Custom Plotting Symbols in R

R has a lot of options for plotting symbols, but sometimes you might want a something not in the base set. This post will cover a way to get a custom plotting symbol in R. The code is available on github.

I will use data is for 24 nations that competed in the 2021 Euros (men’s) tournament, specifically the FIFA rankings of the men’s and women’s national teams (accessed 12 Aug 2021). Using a flag to represent the country seems reasonable, and easier than text or plotting symbol and color combinations. Plus, it might add a bit of visual interest.

Although this might seem specialized, it highlights changing backgrounds in R base graphics, controlling aspect ratios, and adding mathematical notation to plots.

Some prep work before coding comes in handy. First, I downloaded PNG files of each country’s flag and saved it with the country’s name. Next, I checked Wikipedia for the aspect ratio of each flag and added it to my CSV file of the FIFA rankings.

The code begins by loading the library we will be using for a custom PCH and then reading in my data from the CSV. The men’s rankings range from 1 to 62 and the women’s rankings from 2 to 131.

library(png) # library for a custom pch

df <- read.csv('/Users/calvinwhealton/Documents/GitHub/euros_2021_rankings/fifa_national_team_rankings.csv',
               header=T) # read-in data from csv

# calculating ranges for men's and women's teams
men_range <- range(df$Men.s.National.Team)
women_range <- range(df$Women.s.National.Team)

Next, we set the basic parameters of the plot. Using the PNG function allows me to save it directly to a file and have a stable image process I an fine tune. The height and width needed to me modified until the axes were close to the same scale (10 ranks on x = 10 ranks on y). This will make life easier when trying to scale the flags with the aspect ratio.

# setting plot to save as a png file
# checked the output file to make sure
# axes were on the same scale, or close to it

par(mar=c(5,5,5,5)) # plot margins

Now, we can initialize the plot. Because we are not plotting the data yet, we can simply label the axes. Default axis ticks would include 0, so I used more natural axis ticks. The gray background makes flags with white more visible.

# setting up the base scatter plot
# used \ to get the "'" in titles
     ,ylab=expression('Men\'s Ranking')
     ,xlab=expression('Women\'s Ranking')
     ,main='Euros 2021 FIFA Nations\nMen\'s vs Women\'s National Teams'
     ,xlim=rev(women_range) # reverse axes to rank 1 is top right
     ,ylim=rev(men_range) # reverse axes to rank 1 is top right
     ,las=1 # rotate vertical axis labels to be horizontal
     ,xaxt='n' # no default x axis
     ,yaxt='n' # no default y axis

# adding axes
axis(side=1, at=c(1,seq(10,women_range[2],10)),las=1)
axis(side=2, at=c(1,seq(10,men_range[2],12)),las=1)

# adding a gray background to the plot
# many flgs have a white portion that would disappear otherwise

Now comes the fun part. Not all flags are shaped the same. The two extremes in these countries are Switzerland (aspect ratio = 1) and Croatia, Hungary, and North Macedonia (aspect ratio = 2). We will be telling the function the corners of where to plot the flag, so using the same x and y offset for all flags will lead to distortions.

To solve this problem, I set a value for the area of all flags. This will ensure that each country has the same visual impact. After some math, that means the x and y dimensions for each flag can be found based on the aspect ratio. Once we have all these, we can loop through the countries, calculate the coordinates for the flag corners, and use the country’s flag as the plotting symbol.

# setting approximate "area" for each country flag
flag_area <- 25

# looping over all the rankings
for(i in 1:nrow(df)){
  # read in the image and set it to a variable
  img <- readPNG(paste('/Users/calvinwhealton/Documents/GitHub/euros_2021_rankings/flags/',df$Country[i],'.png',sep=''))
  # math for same flag areas (approximately)
  # area = dx*dy = (dy^2)*(aspect_ratio)
  # dy = sqrt(area/aspect_ratio)
  # dx = sqrt(area*aspect_ratio)
  dy <- sqrt(flag_area/df$Aspect.Ratio[i])
  dx <- sqrt(flag_area*df$Aspect.Ratio[i])
  # plot the png image as a raster
  # need the image and coordinates
  # men's team on y axis, women's team on x axis

For a little bit of fun, and to illustrate one way to get Greek letters in a plot notation, I calculated the correlation of these ranks. The last line of this code closes the figure file.

# adding a text correlation
# using linear correlation of ranks, not rank correlation
correl <- round(cor(df$Men.s.National.Team,df$Women.s.National.Team),2)

# adding text notation for correlation
     labels = bquote(paste(," Linear Corr. (",rho,") = ",.(correl),sep=""))

# close file

And we are done! We see that the flags do look to be about the same area and have approximately correct aspect ratios. (I won’t say low long it took me to get that part figured out…) I hope this provides a fun way to change up your plots and help an audience better understand the data. Although I used flags here because it seemed the most natural for this data, any PNG file could be used based on your data and needs.


SALib v0.7.1: Group Sampling & Nonuniform Distributions

This post discusses the changes to the Python library SALib in version 0.7.1, with some examples of how to use the new capabilities. The two major additions in this version were: group sampling for Sobol’ sensitivity analysis and specifying nonuniform distributions for variables.

