Nhiệm vụ 2.1

#Đọc dữ liệu và gán vào object "hnd"
library(xlsx)
hnd <- read.xlsx("D:/naaaaaa/NNLT_ST2/data trong bài MP05 của Fullright.xlsx", sheetIndex = 1, header = T)

Bộ dữ liệu trees

Thông tin tổng quan

#Kiểm tra xem có phải là khung dữ liệu hay không. Nếu đúng hiển thị True và ngược lại
is.data.frame(hnd)
## [1] TRUE
#Hiển thị số cột
length(hnd)
## [1] 7
#Hiện thị tên của các cột
names(hnd)
## [1] "STT"     "Năm"     "CÔNG.TY" "Y"       "X2"      "X3"      "NA."
#Hiển thị số hàng và số cột
dim(hnd)
## [1] 80  7

Một số thông tin mở rộng của bộ dữ liệu

#Hiển thị 10 dòng đầu tiên của bộ dữ liệu
head(hnd,10)
##    STT  Năm CÔNG.TY     Y     X2    X3 NA.
## 1    1 1935      GE  33.1 1170.6  97.8  NA
## 2    2 1936      GE  45.0 2015.8 104.4  NA
## 3    3 1937      GE  77.2 2803.3 118.0  NA
## 4    4 1938      GE  44.6 2039.7 156.2  NA
## 5    5 1939      GE  48.1 2256.2 172.6  NA
## 6    6 1940      GE  74.4 2132.2 186.6  NA
## 7    7 1941      GE 113.0 1834.1 220.9  NA
## 8    8 1942      GE  91.9 1588.0 287.8  NA
## 9    9 1943      GE  61.3 1749.4 319.9  NA
## 10  10 1944      GE  56.8 1687.2 321.3  NA
#Hiển thị 10 dòng cuối cùng của bộ dữ liệu
tail(hnd,10)
##    STT  Năm CÔNG.TY     Y     X2    X3 NA.
## 71  71 1945    WEST 39.27  737.2  92.4  NA
## 72  72 1946    WEST 53.46  760.5  86.0  NA
## 73  73 1947    WEST 55.56  581.4 111.1  NA
## 74  74 1948    WEST 49.56  662.3 130.6  NA
## 75  75 1949    WEST 32.04  583.8 141.8  NA
## 76  76 1950    WEST 32.24  635.2 136.7  NA
## 77  77 1951    WEST 54.38  732.8 129.7  NA
## 78  78 1952    WEST 71.78  864.1 145.5  NA
## 79  79 1953    WEST 90.08 1193.5 174.8  NA
## 80  80 1954    WEST 68.60 1188.9 213.5  NA
#Hiển thị cấu trúc của bộ dữ liệu
str(hnd)
## 'data.frame':    80 obs. of  7 variables:
##  $ STT    : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ Năm    : num  1935 1936 1937 1938 1939 ...
##  $ CÔNG.TY: chr  "GE" "GE" "GE" "GE" ...
##  $ Y      : num  33.1 45 77.2 44.6 48.1 74.4 113 91.9 61.3 56.8 ...
##  $ X2     : num  1171 2016 2803 2040 2256 ...
##  $ X3     : num  97.8 104.4 118 156.2 172.6 ...
##  $ NA.    : logi  NA NA NA NA NA NA ...

Kiểm tra tính hoàn chỉnh của dữ liệu

#Kiểm tra ô trống của bộ dữ liệu. Nếu có dữ liệu thì FALSE và ngược lại
is.na(hnd)
##         STT   Năm CÔNG.TY     Y    X2    X3  NA.
##  [1,] FALSE FALSE   FALSE FALSE FALSE FALSE TRUE
##  [2,] FALSE FALSE   FALSE FALSE FALSE FALSE TRUE
##  [3,] FALSE FALSE   FALSE FALSE FALSE FALSE TRUE
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## [79,] FALSE FALSE   FALSE FALSE FALSE FALSE TRUE
## [80,] FALSE FALSE   FALSE FALSE FALSE FALSE TRUE
#Tính tổng ô trống của bộ dữ liệu
sum(is.na(hnd))
## [1] 80
#Hiển thị vị trí của các ô trống
which(is.na(hnd))
##  [1] 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
## [20] 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518
## [39] 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537
## [58] 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556
## [77] 557 558 559 560

Rút trích dữ liệu

#Đổi tên cho các biến của bộ dữ liệu 
names(hnd) <- c('A','S','D','F','G','H','J')
names(hnd)
## [1] "A" "S" "D" "F" "G" "H" "J"

