Commit ⋅ rich-iannone/DiagrammeR

      starting_edges %>%
      dplyr::semi_join(valid_nodes, by = "to") %>%
      dplyr::left_join(edf, by = c("edge" = "id")) %>%
      dplyr::select_("to.y", copy_attrs_from)
      dplyr::select("to.y",!! enquo(copy_attrs_from))


    if (!is.null(copy_attrs_as)) {

@@ -338,10 +338,9 @@

      nodes %>%
      dplyr::rename(id = to.y) %>%
      dplyr::group_by(id) %>%
      dplyr::summarize_(.dots = stats::setNames(
        list(stats::as.formula(
          paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
        copy_attrs_from)) %>%
      dplyr::summarize(!! copy_attrs_from :=
                         match.fun(!! agg)(!! as.name(copy_attrs_from),
                                           na.rm = TRUE)) %>%
      dplyr::right_join(ndf, by = "id") %>%
      dplyr::select(id, type, label, dplyr::everything()) %>%
      as.data.frame(stringsAsFactors = FALSE)

@@ -312,7 +312,7 @@

    ndf_2 <-
      ndf %>%
      dplyr::filter(id %in% starting_nodes) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::select("id",!! enquo(copy_attrs_from))

    if (!is.null(copy_attrs_as)) {


@@ -318,7 +318,7 @@

      starting_edges %>%
      dplyr::semi_join(valid_nodes, by = "from") %>%
      dplyr::left_join(edf, by = c("edge" = "id")) %>%
      dplyr::select_("from.y", copy_attrs_from)
      dplyr::select("from.y",!! enquo(copy_attrs_from))

    if (!is.null(copy_attrs_as)) {


@@ -336,10 +336,9 @@

      nodes %>%
      dplyr::rename(id = from.y) %>%
      dplyr::group_by(id) %>%
      dplyr::summarize_(.dots = stats::setNames(
        list(stats::as.formula(
          paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
        copy_attrs_from)) %>%
      dplyr::summarize(!! copy_attrs_from :=
                         match.fun(!! agg)(!! as.name(copy_attrs_from),
                                           na.rm = TRUE)) %>%
      dplyr::right_join(ndf, by = "id") %>%
      dplyr::select(id, type, label, dplyr::everything()) %>%
      as.data.frame(stringsAsFactors = FALSE)

@@ -173,53 +173,17 @@

      get_edge_ids(graph),
      suppressMessages(get_selection(graph)))

  for (i in 1:length(exprs)) {

    # Case where mutation occurs for an
    # existing edge attribute
    if (names(exprs)[i] %in% colnames(edf)) {

      edf_replacement <-
        edf %>%
        dplyr::mutate_(
          .dots = stats::setNames(list((exprs %>% paste())[i]),
                           names(exprs)[i]))

      edf_replacement[
        which(edf$id %in% unselected_edges), ] <-
        edf[
          which(edf$id %in% unselected_edges), ]

      # Update the graph's edf
      graph$edges_df <- edf_replacement

      # Reobtain the changed edf for
      # any subsequent mutations
      edf <- get_edge_df(graph)
    }

    # Case where mutation creates a
    # new edge attribute
    if (!(names(exprs)[i] %in% colnames(edf))) {

      edf_replacement <-
        edf %>%
        dplyr::mutate_(
          .dots = stats::setNames(list((exprs %>% paste())[i]),
                           names(exprs)[i]))

      edf_replacement[
        which(edf$id %in% unselected_edges),
        which(colnames(edf_replacement) == names(exprs)[i])] <- NA

      # Update the graph's edf
      graph$edges_df <- edf_replacement

      # Reobtain the changed edf for
      # any subsequent mutations
      edf <- get_edge_df(graph)
    }
  }
  s <- edf$id %in% unselected_edges
  order <- c(which(!s), which(s))

  edf <-
    dplyr::bind_rows(
      edf %>% dplyr::filter(!(!! enquo(s))) %>% dplyr::mutate(!!! enquos(...)),
      edf %>% dplyr::filter( (!! enquo(s)))
    ) %>% dplyr::arrange(!! enquo(order))

  graph$edges_df <- edf


  # Update the `graph_log` df with an action
  graph$graph_log <-

@@ -294,7 +294,7 @@

    ndf_2 <-
      ndf %>%
      dplyr::filter(id %in% starting_nodes) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::select("id",!! enquo(copy_attrs_from))

    if (!is.null(copy_attrs_as)) {


@@ -114,13 +114,7 @@

      reasons = "The variable `id` cannot undergo mutation")
  }

  for (i in 1:length(exprs)) {
    ndf <-
      ndf %>%
      dplyr::mutate_(
        .dots = stats::setNames(list((exprs %>% paste())[i]),
                         names(exprs)[i]))
  }
  ndf <- ndf %>% dplyr::mutate(!!! enquos(...))

