I'm running a function in the 'rnoaa' package that finds the 5 nearest weather stations to a data frame that contains nest box locations. This produces a nested tbl_df, a tibble for each nest box ID. I'd like to convert the tbl_df into a tibble or data frame that retains the corresponding nest box ID, but I'm not sure how to do it. Here's my code and an example of the data.
Import the data:
nests<-structure(list(id = structure(1:5, .Label = c("29", "36", "39",
"41", "42", "43", "45", "47", "48", "50", "51", "52", "53", "54",
"55", "57", "58", "59", "60", "61", "62", "64", "65", "67", "69",
"70", "71", "72", "73", "75", "77", "78", "79", "80", "81", "82",
"84", "87", "88", "89", "90", "91", "92", "93", "95", "97", "99",
"100", "102", "106", "108", "109", "110", "118", "123", "124",
"125", "126", "127", "129", "130", "131", "133", "134", "136",
"138", "140", "141", "144", "147", "149", "151", "155", "157",
"158", "160", "161", "162", "163", "165", "167", "168", "169",
"172", "174", "175", "177", "178", "179", "180", "181", "182",
"186", "189", "190", "193", "195", "202", "205", "207", "208",
"215", "217", "218", "225", "229", "230", "236", "240", "241",
"243", "244", "246", "247", "248", "249", "251", "253", "254",
"255", "257", "258", "259", "260", "261", "262", "263", "269",
"270", "276", "292", "294", "295", "296", "297", "298", "300",
"301", "302", "303", "305", "306", "307", "308", "309", "311",
"316", "317", "318", "322", "323", "324", "326", "329", "330",
"331", "332", "333", "334", "335", "336", "337", "338", "339",
"342", "345", "346", "350", "351", "353", "358", "362", "363",
"365", "366", "368", "369", "372", "379", "380", "381", "382",
"384", "386", "387", "388", "390", "391", "392", "393", "394",
"395", "396", "397", "398", "400", "401", "403", "404", "406",
"410", "411", "414", "415", "416", "418", "420", "424", "425",
"426", "428", "429", "430", "432", "433", "435", "436", "440",
"441", "442", "445", "446", "447", "448", "449", "450", "451",
"453", "458", "459", "461", "462", "463", "464", "465", "466",
"469", "470", "471", "478", "479", "488", "490", "497", "503",
"504", "506", "507", "508", "509", "512", "513", "514", "515",
"516", "517", "518", "519", "520", "521", "527", "528", "529",
"530", "531", "534", "540", "542", "545", "552", "553", "554",
"556", "558", "561", "562", "563", "565", "566", "568", "569",
"570", "571", "572", "573", "574", "575", "576", "577", "578",
"580", "583", "584", "585", "591", "592", "595", "606", "608",
"610", "612", "614", "615", "616", "617", "620", "621", "627",
"628", "634", "635", "636", "637", "638", "639", "643", "647",
"648", "651", "652", "653", "654", "656", "661", "662", "663",
"664", "665", "667", "669", "670", "673", "674", "676", "677",
"679", "680", "681", "684", "685", "690", "693", "694", "695",
"706", "708", "716", "717", "719", "720", "728", "757", "759",
"761", "777", "798", "801", "803", "818", "838", "839", "855",
"856", "864", "865", "867", "868", "880", "890", "899", "901",
"914", "915", "924", "985", "998", "999", "1002", "1003", "1004",
"1019", "1020", "1021", "1022", "1058", "1059", "1116", "1139",
"1146", "1164", "1169", "1170", "1178", "1183", "1186", "1188",
"1193", "1211", "1233", "1235", "1236", "1237", "1251", "1263",
"1285", "1288", "1289", "1294", "1296", "1298", "1299", "1300",
"1302", "1303", "1305", "1307", "1310", "1311", "1328", "1331",
"1332", "1333", "1334", "1335", "1455", "1456", "1459", "1461",
"1462", "1463", "1466", "1467", "1469", "1473", "1474", "1475",
"1476", "1478", "1479", "1480", "1482", "1485", "1487", "1503",
"1506", "1520", "1534", "1564", "1572", "1575", "1582", "1587",
