diff --git a/DESCRIPTION b/DESCRIPTION
index 4dc217a..91eafee 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -11,8 +11,7 @@ Depends:
 Imports:
   ggplot2 (>= 1.0.0),
   dplyr (>= 0.4.3),
-  lazyeval (>= 0.1.10),
-  tibble 
+  rlang
 URL: https://github.com/tinyheero/cofeatureR
 BugReports: https://github.com/tinyheero/cofeatureR/issues
 License: GPL-3
diff --git a/NAMESPACE b/NAMESPACE
index 8691011..f6cce95 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,4 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
 export(plot_cofeature_mat)
+importFrom(rlang,.data)
 importFrom(stats,setNames)
diff --git a/R/plot_cofeature_mat.R b/R/plot_cofeature_mat.R
index d5a24c2..02d0065 100644
--- a/R/plot_cofeature_mat.R
+++ b/R/plot_cofeature_mat.R
@@ -144,63 +144,49 @@ plot_cofeature_mat <- function(
 
   if (type.display.mode == "single") {
     message("Using type.display.mode single")
-    in.df <- dplyr::distinct_(in.df)
+    in.df <- dplyr::distinct(in.df)
 
-    mutate.call <- lazyeval::interp(~ factor(type, levels = rev(type.order)), 
-                                    type = as.name("type"),
-                                    type.order = as.name("type.order"))
+    in.df <- dplyr::mutate(in.df,
+                           type = factor(.data$type, levels = rev(type.order)))
 
-    in.df <- dplyr::mutate_(in.df, 
-                            .dots = setNames(list(mutate.call), "type"))
-
-    in.df <- dplyr::group_by_(in.df, .dots = c("sampleID", "feature"))
-    in.df <- dplyr::arrange_(in.df, .dots = c("type"))
+    in.df <- dplyr::group_by(in.df, .data$sampleID, .data$feature)
+    in.df <- dplyr::arrange(in.df, .data$type)
     in.df <- dplyr::top_n(in.df, n = 1)
     in.df <- dplyr::ungroup(in.df)
   }
 
   message("Setting feature order")
-  mutate.call <- lazyeval::interp(~ as.numeric(
-                                      factor(feature, 
-                                             levels = feature.order)),
-                                  feature = as.name("feature"))
-  in.df <- dplyr::mutate_(in.df, 
-                          .dots = setNames(list(mutate.call), "feature"))
+  in.df <- dplyr::mutate(in.df,
+                         feature = as.numeric(factor(.data$feature,
+                                                     levels = feature.order)))
 
   # Set sample order
   message("Setting sample order")
-  mutate.call <- lazyeval::interp(~ factor(sampleID, 
-                                           levels = sample.id.order),
-                                  sampleID = as.name("sampleID"))
-  in.df <- dplyr::mutate_(in.df, 
-                          .dots = setNames(list(mutate.call), "sampleID"))
+  in.df <- dplyr::mutate(in.df,
+                         sampleID = factor(.data$sampleID,
+                                           levels = sample.id.order))
 
   # Calculate shift
-  in.df <- dplyr::group_by_(in.df, .dots = c("feature", "sampleID"))
-  mutate.call <- lazyeval::interp(~ (1:n())/n() - 
-                                  1/(2 * n()) - 1/2)
-  in.df <- dplyr::mutate_(in.df, 
-                          .dots = setNames(list(mutate.call), "shift"))
+  in.df <- dplyr::group_by(in.df, .data$feature, .data$sampleID)
+  in.df <- dplyr::mutate(in.df,
+                         shift = (1:dplyr::n()) / dplyr::n() -
+                                 1 / (2 * dplyr::n()) - 1/2)
 
   # Calculate height
-  mutate.call <- lazyeval::interp(~ 1/n())
-  in.df <- dplyr::mutate_(in.df, 
-                          .dots = setNames(list(mutate.call), "height"))
+  in.df <- dplyr::mutate(in.df,
+                         height = 1 / dplyr::n())
 
   # Calculate feature_shift
-  mutate.call <- lazyeval::interp(~ feature + shift, 
-                                  feature = as.name("feature"),
-                                  shift = as.name("shift"))
-  in.df <- dplyr::mutate_(in.df, 
-                          .dots = setNames(list(mutate.call), "feature_shift"))
-
-  p1 <- ggplot2::ggplot(in.df, 
-                        ggplot2::aes_string(x = "sampleID", 
-                          y = "feature_shift", 
-                          height = "height",
-                          fill = "type")) +
+  in.df <- dplyr::mutate(in.df,
+                         feature_shift = .data$feature + .data$shift)
+
+  p1 <- ggplot2::ggplot(in.df,
+                        ggplot2::aes(x = .data$sampleID,
+                          y = .data$feature_shift,
+                          height = .data$height,
+                          fill = .data$type)) +
     ggplot2::scale_x_discrete(drop = drop.x) +
-    ggplot2::scale_y_discrete(limits = 1:length(feature.order), 
+    ggplot2::scale_y_discrete(limits = 1:length(feature.order),
                               labels = feature.order) +
     ggplot2::ylab("Feature") +
     ggplot2::xlab("Sample ID")
@@ -213,7 +199,7 @@ plot_cofeature_mat <- function(
   if (missing(missing.fill.col)) {
     if (tile.flag) {
       p1 <- p1 +
-        ggplot2::geom_tile(color = tile.col, size = 1)
+        ggplot2::geom_tile(color = tile.col, linewidth = 1)
     }
   } else {
     p1 <- add_tiles(p1, in.df, tile.col, missing.fill.col, tile.border.size)
@@ -234,7 +220,7 @@ plot_cofeature_mat <- function(
   if (dot.flag) {
     if (!missing(dot.size)) {
       p1 <- p1 +
-        ggplot2::geom_point(ggplot2::aes_string(size = dot.size))
+        ggplot2::geom_point(ggplot2::aes(size = .data[[dot.size]]))
     } else {
       p1 <- p1 +
         ggplot2::geom_point()
@@ -243,27 +229,21 @@ plot_cofeature_mat <- function(
   p1
 }
 
-#' Add tiles to the ggplot2 
+#' Add tiles to the ggplot2
 #'
 #' @param p1 Existing ggplot2
 #' @inheritParams plot_cofeature_mat
 add_tiles <- function(p1, in.df, tile.col, missing.fill.col, tile.border.size) {
 
   # Plot two geom_tile. 1 for data present and 1 for data missing
-  filter.crit.1 <- lazyeval::interp(~ !is.na(type), 
-                                    .values = list(type = as.name("type")))
-  filter.crit.2 <- lazyeval::interp(~ is.na(type), 
-                                    .values = list(type = as.name("type")))
-
-  # No borders for missing data.
-  p1 <- 
+  p1 <-
     p1 +
     ggplot2::geom_tile(
-      data = dplyr::filter_(in.df, filter.crit.1), 
-      color = tile.col, size = tile.border.size
+      data = dplyr::filter(in.df, !is.na(.data$type)),
+      color = tile.col, linewidth = tile.border.size
     ) +
     ggplot2::geom_tile(
-      data = dplyr::filter_(in.df, filter.crit.2), 
+      data = dplyr::filter(in.df, is.na(.data$type)),
       fill = missing.fill.col, color = tile.col
     )