  # Update the graph
  graph$nodes_df <- ndf

@@ -111,11 +111,12 @@


  # Get the aggregate value of total degree based
  # on the aggregate function provided
  fun <- match.fun(agg)

  indegree_agg <-
    indegree_df %>%
    dplyr::group_by() %>%
    dplyr::summarize_(stats::as.formula(
      paste0("~", agg, "(indegree, na.rm = TRUE)"))) %>%
    dplyr::summarize(fun(indegree, na.rm = TRUE)) %>%
    dplyr::ungroup() %>%
    purrr::flatten_dbl()


@@ -305,24 +305,23 @@

      valid_nodes %>%
      dplyr::select(id) %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "from")) %>%
      dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("to" = "id")) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("to" = "id")) %>%
      dplyr::select("id",!! enquo(copy_attrs_from))

    to_join <-
      valid_nodes %>%
      dplyr::select(id) %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "to")) %>%
      dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("from" = "id")) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("from" = "id")) %>%
      dplyr::select("id",!! enquo(copy_attrs_from))

    nodes <-
      from_join %>%
      dplyr::union_all(to_join) %>%
      dplyr::group_by(id) %>%
      dplyr::summarize_(.dots = stats::setNames(
        list(stats::as.formula(
          paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
        copy_attrs_from)) %>%
      dplyr::summarize(!! copy_attrs_from :=
                         match.fun(!! agg)(!! as.name(copy_attrs_from),
                                           na.rm = TRUE)) %>%
      dplyr::right_join(ndf, by = "id") %>%
      dplyr::select(id, type, label, dplyr::everything()) %>%
      as.data.frame(stringsAsFactors = FALSE)

@@ -328,8 +328,8 @@

      valid_nodes %>%
      dplyr::select(id) %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "to")) %>%
      dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("from" = "id")) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("from" = "id")) %>%
      dplyr::select("id",!! enquo(copy_attrs_from))

    # If the values to be copied are numeric,
    # perform aggregation on the values

@@ -337,10 +337,9 @@

      nodes <-
        nodes %>%
        dplyr::group_by(id) %>%
        dplyr::summarize_(.dots = stats::setNames(
          list(stats::as.formula(
            paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
          copy_attrs_from)) %>%
        dplyr::summarize(!! copy_attrs_from :=
                           match.fun(!! agg)(!! as.name(copy_attrs_from),
                                             na.rm = TRUE)) %>%
        dplyr::ungroup()
    }


@@ -111,11 +111,12 @@


  # Get the aggregate value of total degree based
  # on the aggregate function provided
  fun <- match.fun(agg)

  outdegree_agg <-
    outdegree_df %>%
    dplyr::group_by() %>%
    dplyr::summarize_(stats::as.formula(
      paste0("~", agg, "(outdegree, na.rm = TRUE)"))) %>%
    dplyr::summarize(fun(outdegree, na.rm = TRUE)) %>%
    dplyr::ungroup() %>%
    purrr::flatten_dbl()


@@ -173,53 +173,16 @@

      get_node_ids(graph),
      suppressMessages(get_selection(graph)))

  for (i in 1:length(exprs)) {

    # Case where mutation occurs for an
    # existing node attribute
    if (names(exprs)[i] %in% colnames(ndf)) {

      ndf_replacement <-
        ndf %>%
        dplyr::mutate_(
          .dots = stats::setNames(list((exprs %>% paste())[i]),
                           names(exprs)[i]))

      ndf_replacement[
        which(ndf$id %in% unselected_nodes), ] <-
        ndf[
          which(ndf$id %in% unselected_nodes), ]

      # Update the graph's ndf
      graph$nodes_df <- ndf_replacement

      # Reobtain the changed ndf for
      # any subsequent mutations
      ndf <- get_node_df(graph)
    }

    # Case where mutation creates a
    # new node attribute
    if (!(names(exprs)[i] %in% colnames(ndf))) {

      ndf_replacement <-
        ndf %>%
        dplyr::mutate_(
          .dots = stats::setNames(list((exprs %>% paste())[i]),
                           names(exprs)[i]))

      ndf_replacement[
        which(ndf$id %in% unselected_nodes),
        which(colnames(ndf_replacement) == names(exprs)[i])] <- NA

      # Update the graph's ndf
      graph$nodes_df <- ndf_replacement

      # Reobtain the changed ndf for
      # any subsequent mutations
      ndf <- get_node_df(graph)
    }
  }
  s <- ndf$id %in% unselected_nodes
  order <- c(which(!s), which(s))

  ndf <-
    dplyr::bind_rows(
      ndf %>% dplyr::filter(!(!! enquo(s))) %>% dplyr::mutate(!!! enquos(...)),
      ndf %>% dplyr::filter( (!! enquo(s)))
    ) %>% dplyr::arrange(!! enquo(order))

  graph$nodes_df <- ndf

  # Update the `graph_log` df with an action
  graph$graph_log <-

@@ -114,11 +114,12 @@


  # Get the aggregate value of total degree based
  # on the aggregate function provided
  fun <- match.fun(agg)

  total_degree_agg <-
    total_degree_df %>%
    dplyr::group_by() %>%
    dplyr::summarize_(stats::as.formula(
      paste0("~", agg, "(total_degree, na.rm = TRUE)"))) %>%
    dplyr::summarize(fun(total_degree, na.rm = TRUE)) %>%
    dplyr::ungroup() %>%
    purrr::flatten_dbl()


