i have normalized the data using deseq2 internal normalization method. But it seems that my original input data is same as the normalized output. i don't know if the data has been normalized correctly or if there is any issue with my code. Because when i am going with the current normalized data it is giving me a very few up and down regulated genes on adjusted p value <0.1

Code should be placed in three backticks as shown below

library(DESeq2)
library(tidyverse)
count_data <- read.csv('rawcount_data2.csv',row.names = 1)
head(count_data)
colData <- read.csv('rawdatainfo.csv', row.names =1)
all(colnames(count_data) %in% rownames(colData))
all(colnames(count_data)== rownames(colData))

dds <- DESeqDataSetFromMatrix(countData = count_data,
                              colData = colData,
                              design = ~ condition)
levels(dds$condition)
dds
keep <- rowSums(counts(dds)) >=10
dds <- dds[keep,]

dds
#dds$condition <- relevel(dds$condition, ref = "healthy")

dds <- DESeq(dds)
# Get the normalized counts
normalized_counts <- assay(dds)

# Write the normalized counts to a CSV file
write.csv(normalized_counts, file = "normalized_counts3.csv")
res <- results(dds)
res

summary(res)

sessionInfo( )

the original data before normalization after nomaliztion the data looks like

counts(dds, normalized=TRUE)