I have 1000 points of county name data. (ok_field) Also, there are weather data for 1 to 10 days. (m) This data is a datalist.
(The size of the data is so large that if you use the data below as an example, I am grateful!)
ok_field<-structure(list(state = c("oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma", "oklahoma"), county = c("Texas", "Texas", "Texas", "Texas", "Cimarron", "Cimarron", "Texas", "Texas", "Texas", "Texas")), row.names = c(NA, 10L), class = "data.frame") > ok_field state county 1 oklahoma Texas 2 oklahoma Texas 3 oklahoma Texas 4 oklahoma Texas 5 oklahoma Cimarron 6 oklahoma Cimarron 7 oklahoma Texas 8 oklahoma Texas 9 oklahoma Texas 10 oklahoma Texas m <- list(`1` = structure(list(DAY = c(15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15), county = c("Adair", "Alfalfa", "Atoka", "Beaver", "Beckham", "Blaine", "Bryan", "Caddo", "Canadian", "Carter", "Cherokee", "Choctaw", "Cimarron", "Cleveland", "Coal", "Comanche", "Cotton", "Craig", "Creek", "Custer", "Delaware", "Dewey", "Ellis", "Garfield", "Garvin", "Grady", "Grant", "Greer", "Harmon", "Harper", "Haskell", "Hughes", "Jackson", "Jefferson", "Johnston", "Kay", "Kingfisher", "Kiowa", "Latimer", "Le Flore", "Lincoln", "Logan", "Love", "Major", "Marshall", "Mayes", "McClain", "McCurtain", "McIntosh", "Murray", "Muskogee", "Noble", "Nowata", "Okfuskee", "Oklahoma", "Okmulgee", "Osage", "Ottawa", "Pawnee", "Payne", "Pittsburg", "Pontotoc", "Pottawatomie", "Pushmataha", "Roger Mills", "Rogers", "Seminole", "Sequoyah", "Stephens", "Texas", "Tillman", "Tulsa", "Wagoner", "Washington", "Washita", "Woods", "Woodward"), TAVG_C = c(27.6888888888889, 31.1388888888889, 28.6777777777778, 30.2027777777778, 28.8111111111111, 30.25, 28.1111111111111, 29.4851851851852, 29.3055555555556, 28.7972222222222, 27.8805555555556, 29.1722222222222, 26.8166666666667, 28.8444444444444, 28.9222222222222, 29.1388888888889, 30.0722222222222, 27.4222222222222, 28.1611111111111, 29.8638888888889, 28.1277777777778, 29.55, 28.9888888888889, 29.4166666666667, 28.4666666666667, 29.212962962963, 29.9888888888889, 29.7888888888889, 29.9611111111111, 31.8777777777778, 28.3833333333333, 27.7, 29.3, 29.8277777777778, 28.1055555555556, 29.0027777777778, 31.3444444444444, 30.2666666666667, 29.1111111111111, 28.4805555555556, 28.0777777777778, 29.2361111111111, 29.7888888888889, 30.7777777777778, 28.7055555555556, 27.9388888888889, 27.7388888888889, 28.3111111111111, 29.15, 27.6, 28.4055555555556, 29.3666666666667, 28.6555555555556, 28.3, 28.4416666666667, 28.1666666666667, 27.9083333333333, 28.2888888888889, 28.6888888888889, 28.4069444444444, 28.5944444444444, 28.2222222222222, 28.4777777777778, 28.3259259259259, 28.0055555555556, 28.2138888888889, 27.6, 28.75, 29.0777777777778, 29.4555555555556, 30.9111111111111, 29.4166666666667, 28.4222222222222, 27.9, 30.0333333333333, 30.6944444444444, 30.1907407407407)), row.names = c(NA, -77L), class = "data.frame"), `2` = structure(list(DAY = c(16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16), county = c("Adair", "Alfalfa", "Atoka", "Beaver", "Beckham", "Blaine", "Bryan", "Caddo", "Canadian", "Carter", "Cherokee", "Choctaw", "Cimarron", "Cleveland", "Coal", "Comanche", "Cotton", "Craig", "Creek", "Custer", "Delaware", "Dewey", "Ellis", "Garfield", "Garvin", "Grady", "Grant", "Greer", "Harmon", "Harper", "Haskell", "Hughes", "Jackson", "Jefferson", "Johnston", "Kay", "Kingfisher", "Kiowa", "Latimer", "Le