input for DEseq2 differential expression and multi comparisons between samples

[safa]

Hello all,
I have RNAseq data for 24 samples divided as 8 different conditions and 3 biological replicates for each condition. the 8 conditions are for two different plants with 4 similar ages for each plant. the phenotypic data for these plants are either susceptible or resistant according to what age. the first plant has two susceptible ages and two resistant ages while the other plant has three susceptible ages and one resistant age.
I am using the htseq-DESeq2 pipeline to do the differential expression. my goals are: first, compare the different ages S with S and R with R for plant 1 then compare the list of genes from S to the list of genes with R. do the same comparison for plant 2 except that I have 3 S ages: 1 R age so I need to compare all three S ages with the R age.
second, compare two ages 14 and 21 between the two plants as 14 : 14 and 21 : 21.
Third, I need to know if the resistance phenotype is plant dependent means that there are unique R genes for each plant or there are common genes for R phenotype for both plants.
Here is my R code for the DESeq2:
sampleFiles <- list.files(path="/to/htseq-output")
directory <- c("/main directory/")
sampleCondition<- read.table("path/to/phenodata.txt",head=TRUE)
sampleTable <- data.frame(sampleName = sampleFiles, fileName = sampleFiles, condition = sampleCondition)

my sampleCondition file is:
sampleID cultivar phenotype condition
C7-1 C S C7
C7-2 C S C7
C7-3 C S C7
C10-1 C S C10
C10-2 C S C10
C10-3 C S C10
C14-1 C R C14
C14-2 C R C14
C14-3 C R C14
C21-1 C R C21
C21-2 C R C21
C21-3 C R C21
D7-1 D S D7
D7-2 D S D7
D7-3 D S D7
D10-1 D S D10
D10-2 D S D10
D10-3 D S D10
D14-1 D S D14
D14-2 D S D14
D14-3 D S D14
D21-1 D R D21
D21-2 D R D21
D21-3 D R D21

SampleTable file:

sampleName fileName condition.sampleID condition.cultivar condition.phenotype condition.condition

1 C10_1.txt C10_1.txt C10-1 C S C10
2 C10_2.txt C10_2.txt C10-2 C S C10
3 C10_3.txt C10_3.txt C10-3 C S C10
4 C14_1.txt C14_1.txt C14-1 C R C14
5 C14_2.txt C14_2.txt C14-2 C R C14
6 C14_3.txt C14_3.txt C14-3 C R C14
7 C21_1.txt C21_1.txt C21-1 C R C21
8 C21_2.txt C21_2.txt C21-2 C R C21
9 C21_3.txt C21_3.txt C21-3 C R C21
10 C7_1.txt C7_1.txt C7-1 C S C7
11 C7_2.txt C7_2.txt C7-2 C S C7
12 C7_3.txt C7_3.txt C7-3 C S C7
13 D10_1.txt D10_1.txt D10-1 D S D10
14 D10_2.txt D10_2.txt D10-2 D S D10
15 D10_3.txt D10_3.txt D10-3 D S D10
16 D14_1.txt D14_1.txt D14-1 D S D14
17 D14_2.txt D14_2.txt D14-2 D S D14
18 D14_3.txt D14_3.txt D14-3 D S D14
19 D21_1.txt D21_1.txt D21-1 D R D21
20 D21_2.txt D21_2.txt D21-2 D R D21
21 D21_3.txt D21_3.txt D21-3 D R D21
22 D7_1.txt D7_1.txt D7-1 D S D7
23 D7_2.txt D7_2.txt D7-2 D S D7
24 D7_3.txt D7_3.txt D7-3 D S D7

dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design = ~ )

#I am not sure what I should put in the design to achieve my goals. I have tried,
dds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design = ~ condition.cultivar + condition.phenotype + condition.cultivar:condition.phenotype) , #but I am not convinced

dds <- dds[ rowSums(counts(dds)) > 1, ]
gene_de_comparisons <- DESeq(dds)
resultsNames(gene_de_comparisons)

the result is:
[1] "Intercept"
[2] "condition.cultivar_D_vs_C"
[3] "condition.phenotype_S_vs_R"
[4] "condition.cultivarD.condition.phenotypeS"

the result is not my goal I don't understand why I have this pair of comparisons and I am not sure what is the correct code to achieve my goals.

any help is appreciated. and sorry for being too long in my question I just wanted to provide all the details.
Thanks.