Thực hiện thao tác rút trích dữ liệu

# Gán a1 là dữ liệu hàng 1 cột 2
a1 <- hnd[1,2]
a1
## [1] 1935
# Gán a2 là dữ liệu hàng tùy ý của cột 2
a2 <- hnd[,2]
a2
##  [1] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949
## [16] 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944
## [31] 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1935 1936 1937 1938 1939
## [46] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954
## [61] 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949
## [76] 1950 1951 1952 1953 1954
# Gán a3 là dữ liệu hàng 1,2,3 cột 2
a3 <- hnd[c(1,2,3),2]
a3
## [1] 1935 1936 1937
#  Gán a4 là dữ liệu hàng tùy ý lớn hơn 1939 cột tùy ý
a4 <- hnd[hnd$S >=1939,]
a4
##     A    S    D       F      G      H  J
## 5   5 1939   GE   48.10 2256.2  172.6 NA
## 6   6 1940   GE   74.40 2132.2  186.6 NA
## 7   7 1941   GE  113.00 1834.1  220.9 NA
## 8   8 1942   GE   91.90 1588.0  287.8 NA
## 9   9 1943   GE   61.30 1749.4  319.9 NA
## 10 10 1944   GE   56.80 1687.2  321.3 NA
## 11 11 1945   GE   93.60 2007.7  319.6 NA
## 12 12 1946   GE  159.90 2208.3  346.0 NA
## 13 13 1947   GE  147.20 1656.7  456.4 NA
## 14 14 1948   GE  146.30 1604.4  543.4 NA
## 15 15 1949   GE   98.30 1431.8  618.3 NA
## 16 16 1950   GE   93.50 1610.5  647.4 NA
## 17 17 1951   GE  135.20 1819.4  671.3 NA
## 18 18 1952   GE  157.30 2079.7  726.1 NA
## 19 19 1953   GE  179.50 2371.6  800.3 NA
## 20 20 1954   GE  189.60 2759.9  888.9 NA
## 25 25 1939   US  230.40 1957.3  312.7 NA
## 26 26 1940   US  361.60 2202.9  254.2 NA
## 27 27 1941   US  472.80 2380.5  261.4 NA
## 28 28 1942   US  445.60 2168.6  298.7 NA
## 29 29 1943   US  361.60 1985.1  301.8 NA
## 30 30 1944   US  288.20 1813.9  279.1 NA
## 31 31 1945   US  258.70 1850.2  213.8 NA
## 32 32 1946   US  420.30 2067.7  232.6 NA
## 33 33 1947   US  420.50 1796.3  264.8 NA
## 34 34 1948   US  494.50 1625.8  306.9 NA
## 35 35 1949   US  405.10 1667.0  351.1 NA
## 36 36 1950   US  418.80 1677.4  357.8 NA
## 37 37 1951   US  588.20 2289.5  341.1 NA
## 38 38 1952   US  645.20 2159.4  444.2 NA
## 39 39 1953   US  641.00 2031.3  623.6 NA
## 40 40 1954   US  459.30 2115.5  669.7 NA
## 45 45 1939   GM  330.80 4313.2  203.4 NA
## 46 46 1940   GM  461.20 4643.9  207.2 NA
## 47 47 1941   GM  512.00 4551.2  255.2 NA
## 48 48 1942   GM  448.00 3244.1  303.7 NA
## 49 49 1943   GM  499.60 4053.7  264.1 NA
## 50 50 1944   GM  547.50 4379.3  201.6 NA
## 51 51 1945   GM  561.20 4840.9  265.0 NA
## 52 52 1946   GM  688.10 4900.0  402.0 NA
## 53 53 1947   GM  568.90 3526.5  761.5 NA
## 54 54 1948   GM  529.20 3245.7  922.4 NA
## 55 55 1949   GM  555.10 3700.2 1020.1 NA
## 56 56 1950   GM  642.90 3755.6 1099.0 NA
## 57 57 1951   GM  755.90 4833.0 1207.7 NA
## 58 58 1952   GM  891.20 4926.9 1430.5 