r - R - mgsub 问题:被替换的子字符串不是整个字符串
问题描述
我已经从 USPS 下载了街道缩写。这是数据:
dput(usps_streets)
structure(list(common_abbrev = c("allee", "alley", "ally", "aly",
"anex", "annex", "annx", "anx", "arc", "arcade", "av", "ave",
"aven", "avenu", "avenue", "avn", "avnue", "bayoo", "bayou",
"bch", "beach", "bend", "bnd", "blf", "bluf", "bluff", "bluffs",
"bot", "btm", "bottm", "bottom", "blvd", "boul", "boulevard",
"boulv", "br", "brnch", "branch", "brdge", "brg", "bridge", "brk",
"brook", "brooks", "burg", "burgs", "byp", "bypa", "bypas", "bypass",
"byps", "camp", "cp", "cmp", "canyn", "canyon", "cnyn", "cape",
"cpe", "causeway", "causwa", "cswy", "cen", "cent", "center",
"centr", "centre", "cnter", "cntr", "ctr", "centers", "cir",
"circ", "circl", "circle", "crcl", "crcle", "circles", "clf",
"cliff", "clfs", "cliffs", "clb", "club", "common", "commons",
"cor", "corner", "corners", "cors", "course", "crse", "court",
"ct", "courts", "cts", "cove", "cv", "coves", "creek", "crk",
"crescent", "cres", "crsent", "crsnt", "crest", "crossing", "crssng",
"xing", "crossroad", "crossroads", "curve", "dale", "dl", "dam",
"dm", "div", "divide", "dv", "dvd", "dr", "driv", "drive", "drv",
"drives", "est", "estate", "estates", "ests", "exp", "expr",
"express", "expressway", "expw", "expy", "ext", "extension",
"extn", "extnsn", "exts", "fall", "falls", "fls", "ferry", "frry",
"fry", "field", "fld", "fields", "flds", "flat", "flt", "flats",
"flts", "ford", "frd", "fords", "forest", "forests", "frst",
"forg", "forge", "frg", "forges", "fork", "frk", "forks", "frks",
"fort", "frt", "ft", "freeway", "freewy", "frway", "frwy", "fwy",
"garden", "gardn", "grden", "grdn", "gardens", "gdns", "grdns",
"gateway", "gatewy", "gatway", "gtway", "gtwy", "glen", "gln",
"glens", "green", "grn", "greens", "grov", "grove", "grv", "groves",
"harb", "harbor", "harbr", "hbr", "hrbor", "harbors", "haven",
"hvn", "ht", "hts", "highway", "highwy", "hiway", "hiwy", "hway",
"hwy", "hill", "hl", "hills", "hls", "hllw", "hollow", "hollows",
"holw", "holws", "inlt", "is", "island", "islnd", "islands",
"islnds", "iss", "isle", "isles", "jct", "jction", "jctn", "junction",
"junctn", "juncton", "jctns", "jcts", "junctions", "key", "ky",
"keys", "kys", "knl", "knol", "knoll", "knls", "knolls", "lk",
"lake", "lks", "lakes", "land", "landing", "lndg", "lndng", "lane",
"ln", "lgt", "light", "lights", "lf", "loaf", "lck", "lock",
"lcks", "locks", "ldg", "ldge", "lodg", "lodge", "loop", "loops",
"mall", "mnr", "manor", "manors", "mnrs", "meadow", "mdw", "mdws",
"meadows", "medows", "mews", "mill", "mills", "missn", "mssn",
"motorway", "mnt", "mt", "mount", "mntain", "mntn", "mountain",
"mountin", "mtin", "mtn", "mntns", "mountains", "nck", "neck",
"orch", "orchard", "orchrd", "oval", "ovl", "overpass", "park",
"prk", "parks", "parkway", "parkwy", "pkway", "pkwy", "pky",
"parkways", "pkwys", "pass", "passage", "path", "paths", "pike",
"pikes", "pine", "pines", "pnes", "pl", "plain", "pln", "plains",
"plns", "plaza", "plz", "plza", "point", "pt", "points", "pts",
"port", "prt", "ports", "prts", "pr", "prairie", "prr", "rad",
"radial", "radiel", "radl", "ramp", "ranch", "ranches", "rnch",
"rnchs", "rapid", "rpd", "rapids", "rpds", "rest", "rst", "rdg",
"rdge", "ridge", "rdgs", "ridges", "riv", "river", "rvr", "rivr",
"rd", "road", "roads", "rds", "route", "row", "rue", "run", "shl",
"shoal", "shls", "shoals", "shoar", "shore", "shr", "shoars",
"shores", "shrs", "skyway", "spg", "spng", "spring", "sprng",
