Visualizing helmet weights
21 Dec 2016My cousin sent me some data on motorcycle helmet weights for a project he’s working on. I thought it would be fun to slice it up and look at it a bit instead of working on things I should be working on.
The data
The dataset is a spreadsheet with 4 columns and 135 rows. The columns are: a URL, a brand, a model name, and a weight.
Some plots
library(xlsx)
library(ggplot2)
library(stringr)
rm(list=ls())
hels <- read.xlsx("C:/Users/Akhil/Documents/akhilrao.github.io/public/code/helmet_weights/helmet weights.xlsx",1)
# a histogram of weights
#win.graph()
ggplot(data=hels, aes(hels$Grams)) + geom_histogram(binwidth=50, breaks=seq(min(hels$Grams),max(hels$Grams),by=50),col="red") + labs(title="Histogram of helmet weights, 50 gram bins") + labs(x="Grams", y="Count")
# let's slice this by brand
byBrand <- lm(Grams ~ Brand - 1, data = hels)
#summary(byBrand)
brands <- coef(byBrand)
brands <- as.data.frame(brands)
labs <- rownames(brands)
labs <- str_split_fixed(labs,"Brand",2)
brands <- cbind(labs[,2],brands)
colnames(brands) <- c("brand","weight")
rownames(brands) <- NULL
#win.graph()
ggplot(data=brands, aes(x=brand,y=weight)) + geom_bar(stat="identity", col="blue") + labs(title="Average weight by brand")
# which brand has the lightest/heaviest helmets?
brands[which.min(brands[,2]),]## brand weight
## 5 Biltwell 1227.5brands[which.max(brands[,2]),]## brand weight
## 7 GMax 1966# how many models are there per brand?
models <- aggregate(data.frame(count = hels$Brand), list(value = hels$Brand), length)
mean(models$count) # average models per brand## [1] 5.4#win.graph()
ggplot(data=models, aes(x=value,y=count)) + geom_bar(stat="identity", col="blue") + labs(title="Number of models per brand")
What can we say?
It looks like most helmets cluster around the 1.6 kg range. Biltwell has the lightest helmets, with an average weight of 1227.5 grams, and Gmax the heaviest at a whopping 1966 grams - almost 2 kgs!
Of course, Gmax only has 1 helmet in this dataset. AGV and HJC are the kings of variety, with 14 models each in this dataset. Bell and LS2 are close runners up with 12 models each. Fly, GMax, Joe Rocket, Klim, and Vemar only have 1 model each in this dataset. In this dataset, brands have an average of 5.4 models.
The x axis labels on the histograms are messy. I don’t feel like spending the time to pretty them up. I have no idea if this information is in any way useful to anyone, but it was a fun exercise.