diff --git a/R/multi_gene.R b/R/multi_gene.R
index 95e3e2d..da55557 100644
--- a/R/multi_gene.R
+++ b/R/multi_gene.R
@@ -17,7 +17,7 @@
 #' @param minCount numeric(1) passed to passed to \code{spatialLIBD::vis_gene}
 #' @param ... Parameters accepted by \code{spot_plot}, excluding
 #' \code{is_discrete} or \code{var_name}, which are handled internally
-#' 
+#'
 #' @return A \code{ggplot} object containing a "spot plot" of the specified
 #' sample and genes
 #'
@@ -25,44 +25,43 @@
 #' @author Nicholas J. Eagles
 #' @import SpatialExperiment SummarizedExperiment Matrix
 #' @family Spot plots summarizing expression of multiple genes simultaneously
-#' 
-#' @examples 
-#' 
+#'
+#' @examples
+#'
 #' #   Grab an example SpatialExperiment and suppose all of its spots should be
 #' #   plotted (for spatialNAc, 'exclude_overlapping' will only have genuinely
 #' #   overlapping spots be TRUE)
 #' spe <- spatialLIBD::fetch_data(type = "spatialDLPFC_Visium_example_subset")
 #' spe$exclude_overlapping <- FALSE
-#' 
-#' white_matter_genes = c(
+#'
+#' white_matter_genes <- c(
 #'     "ENSG00000197971", "ENSG00000131095", "ENSG00000123560",
 #'     "ENSG00000171885"
 #' )
 #' spot_plot_z_score(
-#'    spe = spe,
-#'    genes = white_matter_genes,
-#'    sample_id = unique(spe)$sample_id[1],
-#'    assayname = 'logcounts'
+#'     spe = spe,
+#'     genes = white_matter_genes,
+#'     sample_id = unique(spe)$sample_id[1],
+#'     assayname = "logcounts"
 #' )
-spot_plot_z_score = function(
-        spe, genes, sample_id, assayname = "logcounts", minCount = 0, ...
-    ) {
+spot_plot_z_score <- function(spe, genes, sample_id, assayname = "logcounts", minCount = 0, ...) {
     #   Check validity of arguments
     .multi_gene_validity_check(
         spe, genes, sample_id, assayname, minCount, ...
     )
 
-    spe = spe[genes, spe$sample_id == sample_id]
+    spe <- spe[genes, spe$sample_id == sample_id]
 
     #   For each spot, average expression Z-scores across all selected genes
-    a = assays(spe)[[assayname]]
-    gene_z = (a - rowMeans(a)) / rowSds(a)
-    spe$temp_var = colMeans(gene_z, na.rm = TRUE)
+    a <- assays(spe)[[assayname]]
+    gene_z <- (a - rowMeans(a)) / rowSds(a)
+    spe$temp_var <- colMeans(gene_z, na.rm = TRUE)
 
     #   Plot spatial distribution of averaged expression Z-scores for this
     #   sample
-    p = spot_plot(
-        spe, sample_id, var_name = 'Z_score', is_discrete = FALSE,
+    p <- spot_plot(
+        spe, sample_id,
+        var_name = "Z_score", is_discrete = FALSE,
         minCount = minCount, assayname = assayname, ...
     )
 
@@ -77,7 +76,7 @@ spot_plot_z_score = function(
 #' is plotted across the entire capture area.
 #'
 #' @inheritParams spot_plot_z_score
-#' 
+#'
 #' @return A \code{ggplot} object containing a "spot plot" of the specified
 #' sample and genes
 #'
@@ -85,41 +84,40 @@ spot_plot_z_score = function(
 #' @author Nicholas J. Eagles
 #' @import SpatialExperiment SummarizedExperiment Matrix
 #' @family Spot plots summarizing expression of multiple genes simultaneously
-#' 
-#' @examples 
-#' 
+#'
+#' @examples
+#'
 #' #   Grab an example SpatialExperiment and suppose all of its spots should be
 #' #   plotted (for spatialNAc, 'exclude_overlapping' will only have genuinely
 #' #   overlapping spots be TRUE)
 #' spe <- spatialLIBD::fetch_data(type = "spatialDLPFC_Visium_example_subset")
 #' spe$exclude_overlapping <- FALSE
-#' 
-#' white_matter_genes = c(
+#'
+#' white_matter_genes <- c(
 #'     "ENSG00000197971", "ENSG00000131095", "ENSG00000123560",
 #'     "ENSG00000171885"
 #' )
 #' spot_plot_sparsity(
-#'    spe = spe,
-#'    genes = white_matter_genes,
-#'    sample_id = unique(spe)$sample_id[1]
+#'     spe = spe,
+#'     genes = white_matter_genes,
+#'     sample_id = unique(spe)$sample_id[1]
 #' )
-spot_plot_sparsity = function(
-        spe, genes, sample_id, assayname = "counts", minCount = 0.1, ...
-    ) {
+spot_plot_sparsity <- function(spe, genes, sample_id, assayname = "counts", minCount = 0.1, ...) {
     #   Check validity of arguments
     .multi_gene_validity_check(
         spe, genes, sample_id, assayname, minCount, ...
     )
 