@@ -116,9 +116,9 @@

  if (return_values == "label") {
    edges_df <-
      edges_df %>%
      dplyr::left_join(graph$nodes_df %>% dplyr::select_("id", "label"), by = c("from" = "id")) %>%
      dplyr::left_join(graph$nodes_df %>% dplyr::select("id", "label"), by = c("from" = "id")) %>%
      dplyr::rename(from_label_ = label) %>%
      dplyr::left_join(graph$nodes_df %>% dplyr::select_("id", "label"), by = c("to" = "id")) %>%
      dplyr::left_join(graph$nodes_df %>% dplyr::select("id", "label"), by = c("to" = "id")) %>%
      dplyr::rename(to_label_ = label)
  }


@@ -159,11 +159,11 @@

    if (return_values == "id") {
      edges_df <-
        edges_df %>%
        dplyr::select_("from", "to")
        dplyr::select("from", "to")
    } else if (return_values == "label") {
      edges_df <-
        edges_df %>%
        dplyr::select_("from_label_", "to_label_")
        dplyr::select("from_label_", "to_label_")
    }

    return(edges_df)

@@ -136,7 +136,7 @@

          trimws() %>%
          stringr::str_split(" ") %>%
          purrr::flatten_chr() %>%
          dplyr::as_tibble(.name_repair = "unique") %>%
          tibble::enframe(name = NULL) %>%
          tidyr::drop_na() %>%
          dplyr::distinct() %>%
          purrr::flatten_chr())

@@ -306,25 +306,25 @@


      from_join <-
        ndf %>%
        dplyr::select_("id", copy_attrs_from) %>%
        dplyr::select("id",!! enquo(copy_attrs_from)) %>%
        dplyr::filter(id %in% starting_nodes) %>%
        dplyr::right_join(
          valid_edges %>%
            dplyr::select(-rel) %>%
            dplyr::rename(e_id = id),
          by = c("id" = "from")) %>%
        dplyr::select_("e_id", copy_attrs_from)
        dplyr::select("e_id",!! enquo(copy_attrs_from))

      to_join <-
        ndf %>%
        dplyr::select_("id", copy_attrs_from) %>%
        dplyr::select("id",!! enquo(copy_attrs_from)) %>%
        dplyr::filter(id %in% starting_nodes) %>%
        dplyr::right_join(
          valid_edges %>%
            dplyr::select(-rel) %>%
            dplyr::rename(e_id = id),
          by = c("id" = "to")) %>%
        dplyr::select_("e_id", copy_attrs_from)
        dplyr::select("e_id",!! enquo(copy_attrs_from))

      if (!is.null(copy_attrs_as)) {


@@ -346,10 +346,9 @@

        dplyr::left_join(edf, by = c("e_id" = "id")) %>%
        dplyr::rename(id = e_id) %>%
        dplyr::group_by(id) %>%
        dplyr::summarize_(.dots = stats::setNames(
          list(stats::as.formula(
            paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
          copy_attrs_from)) %>%
        dplyr::summarize(!! copy_attrs_from :=
                           match.fun(!! agg)(!! as.name(copy_attrs_from),
                                             na.rm = TRUE)) %>%
        dplyr::right_join(edf, by = "id") %>%
        dplyr::select(id, from, to, rel, dplyr::everything()) %>%
        as.data.frame(stringsAsFractions = FALSE)

@@ -359,7 +358,7 @@


      from_join <-
        ndf %>%
        dplyr::select_("id", copy_attrs_from) %>%
        dplyr::select("id",!! enquo(copy_attrs_from)) %>%
        dplyr::filter(id %in% starting_nodes)

      if (!is.null(copy_attrs_as)) {

@@ -403,11 +402,11 @@

      # Drop the ".x" column
      from_join <-
        from_join %>%
        dplyr::select_("e_id", copy_attrs_from)
        dplyr::select("e_id",!! enquo(copy_attrs_from))

      to_join <-
        ndf %>%
        dplyr::select_("id", copy_attrs_from) %>%
        dplyr::select("id",!! enquo(copy_attrs_from)) %>%
        dplyr::filter(id %in% starting_nodes)

      if (!is.null(copy_attrs_as)) {

@@ -449,7 +448,7 @@

      # Drop the ".x" column
      to_join <-
        to_join %>%
        dplyr::select_("e_id", copy_attrs_from)
        dplyr::select("e_id",!! enquo(copy_attrs_from))

      edges <-
        dplyr::bind_rows(

@@ -482,9 +481,9 @@

        dplyr::arrange(e_id) %>%
        dplyr::rename(id = e_id) %>%
        dplyr::group_by(id) %>%
        dplyr::summarize_(.dots = stats::setNames(
          list(stats::as.formula(
            paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))), copy_attrs_from)) %>%
        dplyr::summarize(!! copy_attrs_from :=
                            match.fun(!! agg)(!! as.name(copy_attrs_from),
                                              na.rm = TRUE)) %>%
        dplyr::ungroup() %>%
        dplyr::right_join(edf, by = "id")


