This function takes a pdata.frame and turns all of its columns into objects of class pseries.
pseriesfy(x, ...)A pdata.frame like the input pdata.frame but with all columns turned into pseries.
Background: Initially created pdata.frames have as columns the pure/basic class (e.g., numeric, factor, character). When extracting a column from such a pdata.frame, the extracted column is turned into a pseries.
At times, it can be convenient to apply data transformation operations on
such a pseriesfy-ed pdata.frame, see Examples.
library("plm")
data("Grunfeld", package = "plm")
pGrun <- pdata.frame(Grunfeld[ , 1:4], drop.index = TRUE)
pGrun2 <- pseriesfy(pGrun) # pseriesfy-ed pdata.frame
# compare classes of columns
lapply(pGrun, class)
#> $inv
#> [1] "numeric"
#>
#> $value
#> [1] "numeric"
#>
lapply(pGrun2, class)
#> $inv
#> [1] "pseries" "numeric"
#>
#> $value
#> [1] "pseries" "numeric"
#>
# When using with()
with(pGrun, lag(value)) # dispatches to base R's lag()
#> [1] 3078.50 4661.70 5387.10 2792.20 4313.20 4643.90 4551.20 3244.10 4053.70
#> [10] 4379.30 4840.90 4900.90 3526.50 3254.70 3700.20 3755.60 4833.00 4924.90
#> [19] 6241.70 5593.60 1362.40 1807.10 2676.30 1801.90 1957.30 2202.90 2380.50
#> [28] 2168.60 1985.10 1813.90 1850.20 2067.70 1796.70 1625.80 1667.00 1677.40
#> [37] 2289.50 2159.40 2031.30 2115.50 1170.60 2015.80 2803.30 2039.70 2256.20
#> [46] 2132.20 1834.10 1588.00 1749.40 1687.20 2007.70 2208.30 1656.70 1604.40
#> [55] 1431.80 1610.50 1819.40 2079.70 2371.60 2759.90 417.50 837.80 883.90
#> [64] 437.90 679.70 727.80 643.60 410.90 588.40 698.40 846.40 893.80
#> [73] 579.00 694.60 590.30 693.50 809.00 727.00 1001.50 703.20 157.70
#> [82] 167.90 192.90 156.70 191.40 185.50 199.60 189.50 151.20 187.70
#> [91] 214.70 232.90 249.00 224.50 237.30 240.10 327.30 359.40 398.40
#> [100] 365.70 197.00 210.30 223.10 216.70 286.40 298.00 276.90 272.60
#> [109] 287.40 330.30 324.40 401.90 407.40 409.20 482.20 673.80 676.90
#> [118] 702.00 793.50 927.30 138.00 200.10 210.10 161.20 161.70 145.10
#> [127] 110.60 98.10 108.80 118.20 126.50 156.70 119.40 129.10 134.80
#> [136] 140.80 179.00 178.10 186.80 192.70 191.50 516.00 729.00 560.40
#> [145] 519.90 628.50 537.10 561.20 617.20 626.70 737.20 760.50 581.40
#> [154] 662.30 583.80 635.20 723.80 864.10 1193.50 1188.90 290.60 291.10
#> [163] 335.00 246.00 356.20 289.80 268.20 213.30 348.20 374.20 387.20
#> [172] 347.40 291.90 297.20 276.90 274.60 339.90 474.80 496.00 474.50
#> [181] 70.91 87.94 82.20 58.72 80.54 86.47 77.68 62.16 62.24
#> [190] 61.82 65.85 69.54 64.97 68.00 71.24 69.05 83.04 74.42
#> [199] 63.51 58.12
#> attr(,"tsp")
#> [1] 0 199 1
with(pGrun2, lag(value)) # dispatches to plm's lag() respect. panel structure
