From 0d50b21092bb8dd4b19a2e71a5de90a31cb4612c Mon Sep 17 00:00:00 2001
From: Tiago Olivoto <tiagoolivoto@gmail.com>
Date: Thu, 13 May 2021 13:16:22 -0300
Subject: [PATCH] Prepare for the 0.2.0 release

---
 DESCRIPTION          |  3 +-
 NAMESPACE            |  2 --
 R/count_lesions.R    | 84 ++++++++++++++++++++------------------------
 R/count_objects.R    | 61 +++++++++++++++-----------------
 R/leaf_area.R        | 34 ++++++++----------
 R/objects_rgb.R      | 24 ++++++-------
 R/symptomatic_area.R | 47 ++++++++++---------------
 R/utilities.R        | 13 +++----
 R/utils-pipe.R       | 14 --------
 R/utils_file.R       | 10 +++---
 R/utils_imagem.R     | 22 +++++++-----
 man/pipe.Rd          | 20 -----------
 12 files changed, 135 insertions(+), 199 deletions(-)
 delete mode 100644 R/utils-pipe.R
 delete mode 100644 man/pipe.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index b58a452..032fad4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -18,8 +18,7 @@ Depends:
     R (>= 4.0.0)
 Imports: 
     EBImage,
-    ggplot2,
-    magrittr
+    ggplot2
 Suggests: 
     knitr,
     rmarkdown
diff --git a/NAMESPACE b/NAMESPACE
index 6372a77..c7e6ded 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,7 +2,6 @@
 
 S3method(plot,image_index)
 S3method(plot,objects_rgb)
-export("%>%")
 export(cm_to_dpi)
 export(cm_to_pixels)
 export(count_lesions)
@@ -45,7 +44,6 @@ importFrom(graphics,par)
 importFrom(graphics,points)
 importFrom(graphics,text)
 importFrom(grid,grid.raster)
-importFrom(magrittr,"%>%")
 importFrom(parallel,clusterExport)
 importFrom(parallel,detectCores)
 importFrom(parallel,makeCluster)
diff --git a/R/count_lesions.R b/R/count_lesions.R
index dea1f3a..fa2c273 100644
--- a/R/count_lesions.R
+++ b/R/count_lesions.R
@@ -202,16 +202,14 @@ count_lesions <- function(img,
         ################## no background #############
         if(is.null(img_background)){
           sadio_sintoma <-
-            rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                  sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]) %>%
-            transform(Y = ifelse(CODE == "img_healthy", 1, 0))
+            transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                            sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]),
+                      Y = ifelse(CODE == "img_healthy", 1, 0))
           sadio_sintoma$CODE <- NULL
           usef_area <- nrow(original$df_in)
-          model <-
-            glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma) %>%
-            suppressWarnings()
+          model <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma))
           # isolate plant
-          pred1 <- predict(model, newdata = original$df_in, type="response") %>% round(0)
+          pred1 <- round(predict(model, newdata = original$df_in, type="response"), 0)
           plant_symp <- matrix(pred1, ncol = ncol(original$R))
           plant_symp <- 1 - image_correct(plant_symp, perc = 0.01)
           ID <- c(plant_symp == 0)
@@ -282,30 +280,28 @@ count_lesions <- function(img,
           fundo <- image_to_mat(img_background)
           # separate image from background
           fundo_resto <-
-            rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                  sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
-                  fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]) %>%
-            transform(Y = ifelse(CODE == "img_background", 0, 1))
-          modelo1 <-
-            glm(Y ~ R + G + B, family = binomial("logit"), data = fundo_resto) %>%
-            suppressWarnings()
-          pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
+            transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                            sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
+                            fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]),
+                      Y = ifelse(CODE == "img_background", 0, 1))
+          modelo1 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"),
+                                          data = fundo_resto))
+          pred1 <- round(predict(modelo1, newdata = original$df_in, type="response"), 0)
           plant_background <- matrix(pred1, ncol = ncol(original$R))
           plant_background <- image_correct(plant_background, perc = 0.009)
           plant_background[plant_background == 1] <- 2
           sadio_sintoma <-
-            rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                  sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]) %>%
