Factors and Data frames

 

These exercises are about the factors and data framessections of Introduction to R.

These exercises cover the factors and data frames sections of Introduction to R.

Exercise 1 - Factors

• Create a ordinal factor named “Height” containing – high, low, mid, low, mid, low, mid, high, mid, high.

ordinalFactor <- factor(c("high", "low", "mid", "low", "mid", "low", "mid", "high", "mid", "high"),ordered=T,levels=c("low", "mid", "high"))
ordinalFactor

##  [1] high low  mid  low  mid  low  mid  high mid  high
## Levels: low < mid < high

• Using a logical index, create new factor of only those from “Height”” greater than low.

filteredFactor <- ordinalFactor[ordinalFactor > "low"]
filteredFactor

## [1] high mid  mid  mid  high mid  high
## Levels: low < mid < high

• Replace the last index in “Height” with veryHigh and create new factor with those greater than mid.

newFactor <- factor(ordinalFactor,ordered=T,levels=c("low", "mid", "high","veryHigh"))
newFactor[length(newFactor)] <- "veryHigh"
newFactor[newFactor > "mid"]

## [1] high     high     veryHigh
## Levels: low < mid < high < veryHigh

Exercise 2 - Data frames

• Create data frame called Annotation with a column of gene names (“Gene_1”, “Gene_2”, “Gene_3”,“Gene_4”,“Gene_5”), ensembl gene names (“Ens001”, “Ens003”, “Ens006”, “Ens007”, “Ens010”), pathway information (“Glycolysis”, “TGFb”, “Glycolysis”, “TGFb”, “Glycolysis”) and gene lengths (100, 3000, 200, 1000,1200).

Annotation <- data.frame(geneNames=c("Gene_1", "Gene_2", "Gene_3","Gene_4","Gene_5"), ensembl=c("Ens001", "Ens003", "Ens006", "Ens007", "Ens010"),pathway=c("Glycolysis", "TGFb", "Glycolysis", "TGFb", "Glycolysis"),geneLengths=c(100, 3000, 200, 1000,1200))

• Create data frame called Sample1 with ensembl gene names (“Ens001”, “Ens003”, “Ens006”, “Ens010”) and expression (1000, 3000, 10000,5000)

Sample1 <- data.frame(ensembl=c("Ens001", "Ens003", "Ens006","Ens010"),expression=c(1000, 3000, 10000,5000))

• Create data frame called Sample2 with ensembl gene names (“Ens001”, “Ens003”, “Ens006”, “Ens007”,“Ens010”) and expression (1500, 1500, 17000,500,10000)

Sample2 <- data.frame(ensembl=c("Ens001", "Ens003", "Ens006","Ens007","Ens010"),expression=c(1500, 1500, 17000,500,10000))

• Create a data frame containing only those gene names common to all data frames with all information from Annotation and the expression from Sample 1 and Sample 2.

AnnoSample1 <- merge(Annotation,Sample1,by.x=2,by.y=1,all=F) 
AnnoSample1And2 <- merge(AnnoSample1,Sample2,by=1,all=F) 
AnnoSample1And2

##   ensembl geneNames    pathway geneLengths expression.x expression.y
## 1  Ens001    Gene_1 Glycolysis         100         1000         1500
## 2  Ens003    Gene_2       TGFb        3000         3000         1500
## 3  Ens006    Gene_3 Glycolysis         200        10000        17000
## 4  Ens010    Gene_5 Glycolysis        1200         5000        10000

• Add an extra two columns containing the length normalised expressions for Sample 1 and Sample 2

AnnoSample1And2$Sample1_lne <- AnnoSample1And2$expression.x/AnnoSample1And2$geneLengths
AnnoSample1And2$Sample2_lne <- AnnoSample1And2$expression.y/AnnoSample1And2$geneLengths
AnnoSample1And2

##   ensembl geneNames    pathway geneLengths expression.x expression.y
## 1  Ens001    Gene_1 Glycolysis         100         1000         1500
## 2  Ens003    Gene_2       TGFb        3000         3000         1500
## 3  Ens006    Gene_3 Glycolysis         200        10000        17000
## 4  Ens010    Gene_5 Glycolysis        1200         5000        10000
##   Sample1_lne Sample2_lne
## 1   10.000000   15.000000
## 2    1.000000    0.500000
## 3   50.000000   85.000000
## 4    4.166667    8.333333

• Identify the mean length normalised expression across Sample1 and Sample2 for Ens006 genes

rownames(AnnoSample1And2) <- AnnoSample1And2$ensembl
mean(c(AnnoSample1And2["Ens006","Sample1_lne"],AnnoSample1And2["Ens006","Sample2_lne"]))

## [1] 67.5

• For all genes, identify the log2 fold change in length normalised expression from Sample 1 to Sample 2.

log2FoldChange <- log2(AnnoSample1And2$Sample2_lne) - log2(AnnoSample1And2$Sample1_lne)
names(log2FoldChange) <- AnnoSample1And2$geneNames
log2FoldChange

##     Gene_1     Gene_2     Gene_3     Gene_5 
##  0.5849625 -1.0000000  0.7655347  1.0000000

• Identify the total length of genes in Glycolysis pathway.

sum(AnnoSample1And2[AnnoSample1And2$pathway == "Glycolysis","geneLengths"])

## [1] 1500