#> [1] NA 3078.50 4661.70 5387.10 2792.20 4313.20 4643.90 4551.20 3244.10
#> [10] 4053.70 4379.30 4840.90 4900.90 3526.50 3254.70 3700.20 3755.60 4833.00
#> [19] 4924.90 6241.70 NA 1362.40 1807.10 2676.30 1801.90 1957.30 2202.90
#> [28] 2380.50 2168.60 1985.10 1813.90 1850.20 2067.70 1796.70 1625.80 1667.00
#> [37] 1677.40 2289.50 2159.40 2031.30 NA 1170.60 2015.80 2803.30 2039.70
#> [46] 2256.20 2132.20 1834.10 1588.00 1749.40 1687.20 2007.70 2208.30 1656.70
#> [55] 1604.40 1431.80 1610.50 1819.40 2079.70 2371.60 NA 417.50 837.80
#> [64] 883.90 437.90 679.70 727.80 643.60 410.90 588.40 698.40 846.40
#> [73] 893.80 579.00 694.60 590.30 693.50 809.00 727.00 1001.50 NA
#> [82] 157.70 167.90 192.90 156.70 191.40 185.50 199.60 189.50 151.20
#> [91] 187.70 214.70 232.90 249.00 224.50 237.30 240.10 327.30 359.40
#> [100] 398.40 NA 197.00 210.30 223.10 216.70 286.40 298.00 276.90
#> [109] 272.60 287.40 330.30 324.40 401.90 407.40 409.20 482.20 673.80
#> [118] 676.90 702.00 793.50 NA 138.00 200.10 210.10 161.20 161.70
#> [127] 145.10 110.60 98.10 108.80 118.20 126.50 156.70 119.40 129.10
#> [136] 134.80 140.80 179.00 178.10 186.80 NA 191.50 516.00 729.00
#> [145] 560.40 519.90 628.50 537.10 561.20 617.20 626.70 737.20 760.50
#> [154] 581.40 662.30 583.80 635.20 723.80 864.10 1193.50 NA 290.60
#> [163] 291.10 335.00 246.00 356.20 289.80 268.20 213.30 348.20 374.20
#> [172] 387.20 347.40 291.90 297.20 276.90 274.60 339.90 474.80 496.00
#> [181] NA 70.91 87.94 82.20 58.72 80.54 86.47 77.68 62.16
#> [190] 62.24 61.82 65.85 69.54 64.97 68.00 71.24 69.05 83.04
#> [199] 74.42 63.51
# When lapply()-ing
lapply(pGrun, lag) # dispatches to base R's lag()
#> $inv
#> [1] 317.60 391.80 410.60 257.70 330.80 461.20 512.00 448.00 499.60
#> [10] 547.50 561.20 688.10 568.90 529.20 555.10 642.90 755.90 891.20
#> [19] 1304.40 1486.70 209.90 355.30 469.90 262.30 230.40 361.60 472.80
#> [28] 445.60 361.60 288.20 258.70 420.30 420.50 494.50 405.10 418.80
#> [37] 588.20 645.50 641.00 459.30 33.10 45.00 77.20 44.60 48.10
#> [46] 74.40 113.00 91.90 61.30 56.80 93.60 159.90 147.20 146.30
#> [55] 98.30 93.50 135.20 157.30 179.50 189.60 40.29 72.76 66.26
#> [64] 51.60 52.41 69.41 68.35 46.80 47.40 59.57 88.78 74.12
#> [73] 62.68 89.36 78.98 100.66 160.62 145.00 174.93 172.49 39.68
#> [82] 50.73 74.24 53.51 42.65 46.48 61.40 39.67 62.24 52.32
#> [91] 63.21 59.37 58.02 70.34 67.42 55.74 80.30 85.40 91.90
#> [100] 81.43 20.36 25.98 25.94 27.53 24.60 28.54 43.41 42.81
#> [109] 27.84 32.60 39.03 50.17 51.85 64.03 68.16 77.34 95.30
#> [118] 99.49 127.52 135.72 24.43 23.21 32.78 32.54 26.65 33.71
#> [127] 43.50 34.46 44.28 70.80 44.12 48.98 48.51 50.00 50.59
#> [136] 42.53 64.77 72.68 73.86 89.51 12.93 25.90 35.05 22.89
#> [145] 18.84 28.57 48.51 43.34 37.02 37.81 39.27 53.46 55.56