"spgs", "spngs", "springs", "sprngs", "spur", "spurs", "sq",
"sqr", "sqre", "squ", "square", "sqrs", "squares", "sta", "station",
"statn", "stn", "stra", "strav", "straven", "stravenue", "stravn",
"strvn", "strvnue", "stream", "streme", "strm", "street", "strt",
"st", "str", "streets", "smt", "suite", "sumit", "sumitt", "summit",
"ter", "terr", "terrace", "throughway", "trace", "traces", "trce",
"track", "tracks", "trak", "trk", "trks", "trafficway", "trail",
"trails", "trl", "trls", "trailer", "trlr", "trlrs", "tunel",
"tunl", "tunls", "tunnel", "tunnels", "tunnl", "trnpk", "turnpike",
"turnpk", "underpass", "un", "union", "unions", "valley", "vally",
"vlly", "vly", "valleys", "vlys", "vdct", "via", "viadct", "viaduct",
"view", "vw", "views", "vws", "vill", "villag", "village", "villg",
"villiage", "vlg", "villages", "vlgs", "ville", "vl", "vis",
"vist", "vista", "vst", "vsta", "walk", "walks", "wall", "wy",
"way", "ways", "well", "wells", "wls"), usps_abbrev = c("aly",
"aly", "aly", "aly", "anx", "anx", "anx", "anx", "arc", "arc",
"ave", "ave", "ave", "ave", "ave", "ave", "ave", "byu", "byu",
"bch", "bch", "bnd", "bnd", "blf", "blf", "blf", "blfs", "btm",
"btm", "btm", "btm", "blvd", "blvd", "blvd", "blvd", "br", "br",
"br", "brg", "brg", "brg", "brk", "brk", "brks", "bg", "bgs",
"byp", "byp", "byp", "byp", "byp", "cp", "cp", "cp", "cyn", "cyn",
"cyn", "cpe", "cpe", "cswy", "cswy", "cswy", "ctr", "ctr", "ctr",
"ctr", "ctr", "ctr", "ctr", "ctr", "ctrs", "cir", "cir", "cir",
"cir", "cir", "cir", "cirs", "clf", "clf", "clfs", "clfs", "clb",
"clb", "cmn", "cmns", "cor", "cor", "cors", "cors", "crse", "crse",
"ct", "ct", "cts", "cts", "cv", "cv", "cvs", "crk", "crk", "cres",
"cres", "cres", "cres", "crst", "xing", "xing", "xing", "xrd",
"xrds", "curv", "dl", "dl", "dm", "dm", "dv", "dv", "dv", "dv",
"dr", "dr", "dr", "dr", "drs", "est", "est", "ests", "ests",
"expy", "expy", "expy", "expy", "expy", "expy", "ext", "ext",
"ext", "ext", "exts", "fall", "fls", "fls", "fry", "fry", "fry",
"fld", "fld", "flds", "flds", "flt", "flt", "flts", "flts", "frd",
"frd", "frds", "frst", "frst", "frst", "frg", "frg", "frg", "frgs",
"frk", "frk", "frks", "frks", "ft", "ft", "ft", "fwy", "fwy",
"fwy", "fwy", "fwy", "gdn", "gdn", "gdn", "gdn", "gdns", "gdns",
"gdns", "gtwy", "gtwy", "gtwy", "gtwy", "gtwy", "gln", "gln",
"glns", "grn", "grn", "grns", "grv", "grv", "grv", "grvs", "hbr",
"hbr", "hbr", "hbr", "hbr", "hbrs", "hvn", "hvn", "hts", "hts",
"hwy", "hwy", "hwy", "hwy", "hwy", "hwy", "hl", "hl", "hls",
"hls", "holw", "holw", "holw", "holw", "holw", "inlt", "is",
"is", "is", "iss", "iss", "iss", "isle", "isle", "jct", "jct",
"jct", "jct", "jct", "jct", "jcts", "jcts", "jcts", "ky", "ky",
"kys", "kys", "knl", "knl", "knl", "knls", "knls", "lk", "lk",
"lks", "lks", "land", "lndg", "lndg", "lndg", "ln", "ln", "lgt",
"lgt", "lgts", "lf", "lf", "lck", "lck", "lcks", "lcks", "ldg",
"ldg", "ldg", "ldg", "loop", "loop", "mall", "mnr", "mnr", "mnrs",
"mnrs", "mdw", "mdws", "mdws", "mdws", "mdws", "mews", "ml",
"mls", "msn", "msn", "mtwy", "mt", "mt", "mt", "mtn", "mtn",
"mtn", "mtn", "mtn", "mtn", "mtns", "mtns", "nck", "nck", "orch",
"orch", "orch", "oval", "oval", "opas", "park", "park", "park",
"pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pass",
"psge", "path", "path", "pike", "pike", "pne", "pnes", "pnes",
"pl", "pln", "pln", "plns", "plns", "plz", "plz", "plz", "pt",
"pt", "pts", "pts", "prt", "prt", "prts", "prts", "pr", "pr",
