In Class Exercise 3: Analytical Mapping

Published

January 16, 2023

Modified

April 3, 2023

#Installing and loading packages

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pacman::p_load(sf, tmap, tidyverse)
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NGA_wp <- read_rds("data/rds/NGA_wp.rds")

1 Basic Choropleth Mapping

1.1 Visualising distribution of non-functional water point

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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
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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

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tmap_arrange(p2, p1, nrow = 1)

2 Choropleth Map for Rates

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

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

3 Extreme Value maps

3.1 Percentile Map

Step 1: Exclude records with NA

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NGA_wp <- NGA_wp %>%
  drop_na()

Step 2: Creating customised classification

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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
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# NULL forces NGA_wp["pct_functional"] into var (dataframe)
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get.var <- function(vname, df) {
  v <- df[vname] %>%
    st_set_geometry(NULL)
  v <- unname(v[,1])
  return(v)
}
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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,)

3.2 Box map