TechQA.

Why are NAs being produced from pivot_wider?

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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))
r tidyverts
Original Q&A
1

There are 1 answers

0
RobertoT On BEST ANSWER

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.

  1. If you dont care about the depth of the sample, you can just create a new df without that column and then pivoting.

  2. 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())

  3. Just keep the NAs values because this is the way your dataframe it is.

  4. 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.

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