当术语不准确时，是否可以通过将术语从一列匹配到另一列来填充空白列？
Is there a way to fill blank columns by matching terms from one column to another, when the terms are not exact?
我已经使用 rbind 将两个数据集组合成一个 828 x 5 数据框，称为 vegetation。
这是我当前数据框的示例：
site year sos eos vegetation
EPIC_alligatorriver.csv 2016 176 301
EPIC_alligatorriver.csv 2018 164 291
Landsat_alligatorriver.csv 2016 170 303
Pheno_alligatorriver.csv 2017 152 288
Landsat_NEON.BART42.csv 2017 115 290
Pheno_NEON.BART42.csv 2017 120 290
alligatorriver.csv NA NA NA deciduous broadleaf
NEON.BART42.csv NA NA NA mixed forest
这是我需要的
site year sos eos vegetation
EPIC_alligatorriver.csv 2016 176 301 deciduous broadleaf
EPIC_alligatorriver.csv 2018 164 291 deciduous broadleaf
Landsat_alligatorriver.csv 2016 170 303 deciduous broadleaf
Pheno_alligatorriver.csv 2017 152 288 deciduous broadleaf
Landsat_NEON.BART42.csv 2017 115 290 mixed forest
Pheno_NEON.BART42.csv 2017 120 290 mixed forest
alligatorriver.csv NA NA NA deciduous broadleaf
NEON.BART42.csv NA NA NA mixed forest
基本上，我需要根据站点名称匹配正确的植被类型来填写空白植被栏。
我也可以删除 rbind 并将所有植被数据放在一个完全不同的两列数据框中
site vegetation
alligatorriver.csv deciduous broadleaf
konza.csv grassland
merbleue.csv wetland
NEON.BART42.csv mixed forest
如果这样更容易。三年期间（2016 年、2017 年、2018 年）总共有 7 种植被类型和 99 个站点。感谢您的帮助！
df1 (partial): structure(list(site = c("EPIC_alligatorriver.csv", "EPIC_alligatorriver.csv",
"EPIC_alligatorriver.csv", "EPIC_arbutuslakeinlet.csv", "EPIC_arbutuslakeinlet.csv",
"EPIC_arbutuslakeinlet.csv", "EPIC_archboldavir.csv", "EPIC_archboldavir.csv",
"EPIC_archboldavir.csv", "EPIC_archboldavirx.csv"), year = c(2016L, 2017L, 2018L, 2016L, 2017L, 2018L, 2016L,
2017L, 2018L, 2016L), sos = c(117, 111, 122, 147, 145, 144, 98, 156,
114, 98), eos = c(294, 294, 274, 276, 271, 274, 315, 295, 307, 315), vegetation = c("", "", "", "", "", "", "", "", "", "")), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "data.frame")
df2: structure(list(vegetation = structure(c(2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 6L, 3L, 2L, 4L, 2L, 2L, 3L, 4L, 2L, 3L, 2L,
1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 6L, 6L, 4L, 1L, 4L, 1L, 4L, 2L,
3L, 2L, 4L, 7L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 2L, 4L, 7L, 2L,
4L, 2L, 2L, 5L, 2L, 4L, 2L, 5L, 2L, 5L, 2L, 6L, 2L, 5L, 3L, 2L,
2L, 5L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 6L, 2L,
1L, 4L, 2L, 1L, 3L, 2L, 2L, 4L, 3L, 2L, 3L, 2L, 2L), .Label = c("agriculture",
"deciduous broadleaf", "evergreen needlefeaf", "grassland", "mixed forest",
"shrub", "wetland"), class = "factor"), site = structure(1:99, .Label = c("alligatorriver",
"arbutuslakeinlet", "archboldavir", "archboldavirx", "archboldpnot",
"archboldpnotx", "arsmnswanlake1", "ashburnham", "bartlettir",
"bostoncommon", "bullshoals", "burnssagebrush", "canadaOBS",
"caryinstitute", "cperuvb", "downerwoods", "dukehw", "goodnow",
"grandteton", "harvard", "harvardbarn", "harvardbarn2", "harvardfarmsouth",
"harvardhemlock", "harvardhemlock2", "harvardlph", "howland1",
"howland2", "hubbardbrook", "huyckpreserveny", "jerbajada", "jernort",
"kansas", "kelloggcorn", "kendall", "kingmanfarm", "konza", "lacclair",
"laclaflamme", "laurentides", "lethbridge", "lostcreek", "luckyhills",
"mandanh5", "mandani2", "mead1", "mead2", "mead3", "meadpasture",
"merbleue", "missouriozarks", "montebondonegrass", "montebondonepeat",
"morganmonroe", "nationalelkrefuge", "ncssm", "NEON.BART33",
"NEON.BART42", "NEON.DELA33", "NEON.DSNY33", "NEON.HARV33", "NEON.HARV42",
"NEON.JERC33", "NEON.JERC42", "NEON.LENO33", "NEON.ONAQ33", "NEON.ORNL33",
