This data was provided by Mike. This is the process of my analyses.
First, I imported a dataframe I created that formatted the data in a way I could graph it
mill.abrasion <- read.csv("~/Documents/R/mill abrasion.csv")
head(mill.abrasion)
## lab A B C D E F
## 1 1 2.70 3.4 3.8 3.3 7.1 4.4
## 2 1 2.80 3.3 4.1 3.8 6.4 4.3
## 3 1 2.50 3.2 4.4 3.8 6.7 4.4
## 4 1 3.40 3.0 4.2 3.5 7.2 4.0
## 5 1 2.55 3.2 4.1 3.8 6.2 4.2
## 6 2 3.70 3.4 3.9 3.5 6.5 4.4
From there, I made 6 different boxplots (one for each rock), that compared the abraded materials produced from each lab
boxplot(mill.abrasion$A ~ mill.abrasion$lab, main = "Rock A", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$B ~ mill.abrasion$lab, main = "Rock B", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$C ~ mill.abrasion$lab, main = "Rock C", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$D ~ mill.abrasion$lab, main = "Rock D", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$E ~ mill.abrasion$lab, main = "Rock E", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$F ~ mill.abrasion$lab, main = "Rock F", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
Then I combined all plots into one plot, so that they could be easily compared
allplots <- par(mfrow = c(2, 3))
boxplot(mill.abrasion$A ~ mill.abrasion$lab, main = "Rock A", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$B ~ mill.abrasion$lab, main = "Rock B", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$C ~ mill.abrasion$lab, main = "Rock C", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$D ~ mill.abrasion$lab, main = "Rock D", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$E ~ mill.abrasion$lab, main = "Rock E", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
boxplot(mill.abrasion$F ~ mill.abrasion$lab, main = "Rock F", xlab = "Lab",
ylab = "Ballast Materials", ylim = c(2, 9))
par(allplots)
Next, to do statistical analyses on the data, I imported the dataframe that Mike created
ABRASION <- read.csv("~/Downloads/ABRASION.CSV")
head(ABRASION)
## lab quarry abraded
## 1 1 1 2.70
## 2 1 1 2.80
## 3 1 1 2.50
## 4 1 1 3.40
## 5 1 1 2.55
## 6 2 1 3.70
attach(ABRASION)
The first anova I ran looked at the the effect of what lab was used and the quarry the rock was from on the variability on abraded material
(anova(lm(abraded ~ lab + quarry)))
## Analysis of Variance Table
##
## Response: abraded
## Df Sum Sq Mean Sq F value Pr(>F)
## lab 1 0.1 0.1 0.04 0.84
## quarry 1 133.6 133.6 108.46 <2e-16 ***
## Residuals 207 255.0 1.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
This anova showed that the quarry was a very significant factor in the variability of the abraded material
The second anova I ran looked at the effect of the lab used, the quarry the rock was from, and the interaction of the two on the variability of the abraded material
(anova(lm(abraded ~ lab + quarry + lab * quarry)))
## Analysis of Variance Table
##
## Response: abraded
## Df Sum Sq Mean Sq F value Pr(>F)
## lab 1 0.1 0.1 0.04 0.84
## quarry 1 133.6 133.6 107.97 <2e-16 ***
## lab:quarry 1 0.1 0.1 0.07 0.79
## Residuals 206 254.9 1.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
This anova again showed that the quarry was a very significant factor in the variability of the abraded material