lint R

tools/decontam/test-data/decontam_Rscript.Rmd

@@ -70,8 +70,8 @@ head(metadata, 1000)
 
 # store test data
 # stores the row names as column,
-#see https://stackoverflow.com/questions/2478352
-#/write-table-writes-unwanted-leading-empty-column-to-header-when-has-rownames
+# see https://stackoverflow.com/questions/2478352
+# /write-table-writes-unwanted-leading-empty-column-to-header-when-has-rownames
 write.table(data.frame("SampleID" = rownames(otu), otu),
     file = file.path(getwd(), "otu_input.tsv"),
     sep = "\t",
@@ -111,9 +111,9 @@ meta2$control <- as.logical(meta2[[control_column]])
 sampledata <- sample_data(meta2)
 
 # create dummy tax table (actually not needed,
-#but nice to learn how to load phyloseq objects)
+# but nice to learn how to load phyloseq objects)
 taxmat <- as.data.frame(matrix(sample(letters, 10, replace = TRUE),
-                               nrow = ncol(otu2), ncol = 7))
+    nrow = ncol(otu2), ncol = 7))
 rownames(taxmat) <- colnames(otu2)
 tax <- tax_table(as.matrix(taxmat))
 
@@ -129,25 +129,25 @@ df$LibrarySize <- sample_sums(ps)
 df <- df[order(df$LibrarySize), ]
 df$Index <- seq_len(nrow(df))
 ggplot(data = df, aes(x = Index, y = LibrarySize, color = control)) +
-  geom_point()
+    geom_point()
 ```
 
 # plot 2
 
 ```{r plot prevalence}
 contamdf_prev <- isContaminant(ps,
-                               method = "prevalence",
-                               neg = "control",
-                               threshold = 0.5)
+    method = "prevalence",
+    neg = "control",
+    threshold = 0.5)
 table(contamdf_prev$contaminant)
 
 ps_pa <- transform_sample_counts(ps, function(abund) 1 * (abund > 0))
 ps_pa_neg <- prune_samples(sample_data(ps_pa)$control == TRUE, ps_pa)
 ps_pa_pos <- prune_samples(sample_data(ps_pa)$control == FALSE, ps_pa)
 # Make data_frame of prevalence in positive and negative samples
 df_pa <- data.frame(
-  pa_pos = taxa_sums(ps_pa_pos), pa_neg = taxa_sums(ps_pa_neg),
-  contaminant = contamdf_prev$contaminant
+    pa_pos = taxa_sums(ps_pa_pos), pa_neg = taxa_sums(ps_pa_neg),
+    contaminant = contamdf_prev$contaminant
 )
 ggplot(data = df_pa, aes(x = pa_neg, y = pa_pos, color = contaminant)) +
     geom_point() +