为什么 NA 是从 pivot_wider 生成的？
[英]Why are NAs being produced from pivot_wider?
问题描述
I am trying to pivot my dataframe from tidy format to wide format, using a column with two values, using the following:
我正在尝试将 pivot 我的 dataframe 从整洁格式转换为宽格式，使用具有两个值的列，使用以下内容：
bai_wide = bai_trim %>% pivot_wider(names_from = Species, values_from = BAI)
But when I do this, NAs are produced in the resulting dataframe.但是当我这样做时，在生成的 dataframe 中会产生 NA。 The values should match, and when I inspect the original dataframe I can't find any instances where they do not.这些值应该匹配，当我检查原始 dataframe 时，我找不到任何不匹配的实例。
I'm aware of this question being asked here , but it doesn't seem to address my issue我知道这里有人问这个问题，但它似乎没有解决我的问题
Output of dput(head(bai_trim, 100)) : Output 的dput(head(bai_trim, 100)) ：
structure(list(Site = c("TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2"), Year = c(1930L, 1931L, 1932L,
1933L, 1934L, 1935L, 1936L, 1937L, 1938L, 1939L, 1940L, 1941L,
1942L, 1943L, 1944L, 1945L, 1946L, 1947L, 1948L, 1949L, 1950L,
1951L, 1952L, 1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L,
1960L, 1961L, 1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L,
1969L, 1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L,
1978L, 1979L, 1980L, 1981L, 1982L, 1930L, 1931L, 1932L, 1933L,
1934L, 1935L, 1936L, 1937L, 1938L, 1939L, 1940L, 1941L, 1942L,
1943L, 1944L, 1945L, 1946L, 1947L, 1948L, 1949L, 1950L, 1951L,
1952L, 1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L, 1960L,
1961L, 1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L,
1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L), Species = c("QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR"
), Sample.Depth = c(30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 19L, 29L, 29L, 29L, 30L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L
), Method = c("DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH"), BAI = c(1329.82725527258, 1583.55443950606,
1562.33088797649, 1781.17227674256, 2239.26579940025, 2283.51207558404,
1494.47266835451, 2079.21430793831, 2431.61659002079, 2063.6712031744,
2279.01645480338, 2240.79817505811, 2537.08695503732, 2357.25996541304,
2143.34894709899, 2963.42239899576, 3266.11822944487, 3188.08984551795,
2053.72520969305, 1976.4907044215, 1974.33378516752, 2314.19980193622,
1986.85089493789, 1717.7066077125, 1712.32576613411, 2270.12697244457,
2265.44617869404, 2086.27614664055, 2290.16557632423, 2067.56268776649,
2330.32100341616, 2594.45495623365, 1916.37409435704, 2615.32977322989,
2994.09297309259, 3105.71799117356, 2608.13289994918, 2781.32817927508,
2788.89468459625, 2814.41629406914, 2218.40404749475, 2375.62820321149,
2454.40055519329, 2536.22462576871, 2673.39980127834, 2883.60697407212,
2901.26428554182, 2759.19544971662, 3271.437201359, 3023.01356721046,
2586.11651777101, 2683.77375275508, 2560.55282710926, 1028.27393956856,
1254.97727247239, 1180.00666939284, 1162.75652641982, 1468.21393690705,
1420.29545487908, 870.636254692378, 1558.97134681397, 1680.04973736316,
1807.98548193521, 1887.32063639148, 1916.04119222857, 1949.52683704445,
1921.80868471893, 1600.62264826328, 1859.9149833578, 2184.22704501268,
2364.39029270987, 1853.12296621112, 1533.22199599478, 1797.1627135163,
1738.07965789397, 1687.15007187521, 1592.13731685411, 1656.32266290939,
2337.09276793395, 2353.86414716497, 2290.38356871338, 2562.25811266612,
2576.09112815194, 2595.90714922909, 2892.38644610441, 1926.95398513788,
2040.79373628591, 2636.83713546072, 3216.10408623204, 2399.34264253439,
2411.58302876301, 2150.87125164971, 2456.28295814168, 2401.15926385922,