@@ -184,19 +184,19 @@

    # Filter the graph `ndf`
    ndf_part <-
      ndf %>%
      dplyr::filter_(paste0(node_attr, " == '", node_attr_val, "'"))
      dplyr::filter(!! parse_expr(paste0(node_attr, " == '", node_attr_val, "'")))

    # Optionally apply sorting
    if (!is.null(sort)) {
      if (sort_dir == "desc") {
        ndf_part <-
          ndf_part %>%
          dplyr::arrange_(paste0("desc(", sort_attr, ")"))
          dplyr::arrange(desc(!! sym(sort_attr)))

      } else {
        ndf_part <-
          ndf_part %>%
          dplyr::arrange_(sort_attr)
          dplyr::arrange(!! sym(sort_attr))
      }
    }


@@ -325,8 +325,8 @@

      valid_nodes %>%
      dplyr::select(id) %>%
      dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "from")) %>%
      dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("to" = "id")) %>%
      dplyr::select_("id", copy_attrs_from)
      dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("to" = "id")) %>%
      dplyr::select("id",!! enquo(copy_attrs_from))

    # If the values to be copied are numeric,
    # perform aggregation on the values

@@ -334,10 +334,9 @@

      nodes <-
        nodes %>%
        dplyr::group_by(id) %>%
        dplyr::summarize_(.dots = stats::setNames(
          list(stats::as.formula(
            paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
          copy_attrs_from)) %>%
        dplyr::summarize(!! copy_attrs_from :=
                           match.fun(!! agg)(!! as.name(copy_attrs_from),
                                             na.rm = TRUE)) %>%
        dplyr::ungroup()
    }


@@ -54,7 +54,7 @@

  # exclude the `id` column
  edf <-
    graph$edges_df %>%
    dplyr::select_("-id")
    dplyr::select(-id)

  igraph::graph_from_data_frame(
    d = edf,

@@ -240,12 +240,12 @@


  # Are all values not NA?
  all_is_not_na <-
    df %>% dplyr::select_(attr) %>%
    df %>% dplyr::select(!! enquo(attr)) %>%
    is.na %>% magrittr::not() %>% all()

  # Are all values distinct?
  all_values_distinct <-
    df %>% dplyr::select_(attr) %>% dplyr::distinct() %>% nrow() ==
    df %>% dplyr::select(!! enquo(attr)) %>% dplyr::distinct() %>% nrow() ==
    nrow(df)

  if (all_is_not_na & all_values_distinct) {

@@ -113,44 +113,16 @@

  edf <- graph$edges_df

  # Get the available reverse edges
  reverse_edges <-
    edf %>%
    {
      reverse_edges <- dplyr::as_tibble()
      for (i in 1:length(edges_to)) {
        reverse_edges <-
          edf %>%
          dplyr::filter_(
            paste0(
              "from == ",
              edges_to[i],
              " & to == ",
              edges_from[i])) %>%
          dplyr::bind_rows(reverse_edges)
      }
      reverse_edges
    }
  reverse_edges_df <- data.frame(to = edges_from, from = edges_to)
  reverse_edges <- edf %>% dplyr::inner_join(reverse_edges_df)

  # Add the reverse edges to the existing,
  # selected edges
  if (add_to_selection) {
    edges_df <- data.frame(to = edges_to, from = edges_from)
    edges <-
      edf %>%
      {
        edges <- dplyr::as_tibble()
        for (i in 1:length(edges_to)) {
          edges <-
            edf %>%
            dplyr::filter_(
              paste0(
                "from == ",
                edges_from[i],
                " & to == ",
                edges_to[i])) %>%
            dplyr::bind_rows(edges)
        }
        edges
      } %>%
      dplyr::inner_join(edges_df) %>%
      dplyr::bind_rows(reverse_edges)
  } else {
    edges <- reverse_edges

@@ -116,13 +116,7 @@

      reasons = "The variables `id`, `from`, or `to` cannot undergo mutation")
  }

  for (i in 1:length(exprs)) {
    edf <-
      edf %>%
      dplyr::mutate_(
        .dots = stats::setNames(list((exprs %>% paste())[i]),
                         names(exprs)[i]))
  }
  edf <- edf %>% dplyr::mutate(!!! enquos(...))

