I am trying to pivot my dataframe from tidy format to wide format, using a column with two values, using the following:
bai_wide = bai_trim %>% pivot_wider(names_from = Species, values_from = BAI)
But when I do this, NAs are produced in the resulting dataframe. The values should match, and when I inspect the original dataframe I can't find any instances where they do not.
I'm aware of this question being asked here , but it doesn't seem to address my issue
Output of 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:
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):
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))
[1] 0
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))
CodePudding user response：
I used your dataframe filtered between 1930-1976 for Site: 'TN_C2' to pivot wider:
bai_wide = df %>%
pivot_wider(names_from = Species, values_from = BAI)
And this is the 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. Why? Because you have multiple values in Sample.Depth. So pivoting wider only assign the values for each Sample.Depth, therefore you will have empty values in your columns.
Possible solutions depend on how precise do you want to be using the Depth parameter. That depends on the analysis you want to do.
If you dont care about the depth of the sample, you can just create a new df without that column and then pivoting.
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. Like
>%> group_by(Site,Year) >%> mutate(meanDepth = mean())Just keep the NAs values because this is the way your dataframe it is.
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.
This just depend on you. 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.