diff --git a/R/methods-ComputeResult.R b/R/methods-ComputeResult.R
index 46cbf52..d9d3826 100644
--- a/R/methods-ComputeResult.R
+++ b/R/methods-ComputeResult.R
@@ -90,7 +90,12 @@ mergeComputeResultAndMetadata <- function(computeResult, dataset, metadataVariab
 #'      method='shannon', 
 #'      verbose=FALSE
 #' )
-#' alphaDivDT <- getComputeResultWithMetadata(alphaDivOutput, microbiomeData::DiabImmune, metadataVariables = c('country', 'delivery_mode'))
+#' 
+#' alphaDivDT <- getComputeResultWithMetadata(
+#'      alphaDivOutput, 
+#'      microbiomeData::DiabImmune, 
+#'      metadataVariables = c('country', 'delivery_mode')
+#' )
 #' @param object A Microbiome Dataset
 #' @param dataset The MbioDataset, AbundanceData or Collection object from which the compute result was obtained.
 #' @param format The format you want the compute result in. Currently only "data.table" is supported.
diff --git a/R/methods-MbioDataset.R b/R/methods-MbioDataset.R
index 90ecccb..6ec6611 100644
--- a/R/methods-MbioDataset.R
+++ b/R/methods-MbioDataset.R
@@ -133,11 +133,31 @@ setMethod("updateCollectionName", "MbioDataset", function(object, oldName, newNa
 #' Get a collection from the Microbiome Dataset. The collection will be returned
 #' as an AbundanceData, phyloseq, or Collection object.
 #' 
-#' @examples 
-#' genus <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus")
-#' genus_phyloseq <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", format = "phyloseq")
-#' genus_continuous <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", continuousMetadataOnly = TRUE) ## to pass to correlation method
-#' genus_collection <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", format = "Collection") ## with no metadata
+#' @examples
+#' genus <- getCollection(
+#'      microbiomeData::DiabImmune, 
+#'      "16S (V4) Genus"
+#' )
+#' 
+#' genus_phyloseq <- getCollection(
+#'      microbiomeData::DiabImmune, 
+#'      "16S (V4) Genus", 
+#'      format = "phyloseq"
+#' )
+#' 
+#' ## to pass to correlation method, we want only continuous metadata
+#' genus_continuous <- getCollection(
+#'      microbiomeData::DiabImmune, 
+#'      "16S (V4) Genus", 
+#'      continuousMetadataOnly = TRUE
+#' ) 
+#' 
+#' ## with no metadata
+#' genus_collection <- getCollection(
+#'      microbiomeData::DiabImmune, 
+#'      "16S (V4) Genus", 
+#'      format = "Collection"
+#' )
 #' @param object A Microbiome Dataset
 #' @param collectionName The name of the collection to return
 #' @param format The format of the collection to return. Currently supported options are "AbundanceData", "phyloseq" and "Collection".
diff --git a/R/reexports-microbiomeComputations.R b/R/reexports-microbiomeComputations.R
index ec3e1bf..3ad2fab 100644
--- a/R/reexports-microbiomeComputations.R
+++ b/R/reexports-microbiomeComputations.R
@@ -3,7 +3,10 @@
 #' This function returns abundances, ranked by a selected ranking function
 #' 
 #' @examples
-#' rankedAbundOutput <- rankedAbundance(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "median")
+#' rankedAbundOutput <- rankedAbundance(
+#'      getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+#'      method = "median"
+#' )
 #' @param data AbundanceData object
 #' @param method string defining the ranking strategy by which to order the taxa. Accepted values are 'median','max','q3',and 'variance'. Note that taxa that return a value of 0 for a given method will not be included in the results.
 #' @param cutoff integer indicating the maximium number of taxa to be kept after ranking.
@@ -19,7 +22,10 @@ microbiomeComputations::rankedAbundance
 #' This function returns alpha diversity values for each sample.
 #' 
 #' @examples
-#' alphaDivOutput <- alphaDiv(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "shannon")
+#' alphaDivOutput <- alphaDiv(
+#'      getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+#'      method = "shannon"
+#' )
 #' @param data AbundanceData object
 #' @param method string defining the the alpha diversity method. Accepted values are 'shannon','simpson', and 'evenness'
 #' @param verbose boolean indicating if timed logging is desired
@@ -34,7 +40,11 @@ microbiomeComputations::alphaDiv
 #' This function returns pcoa coordinates calculated from the beta diversity dissimilarity matrix.
