Author: Team BioSakshat
Last update: June 2017
Copyright © 2017 BioSakshat, Inc. All rights reserved.
1. Create a vector x13 with values 2, 3, 4, 5, 6
x13=c(2,3,4,5,6)
2. Create a vector x14 with values 2.0, 2.1, 2.2, 2.3, 2.4, .., 4
x14=seq(from=2,to=4, by=0.1)
3. Create a vector x15 with 10 random values between 4 and 6
x15=sample(4:6,10, replace=T)
4. Create a vector x16 with repeated values 3, 4, 5, 3, 4, 5, 3, 4, 5
x16=rep(c(3,4,5),times=3)
5. create x17 with repeated values 7,7,7,8,8,8,9,9,9
x17=rep(c(7,8,9),each=3)
6. Create a vector x18 with 10 random values between 20 and 30
x18=sample(20:30,10)
7. Create a vector x19 with 10 normally distributed random values
x19=rnorm(10)
8. Create a vector x20 with values of vectors x13 and x16 followed by 3, 5,10
x20=c(x13,x16,3,5,10)
9. Create a vector x21 with values 33,55,66,88,99 and fetch its 3rd, 5th and 2nd values
x21=c(33,55,66,88,99)
x21[c(3,5,2)]
10. Fetch its values from 1 to 4
x21[1:4]
11. Fetch values of x21 vector excluding 2nd and 3rd elements
x22=x21[-c(2,3)]
x22
12 Fetch last element of x21 using length()
x21[length(x21)]
13. Create a vector x23 with values 5, 7, 6, 8, 1, 4.
x23=c(5,7,6,8,1,4)
Delete 1st and last elements.
x23=x23[-c(1,length(x23))]
Reset the value of second element to 12.
x23[2]=12
Add value 0 at the beginning of a vector x23
x23=c(0,x23)
14. Calculate Variance
Write the arithmetic expression to calculate variance of a vector. Cross check your result using var() function.
Formula: Variance= sum((x-mean(x))^2) /n-1 where n is total number of elements.
x=c(5,7,6,8,1,4)
xvar=sum((x-mean(x))^2)/(length(x)-1)
xvar
var(x)
15. Distance between atoms
Given x, y, z coordinates of two atoms. Atom1 (1.2, 2.3, 3.4) and Atom2 (4.5, 5.6, 6.7). Find distance between 2 atoms. Formula: sqrt((x2-x1)2+(y2-y1)2+(z2-z1)2)
atom1=c(1.2,2.3,3.4)
atom2=c(4.5,5.6,6.7)
dist=sum((atom1-atom2)^2)
dist
16. Find out the numbers between 1 to 100, which are divisible by 2 or 3.
x=1:100
x[which(x%%2==0 & x%%3==0)]
17 Create matrix
Create a matrix from a vector cosisting of numbers from 1 to 12 with 3 columns
mt=matrix(1:12,ncol=3)
18. Fetch 2nd row. Fetch 3rd column
mt[2,]
mt[,3]
19. Fetch the value 6
mt[2,2]
20. Fetch the value 8 and 12
mt[4,c(2,3)]
21. Fetch the value 7, 8, 11 and 12
mt[c(3,4),c(2,3)]
22. Create a data frame of gene expression data such that
First column,“Genes”, is a character vector of 6 gene names (G1, G2, …, G6).
Second column, “C1” is a numeric vector of 6 random values from 3 to 5.
Hint: Generate random numbers using function sample(). Use R help to see the syntax of sample.
Third column, “C2” is a numeric vector of 6 random values from 3 to 5.
Fourth column, “T1” is a numeric vector of 6 random values from 5 to 7.
Fifth column, “T2” is a numeric vector of 6 random values from 5 to 7.
Sixth column, “Pathway” is a character vector of which first 3 represent one pathway “P1” and other 3 represent pathway “P2”.
genes=paste(“G”,1:6,sep=“”)
C1=sample(3:5,6, replace=T)
C2=sample(3:5,6, replace=T)
T1=sample(5:7,6, replace=T)
T2=sample(5:7,6, replace=T)
pathway=rep(c(“P1”,“P2”),each=3)
exp=data.frame(“Genes”=genes,“C1”=C1,“C2”=C2,“T1”=T1,“T2”=T2,“Pathway”=pathway)
23. Fetch values of column D2
exp$T2
24. Fetch values of gene G2
exp[2,]
25. Fetch value of gene G3 from C2
exp[3,3]
26. Delete column C2
exp$C2=NULL
27. Insert column C3, which should be numeric vector of 6 random values from 3 to 5.
exp$C3=sample(3:5,6, replace=T)
28. Find mean of column C1
mean(exp$C1)
1. Open iris data set file using read.table() and store in a variable name “iris_data”
iris_data=read.table(“iris.txt”,header=T)
2. Check the structure of “iris_data”. Note the column names. How many categories are there in column named as “Species”. Note the names of species.
str(iris_data)
levels(iris_data$Species)
3. How many rows and columns are there?
dim(iris_data)
4. How many observations (rows) are there for each species?
a. Number of rows with Species as setosa
b. Number of rows with Species as virginica
c. Number of rows with Species as versicolor
table(iris_data$Species)
5. Find the mean of all sepal lengths.
mean(iris_data$Sepal.Length)
6. Find the mean of sepal lengths in Species setosa.
library(dplyr)
setosa=filter(iris_data, Species==“setosa”)
mean(setosa$Sepal.Length)
7. What is the overall correlation between Sepal length and Petal length?
cor(iris_data$Sepal.Length, iris_data$Petal.Length)
8. What is the correlation between Sepal width and Petal width of Species virginica?
virginica=filter(iris_data, Species==“virginica”)
cor(virginica$Sepal.Width, virginica$Petal.Width)
9. Find the difference between sepal lengths of species setosa and versicolor. What is the mean difference between them?
versicolor=filter(iris_data, Species==“versicolor”)
sldiff= setosa$Sepal.Length - versicolor$Sepal.Length
mean(sldiff)
10. Carry out the t-test between sepal lengths of species setosa and versicolor. Is it statistically significant?
Assumptions: Sepal lengths of two species are normally distributed Their variances may not be equal Observations are not paired Two sided t-test, we are just interested to know if sepal lenghts between species are significantly different or not.
