How to use R-stat? – a simple guideline.

Prepared by: Krishna DhakalAcademic level: M.Sc.Ag

Department : Genetics and Plant BreedingDate of final work: March 2, 2016

Agriculture and Forestry University, Chitwan, Nepal

krishnadhakal19@gmail.com

INTRODUCTION TO R It is an elegant, object-oriented programming language

R is an integrated suite of software facilities for data

manipulation, simulation, calculation and graphical display

It handles and analyzes data very effectively and it contains

a suite of operators for calculations on arrays and matrices

R is available in Windows and Macintosh versions, as well

as in various flavors of Unix and Linux

Continued……… It is currently maintained by the R Core development team – a

hard-working, international group of volunteer developers The R project web page is http://www.r-project.org For downloading the software directly Go to http://cran.us.r-project.org/ The R project was started by Robert Gentleman and Ross Ihaka

(that’s where the name “R” is derived) from the Statistics Department in the University of Auckland in 1995

Lacking in R

It has a limited graphical interface (S-Plus has a good one).

This means, it can be harder to learn at the outset

The command language is a programming language so

students must learn to appreciate syntax issues etc.

Starting and quitting with R

First of all download the latest version of R(zip file)

Install in your PC

And the icon of R will appear on your desktop

Double click on it………….

The view when R opens

Continued.. When R is started, the program’s “Gui” (graphical user

interface) window appears

Under the opening message in the R Console is the > (“greater

than”) prompt

At the > prompt, you tell R what you want it to do

Continued…….

You give R a command and R does the work and gives the

answer

If your command is too long to fit on a line or if you submit

an incomplete command, a “+” is used for the continuation

prompt To quit R, type q() or use the Exit option in the File menu

The useful tips While typing instructions in R, you can save yourself a lot of

typing when you learn to use the arrow keys effectively Each command you submit is stored in the History and the up

arrow (↑) will navigate backwards along this history and the down arrow (↓) forwards

The left (←) and right arrow (→) keys move backwards and forwards along the command line

These keys combined with the mouse for copying, cutting/pasting can make it very easy to edit and execute previous commands

The workspace All variables or “objects” created in R are stored in what’s

called the workspace To see what variables are in the workspace, you can use the

function ls() to list them (this function doesn’t need any argument between the parentheses)

To remove objects from the workspace (you’ll want to do this occasionally when your workspace gets too cluttered), use the rm() function

In Windows, you can clear the entire workspace via the “Remove all objects” option under the “Misc” menu

Continued.. When exiting R, the software asks if you would like to save

your workspace image If you click yes, all objects (both new ones created in the

current session and others from earlier sessions) will be available during your next session

If you click no, all new objects will be lost and the workspace will be restored to the last time the image was saved

Get in the habit of saving your work – it will probably help you in the future

Installing packages R is provided with lots of packages, always use reliable and

proven packages, since R does not give guarantee on misuse

Based on the field of your study you have to choose

packages accordingly

For agriculturist packages like lme4, agricolae, lmerTest,

MASS, car etc.

if you have downloaded the packages separately then you

can install it by the following procedure

Continued…… Go to packages(at the top of R screen)- click on “install

packages from local zip files”- choose the zip file and click open

If you don’t have downloaded zip files then you can download it all online

For online install- go to “packages”- click on “install packages”- choose the packages and download them

R is a sea of programs, if you know how to swim you will find everything that is needed for you, what you need is to explore yourself

Analysis of data with R(procedure) During data sheet preparation in excel always use abbreviated form

and always note its full form

dm-days to maturity, ht-plant height, bms-biomass, gps-grain per

spike, gy- grain yield, tw- test weight

Now, Convert the excel file into csv file

Go to menu on excel, click "save as" and choose "csv” (comma

delimited)" and give a short name and remember it

Make a new folder and place the csv file into it(either in C or D

drive whichever you prefer)

Continued.. Now open R and start your job Firstly, get working directory as giving “getwd()” and enter Set working directory : type setwd(“D:/assignment”) and inside

bracket put a inverted double comma and give directory, in example "D" is a drive in which there is a folder named assignment

Uploading data in R: eg. mod=read.csv("heat.csv",header=T), here “mod” is a given name; you can put any, and heat.csv is csv file name you should put yours(exactly the same name without neglecting upper case and lower case letters), read.csv is a command for reading the file data in R screen

When you type mod and enter then, all your data appears on your screen

Continued……. If there is missing data then just leave it vacant in respective

place, if you put it as "0" in data sheet later it will show error in process of calculation

Setting of factors: replication, block and entry(genotypes) should be taken as factors and others are variables

Example. REP=as.factor(mod$rep), here, “REP”- name given, you can use any of your choice, “mod”- it came from(mod=read.csv("heat.csv",header=T)), “rep”- is what used in excel sheet to denote data of replication

BLOCK=as.factor(mod$block),ENTRY=as.factor(mod$ent ry)

Missing value of Data

Continued……. yield, height, biomass, grain per spike, test weight etc. are

variables, in these cases what we do is: HT=(mod$ht),

DM=(mod$dm), GPS=(mod$gps), BMS=(mod$bms),

GY=(mod$gy), TW=(mod$tw)

Making of data frame: example,

Data=data.frame(REP,BLOCK,ENTRY,HT,DM,GPS,BMS,

GY,TW)

Continued …….