Sobol’ Indices Group Sampling

Previous versions of SALib allowed one to calculate the first-order, total-order, and second-order indices for individual input parameters. These same indices can be defined for groups of input parameters (see Saltelli (2002) for more discussion). The main change is adding an item called ‘groups’  to the problem dictionary, which specifies the group of each parameter. Here is some example code. Notice in the ‘groups’  entry in the problem definition.

from SALib.sample import saltelli
from SALib.analyze import sobol
from SALib.util import read_param_file
import numpy as np

# example function
def sampleModel(x):
    y = x[:,0]**1.5 + x[:,1] + 2*x[:,2] + x[:,3] + np.exp(0.3*x[:,4]) + x[:,5] \
         + 2*x[:,1]*x[:,4] + (x[:,0]*x[:,5])**2
    return y

# problem definition
prob_gps_code = {
'names': ['P1','P2','P3','P4','P5','P6'],
'bounds':[[0.0, 1.0], [2.0, 3.0], [0.5, 1.0], [0.0, 5.0], [-0.5, 0.5], [0.0, 1.0]],
# generating parameter values
param_vals_gps_code = saltelli.sample(prob_gps_code, 10000,calc_second_order=True)

# calculating model output values
Y_gps_code = sampleModel(param_vals_gps_code)

# completing Sobol' sensitivity analysis
Si_gps_code = sobol.analyze(prob_gps_code,Y_gps_code,calc_second_order=True,print_to_console=True)

The output from this code is given below. In this case the first-order indices (S1’s) are the index that is closed for the group. The S1 for group1 ( P1, P3, and P4) would be equivalent to summing:  S1 for P1, P3, and P4; S2 for (P1 & P3), (P1 & P4), and (P3 & P4); and S3 for (P1 & P3 & P4). All of the equations used in calculating the sensitivity indices are the same, but now they are for groups of variables.

Group S1 S1_conf ST ST_conf
group1 0.471121 0.081845 0.472901 0.011769
group2 0.498600 0.078950 0.497005 0.013081
group3 0.030502 0.019188 0.031736 0.001041

Group_1 Group_2 S2 S2_conf
group1 group2 0.000618 0.159951
group1 group3 0.002170 0.161403
group2 group3 -0.003324 0.155224

Note: You can also use the read_param_file() function to define the problem. For the above example the problem file would look like:


Nonuniform Distributions

Often the variables in a sensitivity analysis are assumed to be distributed uniformly over some interval. In the updated version of the SALib it is possible to specify whether the each input parameter is triangular, normal, lognormal, or uniform. Each of these distributions interprets the ‘bounds’ in the problem dictionary separately, as listed below.

  • Triangular, “triang” (assumed lower bound of 0)
    • first “bound” is width of distribution (scale, must be greater than 0)
    • second “bound” is location of peak as a fraction of the scale (must be on [0,1])
  • Normal, “norm”
    • first “bound” is the mean (location)
    • second “bound” is the standard deviation (scale, must be greater than 0)
  • Lognormal, “lognorm” (natural logarithms, assumed lower bound of 0)
    • first “bound” is the ln-space mean
    • second “bound” is the ln-space standard deviation (must be greater than 0)
  • Uniform, “unif”
    • first “bound” is the lower bound
    • second “bound” is the upper bound (must be greater than lower bound)

Triangular and lognormal distributions with a non-zero lower bound can be obtained by adding the lower bound to the generated parameters before sending the input data to be evaluated by the model.

Building on the same example as above, the problem dictionary and related analysis would be completed as follows.

# problem definition
prob_dists_code = {
'names': ['P1','P2','P3','P4','P5','P6'],
'bounds':[[0.0,1.0], [1.0, 0.75], [0.0, 0.2], [0.0, 0.2], [-1.0,1.0], [1.0, 0.25]],

# generating parameter values
param_vals_dists_code = saltelli.sample(prob_dists_code, 10000,calc_second_order=True)

# calculating model output
Y_dists_code = sampleModel(param_vals_dists_code)

# complete Sobol' sensitivity analysis
Si_dists_code = sobol.analyze(prob_dists_code,Y_dists_code,calc_second_order=True,print_to_console=True)&lt;/pre&gt;

The output from this analysis is given below, which is consistent with the format in previous versions of SALib.

Parameter S1 S1_conf ST ST_conf
P1 0.106313 0.030983 0.110114 0.003531
P2 0.037785 0.027335 0.085197 0.003743
P3 0.128797 0.029834 0.128702 0.003905
P4 0.034284 0.016997 0.034193 0.001141
P5 0.579715 0.071509 0.627896 0.017935
P6 0.062791 0.021743 0.065357 0.002221
Parameter_1 Parameter_2 S2 S2_conf
P1 P2 0.001783 0.060174
P1 P3 0.001892 0.060389
P1 P4 0.001753 0.060155
P1 P5 0.001740 0.062130
P1 P6 0.004774 0.060436
P2 P3 -0.003539 0.051611
P2 P4 -0.003500 0.051186
P2 P5 0.044591 0.054837
P2 P6 -0.003585 0.051388
P3 P4 -0.000562 0.058972
P3 P5 -0.000533 0.059584
P3 P6 -0.000480 0.059923
P4 P5 -0.000364 0.034382
P4 P6 -0.000191 0.034301
P5 P6 -0.001293 0.137576

Note 1: You can also use the read_param_file() function to define the problem. The one catch is when you want to use nonuniform distributions without grouping the variables. In this case the fourth column in the input file (column for ‘groups’) must be the parameter name repeated from the first column. For the above example the problem file would look like:


Note 2: If you are uncertain that the distribution transformation yielded the desired results, especially since the ‘bounds’ are interpreted differently by each distribution, you can check by plotting histograms of the data. The histograms of the data used in the example are shown below. (The data was actually saved to a .txt file for reference and then imported to R to plot these histograms, but matplotlib has a function histogram().)



Saltelli, Andrea (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications 145(2):280-297. doi:10.1016/S0010-4655(02)00280-1