"pr", "radl", "radl", "radl", "radl", "ramp", "rnch", "rnch",
"rnch", "rnch", "rpd", "rpd", "rpds", "rpds", "rst", "rst", "rdg",
"rdg", "rdg", "rdgs", "rdgs", "riv", "riv", "riv", "riv", "rd",
"rd", "rds", "rds", "rte", "row", "rue", "run", "shl", "shl",
"shls", "shls", "shr", "shr", "shr", "shrs", "shrs", "shrs",
"skwy", "spg", "spg", "spg", "spg", "spgs", "spgs", "spgs", "spgs",
"spur", "spur", "sq", "sq", "sq", "sq", "sq", "sqs", "sqs", "sta",
"sta", "sta", "sta", "stra", "stra", "stra", "stra", "stra",
"stra", "stra", "strm", "strm", "strm", "st", "st", "st", "st",
"sts", "smt", "ste", "smt", "smt", "smt", "ter", "ter", "ter",
"trwy", "trce", "trce", "trce", "trak", "trak", "trak", "trak",
"trak", "trfy", "trl", "trl", "trl", "trl", "trlr", "trlr", "trlr",
"tunl", "tunl", "tunl", "tunl", "tunl", "tunl", "tpke", "tpke",
"tpke", "upas", "un", "un", "uns", "vly", "vly", "vly", "vly",
"vlys", "vlys", "via", "via", "via", "via", "vw", "vw", "vws",
"vws", "vlg", "vlg", "vlg", "vlg", "vlg", "vlg", "vlgs", "vlgs",
"vl", "vl", "vis", "vis", "vis", "vis", "vis", "walk", "walk",
"wall", "way", "way", "ways", "wl", "wls", "wls")), class = "data.frame", row.names = c(NA,
-503L))
我想用它们来处理街道地址和州。玩具数据:
a <- c("10900 harper ave", "12235 davis annex", "24 van cortland parkway")
为了将常用缩写转换为 usps 缩写(标准化数据),我构建了一个小函数:
mr_zip <- function(x){
x <-textclean::mgsub(usps_streets$common_abbrev, usps_streets$usps_abbrev, x, fixed = T,
order.pattern = T)
return(x)
}
当我将函数应用于我的数据时,就会出现问题:
f <- sapply(a, mr_zip)
我得到错误的结果:
"10900 harper avee" "1235 davis anx" "24 van cortland pkway"
因为我应该得到的是:
"10900 harper ave" "1235 davis anx" "24 van cortland pkwy"
我的问题:
- 当我在函数中指定
order.pattern = T
时为什么会发生这种情况?fixed = T
mgsub
- 我能做些什么来解决它?
- 是否有另一种方法可以在文本的多种替换模式中使用向量?
在此先感谢,欢迎所有建议。
编辑:感谢@RichieSacramento,我发现使用单词边界确实有帮助,但在大型数据帧(> 400,000 行)上使用该函数仍然非常慢。使用safe = TRUE
inmgsub
会导致该功能正常工作,但速度非常慢。需要快速的东西——因此是赏金。
解决方案
所以让我们开始玩吧。
第 1 步
首先,我们将您的数据加载到一个tibble
名为USPS
.
library(tidyverse)
USPS = tibble(
common_abbrev = c("allee", "alley", "ally", "aly",
"anex", "annex", "annx", "anx", "arc", "arcade", "av", "ave",
"aven", "avenu", "avenue", "avn", "avnue", "bayoo", "bayou",
"bch", "beach", "bend", "bnd", "blf", "bluf", "bluff", "bluffs",
"bot", "btm", "bottm", "bottom", "blvd", "boul", "boulevard",
"boulv", "br", "brnch", "branch", "brdge", "brg", "bridge", "brk",
"brook", "brooks", "burg", "burgs", "byp", "bypa", "bypas", "bypass",
"byps", "camp", "cp", "cmp", "canyn", "canyon", "cnyn", "cape",
"cpe", "causeway", "causwa", "cswy", "cen", "cent", "center",
"centr", "centre", "cnter", "cntr", "ctr", "centers", "cir",
"circ", "circl", "circle", "crcl", "crcle", "circles", "clf",
"cliff", "clfs", "cliffs", "clb", "club", "common", "commons",
"cor", "corner", "corners", "cors", "course", "crse", "court",
"ct", "courts", "cts", "cove", "cv", "coves", "creek", "crk",
"crescent", "cres", "crsent", "crsnt", "crest", "crossing", "crssng",
"xing", "crossroad", "crossroads", "curve", "dale", "dl", "dam",
"dm", "div", "divide", "dv", "dvd", "dr", "driv", "drive", "drv",
"drives", "est", "estate", "estates", "ests", "exp", "expr",
"express", "expressway", "expw", "expy", "ext", "extension",
"extn", "extnsn", "exts", "fall", "falls", "fls", "ferry", "frry",
"fry", "field", "fld", "fields", "flds", "flat", "flt", "flats",
"flts", "ford", "frd", "fords", "forest", "forests", "frst",
"forg", "forge", "frg", "forges", "fork", "frk", "forks", "frks",
"fort", "frt", "ft", "freeway", "freewy", "frway", "frwy", "fwy",