 #' 
 #' @examples 
-#' betaDivOutput <- betaDiv(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "bray", k = 2)
+#' betaDivOutput <- betaDiv(
+#'      getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+#'      method = "bray", 
+#'      k = 2
+#' )
 #' @param data AbundanceData object
 #' @param method string defining the the beta diversity dissimilarity method. Accepted values are 'bray','jaccard', and 'jsd'
 #' @param k integer determining the number of pcoa axes to return
@@ -50,10 +60,28 @@ microbiomeComputations::betaDiv
 #' This function returns correlation coefficients for variables in one dataset against variables in a second dataset
 #' 
 #' @examples
-#' diabImmune_genus <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", continuousMetadataOnly = TRUE)
-#' correlationDT <- correlation(diabImmune_genus, method = 'spearman', format = 'data.table')
-#' correlationOutput <- correlation(diabImmune_genus, method = 'spearman', format = 'ComputeResult')
-#' alsoCorrelationDT <- getComputeResult(correlationOutput, "data.table")
+#' diabImmune_genus <- getCollection(
+#'      microbiomeData::DiabImmune, 
+#'      "16S (V4) Genus", 
+#'      continuousMetadataOnly = TRUE
+#' )
+#' 
+#' correlationDT <- correlation(
+#'      diabImmune_genus, 
+#'      method = 'spearman', 
+#'      format = 'data.table'
+#' )
+#' 
+#' correlationOutput <- correlation(
+#'      diabImmune_genus, 
+#'      method = 'spearman', 
+#'      format = 'ComputeResult'
+#' )
+#' 
+#' alsoCorrelationDT <- getComputeResult(
+#'      correlationOutput, 
+#'      "data.table"
+#' )
 #' @param data1 first dataset. A data.table
 #' @param data2 second dataset. A data.table
 #' @param method string defining the type of correlation to run. 
@@ -74,9 +102,22 @@ veupathUtils::correlation
 #' convenience wrapper around veupathUtils::correlation, with the exception that it additionally supports sparcc.
 #' 
 #' @examples
-#' correlationDT <- selfCorrelation(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = 'sparcc', format = 'data.table')
-#' correlationOutput <- selfCorrelation(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = 'sparcc', format = 'ComputeResult')
-#' alsoCorrelationDT <- getComputeResult(correlationOutput, "data.table")
+#' correlationDT <- selfCorrelation(
+#'      getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+#'      method = 'sparcc', 
+#'      format = 'data.table'
+#' )
+#' 
+#' correlationOutput <- selfCorrelation(
+#'      getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+#'      method = 'sparcc', 
+#'      format = 'ComputeResult'
+#' )
+#' 
+#' alsoCorrelationDT <- getComputeResult(
+#'      correlationOutput, 
+#'      "data.table"
+#' )
 #' @param data An AbundanceData object
 #' @param method string defining the type of correlation to run. The currently supported values are 'spearman','pearson' and 'sparcc'
 #' @param format string defining the desired format of the result. The currently supported values are 'data.table' and 'ComputeResult'.
diff --git a/man/alphaDiv-methods.Rd b/man/alphaDiv-methods.Rd
index aac6518..61e9362 100644
--- a/man/alphaDiv-methods.Rd
+++ b/man/alphaDiv-methods.Rd
@@ -24,5 +24,8 @@ ComputeResult object
 This function returns alpha diversity values for each sample.
 }
 \examples{
-alphaDivOutput <- alphaDiv(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "shannon")
+alphaDivOutput <- alphaDiv(
+     getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+     method = "shannon"
+)
 }
diff --git a/man/betaDiv-methods.Rd b/man/betaDiv-methods.Rd
index 7cd9852..d70ac6a 100644
--- a/man/betaDiv-methods.Rd
+++ b/man/betaDiv-methods.Rd
@@ -27,5 +27,9 @@ ComputeResult object
 This function returns pcoa coordinates calculated from the beta diversity dissimilarity matrix.
