In Class Exercise 3: Analytical Mapping

1 Installing and loading packages

pacman::p_load(sf, tmap, tidyverse)

NGA_wp <- read_rds("data/rds/NGA_wp.rds")

2 Basic Choropleth Mapping

2.1 Visualising distribution of non-functional Water Point

p1 <- tm_shape(NGA_wp) +
  tm_fill("wp_functional", 
          n= 10,
          style = "equal",
          palette ="Blues") +
  tm_borders(lwd = 0.1, 
             alpha = 1) +
  tm_layout(main.title = "Distribution of functional water",
            legend.outside = FALSE)

# n = 10 indicates 10 range of colors
# style = equal indicates the distribution of data, in this case, equal refers to equal difference per range as per seen in the plot

p2 <- tm_shape(NGA_wp) +
  tm_fill("total_wp", 
          n= 10,
          style = "equal",
          palette ="Blues") +
  tm_borders(lwd = 0.1, 
             alpha = 1) +
  tm_layout(main.title = "Distribution of total water point",
            legend.outside = FALSE)

Arrange both maps into 1 visualisation

tmap_arrange(p2, p1, nrow = 1)

3 Choropleth Map for Rates

3.1 Deriving Proportion of Functional Water Points and Non-Functional WaterPoints

NGA_wp <- NGA_wp %>%
  mutate(pct_functional = wp_functional/total_wp) %>%
  mutate(pct_non_functional = wp_nonfunctional/total_wp)

p3 <- tm_shape(NGA_wp) +
  tm_fill("pct_functional",
          n = 10,
          style = "equal",
          palette = "Blues") +
  tm_borders(lwd= 0.1, 
             alpha = 1) +
  tm_layout(main.title = "Distribution of Proportion of\nFunctional WaterPoints",
            legend.outside = FALSE)

p4 <- tm_shape(NGA_wp) +
  tm_fill("pct_non_functional",
          n = 10,
          style = "equal",
          palette = "Blues") +
  tm_borders(lwd= 0.1, 
             alpha = 1) +
  tm_layout(main.title = "Distribution of Proportion of\nNon_Functional WaterPoints",
            legend.outside = FALSE)

tmap_arrange(p4, p3, nrow = 1)

4 Extreme Value maps

4.1 Percentile Map

Step 1: Exclude records with NA

NGA_wp <- NGA_wp %>%
  drop_na()

Step 2: Creating customised classification

percent <- c(0,.01,.1,.5,.9,.99,1)
var <- NGA_wp["pct_functional"] %>%
  st_set_geometry(NULL)
quantile(var[,1], percent)

        0%         1%        10%        50%        90%        99%       100% 
0.00000000 0.01818182 0.18181818 0.41666667 0.76086957 1.00000000 1.00000000 

# NULL forces NGA_wp["pct_functional"] into var (dataframe)

get.var <- function(vname, df) {
  v <- df[vname] %>%
    st_set_geometry(NULL)
  v <- unname(v[,1])
  return(v)
}

percentmap <- function(vnam, df, legtitle=NA, mtitle = "Percentile Map"){
  percent <- c(0,.01,.1,.5,.9,.99,1)
  var <- get.var(vnam, df)
  bperc <- quantile(var, percent)
  tm_shape(df) + 
    tm_polygons() + 
    tm_shape(df) +
    tm_fill(vnam,
            title = legtitle,
            breaks=bperc,
            palette="Blues",
            labels = c("< 1%","1% - 10%", "10% - 50%", "50% - 90%", "90% - 99%", ">99%")) +
    tm_borders() +
    tm_layout(main.title = mtitle, 
              title.position = c("right", "bottom"))
}

percentmap("pct_functional", NGA_wp,)

4.2 Box map

4.2.1 Using ggplot

ggplot(data = NGA_wp,
       aes(x = "",
           y = wp_nonfunctional)) +
  geom_boxplot()

4.2.2 Creating boxbreaks

boxbreaks <- function(v,mult=1.5) {
  qv <- unname(quantile(v))
  iqr <- qv[4] - qv[2]
  upfence <- qv[4] + mult * iqr
  lofence <- qv[2] - mult * iqr
  # initialize break points vector
  bb <- vector(mode="numeric",length=7)
  # logic for lower and upper fences
  if (lofence < qv[1]) {  # no lower outliers
    bb[1] <- lofence
    bb[2] <- floor(qv[1])
  } else {
    bb[2] <- lofence
    bb[1] <- qv[1]
  }
  if (upfence > qv[5]) { # no upper outliers
    bb[7] <- upfence
    bb[6] <- ceiling(qv[5])
  } else {
    bb[6] <- upfence
    bb[7] <- qv[5]
  }
  bb[3:5] <- qv[2:4]
  return(bb)
}

4.2.3 Creating get.var function

To retrieve a variable as a vector from an sf dataframe

get.var <- function(vname, df) {
  v <- df[vname] %>%
    st_set_geometry(NULL)
  v <- unname(v[,1])
  return(v)
}

4.2.4 Test newly created function

var <- get.var("wp_nonfunctional", NGA_wp)
boxbreaks(var)

[1] -53.5   0.0  14.0  34.0  59.0 126.5 252.0

4.2.5 Boxmap function

boxmap <- function(vnam, df, 
                   legtitle=NA,
                   mtitle="Box Map",
                   mult=1.5){
  var <- get.var(vnam,df)
  bb <- boxbreaks(var)
  tm_shape(df) +
    tm_polygons() +
  tm_shape(df) +
     tm_fill(vnam,title=legtitle,
             breaks=bb,
             palette="Blues",
          labels = c("lower outlier", 
                     "< 25%", 
                     "25% - 50%", 
                     "50% - 75%",
                     "> 75%", 
                     "upper outlier"))  +
  tm_borders() +
  tm_layout(main.title = mtitle, 
            title.position = c("left",
                               "top"))
}

tmap_mode("plot")
boxmap("wp_nonfunctional", NGA_wp)

4.2.6 Recode zero

This is used to recode LGAs with zero total water point into NA

NGA_wp <- NGA_wp %>%
  mutate(wp_functional = na_if(
    total_wp, total_wp < 0))