  # Update the graph
  graph$edges_df <- edf

@@ -123,13 +123,8 @@

  # Get a data frame with node ID and degree types
  node_degree <-
    get_node_info(graph) %>%
    dplyr::select(id, deg, indeg, outdeg)

  for (i in 1:length(expressions)) {
    node_degree <-
      node_degree %>%
      dplyr::filter_(expressions[i])
  }
    dplyr::select(id, deg, indeg, outdeg) %>%
    dplyr::filter(!!! parse_exprs(expressions))

  # Get the node ID values from the filtered table
  nodes_selected <- node_degree$id

Files		•	•	•	Coverage
R	14,098	11,483	0	2,615	81.45%
Project Totals (244 files)	14,098	11,483	0	2,615	81.45%

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173	173		get_edge_ids(graph),
174	174		suppressMessages(get_selection(graph)))
175	175
176		-	for (i in 1:length(exprs)) {
177		-
178		-	# Case where mutation occurs for an
179		-	# existing edge attribute
180		-	if (names(exprs)[i] %in% colnames(edf)) {
181		-
182		-	edf_replacement <-
183		-	edf %>%
184		-	dplyr::mutate_(
185		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
186		-	names(exprs)[i]))
187		-
188		-	edf_replacement[
189		-	which(edf$id %in% unselected_edges), ] <-
190		-	edf[
191		-	which(edf$id %in% unselected_edges), ]
192		-
193		-	# Update the graph's edf
194		-	graph$edges_df <- edf_replacement
195		-
196		-	# Reobtain the changed edf for
197		-	# any subsequent mutations
198		-	edf <- get_edge_df(graph)
199		-	}
200		-
201		-	# Case where mutation creates a
202		-	# new edge attribute
203		-	if (!(names(exprs)[i] %in% colnames(edf))) {
204		-
205		-	edf_replacement <-
206		-	edf %>%
207		-	dplyr::mutate_(
208		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
209		-	names(exprs)[i]))
210		-
211		-	edf_replacement[
212		-	which(edf$id %in% unselected_edges),
213		-	which(colnames(edf_replacement) == names(exprs)[i])] <- NA
214		-
215		-	# Update the graph's edf
216		-	graph$edges_df <- edf_replacement
217		-
218		-	# Reobtain the changed edf for
219		-	# any subsequent mutations
220		-	edf <- get_edge_df(graph)
221		-	}
222		-	}
	176	+	s <- edf$id %in% unselected_edges
	177	+	order <- c(which(!s), which(s))
	178	+
	179	+	edf <-
	180	+	dplyr::bind_rows(
	181	+	edf %>% dplyr::filter(!(!! enquo(s))) %>% dplyr::mutate(!!! enquos(...)),
	182	+	edf %>% dplyr::filter( (!! enquo(s)))
	183	+	) %>% dplyr::arrange(!! enquo(order))
	184	+
	185	+	graph$edges_df <- edf
	186	+
223	187
224	188		# Update the `graph_log` df with an action
225	189		graph$graph_log <-

294	294		ndf_2 <-
295	295		ndf %>%
296	296		dplyr::filter(id %in% starting_nodes) %>%
297		-	dplyr::select_("id", copy_attrs_from)
	297	+	dplyr::select("id",!! enquo(copy_attrs_from))
298	298
299	299		if (!is.null(copy_attrs_as)) {
300	300

173	173		get_node_ids(graph),
174	174		suppressMessages(get_selection(graph)))
175	175
176		-	for (i in 1:length(exprs)) {
177		-
178		-	# Case where mutation occurs for an
179		-	# existing node attribute
180		-	if (names(exprs)[i] %in% colnames(ndf)) {
181		-
182		-	ndf_replacement <-
183		-	ndf %>%
184		-	dplyr::mutate_(
185		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
186		-	names(exprs)[i]))
187		-
188		-	ndf_replacement[
189		-	which(ndf$id %in% unselected_nodes), ] <-
190		-	ndf[
191		-	which(ndf$id %in% unselected_nodes), ]
192		-
193		-	# Update the graph's ndf
194		-	graph$nodes_df <- ndf_replacement
195		-
196		-	# Reobtain the changed ndf for
197		-	# any subsequent mutations
198		-	ndf <- get_node_df(graph)
199		-	}
200		-
201		-	# Case where mutation creates a
202		-	# new node attribute
203		-	if (!(names(exprs)[i] %in% colnames(ndf))) {
204		-
205		-	ndf_replacement <-
206		-	ndf %>%
207		-	dplyr::mutate_(
208		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
209		-	names(exprs)[i]))
210		-
211		-	ndf_replacement[
212		-	which(ndf$id %in% unselected_nodes),
213		-	which(colnames(ndf_replacement) == names(exprs)[i])] <- NA
214		-
215		-	# Update the graph's ndf
216		-	graph$nodes_df <- ndf_replacement
217		-
218		-	# Reobtain the changed ndf for
219		-	# any subsequent mutations
220		-	ndf <- get_node_df(graph)
221		-	}
222		-	}
	176	+	s <- ndf$id %in% unselected_nodes
	177	+	order <- c(which(!s), which(s))
	178	+
	179	+	ndf <-
	180	+	dplyr::bind_rows(
	181	+	ndf %>% dplyr::filter(!(!! enquo(s))) %>% dplyr::mutate(!!! enquos(...)),
	182	+	ndf %>% dplyr::filter( (!! enquo(s)))
	183	+	) %>% dplyr::arrange(!! enquo(order))
	184	+
	185	+	graph$nodes_df <- ndf
223	186
224	187		# Update the `graph_log` df with an action
225	188		graph$graph_log <-