"garden", "gardn", "grden", "grdn", "gardens", "gdns", "grdns",
"gateway", "gatewy", "gatway", "gtway", "gtwy", "glen", "gln",
"glens", "green", "grn", "greens", "grov", "grove", "grv", "groves",
"harb", "harbor", "harbr", "hbr", "hrbor", "harbors", "haven",
"hvn", "ht", "hts", "highway", "highwy", "hiway", "hiwy", "hway",
"hwy", "hill", "hl", "hills", "hls", "hllw", "hollow", "hollows",
"holw", "holws", "inlt", "is", "island", "islnd", "islands",
"islnds", "iss", "isle", "isles", "jct", "jction", "jctn", "junction",
"junctn", "juncton", "jctns", "jcts", "junctions", "key", "ky",
"keys", "kys", "knl", "knol", "knoll", "knls", "knolls", "lk",
"lake", "lks", "lakes", "land", "landing", "lndg", "lndng", "lane",
"ln", "lgt", "light", "lights", "lf", "loaf", "lck", "lock",
"lcks", "locks", "ldg", "ldge", "lodg", "lodge", "loop", "loops",
"mall", "mnr", "manor", "manors", "mnrs", "meadow", "mdw", "mdws",
"meadows", "medows", "mews", "mill", "mills", "missn", "mssn",
"motorway", "mnt", "mt", "mount", "mntain", "mntn", "mountain",
"mountin", "mtin", "mtn", "mntns", "mountains", "nck", "neck",
"orch", "orchard", "orchrd", "oval", "ovl", "overpass", "park",
"prk", "parks", "parkway", "parkwy", "pkway", "pkwy", "pky",
"parkways", "pkwys", "pass", "passage", "path", "paths", "pike",
"pikes", "pine", "pines", "pnes", "pl", "plain", "pln", "plains",
"plns", "plaza", "plz", "plza", "point", "pt", "points", "pts",
"port", "prt", "ports", "prts", "pr", "prairie", "prr", "rad",
"radial", "radiel", "radl", "ramp", "ranch", "ranches", "rnch",
"rnchs", "rapid", "rpd", "rapids", "rpds", "rest", "rst", "rdg",
"rdge", "ridge", "rdgs", "ridges", "riv", "river", "rvr", "rivr",
"rd", "road", "roads", "rds", "route", "row", "rue", "run", "shl",
"shoal", "shls", "shoals", "shoar", "shore", "shr", "shoars",
"shores", "shrs", "skyway", "spg", "spng", "spring", "sprng",
"spgs", "spngs", "springs", "sprngs", "spur", "spurs", "sq",
"sqr", "sqre", "squ", "square", "sqrs", "squares", "sta", "station",
"statn", "stn", "stra", "strav", "straven", "stravenue", "stravn",
"strvn", "strvnue", "stream", "streme", "strm", "street", "strt",
"st", "str", "streets", "smt", "suite", "sumit", "sumitt", "summit",
"ter", "terr", "terrace", "throughway", "trace", "traces", "trce",
"track", "tracks", "trak", "trk", "trks", "trafficway", "trail",
"trails", "trl", "trls", "trailer", "trlr", "trlrs", "tunel",
"tunl", "tunls", "tunnel", "tunnels", "tunnl", "trnpk", "turnpike",
"turnpk", "underpass", "un", "union", "unions", "valley", "vally",
"vlly", "vly", "valleys", "vlys", "vdct", "via", "viadct", "viaduct",
"view", "vw", "views", "vws", "vill", "villag", "village", "villg",
"villiage", "vlg", "villages", "vlgs", "ville", "vl", "vis",
"vist", "vista", "vst", "vsta", "walk", "walks", "wall", "wy",
"way", "ways", "well", "wells", "wls"),
usps_abbrev = c("aly",
"aly", "aly", "aly", "anx", "anx", "anx", "anx", "arc", "arc",
"ave", "ave", "ave", "ave", "ave", "ave", "ave", "byu", "byu",
"bch", "bch", "bnd", "bnd", "blf", "blf", "blf", "blfs", "btm",
"btm", "btm", "btm", "blvd", "blvd", "blvd", "blvd", "br", "br",
"br", "brg", "brg", "brg", "brk", "brk", "brks", "bg", "bgs",
"byp", "byp", "byp", "byp", "byp", "cp", "cp", "cp", "cyn", "cyn",
"cyn", "cpe", "cpe", "cswy", "cswy", "cswy", "ctr", "ctr", "ctr",
"ctr", "ctr", "ctr", "ctr", "ctr", "ctrs", "cir", "cir", "cir",
"cir", "cir", "cir", "cirs", "clf", "clf", "clfs", "clfs", "clb",
"clb", "cmn", "cmns", "cor", "cor", "cors", "cors", "crse", "crse",
