You will find basic R codes

wildtype<- c(103,1125,504,608,794,698,920,535,842,765,945,1005,724,498,727)
print(wildtype)

 [1]  103 1125  504  608  794  698  920  535  842  765  945 1005  724  498  727

#2. Create variable called “resistant”—-

resistant<- c(768, 230, 854 ,637,426, 482, 1118, 524, 604 ,730 ,17 ,421, 587, 782 ,171)
print(resistant)

 [1]  768  230  854  637  426  482 1118  524  604  730   17  421  587  782  171

#3. combine the the vectors into data frame called “miner”—-

miner<- data.frame("wild"=wildtype,"resist"=resistant)
print(miner)

   wild resist
1   103    768
2  1125    230
3   504    854
4   608    637
5   794    426
6   698    482
7   920   1118
8   535    524
9   842    604
10  765    730
11  945     17
12 1005    421
13  724    587
14  498    782
15  727    171

#4. Calculate the mean and variance of the wildtype and resistant from the data frame “miner”—-

print(paste0("The mean of wildtype is ",round(x=mean(miner$wild),digits=3)," and the variance is ",round(x=var(miner$wild),digits=3)))

[1] "The mean of wildtype is 719.533 and the variance is 63014.552"

print(paste0("The mean of resistant is ",round(x=mean(miner$resist),digits=3)," and the variance is ",round(x=var(miner$resist),digits=3)))

[1] "The mean of resistant is 556.733 and the variance is 80529.21"

5. write a function for standard error called “stderror”—-

formula for standard error is standard deviation/square root ($ {(n)}$) of sample size

stderror<- function(x,n=length(x)){
  return(sd(x)/sqrt(n))
}

#…apply the function to the data frame “miner”—-

print(paste0("The standard error of wildtype is ",round(x=stderror(miner$wild),digits=3)))

[1] "The standard error of wildtype is 64.815"

print(paste0("The standard error of resistant is ",round(x=stderror(miner$resist),digits=3)))

[1] "The standard error of resistant is 73.271"

#6. write a function for growth rate called “rate” —-

where, nt is the vector abunace at t,ntplus the vector of abunance at year t+1 and t for vector of years and return plot for growth rate … load data from laptop

abundance <- read.csv("E:/1 Master's/MSU/course classes/Fall 2022/WFA 8134 RMethod/code/abundance.csv",header = TRUE)
head(abundance)

  year abundance nextabundance
1    1        10           215
2    2       215           105
3    3       105           277
4    4       277            44
5    5        44            34
6    6        34            44

#… write that functions

rate<- function(dataframe){
    nt<- dataframe$abundance # abundance at t
    ntplus<- dataframe$nextabundance # abundance at t+1
    t<- dataframe$year # vector of years
    growthrate<- log(ntplus/nt) # growth rate
    plot(t,growthrate,type="l",xlab="Year",ylab="Growth rate",main="Growth rate of abundance")
    return(growthrate)
}

#… apply it over the dataframe

rate(abundance)

 [1]  3.068052935 -0.716677678  0.970057156 -1.839827872 -0.257829109
 [6]  0.257829109  2.087928156  0.514761530 -1.068759326 -0.807260487
[11]  0.836248024  0.762140052 -0.179658439  0.644091024 -0.073893126
[16] -0.055655784 -0.024136329 -0.048383081  0.196466064  0.061285342
[21] -0.114650563  0.110679677  0.027470661  0.053999307  0.082109406
[26]  0.239229689 -0.429692403  0.038749003 -0.059396674  0.336869535
[31]  0.179503953 -0.049329729 -0.224888752 -0.071256817 -0.135375329
[36] -0.106106506 -0.007490672  0.151784833  0.033039854  0.103233255
[41]  0.130850237  0.085278918 -0.016483890 -0.231721325  0.105942418
[46]  0.010416761  0.114438358  0.050461155 -0.005286356 -0.084765267

Figure 1: Calculated Growth rate of abundance dataframe using following formula$r=ln(Nt+1)/Nt$.

git commit -m “comments”

git add .

git push -f origin master