"NEON.ORNL42", "NEON.RMNP33", "NEON.SERC33", "NEON.TREE33", "NEON.TREE42",
"NEON.UNDE33", "NEON.WOOD33", "ninemileprairie", "northattkeboroma",
"oakville", "proctor", "queens", "readingma", "rosemountnprs",
"russellsage", "sanford", "sevilletagrass", "sevilletashrub",
"shalehillsczo", "southerngreatplains", "stjones", "sweetbriar",
"sweetbriargrass", "sylvania", "tonzi", "umichbiological2", "usgseros",
"usmpj", "uwmfieldsta", "warrenwilson", "willowcreek", "worcester"
), class = "factor")), class = "data.frame", row.names = c(NA,
-99L))
这可以通过使用 match() 使用两个数据框中的站点列来完成，使用 sub() 从第一个 data.frame:
中删除前缀
df$vegetation <- df2$vegetation[match(sub("^.*_", "", df$site), df2$site)]
df
site year sos eos vegetation
1 EPIC_alligatorriver.csv 2016 176 301 deciduous broadleaf
2 EPIC_alligatorriver.csv 2018 164 291 deciduous broadleaf
3 Landsat_alligatorriver.csv 2016 170 303 deciduous broadleaf
4 Pheno_alligatorriver.csv 2017 152 288 deciduous broadleaf
5 Landsat_NEON.BART42.csv 2017 115 290 mixed forest
6 Pheno_NEON.BART42.csv 2017 120 290 mixed forest
数据：
df <- structure(list(site = c("EPIC_alligatorriver.csv", "EPIC_alligatorriver.csv",
"Landsat_alligatorriver.csv", "Pheno_alligatorriver.csv", "Landsat_NEON.BART42.csv",
"Pheno_NEON.BART42.csv"), year = c(2016L, 2018L, 2016L, 2017L,
2017L, 2017L), sos = c(176L, 164L, 170L, 152L, 115L, 120L), eos = c(301L,
291L, 303L, 288L, 290L, 290L), vegetation = c("deciduous broadleaf",
"deciduous broadleaf", "deciduous broadleaf", "deciduous broadleaf",
"mixed forest", "mixed forest")), row.names = c(NA, -6L), class = "data.frame")
df2 <- structure(list(site = c("alligatorriver.csv", "konza.csv", "merbleue.csv",
"NEON.BART42.csv"), vegetation = c("deciduous broadleaf", "grassland",
"wetland", "mixed forest")), class = "data.frame", row.names = c(NA,
-4L))
我已经使用 rbind 将两个数据集组合成一个 828 x 5 数据框，称为 vegetation。
这是我当前数据框的示例：
site year sos eos vegetation
EPIC_alligatorriver.csv 2016 176 301
EPIC_alligatorriver.csv 2018 164 291
Landsat_alligatorriver.csv 2016 170 303
Pheno_alligatorriver.csv 2017 152 288
Landsat_NEON.BART42.csv 2017 115 290
Pheno_NEON.BART42.csv 2017 120 290
alligatorriver.csv NA NA NA deciduous broadleaf
NEON.BART42.csv NA NA NA mixed forest
这是我需要的
site year sos eos vegetation
EPIC_alligatorriver.csv 2016 176 301 deciduous broadleaf
EPIC_alligatorriver.csv 2018 164 291 deciduous broadleaf
Landsat_alligatorriver.csv 2016 170 303 deciduous broadleaf
Pheno_alligatorriver.csv 2017 152 288 deciduous broadleaf
Landsat_NEON.BART42.csv 2017 115 290 mixed forest
Pheno_NEON.BART42.csv 2017 120 290 mixed forest
alligatorriver.csv NA NA NA deciduous broadleaf
NEON.BART42.csv NA NA NA mixed forest
基本上，我需要根据站点名称匹配正确的植被类型来填写空白植被栏。
我也可以删除 rbind 并将所有植被数据放在一个完全不同的两列数据框中
site vegetation
alligatorriver.csv deciduous broadleaf
konza.csv grassland
merbleue.csv wetland
NEON.BART42.csv mixed forest
如果这样更容易。三年期间（2016 年、2017 年、2018 年）总共有 7 种植被类型和 99 个站点。感谢您的帮助！
df1 (partial): structure(list(site = c("EPIC_alligatorriver.csv", "EPIC_alligatorriver.csv",
"EPIC_alligatorriver.csv", "EPIC_arbutuslakeinlet.csv", "EPIC_arbutuslakeinlet.csv",
"EPIC_arbutuslakeinlet.csv", "EPIC_archboldavir.csv", "EPIC_archboldavir.csv",
"EPIC_archboldavir.csv", "EPIC_archboldavirx.csv"), year = c(2016L, 2017L, 2018L, 2016L, 2017L, 2018L, 2016L,
2017L, 2018L, 2016L), sos = c(117, 111, 122, 147, 145, 144, 98, 156,
114, 98), eos = c(294, 294, 274, 276, 271, 274, 315, 295, 307, 315), vegetation = c("", "", "", "", "", "", "", "", "", "")), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "data.frame")