NA
## 59 59 1953   GM 1304.40 6241.7 1777.3 NA
## 60 60 1954   GM 1486.70 5593.6  226.3 NA
## 65 65 1939 WEST   18.84  519.9   23.5 NA
## 66 66 1940 WEST   28.57  628.5   26.5 NA
## 67 67 1941 WEST   48.51  537.1   36.2 NA
## 68 68 1942 WEST   43.34  561.2   60.8 NA
## 69 69 1943 WEST   37.02  617.2   84.4 NA
## 70 70 1944 WEST   37.81  626.7   91.2 NA
## 71 71 1945 WEST   39.27  737.2   92.4 NA
## 72 72 1946 WEST   53.46  760.5   86.0 NA
## 73 73 1947 WEST   55.56  581.4  111.1 NA
## 74 74 1948 WEST   49.56  662.3  130.6 NA
## 75 75 1949 WEST   32.04  583.8  141.8 NA
## 76 76 1950 WEST   32.24  635.2  136.7 NA
## 77 77 1951 WEST   54.38  732.8  129.7 NA
## 78 78 1952 WEST   71.78  864.1  145.5 NA
## 79 79 1953 WEST   90.08 1193.5  174.8 NA
## 80 80 1954 WEST   68.60 1188.9  213.5 NA
# Gán a5 là dữ liệu hàng tùy ý lớn hơn 1939 và bé hơn 1946
a5 <- hnd[hnd$S >=1939 & hnd$S <=1946,]
a5
##     A    S    D      F      G     H  J
## 5   5 1939   GE  48.10 2256.2 172.6 NA
## 6   6 1940   GE  74.40 2132.2 186.6 NA
## 7   7 1941   GE 113.00 1834.1 220.9 NA
## 8   8 1942   GE  91.90 1588.0 287.8 NA
## 9   9 1943   GE  61.30 1749.4 319.9 NA
## 10 10 1944   GE  56.80 1687.2 321.3 NA
## 11 11 1945   GE  93.60 2007.7 319.6 NA
## 12 12 1946   GE 159.90 2208.3 346.0 NA
## 25 25 1939   US 230.40 1957.3 312.7 NA
## 26 26 1940   US 361.60 2202.9 254.2 NA
## 27 27 1941   US 472.80 2380.5 261.4 NA
## 28 28 1942   US 445.60 2168.6 298.7 NA
## 29 29 1943   US 361.60 1985.1 301.8 NA
## 30 30 1944   US 288.20 1813.9 279.1 NA
## 31 31 1945   US 258.70 1850.2 213.8 NA
## 32 32 1946   US 420.30 2067.7 232.6 NA
## 45 45 1939   GM 330.80 4313.2 203.4 NA
## 46 46 1940   GM 461.20 4643.9 207.2 NA
## 47 47 1941   GM 512.00 4551.2 255.2 NA
## 48 48 1942   GM 448.00 3244.1 303.7 NA
## 49 49 1943   GM 499.60 4053.7 264.1 NA
## 50 50 1944   GM 547.50 4379.3 201.6 NA
## 51 51 1945   GM 561.20 4840.9 265.0 NA
## 52 52 1946   GM 688.10 4900.0 402.0 NA
## 65 65 1939 WEST  18.84  519.9  23.5 NA
## 66 66 1940 WEST  28.57  628.5  26.5 NA
## 67 67 1941 WEST  48.51  537.1  36.2 NA
## 68 68 1942 WEST  43.34  561.2  60.8 NA
## 69 69 1943 WEST  37.02  617.2  84.4 NA
## 70 70 1944 WEST  37.81  626.7  91.2 NA
## 71 71 1945 WEST  39.27  737.2  92.4 NA
## 72 72 1946 WEST  53.46  760.5  86.0 NA
# Gán a6 là dữ liệu hàng tùy ý bằng 1939 hoặc bằng 1946
a6 <- hnd[hnd$S ==1939 | hnd$S ==1946, ]
a6
##     A    S    D      F      G     H  J
## 5   5 1939   GE  48.10 2256.2 172.6 NA
## 12 12 1946   GE 159.90 2208.3 346.0 NA
## 25 25 1939   US 230.40 1957.3 312.7 NA
## 32 32 1946   US 420.30 2067.7 232.6 NA
## 45 45 1939   GM 330.80 4313.2 203.4 NA
## 52 52 1946   GM 688.10 4900.0 402.0 NA
## 65 65 1939 WEST  18.84  519.9  23.5 NA
## 72 72 1946 WEST  53.46  760.5  86.0 NA