 }
 \examples{
-betaDivOutput <- betaDiv(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "bray", k = 2)
+betaDivOutput <- betaDiv(
+     getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+     method = "bray", 
+     k = 2
+)
 }
diff --git a/man/correlation-methods.Rd b/man/correlation-methods.Rd
index 41b360a..a344034 100644
--- a/man/correlation-methods.Rd
+++ b/man/correlation-methods.Rd
@@ -35,8 +35,26 @@ data.frame with correlation coefficients or a ComputeResult object
 This function returns correlation coefficients for variables in one dataset against variables in a second dataset
 }
 \examples{
-diabImmune_genus <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", continuousMetadataOnly = TRUE)
-correlationDT <- correlation(diabImmune_genus, method = 'spearman', format = 'data.table')
-correlationOutput <- correlation(diabImmune_genus, method = 'spearman', format = 'ComputeResult')
-alsoCorrelationDT <- getComputeResult(correlationOutput, "data.table")
+diabImmune_genus <- getCollection(
+     microbiomeData::DiabImmune, 
+     "16S (V4) Genus", 
+     continuousMetadataOnly = TRUE
+)
+
+correlationDT <- correlation(
+     diabImmune_genus, 
+     method = 'spearman', 
+     format = 'data.table'
+)
+
+correlationOutput <- correlation(
+     diabImmune_genus, 
+     method = 'spearman', 
+     format = 'ComputeResult'
+)
+
+alsoCorrelationDT <- getComputeResult(
+     correlationOutput, 
+     "data.table"
+)
 }
diff --git a/man/getCollection.Rd b/man/getCollection.Rd
index fff77bd..c9a9f3f 100644
--- a/man/getCollection.Rd
+++ b/man/getCollection.Rd
@@ -37,8 +37,28 @@ Get a collection from the Microbiome Dataset. The collection will be returned
 as an AbundanceData, phyloseq, or Collection object.
 }
 \examples{
-genus <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus")
-genus_phyloseq <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", format = "phyloseq")
-genus_continuous <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", continuousMetadataOnly = TRUE) ## to pass to correlation method
-genus_collection <- getCollection(microbiomeData::DiabImmune, "16S (V4) Genus", format = "Collection") ## with no metadata
+genus <- getCollection(
+     microbiomeData::DiabImmune, 
+     "16S (V4) Genus"
+)
+
+genus_phyloseq <- getCollection(
+     microbiomeData::DiabImmune, 
+     "16S (V4) Genus", 
+     format = "phyloseq"
+)
+
+## to pass to correlation method, we want only continuous metadata
+genus_continuous <- getCollection(
+     microbiomeData::DiabImmune, 
+     "16S (V4) Genus", 
+     continuousMetadataOnly = TRUE
+) 
+
+## with no metadata
+genus_collection <- getCollection(
+     microbiomeData::DiabImmune, 
+     "16S (V4) Genus", 
+     format = "Collection"
+)
 }
diff --git a/man/getComputeResultWithMetadata.Rd b/man/getComputeResultWithMetadata.Rd
index de6eb36..7e7ce69 100644
--- a/man/getComputeResultWithMetadata.Rd
+++ b/man/getComputeResultWithMetadata.Rd
@@ -56,5 +56,10 @@ alphaDivOutput <- MicrobiomeDB::alphaDiv(
      method='shannon', 
      verbose=FALSE
 )
-alphaDivDT <- getComputeResultWithMetadata(alphaDivOutput, microbiomeData::DiabImmune, metadataVariables = c('country', 'delivery_mode'))
+
+alphaDivDT <- getComputeResultWithMetadata(
+     alphaDivOutput, 
+     microbiomeData::DiabImmune, 
+     metadataVariables = c('country', 'delivery_mode')
+)
 }
diff --git a/man/rankedAbundance-methods.Rd b/man/rankedAbundance-methods.Rd
index 6e62df3..f51194c 100644
--- a/man/rankedAbundance-methods.Rd
+++ b/man/rankedAbundance-methods.Rd
@@ -27,5 +27,8 @@ ComputeResult object
 This function returns abundances, ranked by a selected ranking function
 }
 \examples{
-rankedAbundOutput <- rankedAbundance(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = "median")
+rankedAbundOutput <- rankedAbundance(
+     getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+     method = "median"
+)
 }
diff --git a/man/selfCorrelation-methods.Rd b/man/selfCorrelation-methods.Rd
index 290b21c..36b6b8f 100644
--- a/man/selfCorrelation-methods.Rd
+++ b/man/selfCorrelation-methods.Rd
@@ -31,7 +31,20 @@ This function returns correlation coefficients for variables in one AbundanceDat
 convenience wrapper around veupathUtils::correlation, with the exception that it additionally supports sparcc.
 }
 \examples{
-correlationDT <- selfCorrelation(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = 'sparcc', format = 'data.table')
-correlationOutput <- selfCorrelation(getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), method = 'sparcc', format = 'ComputeResult')
-alsoCorrelationDT <- getComputeResult(correlationOutput, "data.table")
+correlationDT <- selfCorrelation(
+     getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+     method = 'sparcc', 
+     format = 'data.table'
+)
+
+correlationOutput <- selfCorrelation(
+     getCollection(microbiomeData::DiabImmune, "16S (V4) Genus"), 
+     method = 'sparcc', 
+     format = 'ComputeResult'
+)
+
+alsoCorrelationDT <- getComputeResult(
+     correlationOutput, 
+     "data.table"
+)
 }