2525.4045600946, 2619.28151832898, 2869.37020856327, 2457.47946097768,
2505.49431848312, 2343.63069935373)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -100L), groups = structure(list(
Site = c("TN_C1", "TN_C2"), .rows = structure(list(1:53,
54:100), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L), .drop = TRUE))
Here is a sample of the resulting dataframe:这是生成的 dataframe 的示例：
structure(list(Site = c("TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1",
"TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C1", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2"), Year = c(1930L, 1931L, 1932L,
1933L, 1934L, 1935L, 1936L, 1937L, 1938L, 1939L, 1940L, 1941L,
1942L, 1943L, 1944L, 1945L, 1946L, 1947L, 1948L, 1949L, 1950L,
1951L, 1952L, 1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L,
1960L, 1961L, 1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L,
1969L, 1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L,
1978L, 1979L, 1980L, 1981L, 1982L, 1930L, 1931L, 1932L, 1933L,
1934L, 1935L, 1936L, 1937L, 1938L, 1939L, 1940L, 1941L, 1942L,
1943L, 1944L, 1945L, 1946L, 1947L, 1948L, 1949L, 1950L, 1951L,
1952L, 1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L, 1960L,
1961L, 1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L,
1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L), Sample.Depth = c(30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 29L, 26L,
29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L), Method = c("DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH"), QA = c(1112.01006767124,
949.583411961448, 982.998663254952, 1087.36586010667, 1183.95219489817,
1437.32692024663, 859.941821023156, 1378.82104138941, 1715.11803906048,
1205.20873806085, 1314.40911284639, 1311.60513756934, 1334.12431932916,
1250.4241475598, 1206.45532955227, 1669.46160189739, 1748.00363523953,
1522.13188782807, 1068.48352520193, 1383.60591823409, 1629.94356878758,
1716.81958142787, 1371.09743912618, 1177.56768513191, 1268.53730445105,
1435.3852922059, 1431.72686167387, 1209.19417164828, 1475.2233795444,
1353.14184703705, 1405.88977051333, 1502.28919572968, 1231.9358745554,
1493.22404186533, 1608.26405912164, 1758.40007776153, 1358.26743655462,
1604.19889400061, 1582.77287404955, 1460.38775673841, 1718.89866003169,
1926.87492109503, 2035.25743659833, 2154.8572833228, 2155.72079265846,
1938.13092846124, 2236.80568615272, 1805.23678218424, 1856.36065999217,
1679.99679942377, 1441.26238614602, 1936.44942937414, 2133.45057631534,
1085.3150108096, 974.860473716478, 986.924868102327, 1037.83237831603,
1312.30667301435, 1405.30585427792, 773.207242839713, 1277.61195650029,
1772.52157942987, 1388.91468492248, 1391.06708726821, 1268.28478285902,
1295.29415128352, 1143.11153240523, 1058.28457720443, 1476.33487734882,
1673.26309468627, 1455.5231756649, 1015.5006665268, 1242.52404078483,
1348.94246837961, 1301.55518283897, 1075.92047580797, 977.592546236365,
1046.42643732053, 1426.81431935015, 1475.07415572278, 1455.23907789844,
1649.60781234728, 1563.4820765323, 1642.9919422491, 1865.42560165599,
1329.73932888637, 1795.37507081007, 2413.71424418505, 2499.48425942841,
2007.68534251994, 2279.94325095388, 2250.84540282916, 1988.31215010309,
2384.77641721496, 2719.39349513496, 2888.75729672066, 2955.42338126383,
2908.70715866689, 2724.37859079958, 2901.46999203769), QR = c(1329.82725527258,
1583.55443950606, 1562.33088797649, 1781.17227674256, 2239.26579940025,
2283.51207558404, 1494.47266835451, 2079.21430793831, 2431.61659002079,
2063.6712031744, 2279.01645480338, 2240.79817505811, 2537.08695503732,
2357.25996541304, 2143.34894709899, 2963.42239899576, 3266.11822944487,
