Loading Data in R

rawdata = read.delim("file.txt", sep="\t", header =TRUE)
head(rawdata)

Maybe all rows or col may not needed so need to filter data, In this case it will return all the value where name =sanjay from the entire table.

new_rawdata = subset(rawdata,name=sanjay )

The above only applicable to filter the data from the single col, What if the select multiple col? you can also do piping to make the code more human readable.

col = c("col1", "col2","col3")
new_col = subset(rawdata, select=col)
head(new_col)

To find all the column start with specific alphabet

new_col = substr(colnames(rawdata),1,1)=="Q"
print_data=rawdata[, new_col]
colnames(rawdata)

Two Sided T Test

pt is the Distribution function in R:

df = degree of freedom (n1+n2-2)

q = Value of Test Statistic (Even if the Test Statistics value is -ve we will take the absolute value (+ve values only))

//pt (q, df, lower.tail =FALSE)
pt(3.102, 18, lower.tail=FALSE)*2 

As per the Pvalue we need to compute 2 side so multiply by 2, if the T distribution is Symmetric

Finding critical value

//qt(p,df)
qt(0.975,18)  or qt(0.025.18, lower.tail=FALSE)

Data Transformation Packages

dpylr = tidyverse

To install the package Type install.packages("dplyr")

To load extra package Type library(dplyr) helps to execute, mutate recode

Missing Data

is.na used to find the missing Data