Hello! After running DESeq, I attempted to see the results and the following is returned:

results(dds)
Error in results(dds) : 
  couldn't find results. you should first run DESeq()

This is right after successfully (I think) running DESeq. 

data<-read.csv("TotalRNA_reads.csv", header=TRUE, row.names = 1, stringsAsFactors = FALSE)
colnames(data) <- substring(colnames(data), 2)

colData<-read.csv("TotalRNA_Sample_Coldata.csv",header = TRUE, row.names = 1)
#Double check names match up between Sample and data matrix
all(rownames(colData)==colnames(data))

dds<-DESeqDataSetFromMatrix(countData = data, colData = colData, design= ~condition)

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]  #keeps all but 89 genes 
DESeq(dds)

The DESeq outputs this as it is running:

estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 11650 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
class: DESeqDataSet 
dim: 20451 192 
metadata(1): version
assays(5): counts mu cooks replaceCounts replaceCooks
rownames(20451): UBC|7316 GUCY2D|3000 ... LZTFL1|54585 CSF3|1440
rowData names(22): baseMean baseVar ... maxCooks replace
colnames(192): 649 1132 ... 11921 12010
colData names(4): condition type sizeFactor replaceable

So I believe it ran correctly... has anyone else encountered this issue with the results() function?

Thanks!

You've forgotten to assign the outputs of DESeq to anything; dds <- DESeq(dds) should sort things out for you