Coming back to the R language after several years and trying to remember some basic functions proved to be a bit challenging, even if the syntax is quite simple. Therefore, I considered putting together a few calls as refresher based on Youden-Beale data. To run the below code you'll need to install the R language and RStudio.
In case you don't have the package installed, run the next two lines:
install.packages("ACSWR") #install the Youden-Beale Experiment package library(ACSWR) #load the library
str(yb) #display datasets' structure
'data.frame': 8 obs. of 2 variables:
$ Preparation_1: int 31 20 18 17 9 8 10 7
$ Preparation_2: int 18 17 14 11 10 7 5 6
yb #display the dataset
Preparation_1 Preparation_2
1 31 18
2 20 17
3 18 14
4 17 11
5 9 10
6 8 7
7 10 5
8 7 6
summary(yb) #display the summary for whole dataset
Preparation_1 Preparation_2
Min. : 7.00 Min. : 5.00
1st Qu.: 8.75 1st Qu.: 6.75
Median :13.50 Median :10.50
Mean :15.00 Mean :11.00
3rd Qu.:18.50 3rd Qu.:14.75
Max. :31.00 Max. :18.00
summary(yb$Preparation_1) #display the summary for first column
Min. 1st Qu. Median Mean 3rd Qu. Max.
7.00 8.75 13.50 15.00 18.50 31.00
summary(yb$Preparation_2) #display the summary for second column
Min. 1st Qu. Median Mean 3rd Qu. Max.
5.00 6.75 10.50 11.00 14.75 18.00
min(yb) #display the minimum value for the whole dataset
[1] 5
min(yb$Preparation_1) #display the mininun of first column
[1] 7
min(yb$Preparation_2) #display the minimum of second column
[1] 5
sum(yb) #display the sum of all values
[1] 208
sum(yb$Preparation_1) #display the sum of first column
[1] 120
sum(yb$Preparation_2) #display the sum of second column
[1] 88
#display the percentiles quantile(yb$Preparation_1,seq(0,1,.25))
0% 25% 50% 75% 100%
7.00 8.75 13.50 18.50 31.00
#display the percentiles quantile(yb$Preparation_2,seq(0,1,.25))
0% 25% 50% 75% 100%
5.00 6.75 10.50 14.75 18.00
#display the percentiles quantile(yb$Preparation_2,seq(0,1,.25))
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
7.0 7.7 8.4 9.1 9.8 13.5 17.2 17.9 19.2 23.3 31.0
quantile(yb$Preparation_2,seq(0,1,.1))
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
5.0 5.7 6.4 7.3 9.4 10.5 11.6 13.7 15.8 17.3 18.0
length(yb) #display the number of items ncol(yb) #display the number of columns
[1] 2
sort(yb$Preparation_1) #display the sorted values ascendingly
[1] 7 8 9 10 17 18 20 31
sort(yb$Preparation_1, decreasing = TRUE)
[1] 31 20 18 17 10 9 8 7
#display a vertical poxplot boxplot(yb, notch=FALSE) title("A: Vertical Boxplot for Youden-Beale Data") #display an horizontal poxplot boxplot(yb, horizontal = TRUE) title("B: Horizontal Boxplot for Youden-Beale Data")
plot(yb) #scatter diagram
title("Scatter diagram")
lsfit(yb$Preparation_1, yb$Preparation_2)$coefficients #list square fit coefficients
Intercept X
2.8269231 0.5448718
lsfit(yb$Preparation_1, yb$Preparation_2)$residuals #list square fit residuals
[1] -1.7179487 3.2756410 1.3653846 -1.0897436 2.2692308 -0.1858974
[7] -3.2756410 -0.6410256
Happy coding!
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