We’ll reproduce the example given on page 11 of Statistical Learning with Sparsity: the Lasso and Generalizations by Hastie, Tibshirani, and Wainwright

data = read.csv("https://web.stanford.edu/~hastie/StatLearnSparsity_files/DATA/crime.txt",sep="\t")
names(data) = c("overall","violent","funding","hs","not-hs","college","college4")
library(glmnet)
## Loading required package: Matrix
## Loading required package: foreach
## Loaded glmnet 2.0-5
library(plotmo) 
## Warning: package 'plotmo' was built under R version 3.3.2
## Loading required package: plotrix
## Warning: package 'plotrix' was built under R version 3.3.2
## Loading required package: TeachingDemos
data$overall = log10(data$overall)
par(mfrow=c(1,2))
fit.lasso = glmnet(data.matrix(data[,3:7]),data$overall)
plot_glmnet(fit.lasso,xvar="lambda")
fit.l2 = glmnet(data.matrix(data[,3:7]),data$overall,alpha=0)
plot_glmnet(fit.l2,xvar="lambda")

fit.lasso = cv.glmnet(data.matrix(data[,3:7]),data$overall)
plot(fit.lasso)
fit.l2 = cv.glmnet(data.matrix(data[,3:7]),data$overall,alpha=0)
plot(fit.l2)