我有两个 Dataframe
分类：
structure(list(category_code = 1:12, category_name = c("Kanton",
"Bezirk", "Gemeinde", "Ort", "Ortsteil,Siedlung", "Quartier",
"Region", "Gebiet", "Flurname", "Strasse,Platz", "Hof,Haus",
"Berg"), radius = c(25000L, 8000L, 2500L, 1000L, 500L, 400L,
15000L, 300L, 300L, 150L, 40L, 300L), category_values = list(
c("kanton", "bistum"), "bezirk", c("gemeinde", "stadt", "gletscher",
"see", "gewässer"), c("ort", "dorf", "alp", "gewaesser",
"Gelände", "administrative", "seeteil", "schutzzone"), c("ortsteil",
"siedlung", "areal", "flug", "Wald"), c("quartier", "weiler",
"abhang", "bauzone", "gemeingut", "lehen", "nutzungszone",
"pacht"), "region", c("gebiet", "tal", "ebene", "gebirge",
"massiv", "herrschaft"), c("flur", "mündung", "steinbruch",
"insel", "weiher", "deponie", "lokalname", "kulturland",
"quartierteil", "land", "allmend", "park", "lichtung", "rodung",
"bucht", "pflanzenbestand"), c("strasse", "weg", "kreuzung",
"gasse", "graben", "platz", "bach", "kanal", "verzweigung",
"pfad", "fluss", "fliessend", "spur"), c("haus", "hof", "bauernhof",
"gebaeude", "gebäude", "wohngebäude", "wohngebaeude", "scheune",
"ruine", "burg", "brunnen", "schloss", "käserei", "mühle",
"turm", "sägerei", "kirche", "anstalt", "heim", "gefängnis",
"bild", "stock", "denkmal", "anlage", "stall", "wirtschaft",
"restaurant", "wehr", "halle", "grenzstein", "einkaufszentrum",
"grotte", "hoehle", "museum", "leitung", "bäckerei", "parkplatz",
"wasserfall", "seilbahn", "lift", "kapelle", "bauwerk", "haltestelle",
"aussichtspunkt", "ziegelei", "zisterne", "stadion", "baum",
"werk", "grundstück", "teich", "hafen", "treppe", "quelle",
"kloster", "findling", "mauer", "trotte", "stätte", "bahn",
"bunker", "apotheke", "metzgerei", "block", "walke", "tunnel",
"infrastruktur"), c("gipfel", "grat", "first", "huegel",
"hügel", "graben", "tobel", "pass", "sattel", "rücken",
"aussicht", "berg", "runse", "fels", "geländeerhebung",
"geländevertiefung", "firnschnee", "fischereistelle"))), row.names = c(NA,
-12L), class = "data.frame")现在我想比较categories$category_values和names_df$category_string，对于每个匹配项，最低的（1，2，3，4，5，6，7，8，9，10，11，12）对应categories$category应该写入names_df$category_code，对应的radius也应该写入一个新字段，如names_df$radius。
它也应该是不区分大小写的，然后还检查部分（SQL与%ILIKE%或其他）。例如。类别$category_values是“gewässer”，它还应该检查它的names_df$category是“Fliessgewässer”。
以下是我目前为止的代码，但它没有正确地完成这项工作：
# loop through each row in names_df
for (i in seq_along(names_df$category_string)) {
# check for matches between categories$category_values and names_df$category_string
match_rows <- categories[sapply(categories$category_values, function(x) any(grep(tolower(x), tolower(names_df$category_string[[i]])))), ]
# extract the category codes from the matched rows and add them to the list
matched_codes[[i]] <- match_rows$category_code
# concatenate the matched category codes into a string and write to names_df$category_code
names_df$category_code[i] <- paste0(sort(unlist(matched_codes[[i]])), collapse = ", ")
}谢谢你的帮助
1条答案
按热度按时间mhd8tkvw1#
这里我尝试使用
dplyr和stringr，困难的部分是将字符串与列表的列表进行比较，因此我将每个列表元素合并到一个正则表达式中，并对每个category_string进行逐行比较。然后我取最小索引并连接回categories df。