我能够加载所有的包，并能够看到可用的内核数量，但我在create_cluster(4)中得到
错误:无法找到函数"create_cluster">
library(multidplyr)
library(dplyr)
library(parallel)
numCores <- detectCores()
start.time <- Sys.time()
cluster <- create_cluster(4)
当您可以使用use lsf.str()查看包中呈现的所有函数时，您会看到函数create_cluster()不包括在内。运行:
ls("package:multidplyr")
ls("package:dplyr")
ls("package:parallel")
给出了所有函数的列表。Multiplyr:
[1] "%>%" "cluster_assign"
[3] "cluster_assign_each" "cluster_assign_partition"
[5] "cluster_call" "cluster_copy"
[7] "cluster_library" "cluster_rm"
[9] "cluster_send" "default_cluster"
[11] "new_cluster" "partition"
[13] "party_df"
dplyr:
[1] "%>%" "across" "add_count"
[4] "add_count_" "add_row" "add_rownames"
[7] "add_tally" "add_tally_" "all_equal"
[10] "all_of" "all_vars" "anti_join"
[13] "any_of" "any_vars" "arrange"
[16] "arrange_" "arrange_all" "arrange_at"
[19] "arrange_if" "as.tbl" "as_data_frame"
[22] "as_label" "as_tibble" "auto_copy"
[25] "band_instruments" "band_instruments2" "band_members"
[28] "bench_tbls" "between" "bind_cols"
[31] "bind_rows" "c_across" "case_when"
[34] "changes" "check_dbplyr" "coalesce"
[37] "collapse" "collect" "combine"
[40] "common_by" "compare_tbls" "compare_tbls2"
[43] "compute" "contains" "copy_to"
[46] "count" "count_" "cumall"
[49] "cumany" "cume_dist" "cummean"
[52] "cur_column" "cur_data" "cur_data_all"
[55] "cur_group" "cur_group_id" "cur_group_rows"
[58] "current_vars" "data_frame" "data_frame_"
[61] "db_analyze" "db_begin" "db_commit"
[64] "db_create_index" "db_create_indexes" "db_create_table"
[67] "db_data_type" "db_desc" "db_drop_table"
[70] "db_explain" "db_has_table" "db_insert_into"
[73] "db_list_tables" "db_query_fields" "db_query_rows"
[76] "db_rollback" "db_save_query" "db_write_table"
[79] "dense_rank" "desc" "dim_desc"
[82] "distinct" "distinct_" "distinct_all"
[85] "distinct_at" "distinct_if" "distinct_prepare"
[88] "do" "do_" "dplyr_col_modify"
[91] "dplyr_reconstruct" "dplyr_row_slice" "ends_with"
[94] "enexpr" "enexprs" "enquo"
[97] "enquos" "ensym" "ensyms"
[100] "eval_tbls" "eval_tbls2" "everything"
[103] "explain" "expr" "failwith"
[106] "filter" "filter_" "filter_all"
[109] "filter_at" "filter_if" "first"
[112] "frame_data" "full_join" "funs"
[115] "funs_" "glimpse" "group_by"
[118] "group_by_" "group_by_all" "group_by_at"
[121] "group_by_drop_default" "group_by_if" "group_by_prepare"
[124] "group_cols" "group_data" "group_indices"
[127] "group_indices_" "group_keys" "group_map"
[130] "group_modify" "group_nest" "group_rows"
[133] "group_size" "group_split" "group_trim"
[136] "group_vars" "group_walk" "grouped_df"
[139] "groups" "id" "ident"
[142] "if_all" "if_any" "if_else"
[145] "inner_join" "intersect" "is.grouped_df"
[148] "is.src" "is.tbl" "is_grouped_df"
[151] "lag" "last" "last_col"
[154] "lead" "left_join" "location"
[157] "lst" "lst_" "make_tbl"
[160] "matches" "min_rank" "mutate"
[163] "mutate_" "mutate_all" "mutate_at"
[166] "mutate_each" "mutate_each_" "mutate_if"
[169] "n" "n_distinct" "n_groups"
[172] "na_if" "near" "nest_by"
[175] "nest_join" "new_grouped_df" "nth"
[178] "ntile" "num_range" "one_of"
[181] "order_by" "percent_rank" "progress_estimated"
[184] "pull" "quo" "quo_name"
[187] "quos" "recode" "recode_factor"
[190] "relocate" "rename" "rename_"
[193] "rename_all" "rename_at" "rename_if"
[196] "rename_vars" "rename_vars_" "rename_with"
[199] "right_join" "row_number" "rows_delete"
[202] "rows_insert" "rows_patch" "rows_update"
[205] "rows_upsert" "rowwise" "same_src"
[208] "sample_frac" "sample_n" "select"
[211] "select_" "select_all" "select_at"
[214] "select_if" "select_var" "select_vars"
[217] "select_vars_" "semi_join" "setdiff"
[220] "setequal" "show_query" "slice"
[223] "slice_" "slice_head" "slice_max"
[226] "slice_min" "slice_sample" "slice_tail"
[229] "sql" "sql_escape_ident" "sql_escape_string"
[232] "sql_join" "sql_select" "sql_semi_join"
[235] "sql_set_op" "sql_subquery" "sql_translate_env"
[238] "src" "src_df" "src_local"
[241] "src_mysql" "src_postgres" "src_sqlite"
[244] "src_tbls" "starts_with" "starwars"
[247] "storms" "summarise" "summarise_"
[250] "summarise_all" "summarise_at" "summarise_each"
[253] "summarise_each_" "summarise_if" "summarize"
[256] "summarize_" "summarize_all" "summarize_at"
[259] "summarize_each" "summarize_each_" "summarize_if"
[262] "sym" "syms" "tally"
[265] "tally_" "tbl" "tbl_df"
[268] "tbl_nongroup_vars" "tbl_ptype" "tbl_sum"
[271] "tbl_vars" "tibble" "top_frac"
[274] "top_n" "transmute" "transmute_"
[277] "transmute_all" "transmute_at" "transmute_if"
[280] "tribble" "trunc_mat" "type_sum"
[283] "ungroup" "union" "union_all"
[286] "validate_grouped_df" "vars" "with_groups"
[289] "with_order" "wrap_dbplyr_obj"
并行:
[1] "clusterApply" "clusterApplyLB" "clusterCall"
[4] "clusterEvalQ" "clusterExport" "clusterMap"
[7] "clusterSetRNGStream" "clusterSplit" "detectCores"
[10] "getDefaultCluster" "makeCluster" "makeForkCluster"
[13] "makePSOCKcluster" "mclapply" "mcMap"
[16] "mcmapply" "nextRNGStream" "nextRNGSubStream"
[19] "parApply" "parCapply" "parLapply"
[22] "parLapplyLB" "parRapply" "parSapply"
[25] "parSapplyLB" "pvec" "setDefaultCluster"
[28] "splitIndices" "stopCluster"
这些信息对你有帮助吗?