-            transform(Y = ifelse(CODE == "img_healthy", 1, 0))
+            transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                            sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]),
+                      Y = ifelse(CODE == "img_healthy", 1, 0))
           sadio_sintoma$CODE <- NULL
-          modelo2 <-
-            glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma) %>%
-            suppressWarnings()
+          modelo2 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"),
+                                          data = sadio_sintoma))
           # isolate plant
           ID <- c(plant_background == 2)
           usef_area <- nrow(original$df_in[ID,])
-          pred2 <- predict(modelo2, newdata = original$df_in[ID,], type="response") %>% round(0)
-          pred3 <- predict(modelo2, newdata = original$df_in, type="response") %>% round(0)
+          pred2 <- round(predict(modelo2, newdata = original$df_in[ID,], type="response"), 0)
+          pred3 <- round(predict(modelo2, newdata = original$df_in, type="response"), 0)
           pred3[!ID] <- 1
           leaf_sympts <- matrix(pred3, ncol = ncol(original$R))
           leaf_sympts <- 1 - image_correct(leaf_sympts, perc = 0.009)
@@ -510,21 +506,21 @@ count_lesions <- function(img,
         dev.off()
       }
       stats <-
-        data.frame(area = c(n = length(shape$s.area),
-                            min(shape$s.area),
-                            mean(shape$s.area),
-                            max(shape$s.area),
-                            sd(shape$s.area),
-                            sum(shape$s.area),
-                            sum(shape$s.area) /usef_area * 100),
-                   perimeter = c(NA,
-                                 min(shape$s.perimeter),
-                                 mean(shape$s.perimeter),
-                                 max(shape$s.perimeter),
-                                 sd(shape$s.perimeter),
-                                 sum(shape$s.perimeter),
-                                 NA)) %>%
-        transform(statistics = c("n", "min", "mean", "max", "sd", "sum", "prop"))
+        transform(data.frame(area = c(n = length(shape$s.area),
+                                      min(shape$s.area),
+                                      mean(shape$s.area),
+                                      max(shape$s.area),
+                                      sd(shape$s.area),
+                                      sum(shape$s.area),
+                                      sum(shape$s.area) /usef_area * 100),
+                             perimeter = c(NA,
+                                           min(shape$s.perimeter),
+                                           mean(shape$s.perimeter),
+                                           max(shape$s.perimeter),
+                                           sd(shape$s.perimeter),
+                                           sum(shape$s.perimeter),
+                                           NA)),
+                  statistics = c("n", "min", "mean", "max", "sd", "sum", "prop"))
       stats <- stats[c(3, 1, 2)]
       shape <- shape[,c(1:6, 8:9, 7)]
       colnames(shape) <- c("id", "x", "y", "area", "perimeter", "radius_mean",
@@ -565,7 +561,7 @@ count_lesions <- function(img,
                                 "check_names_dir", "file_extension", "image_import",
                                 "image_binary", "watershed", "distmap", "computeFeatures.moment",
                                 "computeFeatures.shape", "colorLabels", "image_show",
-                                "%>%", "image_to_mat", "image_correct", "bwlabel"),
+                                "image_to_mat", "image_correct", "bwlabel"),
                     envir=environment())
       on.exit(stopCluster(clust))
       if(verbose == TRUE){
@@ -598,17 +594,15 @@ count_lesions <- function(img,
     stats <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["statistics"]] %>%
-                  transform(id =  names(results[i])) %>%
-                  .[,c(4, 1, 2, 3)]
+                  transform(results[[i]][["statistics"]],
+                            id =  names(results[i]))[,c(4, 1, 2, 3)]
               })
       )
     results <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["results"]] %>%
-                  transform(img =  names(results[i])) %>%
-                  .[, c(10, 1:9)]
+                  transform(results[[i]][["results"]],
+                            img =  names(results[i]))[, c(10, 1:9)]
               })
       )
     return(list(statistics = stats,
diff --git a/R/count_objects.R b/R/count_objects.R