df2: structure(list(vegetation = structure(c(2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 6L, 3L, 2L, 4L, 2L, 2L, 3L, 4L, 2L, 3L, 2L,
1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 6L, 6L, 4L, 1L, 4L, 1L, 4L, 2L,
3L, 2L, 4L, 7L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 2L, 4L, 7L, 2L,
4L, 2L, 2L, 5L, 2L, 4L, 2L, 5L, 2L, 5L, 2L, 6L, 2L, 5L, 3L, 2L,
2L, 5L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 6L, 2L,
1L, 4L, 2L, 1L, 3L, 2L, 2L, 4L, 3L, 2L, 3L, 2L, 2L), .Label = c("agriculture",
"deciduous broadleaf", "evergreen needlefeaf", "grassland", "mixed forest",
"shrub", "wetland"), class = "factor"), site = structure(1:99, .Label = c("alligatorriver",
"arbutuslakeinlet", "archboldavir", "archboldavirx", "archboldpnot",
"archboldpnotx", "arsmnswanlake1", "ashburnham", "bartlettir",
"bostoncommon", "bullshoals", "burnssagebrush", "canadaOBS",
"caryinstitute", "cperuvb", "downerwoods", "dukehw", "goodnow",
"grandteton", "harvard", "harvardbarn", "harvardbarn2", "harvardfarmsouth",
"harvardhemlock", "harvardhemlock2", "harvardlph", "howland1",
"howland2", "hubbardbrook", "huyckpreserveny", "jerbajada", "jernort",
"kansas", "kelloggcorn", "kendall", "kingmanfarm", "konza", "lacclair",
"laclaflamme", "laurentides", "lethbridge", "lostcreek", "luckyhills",
"mandanh5", "mandani2", "mead1", "mead2", "mead3", "meadpasture",
"merbleue", "missouriozarks", "montebondonegrass", "montebondonepeat",
"morganmonroe", "nationalelkrefuge", "ncssm", "NEON.BART33",
"NEON.BART42", "NEON.DELA33", "NEON.DSNY33", "NEON.HARV33", "NEON.HARV42",
"NEON.JERC33", "NEON.JERC42", "NEON.LENO33", "NEON.ONAQ33", "NEON.ORNL33",
"NEON.ORNL42", "NEON.RMNP33", "NEON.SERC33", "NEON.TREE33", "NEON.TREE42",
"NEON.UNDE33", "NEON.WOOD33", "ninemileprairie", "northattkeboroma",
"oakville", "proctor", "queens", "readingma", "rosemountnprs",
"russellsage", "sanford", "sevilletagrass", "sevilletashrub",
"shalehillsczo", "southerngreatplains", "stjones", "sweetbriar",
"sweetbriargrass", "sylvania", "tonzi", "umichbiological2", "usgseros",
"usmpj", "uwmfieldsta", "warrenwilson", "willowcreek", "worcester"
), class = "factor")), class = "data.frame", row.names = c(NA,
-99L))
这可以通过使用 match() 使用两个数据框中的站点列来完成，使用 sub() 从第一个 data.frame:
df$vegetation <- df2$vegetation[match(sub("^.*_", "", df$site), df2$site)]
df
site year sos eos vegetation
1 EPIC_alligatorriver.csv 2016 176 301 deciduous broadleaf
2 EPIC_alligatorriver.csv 2018 164 291 deciduous broadleaf
3 Landsat_alligatorriver.csv 2016 170 303 deciduous broadleaf
4 Pheno_alligatorriver.csv 2017 152 288 deciduous broadleaf
5 Landsat_NEON.BART42.csv 2017 115 290 mixed forest
6 Pheno_NEON.BART42.csv 2017 120 290 mixed forest
数据：
df <- structure(list(site = c("EPIC_alligatorriver.csv", "EPIC_alligatorriver.csv",
"Landsat_alligatorriver.csv", "Pheno_alligatorriver.csv", "Landsat_NEON.BART42.csv",
"Pheno_NEON.BART42.csv"), year = c(2016L, 2018L, 2016L, 2017L,
2017L, 2017L), sos = c(176L, 164L, 170L, 152L, 115L, 120L), eos = c(301L,
291L, 303L, 288L, 290L, 290L), vegetation = c("deciduous broadleaf",
"deciduous broadleaf", "deciduous broadleaf", "deciduous broadleaf",
"mixed forest", "mixed forest")), row.names = c(NA, -6L), class = "data.frame")
df2 <- structure(list(site = c("alligatorriver.csv", "konza.csv", "merbleue.csv",
"NEON.BART42.csv"), vegetation = c("deciduous broadleaf", "grassland",
"wetland", "mixed forest")), class = "data.frame", row.names = c(NA,
-4L))