Flore", "Lincoln", "Logan", "Love", "Major", "Marshall", "Mayes", "McClain", "McCurtain", "McIntosh", "Murray", "Muskogee", "Noble", "Nowata", "Okfuskee", "Oklahoma", "Okmulgee", "Osage", "Ottawa", "Pawnee", "Payne", "Pittsburg", "Pontotoc", "Pottawatomie", "Pushmataha", "Roger Mills", "Rogers", "Seminole", "Sequoyah", "Stephens", "Texas", "Tillman", "Tulsa", "Wagoner", "Washington", "Washita", "Woods", "Woodward" ), TAVG_C = c(27.75, 30.0333333333333, 28.65, 27.1083333333333, 28.6583333333333, 29.1722222222222, 28.2277777777778, 28.5814814814815, 28.5055555555556, 28.4083333333333, 27.9833333333333, 29.2388888888889, 22.9194444444444, 28.2333333333333, 29.0333333333333, 28.3833333333333, 29.3944444444444, 27.2166666666667, 27.425, 28.9861111111111, 27.9166666666667, 28.8083333333333, 27.65, 28.5833333333333, 28.1388888888889, 28.5759259259259, 29.2444444444444, 29.6611111111111, 29.9444444444444, 29.8444444444444, 28.4222222222222, 27.7, 29.5777777777778, 29.2833333333333, 28.0388888888889, 28.3805555555556, 30.2833333333333, 29.6722222222222, 29.15, 28.9416666666667, 27.85, 28.5638888888889, 29.2944444444444, 29.6694444444444, 28.6111111111111, 27.9111111111111, 27.2277777777778, 28.7277777777778, 29.1, 27.6055555555556, 28.4916666666667, 28.7555555555556, 28.5166666666667, 28.2055555555556, 27.8138888888889, 28.0444444444444, 27.7361111111111, 28.1666666666667, 28.2944444444444, 27.9152777777778, 28.6638888888889, 27.9527777777778, 28.2611111111111, 28.9777777777778, 27.2888888888889, 28.2527777777778, 27.35, 28.9055555555556, 28.4944444444445, 24.8185185185185, 30.4555555555556, 29.4138888888889, 28.6333333333333, 27.9388888888889, 29.35, 29.4777777777778, 29.3074074074074)), row.names = c(NA, -77L), class = "data.frame")) >m $`1` DAY county TAVG_C 1 15 Adair 27.68889 2 15 Alfalfa 31.13889 3 15 Atoka 28.67778 4 15 Beaver 30.20278 5 15 Beckham 28.81111 6 15 Blaine 30.25000 7 15 Bryan 28.11111 8 15 Caddo 29.48519 9 15 Canadian 29.30556 10 15 Carter 28.79722 11 15 Cherokee 27.88056 12 15 Choctaw 29.17222 13 15 Cimarron 26.81667 14 15 Cleveland 28.84444 15 15 Coal 28.92222 16 15 Comanche 29.13889 17 15 Cotton 30.07222 18 15 Craig 27.42222 19 15 Creek 28.16111 20 15 Custer 29.86389 21 15 Delaware 28.12778 22 15 Dewey 29.55000 23 15 Ellis 28.98889 24 15 Garfield 29.41667 25 15 Garvin 28.46667 26 15 Grady 29.21296 27 15 Grant 29.98889 28 15 Greer 29.78889 29 15 Harmon 29.96111 30 15 Harper 31.87778 31 15 Haskell 28.38333 32 15 Hughes 27.70000 33 15 Jackson 29.30000 34 15 Jefferson 29.82778 35 15 Johnston 28.10556 36 15 Kay 29.00278 37 15 Kingfisher 31.34444 38 15 Kiowa 30.26667 39 15 Latimer 29.11111 40 15 Le Flore 28.48056 41 15 Lincoln 28.07778 42 15 Logan 29.23611 43 15 Love 29.78889 44 15 Major 30.77778 45 15 Marshall 28.70556 46 15 Mayes 27.93889 47 15 McClain 27.73889 48 15 McCurtain 28.31111 49 15 McIntosh 29.15000 50 15 Murray 27.60000 51 15 Muskogee 28.40556 52 15 Noble 29.36667 53 15 Nowata 28.65556 54 15 Okfuskee 28.30000 55 15 Oklahoma 28.44167 56 15 Okmulgee 28.16667 57 15 Osage 27.90833 58 15 Ottawa 28.28889 59 15 Pawnee 28.68889 60 15 Payne 28.40694 61 15 Pittsburg 28.59444 62 15 Pontotoc 28.22222 63 15 Pottawatomie 28.47778 64 15 Pushmataha 28.32593 65 15 Roger Mills 28.00556 66 15 