index 2843908..60fcbd4 100644
--- a/R/count_objects.R
+++ b/R/count_objects.R
@@ -207,13 +207,11 @@ count_objects <- function(img,
         foreground <- image_to_mat(foreground)
         background <- image_to_mat(background)
         back_fore <-
-          rbind(foreground$df_in[sample(1:nrow(foreground$df_in)),][1:nrows,],
-                background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]) %>%
-          transform(Y = ifelse(CODE == "background", 0, 1))
-        modelo1 <-
-          glm(Y ~ R + G + B, family = binomial("logit"), data = back_fore) %>%
-          suppressWarnings()
-        pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
+          transform(rbind(foreground$df_in[sample(1:nrow(foreground$df_in)),][1:nrows,],
+                          background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]),
+                    Y = ifelse(CODE == "background", 0, 1))
+        modelo1 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = back_fore))
+        pred1 <- round(predict(modelo1, newdata = original$df_in, type="response"), 0)
         foreground_background <- matrix(pred1, ncol = ncol(original$R))
         foreground_background <- image_correct(foreground_background, perc = 0.02)
         ID <- c(foreground_background == 1)
@@ -242,7 +240,7 @@ count_objects <- function(img,
         parms2 <- parms[parms$object_size == object_size,]
         rowid <-
           which(sapply(as.character(parms2$resolution), function(x) {
-          eval(parse(text=x))}))
+            eval(parse(text=x))}))
         ext <- ifelse(is.null(extension),  parms2[rowid, 3], extension)
         tol <- ifelse(is.null(tolerance), parms2[rowid, 4], tolerance)
         nmask <- watershed(distmap(img2),
@@ -360,27 +358,27 @@ count_objects <- function(img,
           image_show(im2)
           text(shape[,2],
                shape[,3],
-                 col = marker_col,
-                 pch = 16,
-                 cex = marker_size)
+               col = marker_col,
+               pch = 16,
+               cex = marker_size)
 
         }
         dev.off()
       }
       stats <-
-        data.frame(area = c(n = length(shape$s.area),
-                            min(shape$s.area),
-                            mean(shape$s.area),
-                            max(shape$s.area),
-                            sd(shape$s.area),
-                            sum(shape$s.area)),
-                   perimeter = c(NA,
-                                 min(shape$s.perimeter),
-                                 mean(shape$s.perimeter),
-                                 max(shape$s.perimeter),
-                                 sd(shape$s.perimeter),
-                                 sum(shape$s.perimeter))) %>%
-        transform(statistics = c("n", "min", "mean", "max", "sd", "sum"))
+        transform(data.frame(area = c(n = length(shape$s.area),
+                                      min(shape$s.area),
+                                      mean(shape$s.area),
+                                      max(shape$s.area),
+                                      sd(shape$s.area),
+                                      sum(shape$s.area)),
+                             perimeter = c(NA,
+                                           min(shape$s.perimeter),
+                                           mean(shape$s.perimeter),
+                                           max(shape$s.perimeter),
+                                           sd(shape$s.perimeter),
+                                           sum(shape$s.perimeter))),
+                  statistics = c("n", "min", "mean", "max", "sd", "sum"))
       stats <- stats[c(3, 1, 2)]
       shape <- shape[,c(1:6, 8:9, 7)]
       colnames(shape) <- c("id", "x", "y", "area", "perimeter", "radius_mean",
@@ -424,8 +422,7 @@ count_objects <- function(img,
                     varlist = c("names_plant", "help_count", "file_name",
                                 "check_names_dir", "file_extension", "image_import",
                                 "image_binary", "watershed", "distmap", "computeFeatures.moment",
-                                "computeFeatures.shape", "colorLabels", "image_show",
-                                "%>%"),
+                                "computeFeatures.shape", "colorLabels", "image_show"),
                     envir=environment())
       on.exit(stopCluster(clust))
       if(verbose == TRUE){
@@ -461,20 +458,18 @@ count_objects <- function(img,
     stats <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["statistics"]] %>%
-                  transform(id =  names(results[i])) %>%
