Part 1: How not to lie with choropleth

Grab Census Data (review from Lab 2)

  • Using tidycensus, load in variables on median household income and median housing value from the acs5 2013-2017.
library(tidycensus)
library(tidyverse)
library(viridis)
library(plyr)
library(gtools)
census_api_key("Your Key here")
Var<-c("B19013_001","B25077_001")
## c(Median household Income, Median Housing Value)
  CenDF <- get_acs(geography = "county",
                   variables = Var,
                   year = 2017,
                   survey = "acs5",
                   geometry = TRUE,
                   shift_geo = TRUE)

Manipulate Data

  • Create new variable for the housing price to income ratio.
CenDF<-CenDF %>% 
    mutate(variable=case_when( 
      variable=="B19013_001" ~ "HHIncome",
      variable=="B25077_001" ~ "HouseValue")) %>%
    select(-moe) %>%  
    spread(variable, estimate) %>%  #Spread moves rows into columns
    mutate(HHInc_HousePrice_Ratio=round(HouseValue/HHIncome,2))

Map Data

  • Rely on ggplot and scale_fill_viridis to create natural breaks for color sequencing and to mapHouse-Price-to-Income Ratio
Plot1<-  ggplot(CenDF) +
    geom_sf(aes(fill = HHInc_HousePrice_Ratio), color=NA) +
    coord_sf(datum=NA) +
    labs(title = "House-Price-to-Income Ratio \n R Built-in Color Cut",
         caption = "Source: ACS 5-year, 2013-2017",
         fill = "Price-Income Ratio") +
    scale_fill_viridis(direction=-1)
Plot1

Color Scales in R

R has a variety of functions that allow the user to create color scales easily. These functions generally require you to specify a color on each end of the spectrum and they will interpolate the values between based upon how many levels you desire.

Importance

We select colors to communicate information about our data. If we are using a continuous variable the most basic decision is whether we want to represent it as positive and negative deviations from the average (a divergent scale), or as a continuum of low to high values (a sequential scale). If we have categorical data, it is generally visualized through different colors representing each group (a qualitative scale).

Color Schemes

plot( 1:7, rep(1,7), ylim=c(-0.5,3.5), xlim=c(0,12), yaxt="n", xaxt="n", bty="n", xlab="", ylab=""  )

color.function <- colorRampPalette( c("gray80","darkred") )
col.ramp <- color.function( 7 ) # number of groups you desire
points( 1:7, rep(3,7), pch=15, cex=8, col=col.ramp )

color.function <- colorRampPalette( c("darkred","gray80","steelblue") )
col.ramp <- color.function( 7 ) # number of groups you desire
points( 1:7, rep(2,7), pch=15, cex=8, col=col.ramp )

color.function <- colorRampPalette( c("gray80","black") )
col.ramp <- color.function( 7 ) # number of groups you desire
points( 1:7, rep(1,7), pch=15, cex=8, col=col.ramp )

text( 8, 3, "Sequential", pos=4 )
text( 8, 2, "Divergent", pos=4 )
text( 8, 1, "Grayscale", pos=4 )

Alternative Map: 2 Color Groups

  • Transform HHInc_HousePrice_Ratio from continuous variable to factor variable with 2 levels
## Convert Integer into factor variable
CenDF$fill_factor <- quantcut(CenDF$HHInc_HousePrice_Ratio, q = seq(0, 1, by = 0.5))
CenDF$fill_factor = mapvalues(CenDF$fill_factor, from = levels(CenDF$fill_factor),
                        to = c("low","high"))


## Assign 2 colors
col.ramp <- viridis(n = 2) # number of groups you desire
Plot2<-ggplot(CenDF) +
  geom_sf(aes(fill = fill_factor), color=NA) +
  coord_sf(datum=NA) +
  labs(title = "House-Price-to-Income Ratio",
       caption = "Source: ACS 5-year, 2013-2017",
       fill = "Price-Income Ratio") +
  scale_fill_manual("Price-Income Ratio",values =  col.ramp) 
Plot2