#> [154] 49.56 32.04 32.24 54.38 71.78 90.08 68.60 26.63 23.39
#> [163] 30.65 20.89 28.78 26.93 32.08 32.21 35.69 62.47 52.32
#> [172] 56.95 54.32 40.53 32.54 43.48 56.49 65.98 66.11 49.34
#> [181] 2.54 2.00 2.19 1.99 2.03 1.81 2.14 1.86 0.93
#> [190] 1.18 1.36 2.24 3.81 5.66 4.21 3.42 4.67 6.00
#> [199] 6.53 5.12
#> attr(,"tsp")
#> [1] 0 199 1
#>
#> $value
#> [1] 3078.50 4661.70 5387.10 2792.20 4313.20 4643.90 4551.20 3244.10 4053.70
#> [10] 4379.30 4840.90 4900.90 3526.50 3254.70 3700.20 3755.60 4833.00 4924.90
#> [19] 6241.70 5593.60 1362.40 1807.10 2676.30 1801.90 1957.30 2202.90 2380.50
#> [28] 2168.60 1985.10 1813.90 1850.20 2067.70 1796.70 1625.80 1667.00 1677.40
#> [37] 2289.50 2159.40 2031.30 2115.50 1170.60 2015.80 2803.30 2039.70 2256.20
#> [46] 2132.20 1834.10 1588.00 1749.40 1687.20 2007.70 2208.30 1656.70 1604.40
#> [55] 1431.80 1610.50 1819.40 2079.70 2371.60 2759.90 417.50 837.80 883.90
#> [64] 437.90 679.70 727.80 643.60 410.90 588.40 698.40 846.40 893.80
#> [73] 579.00 694.60 590.30 693.50 809.00 727.00 1001.50 703.20 157.70
#> [82] 167.90 192.90 156.70 191.40 185.50 199.60 189.50 151.20 187.70
#> [91] 214.70 232.90 249.00 224.50 237.30 240.10 327.30 359.40 398.40
#> [100] 365.70 197.00 210.30 223.10 216.70 286.40 298.00 276.90 272.60
#> [109] 287.40 330.30 324.40 401.90 407.40 409.20 482.20 673.80 676.90
#> [118] 702.00 793.50 927.30 138.00 200.10 210.10 161.20 161.70 145.10
#> [127] 110.60 98.10 108.80 118.20 126.50 156.70 119.40 129.10 134.80
#> [136] 140.80 179.00 178.10 186.80 192.70 191.50 516.00 729.00 560.40
#> [145] 519.90 628.50 537.10 561.20 617.20 626.70 737.20 760.50 581.40
#> [154] 662.30 583.80 635.20 723.80 864.10 1193.50 1188.90 290.60 291.10
#> [163] 335.00 246.00 356.20 289.80 268.20 213.30 348.20 374.20 387.20
#> [172] 347.40 291.90 297.20 276.90 274.60 339.90 474.80 496.00 474.50
#> [181] 70.91 87.94 82.20 58.72 80.54 86.47 77.68 62.16 62.24
#> [190] 61.82 65.85 69.54 64.97 68.00 71.24 69.05 83.04 74.42
#> [199] 63.51 58.12
#> attr(,"tsp")
#> [1] 0 199 1
#>
lapply(pGrun2, lag) # dispatches to plm's lag() respect. panel structure
#> $inv
#> [1] NA 317.60 391.80 410.60 257.70 330.80 461.20 512.00 448.00
#> [10] 499.60 547.50 561.20 688.10 568.90 529.20 555.10 642.90 755.90
#> [19] 891.20 1304.40 NA 209.90 355.30 469.90 262.30 230.40 361.60
#> [28] 472.80 445.60 361.60 288.20 258.70 420.30 420.50 494.50 405.10
#> [37] 418.80 588.20 645.50 641.00 NA 33.10 45.00 77.20 44.60
#> [46] 48.10 74.40 113.00 91.90 61.30 56.80 93.60 159.90 147.20
#> [55] 146.30 98.30 93.50 135.20 157.30 179.50 NA 40.29 72.76
#> [64] 66.26 51.60 52.41 69.41 68.35 46.80 47.40 59.57 88.78
#> [73] 74.12 62.68 89.36 78.98 100.66 160.62 145.00 174.93 NA
#> [82] 39.68 50.73 74.24 53.51 42.65 46.48 61.40 39.67 62.24