-    spe = spe[genes, spe$sample_id == sample_id]
+    spe <- spe[genes, spe$sample_id == sample_id]
 
     #   For each spot, compute proportion of marker genes with nonzero
     #   expression
     spe$prop_nonzero_marker <- colMeans(assays(spe)[[assayname]] > 0)
 
     #   Plot spatial distribution of this proportion
-    p = spot_plot(
-        spe, sample_id, var_name = 'prop_nonzero_marker', 
+    p <- spot_plot(
+        spe, sample_id,
+        var_name = "prop_nonzero_marker",
         is_discrete = FALSE, minCount = minCount, assayname = assayname, ...
     )
 
@@ -132,16 +130,14 @@ spot_plot_sparsity = function(
 #' @inheritParams spot_plot_z_score
 #' @import SpatialExperiment SummarizedExperiment
 #' @return NULL
-.multi_gene_validity_check = function(
-        spe, genes, sample_id, assayname, minCount, ...
-    ) {
+.multi_gene_validity_check <- function(spe, genes, sample_id, assayname, minCount, ...) {
     #   'genes'
     if (!all(genes %in% rownames(spe))) {
         stop("The SpatialExperiment does not contain the selected genes in its rownames")
     }
 
     #   'sample_id'
-    if (!('sample_id' %in% colnames(colData(spe))) || !(sample_id %in% spe$sample_id)) {
+    if (!("sample_id" %in% colnames(colData(spe))) || !(sample_id %in% spe$sample_id)) {
         stop(paste("'spe$sample_id' must exist and contain the ID", sample_id))
     }
 
@@ -151,7 +147,7 @@ spot_plot_sparsity = function(
     }
 
     #   Not-allowed '...' parameters
-    if (any(c('var_name', 'is_discrete') %in% names(list(...)))) {
+    if (any(c("var_name", "is_discrete") %in% names(list(...)))) {
         stop("The 'var_name' and 'is_discrete' parameters are internally handled and may not be specified through '...' arguments")
     }
 }
diff --git a/tests/testthat/test-multi_gene.R b/tests/testthat/test-multi_gene.R
index cc2a646..b3e4e46 100644
--- a/tests/testthat/test-multi_gene.R
+++ b/tests/testthat/test-multi_gene.R
@@ -2,18 +2,18 @@ library(SpatialExperiment)
 
 #   Given a SpatialExperiment and multi-gene-plotting function 'plot_fun'
 #   from this package, run tests and return NULL
-test_plot_fun = function(spe, plot_fun) {
+test_plot_fun <- function(spe, plot_fun) {
     #   Bad gene names (gene symbols are not in rownames)
     sample_id <- unique(spe$sample_id)[1]
-    genes = 'MOBP'
+    genes <- "MOBP"
     expect_error(
         plot_fun(spe, genes, sample_id),
         "does not contain the selected genes"
     )
-    
+
     #   Bad sample ID
-    sample_id = 'asdasdasdasd'
-    genes = rownames(spe)[1:10]
+    sample_id <- "asdasdasdasd"
+    genes <- rownames(spe)[1:10]
     expect_error(
         plot_fun(spe, genes, sample_id),
         "must exist and contain the ID"
@@ -21,7 +21,7 @@ test_plot_fun = function(spe, plot_fun) {
 
     #   Bad assay name
     sample_id <- unique(spe$sample_id)[1]
-    genes = rownames(spe)[1:10]
+    genes <- rownames(spe)[1:10]
     expect_error(
         plot_fun(spe, genes, sample_id, assayname = "asfasdad"),
         "is not an assay"
@@ -48,5 +48,9 @@ test_plot_fun = function(spe, plot_fun) {
 spe <- fetch_data(type = "spatialDLPFC_Visium_example_subset")
 spe$exclude_overlapping <- FALSE
 
-test_that("spot_plot_z_score", { test_plot_fun(spe, spot_plot_z_score) })
-test_that("spot_plot_sparsity", { test_plot_fun(spe, spot_plot_sparsity) })
+test_that("spot_plot_z_score", {
+    test_plot_fun(spe, spot_plot_z_score)
+})
+test_that("spot_plot_sparsity", {
+    test_plot_fun(spe, spot_plot_sparsity)
+})