Terciles - 3 Color Groups

  • Transform HHInc_HousePrice_Ratio from continuous variable to factor variable with 3 levels
## Convert Integer into factor variable
CenDF$fill_factor <- quantcut(CenDF$HHInc_HousePrice_Ratio, q = seq(0, 1, by = .33))
CenDF$fill_factor = mapvalues(CenDF$fill_factor, from = levels(CenDF$fill_factor),
                        to = c("low","med", "high"))


## Assign 3 colors
col.ramp <- viridis(n = 3) # number of groups you desire
Plot3<-ggplot(CenDF) +
  geom_sf(aes(fill = fill_factor), color=NA) +
  coord_sf(datum=NA) +
  labs(title = "House-Price-to-Income Ratio",
       caption = "Source: ACS 5-year, 2013-2017",
       fill = "Price-Income Ratio") +
  scale_fill_manual("Price-Income Ratio \n (Terciles)",values =  col.ramp) 
Plot3

Quintiles - 5 Color Groups

  • Transform HHInc_HousePrice_Ratio from continuous variable to factor variable with 5 levels
## Convert Integer into factor variable
CenDF$fill_factor <- quantcut(CenDF$HHInc_HousePrice_Ratio, q = seq(0, 1, by = .2))
CenDF$fill_factor = mapvalues(CenDF$fill_factor, from = levels(CenDF$fill_factor),
                        to = c("1","2","3", "4","5"))


## Assign 5 colors
col.ramp <- viridis(n = 5) # number of groups you desire
Plot4<-ggplot(CenDF) +
  geom_sf(aes(fill = fill_factor), color=NA) +
  coord_sf(datum=NA) +
  labs(title = "House-Price-to-Income Ratio",
       caption = "Source: ACS 5-year, 2013-2017",
       fill = "Price-Income Ratio") +
  scale_fill_manual("Price-Income Ratio  \n (Quintiles)",values =  col.ramp) 
Plot4

Unequal Intervals

  • Transform HHInc_HousePrice_Ratio from continuous variable to factor variable with 6 levels
## Convert Integer into factor variable
CenDF$fill_factor <- quantcut(CenDF$HHInc_HousePrice_Ratio, q = c(0,.1,.25,.5,.75,.9,1))
#CenDF$fill_factor = mapvalues(CenDF$fill_factor, from = levels(CenDF$fill_factor),
 #                       to = c("< .1",".1-.25",".25-.5", ".5-.75",".75-.9","> .9"))


## Assign 6 colors
col.ramp <- viridis(n = 6) # number of groups you desire
Plot5<-ggplot(CenDF) +
  geom_sf(aes(fill = fill_factor), color=NA) +
  coord_sf(datum=NA) +
  labs(title = "House-Price-to-Income Ratio",
       caption = "Source: ACS 5-year, 2013-2017",
       fill = "Price-Income Ratio") +
  scale_fill_manual("Price-Income Ratio",values =  col.ramp) 
Plot5

Compare Maps

library(gridExtra)
grid.arrange(Plot1,Plot3,Plot4,Plot5, nrow=2)

Part 2: Detecting Neighborhood change

Step 1: Get 2012 5-year ACS data

  • In get_acs function, change year from 2017 to 2012, and call new DF as CenDF2012.

  • Then create the ratio of household price divided by median household income for the 2008-2012. Call the variable name HHInc_HousePrice_Ratio2012

    library(tidycensus)
library(tidyverse)
library(viridis)
library(plyr)
library(gtools)

census_api_key("8eab9b16f44cb26460ecbde164482194b7052772")

Var<-c("B19013_001","B25077_001")

# Download 2008-2012 df
  CenDF2012 <- get_acs(geography = "county",
               variables = Var,
               year = 2012,
               survey = "acs5",
               geometry = FALSE)

  #Create new variable for the housing price to income ratio. 
CenDF2012<-CenDF2012 %>% 
  mutate(variable=case_when( 
variable=="B19013_001" ~ "HHIncome2012",
variable=="B25077_001" ~ "HouseValue2012")) %>%
  select(-moe,-NAME) %>%  
  spread(variable, estimate) %>%  #Spread moves rows into columns
  mutate(HHInc_HousePrice_Ratio2012=round(HouseValue2012/HHIncome2012,2))