Lấy dữ liệu trong datasets

d <- iris
d
##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

Kiểm tra cấu trúc của d

str(d)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Xem 5 thông tin đầu trong dữ liệu

head(d,5)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa

Xem 4 thông tin cuối cùng của dữ liệu

tail(d,4)
##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

Lấy thông tin loài setosa

d1 <- d[d$Species=='setosa',]
d1
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa
## 11          5.4         3.7          1.5         0.2  setosa
## 12          4.8         3.4          1.6         0.2  setosa
## 13          4.8         3.0          1.4         0.1  setosa
## 14          4.3         3.0          1.1         0.1  setosa
## 15          5.8         4.0          1.2         0.2  setosa
## 16          5.7         4.4          1.5         0.4  setosa
## 17          5.4         3.9          1.3         0.4  setosa
## 18          5.1         3.5          1.4         0.3  setosa
## 19          5.7         3.8          1.7         0.3  setosa
## 20          5.1         3.8          1.5         0.3  setosa
## 21          5.4         3.4          1.7         0.2  setosa
## 22          5.1         3.7          1.5         0.4  setosa
## 23          4.6         3.6          1.0         0.2  setosa
## 24          5.1         3.3          1.7         0.5  setosa
## 25          4.8         3.4          1.9         0.2  setosa
## 26          5.0         3.0          1.6         0.2  setosa
## 27          5.0         3.4          1.6         0.4  setosa
## 28          5.2         3.5          1.5         0.2  setosa
## 29          5.2         3.4          1.4         0.2  setosa
## 30          4.7         3.2          1.6         0.2  setosa
## 31          4.8         3.1          1.6         0.2  setosa
## 32          5.4         3.4          1.5         0.4  setosa
## 33          5.2         4.1          1.5         0.1  setosa
## 34          5.5         4.2          1.4         0.2  setosa
## 35          4.9         3.1          1.5         0.2  setosa
## 36          5.0         3.2          1.2         0.2  setosa
## 37          5.5         3.5          1.3         0.2  setosa
## 38          4.9         3.6          1.4         0.1  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 40          5.1         3.4          1.5         0.2  setosa
## 41          5.0         3.5          1.3         0.3  setosa
## 42          4.5         2.3          1.3         0.3  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 44          5.0         3.5          1.6         0.6  setosa
## 45          5.1         3.8          1.9         0.4  setosa
## 46          4.8         3.0          1.4         0.3  setosa
## 47          5.1         3.8          1.6         0.2  setosa
## 48          4.6         3.2          1.4         0.2  setosa
## 49          5.3         3.7          1.5         0.2  setosa
## 50          5.0         3.3          1.4         0.2  setosa

Xem cấu trúc của d1

str(d1)
## 'data.frame':    50 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Lấy thông tin của loài setosa hoặc loài versicolor

d2 <- d[d$Species=='setosa'|d$Species=='versicolor',]
d2
##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 1            5.1         3.5          1.4         0.2     setosa
## 2            4.9         3.0          1.4         0.2     setosa
## 3            4.7         3.2          1.3         0.2     setosa
## 4            4.6         3.1          1.5         0.2     setosa
## 5            5.0         3.6          1.4         0.2     setosa
## 6            5.4         3.9          1.7         0.4     setosa
## 7            4.6         3.4          1.4         0.3     setosa
## 8            5.0         3.4          1.5         0.2     setosa
## 9            4.4         2.9          1.4         0.2     setosa
## 10           4.9         3.1          1.5         0.1     setosa
## 11           5.4         3.7          1.5         0.2     setosa
## 12           4.8         3.4          1.6         0.2     setosa
## 13           4.8         3.0          1.4         0.1     setosa
## 14           4.3         3.0          1.1         0.1     setosa
## 15           5.8         4.0          1.2         0.2     setosa
## 16           5.7         4.4          1.5         0.4     setosa
## 17           5.4         3.9          1.3         0.4     setosa
## 18           5.1         3.5          1.4         0.3     setosa
## 19           5.7         3.8          1.7         0.3     setosa
## 20           5.1         3.8          1.5         0.3     setosa
## 21           5.4         3.4          1.7         0.2     setosa
## 22           5.1         3.7          1.5         0.4     setosa
## 23           4.6         3.6          1.0         0.2     setosa
## 24           5.1         3.3          1.7         0.5     setosa
## 25           4.8         3.4          1.9         0.2     setosa
## 26           5.0         3.0          1.6         0.2     setosa
## 27           5.0         3.4          1.6         0.4     setosa
## 28           5.2         3.5          1.5         0.2     setosa
## 29           5.2         3.4          1.4         0.2     setosa
## 30           4.7         3.2          1.6         0.2     setosa
## 31           4.8         3.1          1.6         0.2     setosa
## 32           5.4         3.4          1.5         0.4     setosa
## 33           5.2         4.1          1.5         0.1     setosa
## 34           5.5         4.2          1.4         0.2     setosa
## 35           4.9         3.1          1.5         0.2     setosa
## 36           5.0         3.2          1.2         0.2     setosa
## 37           5.5         3.5          1.3         0.2     setosa
## 38           4.9         3.6          1.4         0.1     setosa
## 39           4.4         3.0          1.3         0.2     setosa
## 40           5.1         3.4          1.5         0.2     setosa
## 41           5.0         3.5          1.3         0.3     setosa
## 42           4.5         2.3          1.3         0.3     setosa
## 43           4.4         3.2          1.3         0.2     setosa
## 44           5.0         3.5          1.6         0.6     setosa
## 45           5.1         3.8          1.9         0.4     setosa
## 46           4.8         3.0          1.4         0.3     setosa
## 47           5.1         3.8          1.6         0.2     setosa
## 48           4.6         3.2          1.4         0.2     setosa
## 49           5.3         3.7          1.5         0.2     setosa
## 50           5.0         3.3          1.4         0.2     setosa
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor

Lấy thông tin của loài setosa có độ dài nhỏ hơn 5

d3 <- d1[d1$Sepal.Length < 5,]
d3
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa
## 12          4.8         3.4          1.6         0.2  setosa
## 13          4.8         3.0          1.4         0.1  setosa
## 14          4.3         3.0          1.1         0.1  setosa
## 23          4.6         3.6          1.0         0.2  setosa
## 25          4.8         3.4          1.9         0.2  setosa
## 30          4.7         3.2          1.6         0.2  setosa
## 31          4.8         3.1          1.6         0.2  setosa
## 35          4.9         3.1          1.5         0.2  setosa
## 38          4.9         3.6          1.4         0.1  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 42          4.5         2.3          1.3         0.3  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 46          4.8         3.0          1.4         0.3  setosa
## 48          4.6         3.2          1.4         0.2  setosa

Lấy thông tin các loài khác loài setosa

d4 <- d[d$Species != 'setosa',]
d4
##     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
## 51           7.0         3.2          4.7         1.4 versicolor
## 52           6.4         3.2          4.5         1.5 versicolor
## 53           6.9         3.1          4.9         1.5 versicolor
## 54           5.5         2.3          4.0         1.3 versicolor
## 55           6.5         2.8          4.6         1.5 versicolor
## 56           5.7         2.8          4.5         1.3 versicolor
## 57           6.3         3.3          4.7         1.6 versicolor
## 58           4.9         2.4          3.3         1.0 versicolor
## 59           6.6         2.9          4.6         1.3 versicolor
## 60           5.2         2.7          3.9         1.4 versicolor
## 61           5.0         2.0          3.5         1.0 versicolor
## 62           5.9         3.0          4.2         1.5 versicolor
## 63           6.0         2.2          4.0         1.0 versicolor
## 64           6.1         2.9          4.7         1.4 versicolor
## 65           5.6         2.9          3.6         1.3 versicolor
## 66           6.7         3.1          4.4         1.4 versicolor
## 67           5.6         3.0          4.5         1.5 versicolor
## 68           5.8         2.7          4.1         1.0 versicolor
## 69           6.2         2.2          4.5         1.5 versicolor
## 70           5.6         2.5          3.9         1.1 versicolor
## 71           5.9         3.2          4.8         1.8 versicolor
## 72           6.1         2.8          4.0         1.3 versicolor
## 73           6.3         2.5          4.9         1.5 versicolor
## 74           6.1         2.8          4.7         1.2 versicolor
## 75           6.4         2.9          4.3         1.3 versicolor
## 76           6.6         3.0          4.4         1.4 versicolor
## 77           6.8         2.8          4.8         1.4 versicolor
## 78           6.7         3.0          5.0         1.7 versicolor
## 79           6.0         2.9          4.5         1.5 versicolor
## 80           5.7         2.6          3.5         1.0 versicolor
## 81           5.5         2.4          3.8         1.1 versicolor
## 82           5.5         2.4          3.7         1.0 versicolor
## 83           5.8         2.7          3.9         1.2 versicolor
## 84           6.0         2.7          5.1         1.6 versicolor
## 85           5.4         3.0          4.5         1.5 versicolor
## 86           6.0         3.4          4.5         1.6 versicolor
## 87           6.7         3.1          4.7         1.5 versicolor
## 88           6.3         2.3          4.4         1.3 versicolor
## 89           5.6         3.0          4.1         1.3 versicolor
## 90           5.5         2.5          4.0         1.3 versicolor
## 91           5.5         2.6          4.4         1.2 versicolor
## 92           6.1         3.0          4.6         1.4 versicolor
## 93           5.8         2.6          4.0         1.2 versicolor
## 94           5.0         2.3          3.3         1.0 versicolor
## 95           5.6         2.7          4.2         1.3 versicolor
## 96           5.7         3.0          4.2         1.2 versicolor
## 97           5.7         2.9          4.2         1.3 versicolor
## 98           6.2         2.9          4.3         1.3 versicolor
## 99           5.1         2.5          3.0         1.1 versicolor
## 100          5.7         2.8          4.1         1.3 versicolor
## 