"ct", "ct", "cts", "cts", "cv", "cv", "cvs", "crk", "crk", "cres",
"cres", "cres", "cres", "crst", "xing", "xing", "xing", "xrd",
"xrds", "curv", "dl", "dl", "dm", "dm", "dv", "dv", "dv", "dv",
"dr", "dr", "dr", "dr", "drs", "est", "est", "ests", "ests",
"expy", "expy", "expy", "expy", "expy", "expy", "ext", "ext",
"ext", "ext", "exts", "fall", "fls", "fls", "fry", "fry", "fry",
"fld", "fld", "flds", "flds", "flt", "flt", "flts", "flts", "frd",
"frd", "frds", "frst", "frst", "frst", "frg", "frg", "frg", "frgs",
"frk", "frk", "frks", "frks", "ft", "ft", "ft", "fwy", "fwy",
"fwy", "fwy", "fwy", "gdn", "gdn", "gdn", "gdn", "gdns", "gdns",
"gdns", "gtwy", "gtwy", "gtwy", "gtwy", "gtwy", "gln", "gln",
"glns", "grn", "grn", "grns", "grv", "grv", "grv", "grvs", "hbr",
"hbr", "hbr", "hbr", "hbr", "hbrs", "hvn", "hvn", "hts", "hts",
"hwy", "hwy", "hwy", "hwy", "hwy", "hwy", "hl", "hl", "hls",
"hls", "holw", "holw", "holw", "holw", "holw", "inlt", "is",
"is", "is", "iss", "iss", "iss", "isle", "isle", "jct", "jct",
"jct", "jct", "jct", "jct", "jcts", "jcts", "jcts", "ky", "ky",
"kys", "kys", "knl", "knl", "knl", "knls", "knls", "lk", "lk",
"lks", "lks", "land", "lndg", "lndg", "lndg", "ln", "ln", "lgt",
"lgt", "lgts", "lf", "lf", "lck", "lck", "lcks", "lcks", "ldg",
"ldg", "ldg", "ldg", "loop", "loop", "mall", "mnr", "mnr", "mnrs",
"mnrs", "mdw", "mdws", "mdws", "mdws", "mdws", "mews", "ml",
"mls", "msn", "msn", "mtwy", "mt", "mt", "mt", "mtn", "mtn",
"mtn", "mtn", "mtn", "mtn", "mtns", "mtns", "nck", "nck", "orch",
"orch", "orch", "oval", "oval", "opas", "park", "park", "park",
"pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pkwy", "pass",
"psge", "path", "path", "pike", "pike", "pne", "pnes", "pnes",
"pl", "pln", "pln", "plns", "plns", "plz", "plz", "plz", "pt",
"pt", "pts", "pts", "prt", "prt", "prts", "prts", "pr", "pr",
"pr", "radl", "radl", "radl", "radl", "ramp", "rnch", "rnch",
"rnch", "rnch", "rpd", "rpd", "rpds", "rpds", "rst", "rst", "rdg",
"rdg", "rdg", "rdgs", "rdgs", "riv", "riv", "riv", "riv", "rd",
"rd", "rds", "rds", "rte", "row", "rue", "run", "shl", "shl",
"shls", "shls", "shr", "shr", "shr", "shrs", "shrs", "shrs",
"skwy", "spg", "spg", "spg", "spg", "spgs", "spgs", "spgs", "spgs",
"spur", "spur", "sq", "sq", "sq", "sq", "sq", "sqs", "sqs", "sta",
"sta", "sta", "sta", "stra", "stra", "stra", "stra", "stra",
"stra", "stra", "strm", "strm", "strm", "st", "st", "st", "st",
"sts", "smt", "ste", "smt", "smt", "smt", "ter", "ter", "ter",
"trwy", "trce", "trce", "trce", "trak", "trak", "trak", "trak",
"trak", "trfy", "trl", "trl", "trl", "trl", "trlr", "trlr", "trlr",
"tunl", "tunl", "tunl", "tunl", "tunl", "tunl", "tpke", "tpke",
"tpke", "upas", "un", "un", "uns", "vly", "vly", "vly", "vly",
"vlys", "vlys", "via", "via", "via", "via", "vw", "vw", "vws",
"vws", "vlg", "vlg", "vlg", "vlg", "vlg", "vlg", "vlgs", "vlgs",
"vl", "vl", "vis", "vis", "vis", "vis", "vis", "walk", "walk",
"wall", "way", "way", "ways", "wl", "wls", "wls"))
USPS
输出
# A tibble: 503 x 2
common_abbrev usps_abbrev
<chr> <chr>
1 allee aly
2 alley aly
3 ally aly
4 aly aly
5 anex anx
6 annex anx
7 annx anx
8 anx anx
9 arc arc
10 arcade arc
# ... with 493 more rows
第 2 步
现在我们将您的USPS
表格转换为带有命名元素的向量。
USPSv = array(data = USPS$usps_abbrev,
dimnames= list(USPS$common_abbrev))
让我们看看它给了我们什么
USPSv['viadct']
# viadct
# "via"
USPSv['coves']
# coves
# "cvs"
看起来很诱人。
第 3 步
现在让我们创建一个转换(矢量化)函数,将我们的USPSv
矢量与命名元素一起使用。
USPS_conv = function(x) {
comm = str_split(x, " ") %>% .[[1]] %>% .[length(.)]
