stay-at-home order lifted" = "dashed",
      "All pandemic orders lifted" = "dotted"
    )
  )

Percent change in Homelessness From Year to Year

years_percent_change <- c(2015,2016,2017,2018, 2019,
           2020, 2021, 2022, 2023, 2024,
          2015, 2016,2017,2018, 2019,
           2020, 2021, 2022, 2023, 2024)
race <- c("white","white","white","white","white","white","white","white","white","white",
          "black","black","black","black","black","black","black","black","black","black")
homelessness_change <- c(786.67, 18.8, -20.89, -0.8, 61.29,
                         -23.5, -37.25, 136.46, 111.01, -24.22,
                         161.66, 10.08, -4.23, 14.66, 10.38,
                         3.53, -45.6, 76.33, -84.07, -20.81)

homeless_change <- data.frame(years_percent_change, race, homelessness_change)

ggplot(homeless_change, aes(x = years_percent_change, y = homelessness_change, color = race)) + geom_line() + 
         labs(title="% change in homelessness from the previous year in SF", x="Year", y= "Percent change in homelessness") +
  scale_x_continuous(breaks = seq(from = 201, to = 2024)) + 
  theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
  
  geom_vline(aes(xintercept = 2020, linetype = "Stay-at-home order enacted")) +
  geom_vline(aes(xintercept = 2021, linetype = "Stay-at-home order lifted")) +
  geom_vline(aes(xintercept = 2023, linetype = "All pandemic orders lifted")) +
  
  scale_linetype_manual(
    name = "COVID Events",
    values = c(
      "Stay-at-home order enacted" = "solid",
      "Stay-at-home order lifted" = "dashed",
      "All pandemic orders lifted" = "dotted"
    )
  )