-                  .[,c(4, 1, 2, 3)]
+                transform(results[[i]][["statistics"]],
+                          id =  names(results[i]))[,c(4, 1, 2, 3)]
               })
       )
     results <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["results"]] %>%
-                  transform(img =  names(results[i])) %>%
-                  .[, c(10, 1:9)]
+                transform(results[[i]][["results"]],
+                          img =  names(results[i]))[, c(10, 1:9)]
               })
       )
-      summ <- stats[stats$statistics == "n",c(1,3)]
+    summ <- stats[stats$statistics == "n",c(1,3)]
     if(verbose == TRUE){
       names(summ) <- c("Image", "Objects")
       cat("--------------------------------------------\n")
diff --git a/R/leaf_area.R b/R/leaf_area.R
index 54b3e0e..80860ba 100644
--- a/R/leaf_area.R
+++ b/R/leaf_area.R
@@ -163,32 +163,28 @@ leaf_area <- function(img,
       background <- image_to_mat(img_background)
       # separate image from background
       background_resto <-
-        rbind(leaf$df_in[sample(1:nrow(leaf$df_in)),][1:nrows,],
-              template$df_in[sample(1:nrow(template$df_in)),][1:nrows,],
-              background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]) %>%
-        transform(Y = ifelse(CODE == "img_background", 1, 0))
+        transform(rbind(leaf$df_in[sample(1:nrow(leaf$df_in)),][1:nrows,],
+                        template$df_in[sample(1:nrow(template$df_in)),][1:nrows,],
+                        background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]),
+                  Y = ifelse(CODE == "img_background", 1, 0))
       background_resto$CODE <- NULL
-      modelo1 <-
-        glm(Y ~ R + G + B, family = binomial("logit"), data = background_resto) %>%
-        suppressWarnings()
-      pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
+      modelo1 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = background_resto))
+      pred1 <- round(predict(modelo1, newdata = original$df_in, type="response"), 0)
       plant_background <- matrix(pred1, ncol = ncol(original$R))
       plant_background <- image_correct(plant_background, perc = 0.009)
       plant_background[plant_background == 1] <- 2
       # image_show(plant_background!=2)
       # separate leaf from template
       leaf_template <-
-        rbind(leaf$df_in[sample(1:nrow(leaf$df_in)),][1:nrows,],
-              template$df_in[sample(1:nrow(template$df_in)),][1:nrows,]) %>%
-        transform(Y = ifelse(CODE == "img_leaf", 0, 1))
+        transform(rbind(leaf$df_in[sample(1:nrow(leaf$df_in)),][1:nrows,],
+                        template$df_in[sample(1:nrow(template$df_in)),][1:nrows,]),
+                  Y = ifelse(CODE == "img_leaf", 0, 1))
       background_resto$CODE <- NULL
-      modelo2 <-
-        glm(Y ~ R + G + B, family = binomial("logit"), data = leaf_template) %>%
-        suppressWarnings()
+      modelo2 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = leaf_template))
       # isolate plant
       ID <- c(plant_background == 0)
-      pred2 <- predict(modelo2, newdata = original$df_in[ID,], type="response") %>% round(0)
-      pred3 <- predict(modelo2, newdata = original$df_in, type="response") %>% round(0)
+      pred2 <- round(predict(modelo2, newdata = original$df_in[ID,], type="response"), 0)
+      pred3 <- round(predict(modelo2, newdata = original$df_in, type="response"), 0)
       leaf_template <- matrix(pred3, ncol = ncol(original$R))
       leaf_template <- image_correct(leaf_template, perc = 0.009)
       plant_background[leaf_template == 1] <- 3
@@ -218,8 +214,8 @@ leaf_area <- function(img,
       shape <- rbind(shape_leaf, shape_template)
       shape$id <- 1:nrow(shape)
       shape <-
-        shape[, c(10, 7, 8, 1, 9, 2:6)] %>%
-        transform(label = paste(id, "|", round(area, text_digits), sep = ""))
+        transform(shape[, c(10, 7, 8, 1, 9, 2:6)],
+                  label = paste(id, "|", round(area, text_digits), sep = ""))
       if(show_original == TRUE){
         im2 <- img
         if(!is.null(col_background)){
@@ -304,7 +300,7 @@ leaf_area <- function(img,
                                 "check_names_dir", "file_extension", "image_import",
                                 "image_binary", "watershed", "distmap", "computeFeatures.moment",
                                 "computeFeatures.shape", "colorLabels", "image_show",
-                                "%>%", "image_to_mat", "image_correct", "bwlabel"),
+                                "image_to_mat", "image_correct", "bwlabel"),
                     envir=environment())
       on.exit(stopCluster(clust))
