Slopegraph examples. Code to reproduce the figures in the blog: https://tonyladson.wordpress.com/2016/09/12/showing-data-in-reports-should-you-use-tables-graphs-or-both/

Slopegraph_blog.R

library(plotrix)
library(RColorBrewer)


###################

# The slopegraphs use the plotrix::bumpchart function

# Here I've tweaked plotrix::bumpchart to increase the spacing between the labels
# 
# This just involves changing one line
#
# old: minspacing <- strheight("M") * 1.5
# new: minspacing <- strheight("M") * 2

body(bumpchart)[[17]] <- substitute( minspacing <- strheight("M") * 2)

# Figure 1
# Complaints

complaints <- matrix(c(
  6.4, 5.0, 4.8, 4.0, 4.3, # Canberra
  3.4, 3.5, 3.9, 3.2, 2.6,  # Sydney
  12.1, 9.5, 0.6, 1.0, 0.8,  # Perth
  53.1, 72.7, 37.5, 49.9, 39.5, # Darwin
  4.8, 6.3, 7.4, 5.3, 4.0, # Melbourne
  NA, 1.5, 2.4, NA, NA), # Adelaide
  nrow = 6, byrow = TRUE)

complaints

cities <- c('Canberra', 'Sydney', 'Perth', 
            'Darwin', 'Melbourne', 
            'Adelaide')

rownames(complaints) <- cities

colnames(complaints) <- c('2010-11', '2011-12', '2012-13', '2013-14', '2014-15')

my_col = brewer.pal(6, name = 'Paired')

op <- par(oma = c(1, 8, 1, 1))
bumpchart(complaints, col = my_col, 
             rank = FALSE,
             main = '')


axis(side = 2, at = c(0, 50), labels = c(0, 50), line = 1, outer = TRUE, las = 2, col = 'black')
mtext(side = 2, 'Total complaints per 1000 properties', outer = TRUE, line = 1.2, adj = 0.3)
par(xpd=TRUE)
boxed.labels(1.5, 1.5, labels =  'Adelaide', border = FALSE)
par(xpd=FALSE)


par(op)


# Figure 2 Capex

capex <- matrix(c(
  700348, 550203, 504668, 262529, 351732, # Perth
  1355928, 942090, 735799, 609752, 686136, # Melbourne
  805693, 792228, 713762, 629305, 641685, # Sydney
  504764, 989579, 653563, 517648, 487636, # South East Queensland
  256668, 230838, 143365, 59393, 48937, # Canberra
  595851, 530075, 331038, 187945, 152124, # Adelaide
  51978, 58080, 64789, 25600, NA), # Darwin
  nrow = 7, byrow = TRUE)



cities <- c('Perth', 'Melbourne', 'Sydney','SE Queensland', 'Canberra', 'Adelaide', 'Darwin')

rownames(capex) <- cities

colnames(capex) <- c('2010-11', '2011-12', '2012-13', '2013-14', '2014-15')

my_col = brewer.pal(7, name = 'Paired')

op <- par(oma = c(1, 1, 1, 8))
bumpchart(capex, col = my_col, 
          rank = FALSE,
          main = '')

axis(side = 4, at = c(0, 1000000), labels = c('$0', '$1 million'), line = 1, outer = TRUE, las = 2)

par(op)



# Figure 3: Include total

capex <-  rbind(capex, apply(capex, 2, sum, na.rm = TRUE))

rownames(capex)[8] <- 'Total'
colnames(capex) <- c('2010-11', '2011-12', '2012-13', '2013-14', '2014-15')

my_col = brewer.pal(9, name = 'Paired')

op <- par(oma = c(1, 1, 1, 8))

bumpchart(capex, col = my_col, 
             rank = FALSE,
             main = '')

axis(side = 4, at = c(0, 1000000, 2000000, 3e+6, 4e+6), 
     labels = c('$0', '$1 million', '$2 million','$3 million', '$4 million'), 
     line = 1, outer = TRUE, las = 2)


par(op)

# Using Thomas Leepers Slopegraph
# https://github.com/leeper/slopegraph
# This isn't working quite right at present but could 
# probably be sorted out

capex <- matrix(c(
  700348, 550203, 504668, 262529, 351732, # Perth
  1355928, 942090, 735799, 609752, 686136, # Melbourne
  805693, 792228, 713762, 629305, 641685, # Sydney
  504764, 989579, 653563, 517648, 487636, # South East Queensland
  256668, 230838, 143365, 59393, 48937, # Canberra
  595851, 530075, 331038, 187945, 152124, # Adelaide
  51978, 58080, 64789, 25600, NA), # Darwin
  nrow = 7, byrow = TRUE)



capex <-  capex/1000
capex <-  data.frame(capex)
cities <- c('Perth', 'Melbourne', 'Sydney','SE Queensland', 'Canberra', 'Adelaide', 'Darwin')
row.names(capex) <- cities
names(capex) <- c('X2010-11', 'X2011-12', 'X2012-13', 'X2013-14', 'X2014-15')

my_col = brewer.pal(9, name = 'Paired')
par(oma = c(1,4,1,1))

slopegraph(capex, 
           labels =  c('2010-11', '2011-12', '2012-13', '2013-14', '2014-15'),
           decimals = 0,
           col.lines = my_col,
           col.lab = my_col,
           col.num = my_col,
           family = 'sans')