I have a rather challenging visualization task.
15 geographical regions (data points for plot), each described by 3 continuous values and all these values have confidence intervals. The aim is to visualise this data in a way, which allows to compare/rank these regions (e.g. from poor to good or from low disparity to high disparity).
For example:
- region A: all descriptive values are very low
- region C: two descriptive values very high, one very low
- region F: all descriptive values are very high
Generated data
library(tidyverse)
set.seed(1)
#make new data
df = tibble(
region = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O"),
prop = rnorm(15, 50, 20),
income = rnorm(15, 10, 4),
sd = rnorm(15, 2, 1))
#add confidence intervals
df = df %>% mutate(
prop_lower = prop - runif(1, 5, 15),
prop_upper = prop + runif(1, 5, 15),
income_lower = income - runif(1, 0.5, 2.2),
income_upper = income + runif(1, 0.5, 2.2),
sd_lower = sd - runif(1, 0.1, 0.6),
sd_upper = sd + runif(1, 0.1, 0.6))
#reorder columns
df = df[c("region", "prop", "prop_lower", "prop_upper", "income", "income_lower", "income_upper", "sd", "sd_lower", "sd_upper")]
<-- 
-->
region prop prop_lower prop_upper income income_lower income_upper sd sd_lower sd_upper
A 37.470924 30.074630 43.06027 9.820266 8.2283755 11.809923 3.3586796 2.8692222 3.857334
B 53.672866 46.276572 59.26221 9.935239 8.3433489 11.924897 1.8972123 1.4077549 2.395867
C 33.287428 25.891134 38.87677 13.775345 12.1834548 15.765003 2.3876716 1.8982143 2.886326
D 81.905616 74.509322 87.49496 13.284885 11.6929947 15.274542 1.9461950 1.4567376 2.444849
E 56.590155 49.193861 62.17950 12.375605 10.7837152 14.365263 0.6229404 0.1334831 1.121595
F 33.590632 26.194338 39.17998 13.675909 12.0840194 15.665567 1.5850054 1.0955481 2.083660
G 59.748581 52.352287 65.33792 13.128545 11.5366552 15.118203 1.6057100 1.1162527 2.104364
H 64.766494 57.370200 70.35584 10.298260 8.7063699 12.287918 1.9406866 1.4512293 2.439341
I 61.515627 54.119333 67.10497 2.042593 0.4507032 4.032251 3.1000254 2.6105680 3.598680
J 43.892232 36.495938 49.48158 12.479303 10.8874130 14.468961 2.7631757 2.2737184 3.261830
K 80.235623 72.839329 85.82497 9.775485 8.1835950 11.765143 1.8354764 1.3460191 2.334131
L 57.796865 50.400571 63.38621 9.376818 7.7849279 11.366476 1.7466383 1.2571810 2.245293
M 37.575188 30.178894 43.16453 4.116990 2.5251004 6.106648 2.6969634 2.2075060 3.195618
N 5.706002 -1.690292 11.29535 8.087400 6.4955097 10.077057 2.5566632 2.0672059 3.055318
O 72.498618 65.102324 78.08796 11.671766 10.0798762 13.661424 1.3112443 0.8217870 1.809899
Best option I have found for plotting so far
library(patchwork)
#panel 1
A = df %>%
ggplot(aes(x = prop, y = income, color = region, label = region))+
geom_errorbarh(aes(xmin = prop_lower, xmax = prop_upper, height = 0), alpha = 0.8)+
geom_linerange(aes(ymin = income_lower, ymax = income_upper), size = 0.5, alpha = 0.8) +
geom_text(size = 3.5, alpha = 0.8, segment.alpha = 0,
nudge_x = 2,
nudge_y = 0.5)+
geom_point(aes(), size = 1.5)+
theme_classic()+
theme(legend.position = "none")
#panel 2
B = df %>%
ggplot(aes(x = region, y = sd))+
geom_linerange(aes(ymin = sd_lower, ymax = sd_upper), size = 0.5, alpha = 0.8) +
geom_point(aes(), size = 1.5)+
theme_classic()+
theme(legend.position = "none")
#panel 1 and 2
A/B + plot_layout(heights = c(6, 2))
The problem is that you have to check multiple plot panels before making any conclusions about a region.
Or should I think out of the box and do something completely different?
E.g. trying to visualise these regions on a map? Any strategies for this (how to choose the colours etc)?
A random example from google photos
How you guys visualise such data? Any promising three-axis plots?
Provided solutions so far
cdalitz suggested multiple coordinate plot. should be doable with CIs while doing 3 separate ggplots, later connection lines and labels can be added in a graphical program. I made a scheme.