       if(verbose == TRUE){
diff --git a/R/objects_rgb.R b/R/objects_rgb.R
index f6ea3ba..9f47825 100644
--- a/R/objects_rgb.R
+++ b/R/objects_rgb.R
@@ -190,13 +190,11 @@ objects_rgb <- function(img,
         foreground <- image_to_mat(foreground)
         background <- image_to_mat(background)
         back_fore <-
-          rbind(foreground$df_in[sample(1:nrow(foreground$df_in)),][1:nrows,],
-                background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]) %>%
-          transform(Y = ifelse(CODE == "background", 0, 1))
-        modelo1 <-
-          glm(Y ~ R + G + B, family = binomial("logit"), data = back_fore) %>%
-          suppressWarnings()
-        pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
+          transform(rbind(foreground$df_in[sample(1:nrow(foreground$df_in)),][1:nrows,],
+                          background$df_in[sample(1:nrow(background$df_in)),][1:nrows,]),
+                    Y = ifelse(CODE == "background", 0, 1))
+        modelo1 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = back_fore))
+        pred1 <- round(predict(modelo1, newdata = original$df_in, type="response"), 0)
         foreground_background <- matrix(pred1, ncol = ncol(original$R))
         foreground_background <- image_correct(foreground_background, perc = 0.02)
         ID <- c(foreground_background == 1)
@@ -369,7 +367,7 @@ objects_rgb <- function(img,
                                 "check_names_dir", "file_extension", "image_import",
                                 "image_binary", "watershed", "distmap", "computeFeatures.moment",
                                 "computeFeatures.shape", "colorLabels", "image_show",
-                                "%>%", "image_resize", "detectCores", "makeCluster", "clusterExport",
+                                "image_resize", "detectCores", "makeCluster", "clusterExport",
                                 "stopCluster", "parLapply"),
                     envir=environment())
       on.exit(stopCluster(clust))
@@ -401,17 +399,15 @@ objects_rgb <- function(img,
     objects <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["objects"]] %>%
-                  transform(img =  names(results[i])) %>%
-                  .[,c(10, 1:9)]
+                  transform(results[[i]][["objects"]],
+                            img =  names(results[i]))[,c(10, 1:9)]
               })
       )
     indexes <-
       do.call(rbind,
               lapply(seq_along(results), function(i){
-                results[[i]][["indexes"]] %>%
-                  transform(img =  names(results[i])) %>%
-                  .[, c(3, 1:2)]
+                  transform(results[[i]][["indexes"]],
+                            img =  names(results[i]))[, c(3, 1:2)]
               })
       )
     invisible(list(objects = objects,
diff --git a/R/symptomatic_area.R b/R/symptomatic_area.R
index 0b6ab7b..bcfbea2 100644
--- a/R/symptomatic_area.R
+++ b/R/symptomatic_area.R
@@ -140,20 +140,17 @@ symptomatic_area <- function(img,
       ################## no background #############
       if(is.null(img_background)){
         sadio_sintoma <-
-          rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]) %>%
-          transform(Y = ifelse(CODE == "img_healthy", 1, 0))
+          transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                          sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]),
+                    Y = ifelse(CODE == "img_healthy", 1, 0))
         usef_area <- nrow(original$df_in)
-        model <-
-          glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma) %>%
-          suppressWarnings()
+        model <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma))
         # isolate plant
-        pred1 <- predict(model, newdata = original$df_in, type="response") %>% round(0)
+        pred1 <- round(predict(model, newdata = original$df_in, type="response"), 0)
         plant_symp <- matrix(pred1, ncol = ncol(original$R))
         plant_symp <- image_correct(plant_symp, perc = 0.01)
         ID <- c(plant_symp == 0)
         pix_sympt <- length(which(ID == TRUE))
-        # pred2 <- predict(model, newdata = original$df_in[ID,], type="response") %>% round(0)
         if(show_original == TRUE){
           if(is.null(col_background)){
             col_background <- col2rgb("green")
@@ -197,28 +194,24 @@ symptomatic_area <- function(img,
         fundo <- image_to_mat(img_background)
         # separate image from background
         fundo_resto <-
-          rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
-                fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]) %>%
