lint R in markdown

tools/decontam/test-data/decontam_Rscript.Rmd

@@ -30,29 +30,34 @@ Create test data for wrapper, it should be able to use a matrix and vector as we
 
 ```{r store test data}
 R.home()
-library(phyloseq); packageVersion("phyloseq")
-library(ggplot2); packageVersion("ggplot2")
-library(decontam); packageVersion("decontam")
+library(phyloseq)
+packageVersion("phyloseq")
+library(ggplot2)
+packageVersion("ggplot2")
+library(decontam)
+packageVersion("decontam")
 
-ps <- readRDS(system.file("extdata", "MUClite.rds", package="decontam"))
+ps <- readRDS(system.file("extdata", "MUClite.rds", package = "decontam"))
 
 # Sample from a physeq object with a sampling function.
 #   ps: physeq object to be sampled
-#   FUN: function to use for sampling (default `sample`)
-#   ...: parameters to be passed to FUN, see `help(sample)` for default parameters
-sample_ps <- function(ps, FUN = sample, ...){
+#   fun: function to use for sampling (default `sample`)
+#   ...: parameters to be passed to fun,
+#see `help(sample)` for default parameters
+sample_ps <- function(ps, fun = sample, ...) {
   ids <- sample_names(ps)
-  sampled_ids <- FUN(ids, ...)
+  sampled_ids <- fun(ids, ...)
   ps <- prune_samples(sampled_ids, ps)
   return(ps)
 }
 
-ps <- sample_ps(ps, size=200) #the initial object is to big for the test case so we subsample 
+# the initial object is to big for the test case so we subsample
+ps <- sample_ps(ps, size = 200)
 
-##ps
+## ps
 # get otu table
 otu <- as(otu_table(ps), "matrix")
-head(otu[,1:10], 10)
+head(otu[, 1:10], 10)
 
 # add control column to sample data
 sample_data(ps)$control <- sample_data(ps)$Sample_or_Control == "Control Sample"
@@ -64,17 +69,21 @@ metadata <- as(sample_data(ps), "matrix")
 head(metadata, 1000)
 
 # store test data
-
-write.table(data.frame("SampleID"=rownames(otu),otu), # stores the row names as column, see https://stackoverflow.com/questions/2478352/write-table-writes-unwanted-leading-empty-column-to-header-when-has-rownames
-            file=file.path(getwd(), "otu_input.tsv"), 
-            sep = "\t", 
-            row.names=FALSE,
-            quote = FALSE)
-write.table(data.frame("SampleID"=rownames(metadata),metadata),
-            file=file.path(getwd(), "metadata_input.tsv"), 
-            sep = "\t",
-            row.names=FALSE,
-            quote = FALSE)
+# stores the row names as column,
+#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",
+  row.names = FALSE,
+  quote = FALSE
+)
+write.table(data.frame("SampleID" = rownames(metadata), metadata),
+  file = file.path(getwd(), "metadata_input.tsv"),
+  sep = "\t",
+  row.names = FALSE,
+  quote = FALSE
+)
 
 saveRDS(ps, file.path(getwd(), "phyloseq_input.rds"))
 ```
@@ -83,76 +92,83 @@ saveRDS(ps, file.path(getwd(), "phyloseq_input.rds"))
 
 ```{r load test data}
 library(tidyverse)
-#heights <- read_csv("data/heights.csv")
 
 # get OTU table (first column is the OTU/ASV ID)
 otu <- read_tsv(file.path(getwd(), "otu_input.tsv"))
-otu2 <- otu %>% tibble::column_to_rownames(colnames(otu)[1]) #use first column as colname
-OTU <- otu_table(otu2,  taxa_are_rows = FALSE)
+# use first column as colname
+otu2 <- otu %>% tibble::column_to_rownames(colnames(otu)[1])
+otu <- otu_table(otu2, taxa_are_rows = FALSE)
 
 # get metadata table must have matching OTU/ASV ID in first column
 meta <- read_tsv(file.path(getwd(), "metadata_input.tsv"))
-meta2 <- meta %>% tibble::column_to_rownames(colnames(meta)[1]) #use first column as colname
+# use first column as colname
 
-control_column = "control"
+meta2 <- meta %>% tibble::column_to_rownames(colnames(meta)[1])
+control_column <- "control"
 
 # convert 0/1 to bool for the control column and store in control column
 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)
-taxmat <- as.data.frame(matrix(sample(letters, 10, replace = TRUE), nrow = ncol(otu2), ncol = 7))
+# create dummy tax table (actually not needed,
+#but nice to learn how to load phyloseq objects)
+taxmat <- as.data.frame(matrix(sample(letters, 10, replace = TRUE),
+                               nrow = ncol(otu2), ncol = 7))
 rownames(taxmat) <- colnames(otu2)
-TAX <- tax_table(as.matrix(taxmat))
+tax <- tax_table(as.matrix(taxmat))
 
-ps <- phyloseq(OTU, TAX, sampledata)
+ps <- phyloseq(otu, tax, sampledata)
 ```
 
 # plot 1
 
 ```{r plot library size vs control}
-df <- as.data.frame(sample_data(ps)) # Put sample_data into a ggplot-friendly data.frame
+# Put sample_data into a ggplot-friendly data.frame
+df <- as.data.frame(sample_data(ps))
 df$LibrarySize <- sample_sums(ps)
-df <- df[order(df$LibrarySize),]
-df$Index <- seq(nrow(df))
-ggplot(data=df, aes(x=Index, y=LibrarySize, color=control)) + geom_point()
+df <- df[order(df$LibrarySize), ]
+df$Index <- seq_len(nrow(df))
+ggplot(data = df, aes(x = Index, y = LibrarySize, color = control)) +
+  geom_point()
 ```
 
 # plot 2
 
 ```{r plot prevalence}
-contamdf.prev <- isContaminant(ps, 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)
-ggplot(data=df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
-  xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
+contamdf_prev <- isContaminant(ps,
+                               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
+)
+ggplot(data = df_pa, aes(x = pa_neg, y = pa_pos, color = contaminant)) +
+  geom_point() +
+  xlab("Prevalence (Negative Controls)") +
+  ylab("Prevalence (True Samples)")
 ```
 
 # generate output
 
 ```{r remove contams}
-
 id_name <- colnames(otu)[1]
 
-ps.noncontam <- prune_taxa(!contamdf.prev$contaminant, ps)
+ps_noncontam <- prune_taxa(!contamdf_prev$contaminant, ps)
 
-otu_table(ps.noncontam) %>%
+otu_table(ps_noncontam) %>%
   as.data.frame() %>%
-  rownames_to_column(id_name) -> otu
-
-#write.csv(otu, file = "phyloseq_biom.csv")
+  rownames_to_column(id_name) <- otu
 
 write.table(otu,
-            file=file.path(getwd(), "otu_output.tsv"), 
-            sep = "\t",
-            row.names=FALSE,
-            )
-
-
-```
+  file = file.path(getwd(), "otu_output.tsv"),
+  sep = "\t",
+  row.names = FALSE,
+)
+```