116	116		if (return_values == "label") {
117	117		edges_df <-
118	118		edges_df %>%
119		-	dplyr::left_join(graph$nodes_df %>% dplyr::select_("id", "label"), by = c("from" = "id")) %>%
	119	+	dplyr::left_join(graph$nodes_df %>% dplyr::select("id", "label"), by = c("from" = "id")) %>%
120	120		dplyr::rename(from_label_ = label) %>%
121		-	dplyr::left_join(graph$nodes_df %>% dplyr::select_("id", "label"), by = c("to" = "id")) %>%
	121	+	dplyr::left_join(graph$nodes_df %>% dplyr::select("id", "label"), by = c("to" = "id")) %>%
122	122		dplyr::rename(to_label_ = label)
123	123		}
124	124

159	159		if (return_values == "id") {
160	160		edges_df <-
161	161		edges_df %>%
162		-	dplyr::select_("from", "to")
	162	+	dplyr::select("from", "to")
163	163		} else if (return_values == "label") {
164	164		edges_df <-
165	165		edges_df %>%
166		-	dplyr::select_("from_label_", "to_label_")
	166	+	dplyr::select("from_label_", "to_label_")
167	167		}
168	168
169	169		return(edges_df)

319	319		starting_edges %>%
320	320		dplyr::semi_join(valid_nodes, by = "to") %>%
321	321		dplyr::left_join(edf, by = c("edge" = "id")) %>%
322		-	dplyr::select_("to.y", copy_attrs_from)
	322	+	dplyr::select("to.y",!! enquo(copy_attrs_from))
323	323
324	324
325	325		if (!is.null(copy_attrs_as)) {

338	338		nodes %>%
339	339		dplyr::rename(id = to.y) %>%
340	340		dplyr::group_by(id) %>%
341		-	dplyr::summarize_(.dots = stats::setNames(
342		-	list(stats::as.formula(
343		-	paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
344		-	copy_attrs_from)) %>%
	341	+	dplyr::summarize(!! copy_attrs_from :=
	342	+	match.fun(!! agg)(!! as.name(copy_attrs_from),
	343	+	na.rm = TRUE)) %>%
345	344		dplyr::right_join(ndf, by = "id") %>%
346	345		dplyr::select(id, type, label, dplyr::everything()) %>%
347	346		as.data.frame(stringsAsFactors = FALSE)

318	318		starting_edges %>%
319	319		dplyr::semi_join(valid_nodes, by = "from") %>%
320	320		dplyr::left_join(edf, by = c("edge" = "id")) %>%
321		-	dplyr::select_("from.y", copy_attrs_from)
	321	+	dplyr::select("from.y",!! enquo(copy_attrs_from))
322	322
323	323		if (!is.null(copy_attrs_as)) {
324	324

336	336		nodes %>%
337	337		dplyr::rename(id = from.y) %>%
338	338		dplyr::group_by(id) %>%
339		-	dplyr::summarize_(.dots = stats::setNames(
340		-	list(stats::as.formula(
341		-	paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
342		-	copy_attrs_from)) %>%
	339	+	dplyr::summarize(!! copy_attrs_from :=
	340	+	match.fun(!! agg)(!! as.name(copy_attrs_from),
	341	+	na.rm = TRUE)) %>%
343	342		dplyr::right_join(ndf, by = "id") %>%
344	343		dplyr::select(id, type, label, dplyr::everything()) %>%
345	344		as.data.frame(stringsAsFactors = FALSE)

114	114		reasons = "The variable `id` cannot undergo mutation")
115	115		}
116	116
117		-	for (i in 1:length(exprs)) {
118		-	ndf <-
119		-	ndf %>%
120		-	dplyr::mutate_(
121		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
122		-	names(exprs)[i]))
123		-	}
	117	+	ndf <- ndf %>% dplyr::mutate(!!! enquos(...))
124	118
125	119		# Update the graph
126	120		graph$nodes_df <- ndf

111	111
112	112		# Get the aggregate value of total degree based
113	113		# on the aggregate function provided
	114	+	fun <- match.fun(agg)
	115	+
114	116		indegree_agg <-
115	117		indegree_df %>%
116	118		dplyr::group_by() %>%
117		-	dplyr::summarize_(stats::as.formula(
118		-	paste0("~", agg, "(indegree, na.rm = TRUE)"))) %>%
	119	+	dplyr::summarize(fun(indegree, na.rm = TRUE)) %>%
119	120		dplyr::ungroup() %>%
120	121		purrr::flatten_dbl()
121	122

