Augmented block design data analysis in R (R-studio).

hi there in this tutorial i am going to talk about the data analysis part of the augmented block design this design will be used when the seed material required for replication is not sufficient and also in the case when the number of treatments are high so let me start with the basics of this experimental design so this is the experimental layout of the augmented block design here we can see there are four blocks block one block two block three and block four and in each and every block the check genotypes which are c1 c2 c3 c4 and c5 are replicated and also randomized and the error degrees of freedom of this experimental layout is 12 which is obtained by multiplying the degrees of freedom for genotypes into the degrees of freedom of blocks which is 4 into 3 so in order to do the data analysis of augmented block design the important thing we need is the r studio in order to download this rstudio we have to go to the chrome and search for rstudio so this is the official website of the art studio in order to download the studio we have to go for products and select our studio and our studio required for us is rstudio desktop so from here we can directly download the r studio desktop so i have already downloaded the rstudio desktop so i will open directly from my pc studio so this is the interface of our studio here we can see there are four pins and the first pane is the program pane here we will write the code required for the data analysis and the second pane is the console and the third pane is the environment and the fourth pin is the packages pane so our file pane in order to do the data analysis the next most important thing is the format with which we have to arrange the data so let me jump into that so this is the format with which we have to arrange the data in this data frame the two important columns are the block column and the treatment column in the block column we will mention the blocks and in the treatment column we will mention the gm types in the beginning of each block we have to mention the data of chucks whatever may be the order of the chucks in different blocks but when mentioning the chucks in excess seed we have to maintain the sequence across each and every block so for example here we can see the order of the chucks in first block is c1 c2 c3 c4 and c5 but in second block the c3 is the first check user and c4 is the lost track used but when mentioning in the excel sheet we have to maintain the uniformity so chapter 1 chapter chapter 3 check 4 chapter 5 in the first block and this order is same in the block 2 also chapter 2 chapter 4 and chapter 5 and this uniformity we have to maintain across all the blocks in this tutorial i am going to consider five quantitative traits for data analysis which are plant height pots per plant seeds per pod under seed weight and yield per plant after arranging the data in this fashion we have to ensure that the spelling of chucks across each statement is same and we have to keep in mind that r is case sensitive suppose here we will mention capital c for check 1 and if we mention small c here in the block two for chuck one it is not going to work because r is case sensitive and we have to make sure that we will avoid this kind of mistakes and after arranging the data we have to save this data frame and we have to go to the r studio for further data analysis this is the program console here we are going to write the code which is required for the data analysis of augmented block design and we all do it for the first time we have to install the package which is required for the analysis of augmented block design and the package is augmented rcbd which this is the augmented rcd package which can be downloaded and installed directly in on console so the code is install dot packages for the first time we have to mention that the package name in inverted commas the package name is augmented capital or c b d and we have to click ctrl and enter to execute the code so the package has been successfully installed in order to load this package we have to write the code that is library augmented rcbd and execute this code now we can see that augmented rcpd has been successfully loaded so after loading this package we have to import the data set so in order to import the data set we have to click here import data set and select from excel so those who are doing this for the first time who have installed the rstudio first time the story asks for certain permission and you have to give yes for all the permission then this console opens after opening of this console we have to browse the location where the file has been located so i have saved the file in desktop in the name of augmented rcbd and so after selecting the file by default it will be there in the first seat okay so if you have if you have multiple number of sheets so that you have to select the sheet in which the data is there so after that we have to change the name into as simple as possible so i will change the name into egm19 so after this you have to click import so after importing we have to check the structure so before checking the structure we have to change the options so that there will be maximum blinds will be available to print in the console so in order to change the print options we have to give the code like this options in that max dot print so we can select up to 10 000 or 1 lakh so just a 10 000 lines that means i need 10 000 lines up to 10 000 lines to be printed in the console after that we can check the structure of the data from structure data from aeg mnt so check the structure of the data frame so here we can see that it is a table which means deeper so here we can see block treatment plant diet pots per plant seed seeds per pore under seed weight and eat per plant so the block and treatments are considered as characters in this table so we need to change the block and treatments into factors so in order to change the block and treatment as factor we have to write the code like this first select the data frame in that we have to select block to convert the core post like this as dot factor that we have to type one second a g m t so select the block execute this line of code next we have to change the treatments so select treatment convert into factor so execute this line of code so in order to cross confirm whether the block and treatments are changed into factors we have to check the structure of the data frame once again to do that just type stm which is the short form for structure and write the data frame name so here we can see that blocks and treatments are now converted into factors factor with four levels means it clearly indicates that there are four different blocks that is one two three and four the fourth box which i have already discussed in the presentation so here in the treatment we can see there are one not one levels which means one or one different genotypes which are used in this experiment 24 in each block so 24 in four blocks means 24 into four 96 96 entries are 96 varieties and five genotypes 96 plus five is equals to one not one levels so after cross confirming this we have to write the code and the code goes like this the code which is needed for the augmented block design analysis goes like this we would like to save the output and then in the factor known as b out so it should be out the function to do the data analysis is augmented rcbd dot bulk select the augmented rcb dot bulk function and the first argument we have to mention is data and here we have to justify the data frame name for data that is easy after that we have to justify for the block so block is equal to the name which we have given for block and our data frame that is below ck here we have to cover it in inverted comma and we have to mention capital b only because as i told earlier r is case sensitive and after justifying block after each and every argument we have to ensure we will put a comma after each and every argument later we have to justify for treatments so treatment is equals to the name we are given for treatment