str_replace(x, comm, USPSv[comm])
}
USPS_conv = Vectorize(USPS_conv)
让我们看看我们的USPS_conv
工作原理。
USPS_conv("10900 harper coves")
# 10900 harper coves
# "10900 harper cvs"
USPS_conv("10900 harper viadct")
# 10900 harper viadct
# "10900 harper via"
很好,但它会处理向量吗?
USPS_conv(c("10900 harper coves", "10900 harper viadct", "10900 harper ave"))
# 10900 harper coves 10900 harper viadct 10900 harper ave
# "10900 harper cvs" "10900 harper via" "10900 harper ave"
到目前为止,一切都很顺利。
第 4 步
现在是时候在USPS_conv
函数中使用我们的函数了mutate
。但是,我们需要一些输入数据。我们将自己生成它们。
n=10
set.seed(1111)
df = tibble(
addresses = paste(
sample(10:10000, n, replace = TRUE),
sample(c("harper", "davis", "van cortland", "marry", "von brown"), n, replace = TRUE),
sample(USPS$common_abbrev, n, replace = TRUE)
)
)
df
输出
# A tibble: 10 x 1
addresses
<chr>
1 8995 davis crk
2 8527 davis tunnl
3 7663 von brown wall
4 3043 harper lake
5 9192 von brown grdn
6 120 marry rvr
7 72 von brown locks
8 8752 marry gardn
9 7754 davis corner
10 3745 davis jcts
让我们执行一个突变
df %>% mutate(addresses = USPS_conv(addresses))
输出
# A tibble: 10 x 1
addresses
<chr>
1 8995 davis crk
2 8527 davis tunl
3 7663 von brown wall
4 3043 harper lk
5 9192 von brown gdn
6 120 marry riv
7 72 von brown lcks
8 8752 marry gdn
9 7754 davis cor
10 3745 davis jcts
看起来没问题吗?好像是最多的。
第 5 步 现在是时候对 1,000,000 个地址进行一次大测试了!我们将像以前一样生成数据。
n=1000000
set.seed(1111)
df = tibble(
addresses = paste(
sample(10:10000, n, replace = TRUE),
sample(c("harper", "davis", "van cortland", "marry", "von brown"), n, replace = TRUE),
sample(USPS$common_abbrev, n, replace = TRUE)
)
)
df
输出
# A tibble: 1,000,000 x 1
addresses
<chr>
1 8995 marry pass
2 8527 davis spng
3 7663 marry loaf
4 3043 davis common
5 9192 marry bnd
6 120 von brown corner
7 72 van cortland plains
8 8752 van cortland crcle
9 7754 von brown sqrs
10 3745 marry key
# ... with 999,990 more rows
所以我们走吧。但是让我们立即测量需要多长时间。
start_time =Sys.time()
df %>% mutate(addresses = USPS_conv(addresses))
Sys.time()-start_time
#Time difference of 3.610211 mins
如您所见,我花了不到 4 分钟的时间。我不知道您是否期待更快的东西,以及您是否对这次感到满意。我将等待您的评论。
最后一分钟更新
事实证明,USPS_conv
如果我们稍微更改它的代码,可以稍微加快速度。
USPS_conv2 = function(x) {
t = str_split(x, " ")
comm = t[[1]][length(t[[1]])]
str_replace(x, comm, USPSv[comm])
}
USPS_conv2 = Vectorize(USPS_conv2)
新USPS_conv2
功能的运行速度稍快。
所有这一切都转化为将一百万条记录的突变时间减少到 3.3 分钟。
超级速度的大更新!