305	305		valid_nodes %>%
306	306		dplyr::select(id) %>%
307	307		dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "from")) %>%
308		-	dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("to" = "id")) %>%
309		-	dplyr::select_("id", copy_attrs_from)
	308	+	dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("to" = "id")) %>%
	309	+	dplyr::select("id",!! enquo(copy_attrs_from))
310	310
311	311		to_join <-
312	312		valid_nodes %>%
313	313		dplyr::select(id) %>%
314	314		dplyr::inner_join(edf %>% dplyr::select(from, to), by = c("id" = "to")) %>%
315		-	dplyr::inner_join(ndf %>% dplyr::select_("id", copy_attrs_from), by = c("from" = "id")) %>%
316		-	dplyr::select_("id", copy_attrs_from)
	315	+	dplyr::inner_join(ndf %>% dplyr::select("id",!! enquo(copy_attrs_from)), by = c("from" = "id")) %>%
	316	+	dplyr::select("id",!! enquo(copy_attrs_from))
317	317
318	318		nodes <-
319	319		from_join %>%
320	320		dplyr::union_all(to_join) %>%
321	321		dplyr::group_by(id) %>%
322		-	dplyr::summarize_(.dots = stats::setNames(
323		-	list(stats::as.formula(
324		-	paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
325		-	copy_attrs_from)) %>%
	322	+	dplyr::summarize(!! copy_attrs_from :=
	323	+	match.fun(!! agg)(!! as.name(copy_attrs_from),
	324	+	na.rm = TRUE)) %>%
326	325		dplyr::right_join(ndf, by = "id") %>%
327	326		dplyr::select(id, type, label, dplyr::everything()) %>%
328	327		as.data.frame(stringsAsFactors = FALSE)

136	136		trimws() %>%
137	137		stringr::str_split(" ") %>%
138	138		purrr::flatten_chr() %>%
139		-	dplyr::as_tibble(.name_repair = "unique") %>%
	139	+	tibble::enframe(name = NULL) %>%
140	140		tidyr::drop_na() %>%
141	141		dplyr::distinct() %>%
142	142		purrr::flatten_chr())

306	306
307	307		from_join <-
308	308		ndf %>%
309		-	dplyr::select_("id", copy_attrs_from) %>%
	309	+	dplyr::select("id",!! enquo(copy_attrs_from)) %>%
310	310		dplyr::filter(id %in% starting_nodes) %>%
311	311		dplyr::right_join(
312	312		valid_edges %>%
313	313		dplyr::select(-rel) %>%
314	314		dplyr::rename(e_id = id),
315	315		by = c("id" = "from")) %>%
316		-	dplyr::select_("e_id", copy_attrs_from)
	316	+	dplyr::select("e_id",!! enquo(copy_attrs_from))
317	317
318	318		to_join <-
319	319		ndf %>%
320		-	dplyr::select_("id", copy_attrs_from) %>%
	320	+	dplyr::select("id",!! enquo(copy_attrs_from)) %>%
321	321		dplyr::filter(id %in% starting_nodes) %>%
322	322		dplyr::right_join(
323	323		valid_edges %>%
324	324		dplyr::select(-rel) %>%
325	325		dplyr::rename(e_id = id),
326	326		by = c("id" = "to")) %>%
327		-	dplyr::select_("e_id", copy_attrs_from)
	327	+	dplyr::select("e_id",!! enquo(copy_attrs_from))
328	328
329	329		if (!is.null(copy_attrs_as)) {
330	330

346	346		dplyr::left_join(edf, by = c("e_id" = "id")) %>%
347	347		dplyr::rename(id = e_id) %>%
348	348		dplyr::group_by(id) %>%
349		-	dplyr::summarize_(.dots = stats::setNames(
350		-	list(stats::as.formula(
351		-	paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))),
352		-	copy_attrs_from)) %>%
	349	+	dplyr::summarize(!! copy_attrs_from :=
	350	+	match.fun(!! agg)(!! as.name(copy_attrs_from),
	351	+	na.rm = TRUE)) %>%
353	352		dplyr::right_join(edf, by = "id") %>%
354	353		dplyr::select(id, from, to, rel, dplyr::everything()) %>%
355	354		as.data.frame(stringsAsFractions = FALSE)

359	358
360	359		from_join <-
361	360		ndf %>%
362		-	dplyr::select_("id", copy_attrs_from) %>%
	361	+	dplyr::select("id",!! enquo(copy_attrs_from)) %>%
363	362		dplyr::filter(id %in% starting_nodes)
364	363
365	364		if (!is.null(copy_attrs_as)) {

449	448		# Drop the ".x" column
450	449		to_join <-
451	450		to_join %>%
452		-	dplyr::select_("e_id", copy_attrs_from)
	451	+	dplyr::select("e_id",!! enquo(copy_attrs_from))
453	452
454	453		edges <-
455	454		dplyr::bind_rows(

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482	481		dplyr::arrange(e_id) %>%
483	482		dplyr::rename(id = e_id) %>%
484	483		dplyr::group_by(id) %>%
485		-	dplyr::summarize_(.dots = stats::setNames(
486		-	list(stats::as.formula(
487		-	paste0("~", agg, "(", copy_attrs_from, ", na.rm = TRUE)"))), copy_attrs_from)) %>%
	484	+	dplyr::summarize(!! copy_attrs_from :=
	485	+	match.fun(!! agg)(!! as.name(copy_attrs_from),
	486	+	na.rm = TRUE)) %>%
488	487		dplyr::ungroup() %>%
489	488		dplyr::right_join(edf, by = "id")
490	489