101          6.3         3.3          6.0         2.5  virginica
## 102          5.8         2.7          5.1         1.9  virginica
## 103          7.1         3.0          5.9         2.1  virginica
## 104          6.3         2.9          5.6         1.8  virginica
## 105          6.5         3.0          5.8         2.2  virginica
## 106          7.6         3.0          6.6         2.1  virginica
## 107          4.9         2.5          4.5         1.7  virginica
## 108          7.3         2.9          6.3         1.8  virginica
## 109          6.7         2.5          5.8         1.8  virginica
## 110          7.2         3.6          6.1         2.5  virginica
## 111          6.5         3.2          5.1         2.0  virginica
## 112          6.4         2.7          5.3         1.9  virginica
## 113          6.8         3.0          5.5         2.1  virginica
## 114          5.7         2.5          5.0         2.0  virginica
## 115          5.8         2.8          5.1         2.4  virginica
## 116          6.4         3.2          5.3         2.3  virginica
## 117          6.5         3.0          5.5         1.8  virginica
## 118          7.7         3.8          6.7         2.2  virginica
## 119          7.7         2.6          6.9         2.3  virginica
## 120          6.0         2.2          5.0         1.5  virginica
## 121          6.9         3.2          5.7         2.3  virginica
## 122          5.6         2.8          4.9         2.0  virginica
## 123          7.7         2.8          6.7         2.0  virginica
## 124          6.3         2.7          4.9         1.8  virginica
## 125          6.7         3.3          5.7         2.1  virginica
## 126          7.2         3.2          6.0         1.8  virginica
## 127          6.2         2.8          4.8         1.8  virginica
## 128          6.1         3.0          4.9         1.8  virginica
## 129          6.4         2.8          5.6         2.1  virginica
## 130          7.2         3.0          5.8         1.6  virginica
## 131          7.4         2.8          6.1         1.9  virginica
## 132          7.9         3.8          6.4         2.0  virginica
## 133          6.4         2.8          5.6         2.2  virginica
## 134          6.3         2.8          5.1         1.5  virginica
## 135          6.1         2.6          5.6         1.4  virginica
## 136          7.7         3.0          6.1         2.3  virginica
## 137          6.3         3.4          5.6         2.4  virginica
## 138          6.4         3.1          5.5         1.8  virginica
## 139          6.0         3.0          4.8         1.8  virginica
## 140          6.9         3.1          5.4         2.1  virginica
## 141          6.7         3.1          5.6         2.4  virginica
## 142          6.9         3.1          5.1         2.3  virginica
## 143          5.8         2.7          5.1         1.9  virginica
## 144          6.8         3.2          5.9         2.3  virginica
## 145          6.7         3.3          5.7         2.5  virginica
## 146          6.7         3.0          5.2         2.3  virginica
## 147          6.3         2.5          5.0         1.9  virginica
## 148          6.5         3.0          5.2         2.0  virginica
## 149          6.2         3.4          5.4         2.3  virginica
## 150          5.9         3.0          5.1         1.8  virginica

Rút trích dữ liệu từ packages tidyverse

Gọi package tidyverse

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Gán dữ liệu diamonds cho D

d <- diamonds

Lọc thông tin về kim cương có color là D hoặc carat > 1

D1 <- filter(d,color=='D'|carat > 1)
D1
## # A tibble: 22,956 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Very Good D     VS2      60.5    61   357  3.96  3.97  2.4 
##  2  0.23 Very Good D     VS1      61.9    58   402  3.92  3.96  2.44
##  3  0.26 Very Good D     VS2      60.8    59   403  4.13  4.16  2.52
##  4  0.26 Good      D     VS2      65.2    56   403  3.99  4.02  2.61
##  5  0.26 Good      D     VS1      58.4    63   403  4.19  4.24  2.46
##  6  0.22 Premium   D     VS2      59.3    62   404  3.91  3.88  2.31
##  7  0.3  Premium   D     SI1      62.6    59   552  4.23  4.27  2.66
##  8  0.3  Ideal     D     SI1      62.5    57   552  4.29  4.32  2.69
##  9  0.3  Ideal     D     SI1      62.1    56   552  4.3   4.33  2.68
## 10  0.24 Very Good D     VVS1     61.5    60   553  3.97  4     2.45
## # ℹ 22,946 more rows

Gán thông tin ở d cho D1 với điều kiện là những kim cương color là D hoặc carat > 1