3188.08984551795, 2053.72520969305, 1976.4907044215, 1974.33378516752,
2314.19980193622, 1986.85089493789, 1717.7066077125, 1712.32576613411,
2270.12697244457, 2265.44617869404, 2086.27614664055, 2290.16557632423,
2067.56268776649, 2330.32100341616, 2594.45495623365, 1916.37409435704,
2615.32977322989, 2994.09297309259, 3105.71799117356, 2608.13289994918,
2781.32817927508, 2788.89468459625, 2814.41629406914, 2218.40404749475,
2375.62820321149, 2454.40055519329, 2536.22462576871, 2673.39980127834,
2883.60697407212, 2901.26428554182, 2759.19544971662, 3271.437201359,
3023.01356721046, 2586.11651777101, NA, NA, 1028.27393956856,
NA, NA, 1162.75652641982, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -100L), groups = structure(list(Site = c("TN_C1",
"TN_C2"), .rows = structure(list(1:53, 54:100), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L), .drop = TRUE))
Summary output of summary(bai_trim) :摘要 output 的summary(bai_trim) ：
Site Year Species Sample.Depth Method BAI
Length:6102 Min. :1793 Length:6102 Min. : 5.00 Length:6102 Min. :-410.3
Class :character 1st Qu.:1918 Class :character 1st Qu.: 15.00 Class :character 1st Qu.:1383.0
Mode :character Median :1945 Mode :character Median : 28.00 Mode :character Median :2031.3
Mean :1938 Mean : 25.26 Mean :2302.9
3rd Qu.:1967 3rd Qu.: 30.00 3rd Qu.:2891.8
Max. :2014 Max. :105.00 Max. :8924.4
Output of sum(is.na(bai_trim) : sum(is.na(bai_trim) ：
sum(is.na(bai_trim))
[1] 0
Output of dput(new_df) on new_df = bai_trim %>% filter(Year > 1929, Year < 1977, Site == 'TN_C2') Output of dput(new_df) on new_df = bai_trim %>% filter(Year > 1929, Year < 1977, Site == 'TN_C2')
structure(list(Site = c("TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2",
"TN_C2", "TN_C2", "TN_C2", "TN_C2", "TN_C2"), Year = c(1930L,
1931L, 1932L, 1933L, 1934L, 1935L, 1936L, 1937L, 1938L, 1939L,
1940L, 1941L, 1942L, 1943L, 1944L, 1945L, 1946L, 1947L, 1948L,
1949L, 1950L, 1951L, 1952L, 1953L, 1954L, 1955L, 1956L, 1957L,
1958L, 1959L, 1960L, 1961L, 1962L, 1963L, 1964L, 1965L, 1966L,
1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L, 1974L, 1975L,
1976L, 1930L, 1931L, 1932L, 1933L, 1934L, 1935L, 1936L, 1937L,
1938L, 1939L, 1940L, 1941L, 1942L, 1943L, 1944L, 1945L, 1946L,
1947L, 1948L, 1949L, 1950L, 1951L, 1952L, 1953L, 1954L, 1955L,
1956L, 1957L, 1958L, 1959L, 1960L, 1961L, 1962L, 1963L, 1964L,
1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L,
1974L, 1975L, 1976L), Species = c("QA", "QA", "QA", "QA", "QA",
"QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA",
"QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA",
"QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA",
"QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QA", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR", "QR",
"QR"), Sample.Depth = c(29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
29L, 29L, 29L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L), Method = c("DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH", "DBH",
"DBH", "DBH"), BAI = c(1085.3150108096, 974.860473716478, 986.924868102327,
1037.83237831603, 1312.30667301435, 1405.30585427792, 773.207242839713,
1277.61195650029, 1772.52157942987, 1388.91468492248, 1391.06708726821,
1268.28478285902, 1295.29415128352, 1143.11153240523, 1058.28457720443,
1476.33487734882, 1673.26309468627, 1455.5231756649, 1015.5006665268,
1242.52404078483, 1348.94246837961, 1301.55518283897, 1075.92047580797,
977.592546236365, 1046.42643732053, 1426.81431935015, 1475.07415572278,
1455.23907789844, 1649.60781234728, 1563.4820765323, 1642.9919422491,