"1588", "1592", "1593", "1594", "1597", "1602", "1607", "1611",
"1612", "1613", "1615", "1616", "1617", "1619", "1633", "1656",
"1657", "1658", "1660", "1663", "1664", "1667", "1668", "1669",
"1676", "1677", "1679", "1691", "1704", "1716", "1734", "1735",
"1736", "1766", "1771", "1772", "1773", "1775", "1777", "1783",
"1801", "1814", "1818", "1834", "1835", "1836", "1837", "1838",
"1840", "1843", "1845", "1846", "1847", "1850", "1852", "1856",
"1857", "1858", "1859", "1860", "1882", "1883", "1890", "1891",
"1897", "1899", "1901", "1902", "1909", "1910", "1912", "1914",
"1923", "1926", "1928", "1929", "1935", "1941", "1956", "1958",
"1960", "1968", "1991", "1994", "1998", "2002", "2010", "2012",
"2016", "2019", "2024", "2026", "2029", "2030", "2032", "2033",
"2034", "2035", "2036", "2039", "2042", "2046", "2049", "2053",
"2055", "2056", "2057", "2059", "2093", "2101", "2103", "2121",
"2134", "2146", "2147", "2152", "2184", "2185", "2186", "2187",
"2188", "2190", "2197", "2201", "2239", "2240", "2249", "2250",
"2291", "2313", "2322", "2347", "2351", "2353", "2354", "2355",
"2360", "2361", "2369", "2370", "2372", "2373", "2374", "2375",
"2376", "2402", "2426", "2427", "2445", "2447", "2449", "2459",
"2460", "2462", "2467", "2468", "2469", "2471", "2484", "2485",
"2486", "2488", "2490", "2494", "2496", "2517", "2613", "2623",
"2624", "2625", "2641", "2696", "2697", "2709", "2711", "2712",
"2713", "2714", "2997", "3000", "3004"), class = "factor"), latitude = c(43.29515222,
44.02074565, 44.44193, 44.146666, 43.98897), longitude = c(-89.29077182,
-92.04753707, -121.40635, -121.347223, -121.18639)), .Names = c("id",
"latitude", "longitude"), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
This grabs the 5 nearest weather stations and produces a tbl_df:
nearest_station<-meteo_nearby_stations(lat_lon_df = nests, station_data = station_data,
limit = 5, var = c("TAVG"),
year_min = 2011, year_max = 2016)
nearest_station
Finally, I used do.call to produce a single data frame:
ns <- do.call(rbind, lapply(nearest_station, data.frame, stringsAsFactors=FALSE))
head(ns)
While the resulting data table shows the nest box ID next to the weather station ID (under id), the first column really only contains the weather station ID:
id name latitude longitude distance
29.1 USW00014837 MADISON DANE RGNL AP 43.1406 -89.3453 17.74438
29.2 USR0000WDDG DODGEVILLE WISCONSIN 43.1000 -90.0000 61.44939
29.3 USW00014839 MILWAUKEE MITCHELL AP 42.9550 -87.9044 118.69939
29.4 USW00094822 ROCKFORD GTR ROCKFORD AP 42.1928 -89.0931 123.63416
29.5 USW00094908 DUBUQUE RGNL AP 42.3978 -90.7036 152.38709
36.1 USW00014925 ROCHESTER INTL AP 43.9042 -92.4917 37.83807
ns[,1]
USW00014837
Is there a way to keep the nest box information in the weather station data frame?
(sorry for late reply)
If you use something like dplyr::bind_rows, you can do:
dplyr::bind_rows(nearest_station, .id = "nest_box_id")
to get
#> # A tibble: 25 x 6
#> nest_box_id id name latitude longitude distance
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 29 USW00014837 MADISON DANE RGNL AP 43.1 -89.3 17.7
#> 29 USR0000WDDG DODGEVILLE WISCONSIN 43.1 -90.0 61.4
#> 29 USW00014839 MILWAUKEE MITCHELL AP 43.0 -87.9 119.
#> 29 USW00094822 ROCKFORD GTR ROCKFORD AP 42.2 -89.1 124.
#> 29 USW00094908 DUBUQUE RGNL AP 42.4 -90.7 152.
#> 36 USW00014925 ROCHESTER INTL AP 43.9 -92.5 37.8
#> 36 USW00014920 LA CROSSE MUNI AP 43.9 -91.3 65.5
#> 36 USR0000WBRF BLACK RIVER FALLS WISCONSIN 44.3 -90.8 102.
#> 36 USR0000WAUG AUGUSTA WISCONSIN 44.7 -91.1 105.
#> 36 USW00014922 MINNEAPOLIS/ST PAUL AP 44.9 -93.2 134.
#> # ... with 15 more rows