#> [91] 52.32 63.21 59.37 58.02 70.34 67.42 55.74 80.30 85.40
#> [100] 91.90 NA 20.36 25.98 25.94 27.53 24.60 28.54 43.41
#> [109] 42.81 27.84 32.60 39.03 50.17 51.85 64.03 68.16 77.34
#> [118] 95.30 99.49 127.52 NA 24.43 23.21 32.78 32.54 26.65
#> [127] 33.71 43.50 34.46 44.28 70.80 44.12 48.98 48.51 50.00
#> [136] 50.59 42.53 64.77 72.68 73.86 NA 12.93 25.90 35.05
#> [145] 22.89 18.84 28.57 48.51 43.34 37.02 37.81 39.27 53.46
#> [154] 55.56 49.56 32.04 32.24 54.38 71.78 90.08 NA 26.63
#> [163] 23.39 30.65 20.89 28.78 26.93 32.08 32.21 35.69 62.47
#> [172] 52.32 56.95 54.32 40.53 32.54 43.48 56.49 65.98 66.11
#> [181] NA 2.54 2.00 2.19 1.99 2.03 1.81 2.14 1.86
#> [190] 0.93 1.18 1.36 2.24 3.81 5.66 4.21 3.42 4.67
#> [199] 6.00 6.53
#>
#> $value
#> [1] NA 3078.50 4661.70 5387.10 2792.20 4313.20 4643.90 4551.20 3244.10
#> [10] 4053.70 4379.30 4840.90 4900.90 3526.50 3254.70 3700.20 3755.60 4833.00
#> [19] 4924.90 6241.70 NA 1362.40 1807.10 2676.30 1801.90 1957.30 2202.90
#> [28] 2380.50 2168.60 1985.10 1813.90 1850.20 2067.70 1796.70 1625.80 1667.00
#> [37] 1677.40 2289.50 2159.40 2031.30 NA 1170.60 2015.80 2803.30 2039.70
#> [46] 2256.20 2132.20 1834.10 1588.00 1749.40 1687.20 2007.70 2208.30 1656.70
#> [55] 1604.40 1431.80 1610.50 1819.40 2079.70 2371.60 NA 417.50 837.80
#> [64] 883.90 437.90 679.70 727.80 643.60 410.90 588.40 698.40 846.40
#> [73] 893.80 579.00 694.60 590.30 693.50 809.00 727.00 1001.50 NA
#> [82] 157.70 167.90 192.90 156.70 191.40 185.50 199.60 189.50 151.20
#> [91] 187.70 214.70 232.90 249.00 224.50 237.30 240.10 327.30 359.40
#> [100] 398.40 NA 197.00 210.30 223.10 216.70 286.40 298.00 276.90
#> [109] 272.60 287.40 330.30 324.40 401.90 407.40 409.20 482.20 673.80
#> [118] 676.90 702.00 793.50 NA 138.00 200.10 210.10 161.20 161.70
#> [127] 145.10 110.60 98.10 108.80 118.20 126.50 156.70 119.40 129.10
#> [136] 134.80 140.80 179.00 178.10 186.80 NA 191.50 516.00 729.00
#> [145] 560.40 519.90 628.50 537.10 561.20 617.20 626.70 737.20 760.50
#> [154] 581.40 662.30 583.80 635.20 723.80 864.10 1193.50 NA 290.60
#> [163] 291.10 335.00 246.00 356.20 289.80 268.20 213.30 348.20 374.20
#> [172] 387.20 347.40 291.90 297.20 276.90 274.60 339.90 474.80 496.00
#> [181] NA 70.91 87.94 82.20 58.72 80.54 86.47 77.68 62.16
#> [190] 62.24 61.82 65.85 69.54 64.97 68.00 71.24 69.05 83.04
#> [199] 74.42 63.51
#>
# as.list(., keep.attributes = TRUE) on a non-pseriesfy-ed
# pdata.frame is similar and dispatches to plm's lag
lapply(as.list(pGrun, keep.attributes = TRUE), lag)
#> $inv
#> 1-1935 1-1936 1-1937 1-1938 1-1939 1-1940 1-1941 1-1942 1-1943 1-1944
#> NA 317.60 391.80 410.60 257.70 330.80 461.20 512.00 448.00 499.60
#> 1-1945 1-1946 1-1947 1-1948 1-1949 1-1950 1-1951 1-1952 1-1953 1-1954