Step 2: Merge 2008-12 and 2013-2017 dataframes

  • Hint: you can not merge to sf DFs. Go back and turn Geometry=FALSE for one of your 2012 DF

    CenDF<-merge(CenDF,CenDF2012,by.all="GEOID", all.x=TRUE) # all.x=TRUE makes sure that the merged dataframe keeps all counties in CenDF even if missing in CenDF2012

Step 3: Compare Descriptive statistics

  • Look at summary statistics and plot histograms for the 2008-2012 housing price to income ratio versus the 2013-2017 housing price to income ratio.

    Hist1<-ggplot(CenDF, aes(HHInc_HousePrice_Ratio)) +
  geom_histogram(fill = "firebrick2", 
             color = "white", bins = 60) +
  xlab("house price to income ratio by county, 2017") +
  theme(plot.title = element_text(hjust = 0.5)) +
  geom_vline(xintercept = mean(CenDF$HHInc_HousePrice_Ratio,na.rm=TRUE), lty = "dashed")

Hist2<-ggplot(CenDF, aes(HHInc_HousePrice_Ratio2012)) +
  geom_histogram(fill = "firebrick2", 
             color = "white", bins = 60) +
  xlab("house price to income ratio by county, 2012") +
  theme(plot.title = element_text(hjust = 0.5)) +
  geom_vline(xintercept = mean(CenDF$HHInc_HousePrice_Ratio2012,na.rm=TRUE), lty = "dashed")

library(gridExtra)
grid.arrange(Hist1,Hist2, nrow=2)

    summary(CenDF$HHInc_HousePrice_Ratio2012)
    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.900   2.090   2.570   2.801   3.215  12.440       3
    summary(CenDF$HHInc_HousePrice_Ratio)
    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.670   2.130   2.535   2.758   3.112  11.820       2

Step 4: Create a new variable to track changes in house price to income ratio over the 2008-2017 time period.

Hint: To calculate percentage change of a variable use the following equation: pct_change = 100 * (Present - Past) / Past. Use mutate and replace present and past with the 2017 and 2012 house price to income ratios, respectively.

    ## Change Variable
CenDF<-CenDF %>%
mutate(pct_change = 100 * (`HHInc_HousePrice_Ratio` - `HHInc_HousePrice_Ratio2012`) / `HHInc_HousePrice_Ratio2012`)

Step 5: Create a cloropleth map to visualize changes

  • Create cloropleth map to visualize changes in the house price to income ratio from the 2008-2012 5-year ACS estimate verus the 2013-2017 estimate

Hint: You will make minor changes to the code you used to create Plot 1 above. e.g. change the fill variable in ggplot to the name of the change variable you created in the previous step.

upper_limit <- round(max(CenDF$pct_change,na.rm=TRUE) + 10, -1)
lower_limit <- round(min(CenDF$pct_change,na.rm=TRUE) - 10, -1)

Plot6<-  ggplot(CenDF,aes(fill = pct_change)) +
  geom_sf(size = 0) +
  #geom_sf(data = major_roads_geo, color = "white", size = 0.8, fill = NA) +
  #geom_sf(data = minor_roads_geo, color = "white", size = 0.4, fill = NA) +
  scale_fill_viridis(option = "viridis", name = "% Change", limits = c(lower_limit, upper_limit), breaks = seq(lower_limit, upper_limit, 20)) +
  labs(title="Changes in House Price to Income Ratio",
       subtitle = "2017 5-Year Estimates vs. 2012 5-Year Estimates for Census Tracts",
       caption = paste0(
         "Data sources:",
         "\n  U.S. Census Bureau, 2012 and 2017 American Community Survey 5-Year Estimates"
       )
  ) +
  theme(plot.caption = element_text(hjust = 0, margin = margin(t = 15))) +
  theme(axis.ticks = element_blank(), axis.text = element_blank()) +
  theme(panel.background = element_blank())

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