我意识到我的第一个答案版本虽然结构简单,但有点慢:-(。所以我决定想出更快的东西。我会在这里分享我的想法,但请注意,一些解决方案会有点“神奇”。
魔法词典-环境
为了加快操作,我们需要创建一个字典,将键快速转换为值。我们将使用 R 中的环境创建它。
这是我们字典的一个小界面。
#Simple Dictionary (hash Table) Interface for R
ht.create = function() new.env()
ht.insert = function(ht, key, value) ht[[key]] <- value
ht.insert = Vectorize(ht.insert, c("key", "value"))
ht.lookup = function(ht, key) ht[[key]]
ht.lookup = Vectorize(ht.lookup, "key")
ht.delete = function(ht, key) rm(list=key,envir=ht,inherits=FALSE)
ht.delete = Vectorize(ht.delete, "key")
它是怎么发生的。我已经展示了。下面我将创建一个新的字典环境ht.create()
,我将在其中添加两个元素“a1”和“a2” ht.insert
,其值分别为“va1”和“va2”。最后,我将向我的环境字典询问这些ht.lookup
键的值。
ht1 = ht.create()
ht.insert(ht1, "a1", "va1" )
ht1 %>% ht.insert("a2", "va2")
ht.lookup(ht1, "a1")
# a1
# "va1"
ht1 %>% ht.lookup("a2")
# a2
# "va2"
请注意,函数ht.insert
和 ht.lookup
是矢量化的,这意味着我将能够将整个向量添加到字典中。以同样的方式,我将能够通过给出整个向量来查询我的字典。
ht.insert(ht1, paste0("a", 1:10),paste0("va", 1:10))
ht1 %>% ht.insert( paste0("a", 11:20),paste0("va", 11:20))
ht.lookup(ht1, paste0("a", 10:1))
# a10 a9 a8 a7 a6 a5 a4 a3 a2 a1
# "va10" "va9" "va8" "va7" "va6" "va5" "va4" "va3" "va2" "va1"
ht1 %>% ht.lookup(paste0("a", 20:11))
# a20 a19 a18 a17 a16 a15 a14 a13 a12 a11
# "va20" "va19" "va18" "va17" "va16" "va15" "va14" "va13" "va12" "va11"
魔法属性
现在我们将执行一个函数,该函数将向选定的字典环境表添加一个附加属性。
#Functions that add a dictionary attribute to tibble
addHashTable = function(.data, key, value){
key = enquo(key)
value = enquo(value)
if (!all(c(as_label(key), as_label(value)) %in% names(.data))) {
stop(paste0("`.data` must contain `", as_label(key),
"` and `", as_label(value), "` columns"))
}
if((.data %>% distinct(!!key, !!value) %>% nrow)!=
(.data %>% distinct(!!key) %>% nrow)){
warning(paste0(
"\nThe number of unique values of the ", as_label(key),
" variable is different\n",
" from the number of unique values of the ",
as_label(key), " and ", as_label(value)," pairs!\n",
"The dictionary will only return the last values for a given key!"))
}
ht = ht.create()
ht %>% ht.insert(.data %>% distinct(!!key, !!value) %>% pull(!!key),
.data %>% distinct(!!key, !!value) %>% pull(!!value))
attr(.data, "hashTab") = ht
.data
}
addHashTable2 = function(.x, .y, key, value){
key = enquo(key)
value = enquo(value)
if (!all(c(as_label(key), as_label(value)) %in% names(.y))) {
stop(paste0("`.y` must contain `", as_label(key),
"` and `", as_label(value), "` columns"))
}
if((.y %>% distinct(!!key, !!value) %>% nrow)!=
(.y %>% distinct(!!key) %>% nrow)){
warning(paste0(
"\nThe number of unique values of the ", as_label(key),
" variable is different\n",
" from the number of unique values of the ",
as_label(key), " and ", as_label(value)," pairs!\n",
"The dictionary will only return the last values for a given key!"))
}
ht = ht.create()
ht %>% ht.insert(.y %>% distinct(!!key, !!value) %>% pull(!!key),
.y %>% distinct(!!key, !!value) %>% pull(!!value))
attr(.x, "hashTab") = ht
.x
}
那里实际上有两个功能。该addHashTable
函数将字典环境属性添加到它从中获取键值对的同一个表中。该addHashTable2
函数同样添加到字典环境表,但从另一个表中检索密钥对。
让我们看看是如何addHashTable
工作的。
USPS = USPS %>% addHashTable(common_abbrev, usps_abbrev)
str(USPS)
# tibble [503 x 2] (S3: tbl_df/tbl/data.frame)
# $ common_abbrev: chr [1:503] "allee" "alley" "ally" "aly" ...
# $ usps_abbrev : chr [1:503] "aly" "aly" "aly" "aly" ...
# - attr(*, "hashTab")=<environment: 0x000000001591bbf0>
如您所见,USPS
表中添加了一个指向0x000000001591bbf0
环境的属性。
替换功能
我们需要创建一个函数,该函数将使用以这种方式添加的字典环境,在这种情况下,将指定变量中的最后一个单词替换为字典中的相应值。这里是。
replaceString = function(.data, value){
value = enquo(value)
#Test whether the value variable is in .data
if(!(as_label(value) %in% names(.data))){
stop(paste("The", as_label(value),
"variable does not exist in the .data table!"))