184	184		# Filter the graph `ndf`
185	185		ndf_part <-
186	186		ndf %>%
187		-	dplyr::filter_(paste0(node_attr, " == '", node_attr_val, "'"))
	187	+	dplyr::filter(!! parse_expr(paste0(node_attr, " == '", node_attr_val, "'")))
188	188
189	189		# Optionally apply sorting
190	190		if (!is.null(sort)) {
191	191		if (sort_dir == "desc") {
192	192		ndf_part <-
193	193		ndf_part %>%
194		-	dplyr::arrange_(paste0("desc(", sort_attr, ")"))
	194	+	dplyr::arrange(desc(!! sym(sort_attr)))
195	195
196	196		} else {
197	197		ndf_part <-
198	198		ndf_part %>%
199		-	dplyr::arrange_(sort_attr)
	199	+	dplyr::arrange(!! sym(sort_attr))
200	200		}
201	201		}
202	202

54	54		# exclude the `id` column
55	55		edf <-
56	56		graph$edges_df %>%
57		-	dplyr::select_("-id")
	57	+	dplyr::select(-id)
58	58
59	59		igraph::graph_from_data_frame(
60	60		d = edf,

240	240
241	241		# Are all values not NA?
242	242		all_is_not_na <-
243		-	df %>% dplyr::select_(attr) %>%
	243	+	df %>% dplyr::select(!! enquo(attr)) %>%
244	244		is.na %>% magrittr::not() %>% all()
245	245
246	246		# Are all values distinct?
247	247		all_values_distinct <-
248		-	df %>% dplyr::select_(attr) %>% dplyr::distinct() %>% nrow() ==
	248	+	df %>% dplyr::select(!! enquo(attr)) %>% dplyr::distinct() %>% nrow() ==
249	249		nrow(df)
250	250
251	251		if (all_is_not_na & all_values_distinct) {

113	113		edf <- graph$edges_df
114	114
115	115		# Get the available reverse edges
116		-	reverse_edges <-
117		-	edf %>%
118		-	{
119		-	reverse_edges <- dplyr::as_tibble()
120		-	for (i in 1:length(edges_to)) {
121		-	reverse_edges <-
122		-	edf %>%
123		-	dplyr::filter_(
124		-	paste0(
125		-	"from == ",
126		-	edges_to[i],
127		-	" & to == ",
128		-	edges_from[i])) %>%
129		-	dplyr::bind_rows(reverse_edges)
130		-	}
131		-	reverse_edges
132		-	}
	116	+	reverse_edges_df <- data.frame(to = edges_from, from = edges_to)
	117	+	reverse_edges <- edf %>% dplyr::inner_join(reverse_edges_df)
133	118
134	119		# Add the reverse edges to the existing,
135	120		# selected edges
136	121		if (add_to_selection) {
	122	+	edges_df <- data.frame(to = edges_to, from = edges_from)
137	123		edges <-
138	124		edf %>%
139		-	{
140		-	edges <- dplyr::as_tibble()
141		-	for (i in 1:length(edges_to)) {
142		-	edges <-
143		-	edf %>%
144		-	dplyr::filter_(
145		-	paste0(
146		-	"from == ",
147		-	edges_from[i],
148		-	" & to == ",
149		-	edges_to[i])) %>%
150		-	dplyr::bind_rows(edges)
151		-	}
152		-	edges
153		-	} %>%
	125	+	dplyr::inner_join(edges_df) %>%
154	126		dplyr::bind_rows(reverse_edges)
155	127		} else {
156	128		edges <- reverse_edges

116	116		reasons = "The variables `id`, `from`, or `to` cannot undergo mutation")
117	117		}
118	118
119		-	for (i in 1:length(exprs)) {
120		-	edf <-
121		-	edf %>%
122		-	dplyr::mutate_(
123		-	.dots = stats::setNames(list((exprs %>% paste())[i]),
124		-	names(exprs)[i]))
125		-	}
	119	+	edf <- edf %>% dplyr::mutate(!!! enquos(...))
126	120
127	121		# Update the graph
128	122		graph$edges_df <- edf

123	123		# Get a data frame with node ID and degree types
124	124		node_degree <-
125	125		get_node_info(graph) %>%
126		-	dplyr::select(id, deg, indeg, outdeg)
127		-
128		-	for (i in 1:length(expressions)) {
129		-	node_degree <-
130		-	node_degree %>%
131		-	dplyr::filter_(expressions[i])
132		-	}
	126	+	dplyr::select(id, deg, indeg, outdeg) %>%
	127	+	dplyr::filter(!!! parse_exprs(expressions))
133	128
134	129		# Get the node ID values from the filtered table
135	130		nodes_selected <- node_degree$id

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