D1 <- d %>% filter(color=='D'|carat > 1)
D1
## # A tibble: 22,956 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Very Good D     VS2      60.5    61   357  3.96  3.97  2.4 
##  2  0.23 Very Good D     VS1      61.9    58   402  3.92  3.96  2.44
##  3  0.26 Very Good D     VS2      60.8    59   403  4.13  4.16  2.52
##  4  0.26 Good      D     VS2      65.2    56   403  3.99  4.02  2.61
##  5  0.26 Good      D     VS1      58.4    63   403  4.19  4.24  2.46
##  6  0.22 Premium   D     VS2      59.3    62   404  3.91  3.88  2.31
##  7  0.3  Premium   D     SI1      62.6    59   552  4.23  4.27  2.66
##  8  0.3  Ideal     D     SI1      62.5    57   552  4.29  4.32  2.69
##  9  0.3  Ideal     D     SI1      62.1    56   552  4.3   4.33  2.68
## 10  0.24 Very Good D     VVS1     61.5    60   553  3.97  4     2.45
## # ℹ 22,946 more rows

Gán thông tin d1 cho d2 với điều kiện 5 biến được chọn là color,carat,x,y,z

D2 <- D1 %>% select(color,carat,x,y,z)
D2
## # A tibble: 22,956 × 5
##    color carat     x     y     z
##    <ord> <dbl> <dbl> <dbl> <dbl>
##  1 D      0.23  3.96  3.97  2.4 
##  2 D      0.23  3.92  3.96  2.44
##  3 D      0.26  4.13  4.16  2.52
##  4 D      0.26  3.99  4.02  2.61
##  5 D      0.26  4.19  4.24  2.46
##  6 D      0.22  3.91  3.88  2.31
##  7 D      0.3   4.23  4.27  2.66
##  8 D      0.3   4.29  4.32  2.69
##  9 D      0.3   4.3   4.33  2.68
## 10 D      0.24  3.97  4     2.45
## # ℹ 22,946 more rows

Gán thông tin từ d cho D22 với color là D hoặc carat > 1 và lấy 5 biến là color,carat,x,y,z

D22 <- d %>% filter(color=='D'|carat > 1) %>% select(color,carat,x,y,z)
D22
## # A tibble: 22,956 × 5
##    color carat     x     y     z
##    <ord> <dbl> <dbl> <dbl> <dbl>
##  1 D      0.23  3.96  3.97  2.4 
##  2 D      0.23  3.92  3.96  2.44
##  3 D      0.26  4.13  4.16  2.52
##  4 D      0.26  3.99  4.02  2.61
##  5 D      0.26  4.19  4.24  2.46
##  6 D      0.22  3.91  3.88  2.31
##  7 D      0.3   4.23  4.27  2.66
##  8 D      0.3   4.29  4.32  2.69
##  9 D      0.3   4.3   4.33  2.68
## 10 D      0.24  3.97  4     2.45
## # ℹ 22,946 more rows

