One approach is to nest the data in a data frame with a list column. You can then use purrr::map to run a function for each group, tidy the results and unnest.

Using the same df1 as in the answer from @zx8754:

library(dplyr)
library(tidyr)
library(purrr)

df1 %>% 
  nest(-Class) %>% 
  mutate(model = map(data, ~anova(lm(Value ~ Sample, .))), 
         tidy = map(model, tidy)) %>% 
select(Class, td) %>% 
unnest()

Result:

# A tibble: 6 x 7
  Class     term         df  sumsq meansq statistic p.value
  <chr>     <chr>     <int>  <dbl>  <dbl>     <dbl>   <dbl>
1 Macrolide Sample        2 0.0471 0.0235     1.22    0.358
2 Macrolide Residuals     6 0.115  0.0192    NA      NA    
3 Ceph      Sample        2 0.103  0.0515     0.662   0.564
4 Ceph      Residuals     4 0.311  0.0777    NA      NA    
5 MCeph     Sample        2 0.161  0.0804     0.447   0.727
6 MCeph     Residuals     1 0.18   0.18      NA      NA

More details: Running a model on separate groups.

Split data by class, then run your favourite function, see example:

# example data
df1 <- read.table(text = "Sample    Class   Value
A   Macrolide   0.22
A   Macrolide   0.45
A   Macrolide   0.63
B   Macrolide   0.25
B   Macrolide   0.28
B   Macrolide   0.47
C   Macrolide   0.22
C   Macrolide   0.26
C   Macrolide   0.29
A   Ceph    0.32
A   Ceph    0.42
A   Ceph    0.62
B   Ceph    0.42
B   Ceph    0.2
B   Ceph    0.91
C   Ceph    0.82
C   MCeph   0.92
A   MCeph   0.2
B   MCeph   0.72
C   MCeph   0.32
", header = TRUE, stringsAsFactors = FALSE)

# split and apply function, result is a named list:
lapply(split(df1, df1$Class), function(i){
  anova(lm(Value ~ Sample, data = i))
})

$Ceph
Analysis of Variance Table

Response: Value
          Df  Sum Sq  Mean Sq F value Pr(>F)
Sample     2 0.10293 0.051467  0.6622 0.5644
Residuals  4 0.31087 0.077717               

$Macrolide
Analysis of Variance Table

Response: Value
          Df   Sum Sq  Mean Sq F value Pr(>F)
Sample     2 0.047089 0.023544  1.2241 0.3582
Residuals  6 0.115400 0.019233               

$MCeph
Analysis of Variance Table

Response: Value
          Df Sum Sq Mean Sq F value Pr(>F)
Sample     2 0.1608  0.0804  0.4467 0.7268
Residuals  1 0.1800  0.1800

Couldn't you just subset your dataframe for Class. i.e.

> library(lmPerm)
> data.df = read.csv("data.txt")

> aov.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "Macrolide")  )
[1] "Settings:  unique SS "
> summary(aov.macro)
Component 1 :
            Df R Sum Sq R Mean Sq Pr(Exact)
Sample       2 0.047089  0.023544         1
Residuals    6 0.115400  0.019233          


> ceph.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "Ceph")  )
[1] "Settings:  unique SS "
> summary(ceph.macro)
Component 1 :
            Df R Sum Sq R Mean Sq Pr(Exact)
Sample       2  0.10293  0.051467         1
Residuals    4  0.31087  0.077717          

> mceph.macro <- lmPerm::aovp(Value ~ Sample, subset(data.df,data.df$Class == "MCeph")  )
Error in ctrfn(levels(x), contrasts = contrasts) : 
  not enough degrees of freedom to define contrasts

Not sure if that was intentional but you have one class labelled as MCeph. Seems like a typo, or the data is truncated? If not, you don't have enough degrees of freedom to do an anova on that class (you'd need at least one of each Sample for the function to even work).