-          transform(Y = ifelse(CODE == "img_background", 0, 1))
-        modelo1 <-
-          glm(Y ~ R + G + B, family = binomial("logit"), data = fundo_resto) %>%
-          suppressWarnings()
-        pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
+          transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                          sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
+                          fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]),
+                    Y = ifelse(CODE == "img_background", 0, 1))
+        modelo1 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = fundo_resto))
+        pred1 <- round(predict(modelo1, newdata = original$df_in, type="response"), 0)
         plant_background <- matrix(pred1, ncol = ncol(original$R))
         plant_background <- image_correct(plant_background, perc = 0.009)
         plant_background[plant_background == 1] <- 2
         sadio_sintoma <-
-          rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
-                sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]) %>%
-          transform(Y = ifelse(CODE == "img_healthy", 1, 0))
-        modelo2 <-
-          glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma) %>%
-          suppressWarnings()
+          transform(rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
+                          sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,]),
+                    Y = ifelse(CODE == "img_healthy", 1, 0))
+        modelo2 <- suppressWarnings(glm(Y ~ R + G + B, family = binomial("logit"), data = sadio_sintoma))
         # isolate plant
         ID <- c(plant_background == 2)
         usef_area <- nrow(original$df_in[ID,])
-        pred3 <- predict(modelo2, newdata = original$df_in[ID,], type="response") %>% round(0)
+        pred3 <- round(predict(modelo2, newdata = original$df_in[ID,], type="response"), 0)
         pix_sympt <- length(which(pred3 == 0))
         if(show_original == TRUE){
           im2 <- img
@@ -298,7 +291,7 @@ symptomatic_area <- function(img,
                                 "check_names_dir", "file_extension", "image_import",
                                 "image_binary", "watershed", "distmap", "computeFeatures.moment",
                                 "computeFeatures.shape", "colorLabels", "image_show",
-                                "%>%", "image_to_mat", "image_correct", "image_export"),
+                                "image_to_mat", "image_correct", "image_export"),
                     envir=environment())
       on.exit(stopCluster(clust))
       if(verbose == TRUE){
@@ -327,10 +320,8 @@ symptomatic_area <- function(img,
                    save_image, dir_original, dir_processed)
     }
     }
-    results <-
-      do.call(rbind, results) %>%
-      transform(sample = names_plant)
-    results <- results[c(3, 1, 2)]
+    results <- transform(do.call(rbind, results),
+                         sample = names_plant)[c(3, 1, 2)]
     return(results)
   }
 }
diff --git a/R/utilities.R b/R/utilities.R
index b1e69a3..dd01d88 100644
--- a/R/utilities.R
+++ b/R/utilities.R
@@ -32,9 +32,7 @@ run_progress <- function(pb,
                          digits = 0,
                          sleep = 0){
   Sys.sleep(sleep)
-  elapsed <-
-    as.numeric(difftime(Sys.time(), pb$time, units = "secs")) %>%
-    sec_to_hms()
+  elapsed <- sec_to_hms(as.numeric(difftime(Sys.time(), pb$time, units = "secs")))
   temp <- switch(
     pb$style,
     list(extra = nchar(text) + nchar(pb$leftd) + nchar(pb$rightd),
@@ -47,11 +45,10 @@ run_progress <- function(pb,
          text = paste(text, paste(pb$leftd, '%s%s', pb$rightd, sep = ""), '% s%%', elapsed))
   )
   step <- round(actual / pb$max * (pb$width - temp$extra))
-  temp$text %>%
-    sprintf(strrep(pb$char, step),
-            strrep(' ', pb$width - step - temp$extra),
-            round(actual / pb$max * 100, digits = digits)) %>%
-    cat("\r")
+    cat(sprintf(temp$text,
+                strrep(pb$char, step),
+                strrep(' ', pb$width - step - temp$extra),
+                round(actual / pb$max * 100, digits = digits)), "\r")
   if(actual == pb$max){
     cat("\n")
   }
diff --git a/R/utils-pipe.R b/R/utils-pipe.R
deleted file mode 100644
index fd0b1d1..0000000
--- a/R/utils-pipe.R
+++ /dev/null
@@ -1,14 +0,0 @@
-#' Pipe operator
-#'
-#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
-#'
-#' @name %>%
-#' @rdname pipe
-#' @keywords internal
-#' @export
-#' @importFrom magrittr %>%
-#' @usage lhs \%>\% rhs
-#' @param lhs A value or the magrittr placeholder.
