Add tidyr:: and dplyr:: where needed

Adds a few additional dplyr functions to the package imports, in particular: slice, data_frame, and row_number.

NAMESPACE

 importFrom(dplyr,arrange)
 importFrom(dplyr,bind_cols)
 importFrom(dplyr,bind_rows)
+importFrom(dplyr,data_frame)
 importFrom(dplyr,filter)
 importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
 importFrom(dplyr,right_join)
+importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,semi_join)
+importFrom(dplyr,slice)
 importFrom(magrittr,"%>%")

R/move_together.R

   all <- bind_rows(lhs, rhs)
 
   # separate column and row-filter (ids)
-  x_cols <- distinct(lhs, .col)
-  y_cols <- distinct(rhs, .col)
+  x_cols <- dplyr::distinct(lhs, .col)
+  y_cols <- dplyr::distinct(rhs, .col)
 
   # separate header columns from ids and treat them as columns
-  x_ids <- distinct(lhs, .id, .id_long)
-  y_ids <- distinct(rhs, .id, .id_long)
+  x_ids <- dplyr::distinct(lhs, .id, .id_long)
+  y_ids <- dplyr::distinct(rhs, .id, .id_long)
 
   x_headers <- filter(x_ids, grepl("^\\.header", .id_long))
   y_headers <- filter(y_ids, grepl("^\\.header", .id_long))

R/process_data_helpers.R

   }
 
   x <- x %>%
-    unite(one_of(by), col = ".id", remove = FALSE) %>%
+    tidyr::unite(dplyr::one_of(by), col = ".id", remove = FALSE) %>%
     mutate(.id_long = add_duplicate_number(.id))
 
   y <- y %>%
-    unite(one_of(by), col = ".id", remove = FALSE)  %>%
+    tidyr::unite(dplyr::one_of(by), col = ".id", remove = FALSE)  %>%
     mutate(.id_long = add_duplicate_number(.id))
 
   ids <- dplyr::union(x %>% dplyr::select(.id, .id_long),
 
   x <- x %>%
     mutate(.r = row_number()) %>%
-    gather_(key = ".col", value = ".val", names(x)[grepl("^[^.]", names(x))]) %>%
+    # TODO re-evaluate gather_ here
+    tidyr::gather_(key = ".col", value = ".val", names(x)[grepl("^[^.]", names(x))]) %>%
     mutate(.x = x_keys[.col],
            .y = -.r) %>%
     bind_rows(data_frame(.id = ".header",

R/tidyr_helpers.R

   key_values <- key_values[!key_values %in% ids]
 
   id_values <- x %>% select(one_of(ids))
-  id_values <- id_values %>% gather(key = ".key_map", value = ".id_map")
+  id_values <- id_values %>% tidyr::gather(key = ".key_map", value = ".id_map")
 
   x <- x %>% mutate(.r = row_number()) %>%
-    unite(one_of(ids), col = ".id_map", remove = F)
+    tidyr::unite(dplyr::one_of(ids), col = ".id_map", remove = F)
 
   x <- x %>%
     gather(key = ".col", value = ".val", names(x)[grepl("^[^\\.]", names(x))]) %>%
   tmp <- x %>% filter(.key_map %in% ids)
   x <- bind_rows(
     left_join(tmp %>% select(-.key_map),
-              tmp %>% select(.id_map) %>% crossing(.key_map = key_values),
+              tmp %>% select(.id_map) %>% tidyr::crossing(.key_map = key_values),
               by = ".id_map"),
     x %>% filter(!.key_map %in% ids)
   )
 
   # add header:
-  crosser <- crossing(.id_map = as.character(id_values$.id_map),
+  crosser <- tidyr::crossing(.id_map = as.character(id_values$.id_map),
                       .key_map = key_values)
   key_header <- data_frame(
     .key_map = key_values,
                .x = 1:length(ids),
                .y = 0,
                .header = TRUE),
-    crossing(.id_map = ids, .key_map = key_values),
+    tidyr::crossing(.id_map = ids, .key_map = key_values),
     by = ".id_map"
   )
 
   x <- bind_rows(id_header, key_header, x)
 
-  x <- x %>% unite(.key_map, .id_map, .val, col = ".id", remove = F)
+  x <- x %>% tidyr::unite(.key_map, .id_map, .val, col = ".id", remove = F)
 
   x %>%
     add_color_tidyr(key_values = key_values) %>%
   xn <- names(x)
 
   x <- x %>% mutate(.r = row_number()) %>%
-    unite(ids, col = ".id_map", remove = F) %>%
-    unite(key, col = ".key_map", remove = F)
+    tidyr::unite(ids, col = ".id_map", remove = F) %>%
+    tidyr::unite(key, col = ".key_map", remove = F)
 
   key_values <- x %>% pull(key) %>% unique()
 
   names(x_dict) <- xn
 
   x <- x %>%
-    gather(key = ".col", value = ".val", names(x)[grepl("^[^\\.]", names(x))]) %>%
+    tidyr::gather(key = ".col", value = ".val", names(x)[grepl("^[^\\.]", names(x))]) %>%
     mutate(
       .x = x_dict[.col],
       .y = -rep(1:nr, nc),
 
   # add headers:
 
-  id_headers <- crossing(.id_map = ids, # x$.id_map %>% unique()
-                         .key_map = key_values,
-                         ) %>%
+  id_headers <- tidyr::crossing(.id_map = ids, # x$.id_map %>% unique()
+                                .key_map = key_values,
+  ) %>%
     mutate(
       .r = 0,
       .col = "id",
   )
 
   x <- x %>%
-    add_row(
+    dplyr::add_row(
       .before = T,
       .id_map = c(rep("key", length(key)), rep("value", length(value))),
       .key_map = c(rep("key", length(key)), rep("value", length(value))),
   x <- bind_rows(id_headers, x)
 
   x <- x %>%
-    unite(.key_map, .id_map, .val, col = ".id", remove = F)
+    tidyr::unite(.key_map, .id_map, .val, col = ".id", remove = F)
 
   x %>% add_color_tidyr(key_values = key_values) %>%
     mutate(.alpha = ifelse(.header == TRUE, 1, 0.6))

R/zzzz-package.R

 #' @importFrom dplyr left_join right_join full_join inner_join semi_join anti_join
-#' @importFrom dplyr mutate select filter arrange bind_rows bind_cols group_by pull
+#' @importFrom dplyr mutate select filter arrange bind_rows bind_cols group_by pull slice data_frame row_number
 #' @keywords internal
 "_PACKAGE"