#> 547.50 561.20 688.10 568.90 529.20 555.10 642.90 755.90 891.20 1304.40
#> 2-1935 2-1936 2-1937 2-1938 2-1939 2-1940 2-1941 2-1942 2-1943 2-1944
#> NA 209.90 355.30 469.90 262.30 230.40 361.60 472.80 445.60 361.60
#> 2-1945 2-1946 2-1947 2-1948 2-1949 2-1950 2-1951 2-1952 2-1953 2-1954
#> 288.20 258.70 420.30 420.50 494.50 405.10 418.80 588.20 645.50 641.00
#> 3-1935 3-1936 3-1937 3-1938 3-1939 3-1940 3-1941 3-1942 3-1943 3-1944
#> NA 33.10 45.00 77.20 44.60 48.10 74.40 113.00 91.90 61.30
#> 3-1945 3-1946 3-1947 3-1948 3-1949 3-1950 3-1951 3-1952 3-1953 3-1954
#> 56.80 93.60 159.90 147.20 146.30 98.30 93.50 135.20 157.30 179.50
#> 4-1935 4-1936 4-1937 4-1938 4-1939 4-1940 4-1941 4-1942 4-1943 4-1944
#> NA 40.29 72.76 66.26 51.60 52.41 69.41 68.35 46.80 47.40
#> 4-1945 4-1946 4-1947 4-1948 4-1949 4-1950 4-1951 4-1952 4-1953 4-1954
#> 59.57 88.78 74.12 62.68 89.36 78.98 100.66 160.62 145.00 174.93
#> 5-1935 5-1936 5-1937 5-1938 5-1939 5-1940 5-1941 5-1942 5-1943 5-1944
#> NA 39.68 50.73 74.24 53.51 42.65 46.48 61.40 39.67 62.24
#> 5-1945 5-1946 5-1947 5-1948 5-1949 5-1950 5-1951 5-1952 5-1953 5-1954
#> 52.32 63.21 59.37 58.02 70.34 67.42 55.74 80.30 85.40 91.90
#> 6-1935 6-1936 6-1937 6-1938 6-1939 6-1940 6-1941 6-1942 6-1943 6-1944
#> NA 20.36 25.98 25.94 27.53 24.60 28.54 43.41 42.81 27.84
#> 6-1945 6-1946 6-1947 6-1948 6-1949 6-1950 6-1951 6-1952 6-1953 6-1954
#> 32.60 39.03 50.17 51.85 64.03 68.16 77.34 95.30 99.49 127.52
#> 7-1935 7-1936 7-1937 7-1938 7-1939 7-1940 7-1941 7-1942 7-1943 7-1944
#> NA 24.43 23.21 32.78 32.54 26.65 33.71 43.50 34.46 44.28
#> 7-1945 7-1946 7-1947 7-1948 7-1949 7-1950 7-1951 7-1952 7-1953 7-1954
#> 70.80 44.12 48.98 48.51 50.00 50.59 42.53 64.77 72.68 73.86
#> 8-1935 8-1936 8-1937 8-1938 8-1939 8-1940 8-1941 8-1942 8-1943 8-1944
#> NA 12.93 25.90 35.05 22.89 18.84 28.57 48.51 43.34 37.02
#> 8-1945 8-1946 8-1947 8-1948 8-1949 8-1950 8-1951 8-1952 8-1953 8-1954
#> 37.81 39.27 53.46 55.56 49.56 32.04 32.24 54.38 71.78 90.08
#> 9-1935 9-1936 9-1937 9-1938 9-1939 9-1940 9-1941 9-1942 9-1943 9-1944
#> NA 26.63 23.39 30.65 20.89 28.78 26.93 32.08 32.21 35.69
#> 9-1945 9-1946 9-1947 9-1948 9-1949 9-1950 9-1951 9-1952 9-1953 9-1954
#> 62.47 52.32 56.95 54.32 40.53 32.54 43.48 56.49 65.98 66.11
#> 10-1935 10-1936 10-1937 10-1938 10-1939 10-1940 10-1941 10-1942 10-1943 10-1944
#> NA 2.54 2.00 2.19 1.99 2.03 1.81 2.14 1.86 0.93
#> 10-1945 10-1946 10-1947 10-1948 10-1949 10-1950 10-1951 10-1952 10-1953 10-1954
#> 1.18 1.36 2.24 3.81 5.66 4.21 3.42 4.67 6.00 6.53
#>
#> $value
#> 1-1935 1-1936 1-1937 1-1938 1-1939 1-1940 1-1941 1-1942 1-1943 1-1944
#> NA 3078.50 4661.70 5387.10 2792.20 4313.20 4643.90 4551.20 3244.10 4053.70
#> 1-1945 1-1946 1-1947 1-1948 1-1949 1-1950 1-1951 1-1952 1-1953 1-1954