-#' @param rhs A function call using the magrittr semantics.
-#' @return The result of calling `rhs(lhs)`.
-NULL
diff --git a/R/utils_file.R b/R/utils_file.R
index d7cbabf..c8e3313 100644
--- a/R/utils_file.R
+++ b/R/utils_file.R
@@ -126,11 +126,11 @@ manipulate_files <- function(pattern,
    name <- names
   } else{
     if(length(name) == 1){
-    name <- lapply(seq_along(names),
-                   function(i){
-                     paste0(name, i, collapse = "_")
-                   }) %>%
-      unlist()
+    name <-
+      unlist(lapply(seq_along(names),
+                    function(i){
+                      paste0(name, i, collapse = "_")
+                    }))
     } else{
       name <- name
       if(length(name) != length(names)){
diff --git a/R/utils_imagem.R b/R/utils_imagem.R
index 7dc69f0..a5f5bfd 100644
--- a/R/utils_imagem.R
+++ b/R/utils_imagem.R
@@ -190,15 +190,17 @@ image_dimension <- function(image,
         message("Image processing using multiple sessions (",nworkers, "). Please wait.")
       }
       res <-
+        as.data.frame(
         do.call(rbind,
-                parLapply(clust, image,  image_dimension, verbose =  FALSE)) %>%
-        as.data.frame()
+                parLapply(clust, image,  image_dimension, verbose =  FALSE))
+        )
       res <- transform(res, image = rownames(res))[,c(3, 1, 2)]
     } else{
     res <-
+      as.data.frame(
       do.call(rbind,
-              lapply(image, image_dimension, verbose =  FALSE)) %>%
-      as.data.frame()
+              lapply(image, image_dimension, verbose =  FALSE))
+      )
       res <- transform(res, image = rownames(res))[,c(3, 1, 2)]
     }
     if(verbose == TRUE){
@@ -742,11 +744,13 @@ image_index <- function(image,
 #'ind <- image_index(img2)
 #'plot(ind)
 plot.image_index <- function(x, facet = TRUE, ...){
-  mat <- do.call(cbind,
-                 lapply(x, function(i){
-                   as.vector(i)}
-                 )) %>%
-    as.data.frame()
+  mat <-
+    as.data.frame(
+      do.call(cbind,
+              lapply(x, function(i){
+                as.vector(i)}
+              ))
+    )
   colnames(mat) <- names(x)
   mat$id <- rownames(mat)
   if(length(x) == 1){
diff --git a/man/pipe.Rd b/man/pipe.Rd
deleted file mode 100644
index a648c29..0000000
--- a/man/pipe.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils-pipe.R
-\name{\%>\%}
-\alias{\%>\%}
-\title{Pipe operator}
-\usage{
-lhs \%>\% rhs
-}
-\arguments{
-\item{lhs}{A value or the magrittr placeholder.}
-
-\item{rhs}{A function call using the magrittr semantics.}
-}
-\value{
-The result of calling \code{rhs(lhs)}.
-}
-\description{
-See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
-}
-\keyword{internal}