Always use the exact names that you have given to the

respective factors and variables

To get summary of your data, perform “summary(data)” and

press “enter”

Data summary will give you the mean, median, and quartiles

values

QQPLOT Qqplot is required to find the distribution of data of a particular

variable

It helps us to find the extreme outliers

For qqPlot , package “car” is required to install

After installation of “car”, give command: require(car) then enter

QqPlot also requires package “lme4” and “lmerTest”

Now, mod=lmer(gy~entry+(1|rep),data)

Then qqPlot(resid(mod)) then enter

You will see the picture on your screen

-2 -1 0 1 2

-10

-50

510

15

norm quantiles

resid(

mod

.ht)

-2 -1 0 1 2

-20

-10

010

2030

40

norm quantiles

resid(

mod

.gps

)

To find extreme outliers in your data

The process is same as in qqPlot

The only difference is you give command

like :plot(resid(mod.ht)) for getting extreme outliers in

height

Similarly, you can get outliers in other data just by

interchanging the command name

In screen you will see the extreme outliers with their

respective entry number

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

-4-2

02

4

Leverage

Stand

ardize

d residu

als

lm(dm ~ entry + rep)

Cook's distance

Residuals vs Leverage

78

77

42

Plotting histogram

To plot histogram you can simply give command: hist(tw)-

this means you want to plot histogram of the test weight

Similarly you can plot histogram of any other variables

which you want

Box plot To produce box plot you can simply perform:

boxplot(gy~rep)

Here the box plot will show the result of grain yield with

respect to the replication

Box plot generally have 5 components the tail regions gives

two extreme values the middle line inside the box gives

median or Q2 value, top part of box shows Q1, bottom part

shows Q3

Correlation

To find out the correlation between the yield and other

variables: cor.test(gy,tw) or any other which you want

Correlation test gives you the value with either positive or

negative correlation

Numerical value and graph

50 100 150 200 250 300 350

7080

9010

011

0

gy

ht

Shapiro test

It is used to see the normality of the variables

Shapiro.test(tw), shapiro.test(gy) etc.

ANOVA from linear model

In this model: eg. analysis=lm(gy~en+rep,data)

Here analysis is a name given to the command, and the gy-

grain yield, in relation to the en- genotype, and replication

Similarly you can give command: anova(analysis) and enter

then you will get your anova

Here analysis is the name given to the command, you can use

on your own

ANOVA from linear mix model Linear mix model is more reliable to get ANOVA then linear

model as it reduces the randomness due to replication To produce anova: mod.ht=lmer(ht~entry+(1|rep),data) Here, mod.ht is a name given, lmer is the function code, ht-

height, entry- genotype, rep- replication, data- from data frame

Similarly you can get anova of other variables just interchanging “ht”. It means that if you want to produce anova of grain yield (gy) then,: mod.gy=lmer(gy~entry+(1|rep),data)

Then type: anova(mod.gy) and enter

Continue…….

Linear mix model is used when the data is obtained from

“RCBD” design

When the design is different, other methods should be used

PBIB test for alpha lattice design If the field is designed according to alpha lattice design then

analysis is to be done by using PBIB test It comes under package “agricolae” And it has the following command modelPBIB=PBIB.test(block,entry,rep,gy,k=12,method="VC

"or"REML",test=“lsd"or"tukey",alpha=0.05,console=T,group=T)

Here, “modelPBIB” is a name given, k- no of plots or treatments in a block, method should be used only one either vc or reml, test may be either lsd or tukey

This command is for grain yield, similarly you can find the value for other variables

Other functions in R The R under “agricolae” offers many functions like AUDPC

analysis, AMMI analysis- for finding G×E interactions As I mention earlier, R is a sea, what you need is to explore

these all To find correlation directly from data frame you just remove

the factors and retain only the variables Eg. data=data.frame (rep,entry,block,gy,ht,dm,tw,bms) Remove rep, entry, block :

data=data.frame(ht,dm,bms,gy,tw) Now give command: plot(data) and enter you will see the

corrrelation

ht

102 106 110 0.6 1.2 1.8 25 35 45

7090

110

102

106

110

dm

gps

2060

0.6

1.2

1.8

bms

gy

5015

030

0

70 90 110

2535

45

20 60 50 150 300

tw

After climbing a great hill, one only finds that there are many more hills to climb.

Nelson Mandela

Thank you for your patience