}
#Dictionary attribute presence test
if(!("hashTab" %in% names(attributes(.data)))) {
stop(paste0(
"\nThere is no dictionary attribute in the .data table!\n",
"Use addHashTable or addHashTable2 to add a dictionary attribute."))
}
txt = .data %>% pull(!!value)
i = sapply(strsplit(txt, ""), function(x) max(which(x==" ")))
txt = paste0(str_sub(txt, end=i),
ht.lookup(attr(.data, "hashTab"),
str_sub(txt, start=i+1)))
.data %>% mutate(!!value := txt)
}
第一次测试
是时候写第一个文本了。为了避免复制代码,我添加了一个小函数,它返回一个包含随机选择的地址的表。
randomAddresses = function(n){
tibble(
addresses = paste(
sample(10:10000, n, replace = TRUE),
sample(c("harper", "davis", "van cortland", "marry", "von brown"), n, replace = TRUE),
sample(USPS$common_abbrev, n, replace = TRUE)
)
)
}
set.seed(1111)
df = randomAddresses(10)
df
# # A tibble: 10 x 1
# addresses
# <chr>
# 1 74 marry forges
# 2 787 von brown knol
# 3 2755 van cortland summit
# 4 9405 harper plaza
# 5 5376 marry pass
# 6 1857 marry trailer
# 7 9810 von brown drv
# 8 7984 davis garden
# 9 9110 marry alley
# 10 6458 von brown row
是时候使用我们神奇的文本替换功能了。但是,请记住首先将字典环境添加到表中。
df = df %>% addHashTable2(USPS, common_abbrev, usps_abbrev)
df %>% replaceString(addresses)
# A tibble: 10 x 1
# addresses
# <chr>
# 1 74 marry frgs
# 2 787 von brown knl
# 3 2755 van cortland smt
# 4 9405 harper plz
# 5 5376 marry pass
# 6 1857 marry trlr
# 7 9810 von brown dr
# 8 7984 davis gdn
# 9 9110 marry aly
# 10 6458 von brown row
它看起来像它的作品!
大考验
好吧,没有什么可以等待的。现在让我们在一个有一百万行的表上试试。让我们立即测量绘制地址和添加字典环境需要多长时间。
start_time =Sys.time()
df = randomAddresses(1000000)
df = df %>% addHashTable2(USPS, common_abbrev, usps_abbrev)
Sys.time()-start_time
#Time difference of 1.56609 secs
输出
df
# A tibble: 1,000,000 x 1
# addresses
# <chr>
# 1 8995 marry pass
# 2 8527 davis spng
# 3 7663 marry loaf
# 4 3043 davis common
# 5 9192 marry bnd
# 6 120 von brown corner
# 7 72 van cortland plains
# 8 8752 van cortland crcle
# 9 7754 von brown sqrs
# 10 3745 marry key
# # ... with 999,990 more rows
1.6秒可能不算多。但是,最大的问题是替换缩写需要多长时间。
start_time =Sys.time()
df = df %>% replaceString(addresses)
Sys.time()-start_time
#Time difference of 8.316476 secs
输出
# A tibble: 1,000,000 x 1
# addresses
# <chr>
# 1 8995 marry pass
# 2 8527 davis spg
# 3 7663 marry lf
# 4 3043 davis cmn
# 5 9192 marry bnd
# 6 120 von brown cor
# 7 72 van cortland plns
# 8 8752 van cortland cir
# 9 7754 von brown sqs
# 10 3745 marry ky
# # ... with 999,990 more rows
繁荣!!我们有 8 秒!
我确信在 R 中无法建立更快的机制。
@ThomasIsCoding 的小更新
下面是一个小的基准测试。注意我借用了函数的代码f_MK_conv2
, f_TIC1
以及f_TIC2
来自@ThomasIsCoding。
set.seed(1111)
df = randomAddresses(10000)
df = df %>% addHashTable2(USPS, common_abbrev, usps_abbrev)
library(microbenchmark)
mb1 = microbenchmark(
f_MK_conv2(df$addresses),
f_TIC1(df$addresses),
f_TIC2(df$addresses),
replaceString(df, addresses),
times = 20L
)
ggplot2::autoplot(mb1)
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