1865.42560165599, 1329.73932888637, 1795.37507081007, 2413.71424418505,
2499.48425942841, 2007.68534251994, 2279.94325095388, 2250.84540282916,
1988.31215010309, 2384.77641721496, 2719.39349513496, 2888.75729672066,
2955.42338126383, 2908.70715866689, 2724.37859079958, 2901.46999203769,
1028.27393956856, 1254.97727247239, 1180.00666939284, 1162.75652641982,
1468.21393690705, 1420.29545487908, 870.636254692378, 1558.97134681397,
1680.04973736316, 1807.98548193521, 1887.32063639148, 1916.04119222857,
1949.52683704445, 1921.80868471893, 1600.62264826328, 1859.9149833578,
2184.22704501268, 2364.39029270987, 1853.12296621112, 1533.22199599478,
1797.1627135163, 1738.07965789397, 1687.15007187521, 1592.13731685411,
1656.32266290939, 2337.09276793395, 2353.86414716497, 2290.38356871338,
2562.25811266612, 2576.09112815194, 2595.90714922909, 2892.38644610441,
1926.95398513788, 2040.79373628591, 2636.83713546072, 3216.10408623204,
2399.34264253439, 2411.58302876301, 2150.87125164971, 2456.28295814168,
2401.15926385922, 2525.4045600946, 2619.28151832898, 2869.37020856327,
2457.47946097768, 2505.49431848312, 2343.63069935373)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -94L), groups = structure(list(
Site = "TN_C2", .rows = structure(list(1:94), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
1 个解决方案
解决方案1
1 已采纳 2021-12-05 15:56:42
I used your dataframe filtered between 1930-1976 for Site: 'TN_C2' to pivot wider:我使用了您的 dataframe 在 1930-1976 之间过滤的站点：“TN_C2”到 pivot 更宽：
bai_wide = df %>%
pivot_wider(names_from = Species, values_from = BAI)
And this is the output:这是 output：
> bai_wide
# A tibble: 92 x 6
# Groups: Site [1]
Site Year Sample.Depth Method QA QR
<chr> <int> <int> <chr> <dbl> <dbl>
1 TN_C2 1930 29 DBH 1085. 1028.
2 TN_C2 1931 30 DBH 975. NA
3 TN_C2 1931 29 DBH NA 1255.
4 TN_C2 1932 30 DBH 987. NA
5 TN_C2 1932 29 DBH NA 1180.
6 TN_C2 1933 30 DBH 1038. 1163.
7 TN_C2 1934 30 DBH 1312. NA
8 TN_C2 1934 31 DBH NA 1468.
9 TN_C2 1935 30 DBH 1405. NA
10 TN_C2 1935 31 DBH NA 1420.
# ... with 82 more rows
We can see there is a lot of Nas.我们可以看到有很多Nas。 Why?为什么？ Because you have multiple values in Sample.Depth .因为您在Sample.Depth中有多个值。 So pivoting wider only assign the values for each Sample.Depth, therefore you will have empty values in your columns.因此，更宽地旋转只会为每个 Sample.Depth 分配值，因此您的列中将有空值。
Possible solutions depend on how precise do you want to be using the Depth parameter.可能的解决方案取决于您希望使用“深度”参数的精确程度。 That depends on the analysis you want to do.这取决于您要进行的分析。
If you dont care about the depth of the sample, you can just create a new df without that column and then pivoting.
如果您不关心样本的深度，您可以只创建一个没有该列的新 df 然后旋转。If you just care but not really precise, you can combine the values of Depth for each year grouping by Site and Year and mutating a new column to use the mean of Depth.
如果您只是关心但不是很精确，您可以将每年按站点和年份分组的深度值组合起来，并变异一个新列以使用深度的平均值。 Like >%> group_by(Site,Year) >%> mutate(meanDepth = mean())像>%> group_by(Site,Year) >%> mutate(meanDepth = mean())Just keep the NAs values because this is the way your dataframe it is.
只需保留 NAs 值，因为这是您的 dataframe 的方式。There are more difficult ways to try to create relations to only have 1 row per year per site altering Sample.Depth but I don't have enough time to elaborate.
尝试创建关系以使每个站点每年只有 1 行更改 Sample.Depth 有更困难的方法，但我没有足够的时间来详细说明。
This just depend on you.这仅取决于您。 Main thing you have to know it is that Sample.Depth is creating you the empty values because pivot_wider() create the new columns related to the other column values which were in the same row.您必须知道的主要事情是 Sample.Depth 正在为您创建空值，因为 pivot_wider() 创建了与同一行中的其他列值相关的新列。
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