This is a small portion of the dataframe I am working with for reference.I am working with a data frame (MG53_HanLab) in R that has a column for Time, several columns with the name "MG53" in them, several columns with the name "F2" and several with "Iono" in them. I would like to compare the means of each group for each time point. I understand that I have to subset the data and have tried doing
control <- MG53_HanLab[c(2:11)]
F2 <- MG53_HanLab[c(12:23)]
iono <- MG53_HanLab[c(24:33)]
which has created 3 new dataframes.
My question is: How do I compare two dataframes row by row to see if there is a difference in the means for each table?
rowMeans feels simpler as @Chi Pak suggested.
#create example data
time<-seq(1.0,6.0,.5)
A_1<-runif(11)
A_2<-runif(11)
B_1_1<-runif(11)
B_1_2<-runif(11)
B_2<-runif(11)
#create data frame
df<-data.frame(time,A_1,A_2,B_1_1,B_1_2,B_2)
#subset column groups into individual data frames using regular expression
df.a<-df[,grep('A_',colnames(df))]
#calculate rowMeans
a.mean<-rowMeans(df.a)
#repeat for other column groups
df.b<-df[,grep('B_',colnames(df))]
b.mean<-rowMeans(df.b)
#recombine to view side by side
df.mean<-data.frame(a.mean,b.mean)