Rogers 28.21389 67 15 Seminole 27.60000 68 15 Sequoyah 28.75000 69 15 Stephens 29.07778 70 15 Texas 29.45556 71 15 Tillman 30.91111 72 15 Tulsa 29.41667 73 15 Wagoner 28.42222 74 15 Washington 27.90000 75 15 Washita 30.03333 76 15 Woods 30.69444 77 15 Woodward 30.19074 $`2` DAY county TAVG_C 1 16 Adair 27.75000 2 16 Alfalfa 30.03333 3 16 Atoka 28.65000 4 16 Beaver 27.10833 5 16 Beckham 28.65833 6 16 Blaine 29.17222 7 16 Bryan 28.22778 8 16 Caddo 28.58148 9 16 Canadian 28.50556 10 16 Carter 28.40833 11 16 Cherokee 27.98333 12 16 Choctaw 29.23889 13 16 Cimarron 22.91944 14 16 Cleveland 28.23333 15 16 Coal 29.03333 16 16 Comanche 28.38333 17 16 Cotton 29.39444 18 16 Craig 27.21667 19 16 Creek 27.42500 20 16 Custer 28.98611 21 16 Delaware 27.91667 22 16 Dewey 28.80833 23 16 Ellis 27.65000 24 16 Garfield 28.58333 25 16 Garvin 28.13889 26 16 Grady 28.57593 27 16 Grant 29.24444 28 16 Greer 29.66111 29 16 Harmon 29.94444 30 16 Harper 29.84444 31 16 Haskell 28.42222 32 16 Hughes 27.70000 33 16 Jackson 29.57778 34 16 Jefferson 29.28333 35 16 Johnston 28.03889 36 16 Kay 28.38056 37 16 Kingfisher 30.28333 38 16 Kiowa 29.67222 39 16 Latimer 29.15000 40 16 Le Flore 28.94167 41 16 Lincoln 27.85000 42 16 Logan 28.56389 43 16 Love 29.29444 44 16 Major 29.66944 45 16 Marshall 28.61111 46 16 Mayes 27.91111 47 16 McClain 27.22778 48 16 McCurtain 28.72778 49 16 McIntosh 29.10000 50 16 Murray 27.60556 51 16 Muskogee 28.49167 52 16 Noble 28.75556 53 16 Nowata 28.51667 54 16 Okfuskee 28.20556 55 16 Oklahoma 27.81389 56 16 Okmulgee 28.04444 57 16 Osage 27.73611 58 16 Ottawa 28.16667 59 16 Pawnee 28.29444 60 16 Payne 27.91528 61 16 Pittsburg 28.66389 62 16 Pontotoc 27.95278 63 16 Pottawatomie 28.26111 64 16 Pushmataha 28.97778 65 16 Roger Mills 27.28889 66 16 Rogers 28.25278 67 16 Seminole 27.35000 68 16 Sequoyah 28.90556 69 16 Stephens 28.49444 70 16 Texas 24.81852 71 16 Tillman 30.45556 72 16 Tulsa 29.41389 73 16 Wagoner 28.63333 74 16 Washington 27.93889 75 16 Washita 29.35000 76 16 Woods 29.47778 77 16 Woodward 29.30741 Like vlookup in Excel, I want to combine the weather data that matches the county name[enter image description here][1] by date. I can combine each 1 day using this code, but I would like to combine weather data for 1-10 days using a loop.
z <- inner_join(ok_field, m$`1`,by="county",copy=TRUE) | county | DAY | TAVG_C |
|---|---|---|
| Woods | 15 | 30.69444444 |
| Alfalfa | 15 | 31.13888889 |
| Alfalfa | 15 | 31.13888889 |
| Grant | 15 | 29.98888889 |
| Alfalfa | 15 | 31.13888889 |
| Major | 15 | 30.77777778 |
However, the table I want is as follows.
| county | TAVG_C_15 | TAVG_C_16 |
|---|---|---|
| Woods | 30.69444444 | 24.81852 |
| Alfalfa | 31.13888889 | 22.91944 |
| Alfalfa | 31.13888889 | 24.81852 |
| Grant | 29.98888889 | 22.91944 |
| Alfalfa | 31.13888889 | 22.91944 |
| Major | 30.77777778 | 24.81852 |
The code I've tried is the code below.
1.
look <- data.frame(matrix(nrow=(length(field$Id)),ncol=nrow(A))) for (i in 1:nrow(A)){ look[,i]<-ok_field[list(m[i]), on="county", nomatch=0] } for (i in 1:nrow(A)){ look[,i] <- inner_join(ok_field, (m[i]),by="county",copy=TRUE) } If you can help me, I am grateful!
https://stackoverflow.com/questions/66575752/inner-join-function-from-datalist-in-r-using-looplike-vlookup-in-excel March 11, 2021 at 10:44AM
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