#> 4379.30 4840.90 4900.90 3526.50 3254.70 3700.20 3755.60 4833.00 4924.90 6241.70
#> 2-1935 2-1936 2-1937 2-1938 2-1939 2-1940 2-1941 2-1942 2-1943 2-1944
#> NA 1362.40 1807.10 2676.30 1801.90 1957.30 2202.90 2380.50 2168.60 1985.10
#> 2-1945 2-1946 2-1947 2-1948 2-1949 2-1950 2-1951 2-1952 2-1953 2-1954
#> 1813.90 1850.20 2067.70 1796.70 1625.80 1667.00 1677.40 2289.50 2159.40 2031.30
#> 3-1935 3-1936 3-1937 3-1938 3-1939 3-1940 3-1941 3-1942 3-1943 3-1944
#> NA 1170.60 2015.80 2803.30 2039.70 2256.20 2132.20 1834.10 1588.00 1749.40
#> 3-1945 3-1946 3-1947 3-1948 3-1949 3-1950 3-1951 3-1952 3-1953 3-1954
#> 1687.20 2007.70 2208.30 1656.70 1604.40 1431.80 1610.50 1819.40 2079.70 2371.60
#> 4-1935 4-1936 4-1937 4-1938 4-1939 4-1940 4-1941 4-1942 4-1943 4-1944
#> NA 417.50 837.80 883.90 437.90 679.70 727.80 643.60 410.90 588.40
#> 4-1945 4-1946 4-1947 4-1948 4-1949 4-1950 4-1951 4-1952 4-1953 4-1954
#> 698.40 846.40 893.80 579.00 694.60 590.30 693.50 809.00 727.00 1001.50
#> 5-1935 5-1936 5-1937 5-1938 5-1939 5-1940 5-1941 5-1942 5-1943 5-1944
#> NA 157.70 167.90 192.90 156.70 191.40 185.50 199.60 189.50 151.20
#> 5-1945 5-1946 5-1947 5-1948 5-1949 5-1950 5-1951 5-1952 5-1953 5-1954
#> 187.70 214.70 232.90 249.00 224.50 237.30 240.10 327.30 359.40 398.40
#> 6-1935 6-1936 6-1937 6-1938 6-1939 6-1940 6-1941 6-1942 6-1943 6-1944
#> NA 197.00 210.30 223.10 216.70 286.40 298.00 276.90 272.60 287.40
#> 6-1945 6-1946 6-1947 6-1948 6-1949 6-1950 6-1951 6-1952 6-1953 6-1954
#> 330.30 324.40 401.90 407.40 409.20 482.20 673.80 676.90 702.00 793.50
#> 7-1935 7-1936 7-1937 7-1938 7-1939 7-1940 7-1941 7-1942 7-1943 7-1944
#> NA 138.00 200.10 210.10 161.20 161.70 145.10 110.60 98.10 108.80
#> 7-1945 7-1946 7-1947 7-1948 7-1949 7-1950 7-1951 7-1952 7-1953 7-1954
#> 118.20 126.50 156.70 119.40 129.10 134.80 140.80 179.00 178.10 186.80
#> 8-1935 8-1936 8-1937 8-1938 8-1939 8-1940 8-1941 8-1942 8-1943 8-1944
#> NA 191.50 516.00 729.00 560.40 519.90 628.50 537.10 561.20 617.20
#> 8-1945 8-1946 8-1947 8-1948 8-1949 8-1950 8-1951 8-1952 8-1953 8-1954
#> 626.70 737.20 760.50 581.40 662.30 583.80 635.20 723.80 864.10 1193.50
#> 9-1935 9-1936 9-1937 9-1938 9-1939 9-1940 9-1941 9-1942 9-1943 9-1944
#> NA 290.60 291.10 335.00 246.00 356.20 289.80 268.20 213.30 348.20
#> 9-1945 9-1946 9-1947 9-1948 9-1949 9-1950 9-1951 9-1952 9-1953 9-1954
#> 374.20 387.20 347.40 291.90 297.20 276.90 274.60 339.90 474.80 496.00
#> 10-1935 10-1936 10-1937 10-1938 10-1939 10-1940 10-1941 10-1942 10-1943 10-1944
#> NA 70.91 87.94 82.20 58.72 80.54 86.47 77.68 62.16 62.24
#> 10-1945 10-1946 10-1947 10-1948 10-1949 10-1950 10-1951 10-1952 10-1953 10-1954
#> 61.82 65.85 69.54 64.97 68.00 71.24 69.05 83.04 74.42 63.51
#>