Healy and Moody (2014) recommend log-log histograms as an alternative to regular histograms. Given the fact that important variables in Sociology are often highly skewed, log-log histograms reveal greater details at the upper end of the distribution
// Membership variables of the first round (2002) of the ESS
use sptcmmb cltommb trummb prfommb ///
cnsommb hmnommb epaommb rlgommb ///
prtymmb setommb sclcmmb othvmmb using "ESS1e06.3_F1.dta", clear
// Generate count variable of memberships
scores memberships = total(sptcmmb cltommb trummb prfommb ///
cnsommb hmnommb epaommb rlgommb ///
prtymmb setommb sclcmmb othvmmb)
// Standard histogram
twoway (histogram memberships, percent discrete), ///
xtitle("Total no. of memberships in" "voluntary associations in last 12 mo.") ///
ytitle("Respondents in ESS round 1 (%)") xlabel(0/12) name(a, replace)
// Log-log histogram
twoway__histogram_gen memberships, gen(y x) width(1) fraction
replace y = y * 100 // Convert fractions to %
scatter y x, yscale(log) xscale(log) ///
xtitle("Total no. of memberships in" "voluntary associations in last 12 mo.") ///
ytitle("Respondents in ESS round 1 (%)") xlabel(0/12) ///
ylab(.1 1 10 20 40) ///
name(b, replace)
graph combine a b
Reference
Healy, Kieran, and James Moody. 2014. "Data Visualization in Sociology."
Annual Review of Sociology 40:105-128. doi: 10.1146/annurev-soc-071312-145551