that is capital t and this is also be enclosed in double inverted comma treatment so after this argument we have to consider the triads since we are having more than one red we have to mention in c open the parenthesis after opening parenthesis c small bracket two small brackets so in that small brackets mention the triad's name which is given in this data frame that is ph triple p c is performed spp under seed weight yield purple land as the shortcuts which we have given here has to be mentioned in this bracket inside these brackets also we have to enclose them in double inverted karma the first right is plant height after each of you every triad make sure that you will give a comma the next right is what's per plant triple p after this trap next right is fifth perform s p p for this strike the fourth right is 100c big 100 see wait and the last right is brand y p after mentioning the triads that we have to give and defer command which is such small c h u yes so x is equals to null don't worry much about this command just give as it is after giving this command we have to describe the level of significance that is alpha for field experiment it will be 5 0.05 after describing the level of significance and we have to specify that we need descriptive statistics and the argument to get the descriptive statistics is describe so we need descriptive statistics so we will give true after that if uh frequency distribution of this quantitative is also important for us and we will need that too so frequency distribution is equal to true after that we need also the genetic variability parameters that is gcv and pcb so in order to get that we have to give the core gva is equal to true after this we have to give color for each and every sub user so in our in this data analysis we are going to use five checks so we have to specify five different colors so it is so five different colors means more than one color which has to be mentioned within c and parenthesis so so color check dot co so we have to mention it in c that is we have to select five different colors those colors which has to be compatible in our studio also so if you don't know much about these colors you just go and search for the colors which are compatible in r so i will select five colors the five colors are brown for the first check dark cyan for the second check for screen for third check deep pink for the fifth chick after mentioning these colors inside parenthesis we have to give another default argument that is console console which is equal to true the true should be given in capitals only after this we can execute this line of code let me see whether this kind of code doesn't have any errors no this line of code doesn't have any error so within two or three seconds all the data analysis of this number of entries has been completed successfully here we can see the anova table here the anova with treatment adjusted here it is the anova with blocker just said and we can get this report in word format also so in order to get the report in word format we have to give the code like this the function is report augmented rcbd dot bulk because we are using augmented rcp bulk when the number of triads are more than one [Music] after writing this function you have to mention r dot bulk which is and we have to mention the factor where we have stored the output that is b out so just type b out and keep a target just now i will give the temporary directory where this file will be stored targets file path is equal to temporary directory after this we have to give the name of the word file so i will write it is easymnt dot doc x so after this you just want to execute this line of code also so this line of code has some error here [Music] so sorry here we have to mention the file name in inverted commas also so easy dot dot x we have to mention and the inverted commas so let me execute this now yeah now it is working fine here we can see the red mark so if we if you see this means the report has been generating so after the red mark has been started stopped here we have to know where the file has been located so in order to find the temporary directory just write the function temporary directory and execute this so the temporary directory where the word file has been saved is we have in c drive we have to follow this path yeah this path in order to find this temporary directory and in order to find the word file so let's go to the windows explorer and before looking into the temporary directory we have to ensure that all the hidden files are shown so just go to the view and click on hidden hidden items go to view and click on hidden items so after that it goes to this pc windows c so on the location where it is saved is users and customer app data local local yeah local temporary directory and the name of the temporary directory is rtm pww kt3x yeah kt3x find here kt3x yeah yeah and this temporary directory yeah we can see the word file with the name what we mentioned in the concept so just copy the entire folder copy and keep it on desktop let me go to the desktop paste here so we can rename this folder clean here let me show what are the things we will get so these are all the graphs the frequency distribution graphs and the gcv and pcb graphs so we will be getting inside this water file so let me show you how this workflow looks and this will uh ask this permission this document contains fields that may refer to other phase so we have to give yes so simply give us after this we will get a clean and nice report of our augmented block design so here we can see the anova with treatment registered on our so the anova which we use for this data analysis is anova of block register and a number of treatment adjusted is not so much popular and we will also get the standard errors and the standard errors in different combination that is a test treatment and a control treatment between two test treatments of different blocks and two test treatments of same lock like that and it it will be same similar for critical difference also cd at five percent and here we can see the cv the coefficient of variation and it is high for the yields per year plan which is usually and in the agriculture the cv will be our the recommended cvr the considered amount of cv is around 6 to 24 as given in icr guidelines so these are the statistics so this is the descriptive statistics here we can see the minimum value on the maximum value these are all the values with these are the values which we have to which we which is very much needed so this is the frequency distribution table not frequency distribution table this is the frequency distribution chart so continuous distribution here we can see so this is the table which has taken more place and not aligned so we have to align this is one of the problem in this package but this problem is manageable just realigning it we can see here here you can see the genetic variability parameters that is gcv pcb and ecv and with category also we will get and for this the graphs also will get see these are the graphs and these are the adjacent means so these adjusted means will be used in order to calculate the genotypic correlations and the original data what we have used for the data analysis will be used for calculating phenotypic correlation and this will be same for phenotypic path analysis and genotypic path analysis also for genotypic path analysis we will use the same data data that is the adjacent means data so we can copy this as adjacent means data into excel sheet and we can further proceed into correlation and path analysis assume if you have any doubts regarding this data analysis tutorial just please comment in the comment section and i will reply for your comments as soon as possible thank you

Channel: The Outlier

Views: 2,019

Rating: 5 out of 5

Keywords: Augmented RCBD, augmented block design data analysis, augmented randomised block design, augmented data analysis, augmented block design ANOVA, augmented block design, augmented design, augmented RCBD

Id: kXtC7uiXxhs

Length: 26min 44sec (1604 seconds)

Published: Mon Dec 28 2020