@@ -28,7 +28,7 @@ cluster_reads <- function(x, chr, start, end, min_pts = 5) {

     }

     methy_data <- methy_data %>%

-        dplyr::filter(pos >= start & pos < end)

+        dplyr::filter(.data$pos >= .data$start & .data$pos < .data$end)

     read_stats <- get_read_stats(methy_data)

@@ -37,13 +37,13 @@ cluster_reads <- function(x, chr, start, end, min_pts = 5) {

     max_span <- max(read_stats$span)

     keep_reads <- read_stats$read_name[read_stats$span > 0.9 * max_span]

     methy_data <- methy_data %>%

-        dplyr::filter(read_name %in% keep_reads)

+        dplyr::filter(.data$read_name %in% keep_reads)

     # convert methylation data into a matrix with one row for each read name

     mod_mat <- methy_data %>%

-        dplyr::select(read_name, pos, mod_prob) %>%

-        dplyr::arrange(pos) %>%

-        tidyr::pivot_wider(names_from = pos, values_from = mod_prob) %>%

+        dplyr::select("read_name", "pos", "mod_prob") %>%

+        dplyr::arrange(.data$pos) %>%

+        tidyr::pivot_wider(names_from = "pos", values_from = "mod_prob") %>%

         df_to_matrix()

     # pre-check before filtering

@@ -75,26 +75,26 @@ cluster_reads <- function(x, chr, start, end, min_pts = 5) {

     # merge and process results of cluster analysis and read statistics

     clust_df %>%

         dplyr::left_join(read_stats, by = "read_name") %>%

-        dplyr::arrange(cluster_id) %>%

+        dplyr::arrange(.data$cluster_id) %>%

         dplyr::mutate(

-            cluster_id = as.factor(cluster_id),

-            start = as.integer(start),

-            end = as.integer(end),

-            span = as.integer(span)

+            cluster_id = as.factor(.data$cluster_id),

+            start = as.integer(.data$start),

+            end = as.integer(.data$end),

+            span = as.integer(.data$span)

         )

 }

 # summarize read statistics (start, end, strand) based on same read name

 get_read_stats <- function(methy_data) {

     methy_data %>%

-        group_by(read_name) %>%

+        group_by(.data$read_name) %>%

         summarise(

-            start = min(pos),

-            end = max(pos),

-            mean = mean(mod_prob, na.rm = TRUE),

-            span = end - start,

-            strand = unique(strand)

+            start = min(.data$pos),

+            end = max(.data$pos),

+            mean = mean(.data$mod_prob, na.rm = TRUE),

+            span = .data$end - .data$start,

+            strand = unique(.data$strand)

         ) %>%

-        arrange(strand)

+        arrange(.data$strand)

 }

@@ -83,7 +83,7 @@ plot_methylation_internal <- function(

     # add points

     if (points) {

         p <- p +

-            geom_point(

+            ggplot2::geom_point(

                 aes(y = .data$mod_prob),

                 alpha = 0.75

             )