t.test(setosa$Sepal.Length, versicolor$Sepal.Length)
Based on p-value we reject null hypothesis at 95 % confidence interval Hence sepal lengths of species setosa and versicolor are significantly different
Note: Please see the help for t.test to find out various options for t-test
Plot1: Scatter plot of Sepal length vs Petal length of all 150 flowers, color according to species/plants.
library(gdata)
irisd=read.xls("iris.xls");plot(irisd$SL,irisd$PL,col=rep(c("red","blue","green"),each=50),pch=15,xlab="Sepal Length",ylab="Petal Length",cex.lab=1.2,main="Sepal Vs Petal Length",cex.axis=1.2)
legend(4.2,7,c("Setosa","Versicolor","virginia"),col=c("red","blue","green"),pch=15)Plot2: Barplot showing distribution of Sepal lengths among 6 classes of flowers (3 plants and 2 colors).
barplot(t(table(irisd$SP,irisd$Col)),beside=T,col=c("red","blue"),ylim=c(0,50),cex.axis=1.5,cex.name=1.5)
legend(1,50,c("Red","Blue"),pch=15,col=c("red","blue"))Plot3: Multi panel plot showing the histogram of SL, PL, SW, PW of all 150 flowers.
par(mfrow=c(2,2))
hist(irisd[,1],xlab="Sepal Length",ylab="Frequency",main="",cex.lab=1.2,col="gray")
hist(irisd[,2],xlab="Sepal width",ylab="Frequency",main="",cex.lab=1.2,col="gray")
hist(irisd[,3],xlab="Petal Length",ylab="Frequency",main="",cex.lab=1.2,col="gray")
hist(irisd[,4],xlab="Petal Width",ylab="Frequency",main="",cex.lab=1.2,col="gray")
par(mfrow=c(1,1))Plot4: Box plot showing SL, SW, PL, PW distribution along with a line joining their mean lengths.
boxplot(irisd[,1:4],col=c("red","green","blue","orange"),main="Length Distribution",cex.axis=1.2,ylab="Length (cm)",cex.lab=1.2)
l=summarise(irisd,mean(SL),mean(SW),mean(PL),mean(PW))
lines(c(1,2,3,4),l,lwd=4,col="brown")
legend(3.5,8,c("Mean\nLength"),lty=1,lwd=4,col="brown")Plot5: Probability density plot of Sepal lengths among three different categories of plants.
# First filter
library(dplyr)
set_SL=filter(irisd,SP=="setosa") %>% select(SL)
versi_SL=filter(irisd,SP=="versicolor") %>% select(SL)
virgi_SL=filter(irisd,SP=="virginica") %>% select(SL)
# Convert them to vector
set_SL=set_SL$SL
versi_SL=versi_SL$SL;
virgi_SL=virgi_SL$SL;
# use density to plot
plot(density(set_SL),xlim=c(4,10),lwd=2,xlab="Length (cm)",main="Prob. Density of Sepal Length across different flowers")
lines(density(versi_SL),col="red",lwd=2)
lines(density(virgi_SL),col="green",lwd=2)
legend(8,1.2,lwd=2,c("Setosa","Versicolor","Virginica"),col=c("black","red","green"))Plot6: 3D plot showing distinct clustering of flowers in terms of SL, SW and PL. Different colors for different plants.
library(rgl)
plot3d(irisd[,1:3],col=rep(c("red","green","blue"),each=50))Plot7: Scatter plot matrix showing a global view of the distribution of SL, SW, PL and PW across 3 plants.
pairs(irisd[,1:4],col=rep(c("red","green","blue"),each=50))Plot8: Heatmap showing clustering of flowers in terms of their SL, SW, PL and PW properties.
m=as.matrix(irisd[,1:4]);
library("gplots")
heatmap.2(m, trace="none")Plot9: Pie chart showing the number of flowers in 6 categories (3 plants and 2 colors)
#Subset species by colors
library(dplyr)
Set_B=filter(irisd, SP=="setosa" & Col=="Blue" )
Set_R=filter(irisd, SP=="setosa" & Col=="Red" )
Ver_B=filter(irisd, SP=="versicolor" & Col=="Blue" )
Ver_R=filter(irisd, SP=="versicolor" & Col=="Red" )
Vir_B=filter(irisd, SP=="virginica" & Col=="Blue" )
Vir_R=filter(irisd, SP=="virginica" & Col=="Red" )
#Create vector of counts in each subset
irisp=c(nrow(Set_B),nrow(Set_R),nrow(Ver_B),nrow(Ver_R),nrow(Vir_B),nrow(Vir_R))
pie(irisp, labels=irisp, col=c("Red", "Cyan", "yellow", "Blue", "Green", "Magenta"))
legend(0.9,1,c("Set_B","Ver_B","Vir_B","Set_R","Ver_R","Vir_R"),pch=15, col=c("Red","Yellow","Green", "Cyan","Blue","Magenta"))Plot10: Dot chart showing clear distribution of SL among 3 plants.
dotchart(irisd$SL,group=irisd$SP, main="Sepal Length across 3 Plants")