PGDARSMA (2022)
Introduction with R software
It was begun in the 1990s by Robert Gentleman and Ross Ihaka of the Statistics Department of the University of Auckland. Nearly 20 senior statisticians provide the core development group of the R language, including the primary developer of the original §language, John Chambers, of Bell Labs. It contains a large, coherent, integrated collection of intermediate tools for data analysis. The software is FREE and can be downloaded from http://www.r-project.org/ or you can download from R software . The versatility of this software is that it can be used in every domain and the coding structure in this software is quite easy rather than the others. Moreover, R software is used not only for coding purpose, you can also prepare any manuscript or prepare any sorts of presentation in this software. R markdown will help in this case. The major yardstick of the software is the R packages. Basically, Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. Currently, the CRAN package repository features 10964 available packages.https://cran.r-project.org/web/packages/. To install packages type install.packages(pkgs, dependencies=TRUE) in the console.
In most of my research work i have used this software. Moreover, for any sorts of discrepancy do not hesitate to contact me. The contact details is provided in my home page. Don't worry this page will be update as per your needs.
References:
An introduction to R, Longhow Lam. (Link for the material https://cran.r-project.org/doc/contrib/Lam-IntroductionToR_LHL.pdf )
Applied Statistical Inference: Likelihood and Bayes by Leohard Held and Daniel Sabanes Bove, Springer-Verlag Berlin 2014
The R Student Companion, Brian Dennis, CRC Press, 2013.
An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie and Tibshirani, Springer Text in Statistics 2013
Using R for Numerical Analysis in Science and Engineering by Victor A. Broomfield, CRC Press. Taylor and Francis Group 2014
A Primer of Ecology with R by M. Henry and H. Stevens, Springer 2009
Statistical Modeling: The Two Cultures by Leo Breiman, Statistical Science 2001, Vol. 16, No. 3, 199-231.
The Art of R Programming; Norman Matloff
AN R COMPANION FOR THE HANDBOOK OF BIOLOGICAL STATISTICS; SALVATORE S. MANGIAFICO : https://rcompanion.org/documents/RCompanionBioStatistics.pdf
Visualization of Statistics by the R software
12th February 2022 (11.00 a.m. - 11.30 a.m.)
In today's class we will learn the process to handle with the real life data set. Then we will understand about the procedure of statistical analysis with respect to this real life data set. Initially Prof. Bhattacharya will demonstrate the technique sampling theory through the R software. Whatever you have learnt by the theoretical methods that will be visualized by the R software.
####### Data
Marks = rnorm(1000, 65, 10)
### rnorm is the notation for collecting
## random sample from the normal distribution
Marks = round(Marks, 0)
Marks
#######graphical representation
plot(Marks, xlab="students no.", ylab="Marks")
######frequency distribution
par(mfrow=c(2, 2))
hist(Marks, probability = F)
#prob=T)
#lines(density(Marks, kernal="gaussian"))
##########sample of size 100
sample100=sample(Marks, 100)
sample100
######sample frequency distribution
hist(sample100, col="red")
##########sample of size 500
sample500=sample(Marks, 500)
sample500
######sample frequency distribution
hist(sample500, col="blue")
##########sample of size 100 100 times
samplecomb=matrix(0, 100, 100)
## matrix command is used to create the matrix in R Software.
for (i in 1:100)
{
samplecomb[i, ]=sample(Marks, 100)
}
samplecombrwmean=rowMeans(samplecomb[, 1:100])
hist(samplecombrwmean, col="green")
############# Data
Marksmsc1 = rnorm(1000, 65, 10)
Marksmsc1 = round(Marksmsc1, 0)
############# Data
set.seed(897)
Marksmsc2 = rnorm(1000, 71, 10) + rnorm(1000, 0, 2)
Marksmsc2 = round(Marksmsc2, 0)
#######graphical representation
plot(Marksmsc1, Marksmsc2)
Marksmscdata=data.frame(Marksmsc1, Marksmsc2)
cormsc12=cor(Marksmsc1, Marksmsc2)
regmsc12=lm(Marksmsc2~Marksmsc1, Marksmscdata)
abline(regmsc12, col = "red")
Introduction in R Programming:
12th November 2022 (11.30 p.m. - 12.30 p.m.)
Before diving into the statistical analysis of the anthropometric data, we should accustomed or familiar with the R software a little bit. The following lectures will help you to establish a basic understanding with the R code.
x = 3
y = 4
print(x)
z = x + y
print(z)
print(z)
z = x-y
print(z)
z = x*y
print(z)
z = x^2
print(z)
# Idea of storing numbers in R
time = 1:12
print(time)
length(time)
size = c(3.4, 4.2, 4.6, 5.1, 5.8, 6.1, 6.3, 6.8,7.1,9.3, 9.5, 9.9)
length(size)
data = data.frame(time, size)
print(data)
View(data)
plot(time, size)
plot(time, size, col = "red")
plot(time, size, col = "red", type = "l")
plot(time, size, col = "red", type = "b")
plot(time, size, col = "red", type = "b", pch = "*")
plot(time, size, col = "red", type = "b", pch = "*", cex = 3)
plot(time, size, col = "red", type = "b", pch = "*", cex = 3, lwd = 2)
plot(time, size, col = "red", type = "b", pch = "*", cex = 3, lwd = 2, xlab ="Time (weeks)")
plot(time, size, col = "red", type = "b", pch = "*", cex = 3, lwd = 2, xlab ="Time (weeks)", ylab = "Size (in cm)")
Fundamental of Statistics by the R software
12th November 2022 (4.00 p.m. - 5.00 p.m.)
In this session we will learn the basic properties of the statistical methods through the R software, which is designed according to the syllabus structure of PGDARSMA. I am now first listing the tools, which will be demonstrated below.
Cumulative Frequency/ Ogive
Data Extraction from Excel file.
Histogram and Bar plot.
Box plot and the outlier detection.
Mean, Median, Mode.
Variance, Standard Deviation.
Skewness, Kurtosis.
Correlation coefficient.
#### Cumulative Frequency/Ogive #####
# declaring data points
data_points= c(1, 2, 3, 5, 1, 1, 2,4, 5, 1, 2, 3, 3)
# declaring the break points
break_points = seq(0, 6, by=1)
# transforming the data
data_transform = cut(data_points, break_points,right=FALSE)
# creating the frequency table
freq_table = table(data_transform)
# printing the frequency table
print("Frequency Table")
print(freq_table)
# calculating cumulative frequency
cumulative_freq = c(0, cumsum(freq_table))
print("Cumulative Frequency")
print(cumulative_freq)
# plotting the data
plot(break_points, cumulative_freq,
xlab="Data Points",
ylab="Cumulative Frequency")
# creating line graph
lines(break_points, cumulative_freq)
### Histogram and Bar diagram draw ###
### Through the excel data ###
setwd("C:/Users/user/Desktop/Giridih")
data = read.csv("agri.csv")
View(data)
age = data$age
hist(age, probability = T)
barplot(age)
## Box plot
boxplot(age)
### Mean, median, mode ## na.rm
mean(age)
median(age)
library(DescTools)
Mode(age)
## Variance, SD
var(age)
sd = sqrt(var(age))
## Skewness and Kurtosis
x = c(0.012,0.092,0.107,0.026,0, 0, 0, 0,0,0)
library(e1071)
skewness(x)
kurtosis(x)
### Correlation coefficient
sbp = data$sbp
cor(age, sbp)