Tạo dữ liệu mới từ dữ liệu có sẵn

Gán bộ dữ trees cho P

P <- trees

##Các hàm toán học

P$tich <- P$Girth*P$Height*P$Volume
P1 <- P %>% mutate(l = log(tich))
P1
##    Girth Height Volume      tich         l
## 1    8.3     70   10.3   5984.30  8.696895
## 2    8.6     65   10.3   5757.70  8.658293
## 3    8.8     63   10.2   5654.88  8.640274
## 4   10.5     72   16.4  12398.40  9.425323
## 5   10.7     81   18.8  16293.96  9.698550
## 6   10.8     83   19.7  17659.08  9.779005
## 7   11.0     66   15.6  11325.60  9.334821
## 8   11.0     75   18.2  15015.00  9.616805
## 9   11.1     80   22.6  20068.80  9.906922
## 10  11.2     75   19.9  16716.00  9.724122
## 11  11.3     79   24.2  21603.34  9.980603
## 12  11.4     76   21.0  18194.40  9.808869
## 13  11.4     76   21.4  18540.96  9.827738
## 14  11.7     69   21.3  17195.49  9.752402
## 15  12.0     75   19.1  17190.00  9.752083
## 16  12.9     74   22.2  21192.12  9.961385
## 17  12.9     85   33.8  37061.70 10.520339
## 18  13.3     86   27.4  31340.12 10.352654
## 19  13.7     71   25.7  24998.39 10.126567
## 20  13.8     64   24.9  21991.68  9.998419
## 21  14.0     78   34.5  37674.00 10.536725
## 22  14.2     80   31.7  36011.20 10.491585
## 23  14.5     74   36.3  38949.90 10.570031
## 24  16.0     72   38.3  44121.60 10.694705
## 25  16.3     77   42.6  53467.26 10.886825
## 26  17.3     81   55.4  77632.02 11.259735
## 27  17.5     82   55.7  79929.50 11.288900
## 28  17.9     80   58.3  83485.60 11.332429
## 29  18.0     80   51.5  74160.00 11.213980
## 30  18.0     80   51.0  73440.00 11.204224
## 31  20.6     87   77.0 137999.40 11.835005
P2 <- P %>% mutate(sq = sqrt(tich))
P2
##    Girth Height Volume      tich        sq
## 1    8.3     70   10.3   5984.30  77.35826
## 2    8.6     65   10.3   5757.70  75.87951
## 3    8.8     63   10.2   5654.88  75.19894
## 4   10.5     72   16.4  12398.40 111.34810
## 5   10.7     81   18.8  16293.96 127.64780
## 6   10.8     83   19.7  17659.08 132.88747
## 7   11.0     66   15.6  11325.60 106.42180
## 8   11.0     75   18.2  15015.00 122.53571
## 9   11.1     80   22.6  20068.80 141.66439
## 10  11.2     75   19.9  16716.00 129.29037
## 11  11.3     79   24.2  21603.34 146.98075
## 12  11.4     76   21.0  18194.40 134.88662
## 13  11.4     76   21.4  18540.96 136.16519
## 14  11.7     69   21.3  17195.49 131.13158
## 15  12.0     75   19.1  17190.00 131.11064
## 16  12.9     74   22.2  21192.12 145.57514
## 17  12.9     85   33.8  37061.70 192.51416
## 18  13.3     86   27.4  31340.12 177.03141
## 19  13.7     71   25.7  24998.39 158.10879
## 20  13.8     64   24.9  21991.68 148.29592
## 21  14.0     78   34.5  37674.00 194.09791
## 22  14.2     80   31.7  36011.20 189.76617
## 23  14.5     74   36.3  38949.90 197.35729
## 24  16.0     72   38.3  44121.60 210.05142
## 25  16.3     77   42.6  53467.26 231.22989
## 26  17.3     81   55.4  77632.02 278.62523
## 27  17.5     82   55.7  79929.50 282.71806
## 28  17.9     80   58.3  83485.60 288.93875
## 29  18.0     80   51.5  74160.00 272.32334
## 30  18.0     80   51.0  73440.00 270.99815
## 31  20.6     87   77.0 137999.40 371.48270

Mã hóa dữ liệu

hnd <- iris

names(hnd) <- c('SL','SW','PL','PW','S')
str(hnd)
## 'data.frame':    150 obs. of  5 variables:
##  $ SL: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ SW: num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ PL: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ PW: num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ S : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
hnd$S.Coded <- ifelse(hnd$S == 'setosa','setosa','Not setosa')
hnd$S.Coded1 <- recode(hnd$S,setosa = 'Loại 1',  versicolor = 'Loại 2')
hnd$SL.Coded <- ifelse(hnd$SL >= 5,'Đạt', 'Không đạt')
hnd$SL.Coded1 <- ifelse(hnd$SL >= 5 & hnd$SL <= 6, 'Nhận', 'Loại')
hnd$SL.Coded2 <- case_when(hnd$SL < 5 ~ 'Quá nhỏ', hnd$SL >= 5 & hnd$SL <= 6.5 ~ 'OK', hnd$SL >6.5 ~ 'Quá lớn')
hnd$SL.Coded2 <- cut(hnd$SL,3,labels = c('Loại 1','Loại 2','Loại 3'))

Lập bảng tần số

Lập bảng tần số 1 biến

L <- iris
table(L$Species)
## 
##     setosa versicolor  virginica 
##         50         50         50
cut(L$Sepal.Length,3)
##   [1] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##   [8] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##  [15] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (4.3,5.5] (4.3,5.5]
##  [22] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##  [29] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##  [36] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##  [43] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5] (4.3,5.5]
##  [50] (4.3,5.5] (6.7,7.9] (5.5,6.7] (6.7,7.9] (4.3,5.5] (5.5,6.7] (5.5,6.7]
##  [57] (5.5,6.7] (4.3,5.5] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (5.5,6.7]
##  [64] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7]
##  [71] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9]
##  [78] (5.5,6.7] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5] (5.5,6.7] (5.5,6.7]
##  [85] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (4.3,5.5] (4.3,5.5]
##  [92] (5.5,6.7] (5.5,6.7] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7]
##  [99] (4.3,5.5] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7]
## [106] (6.7,7.9] (4.3,5.5] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7]
## [113] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (6.7,7.9]
## [120] (5.5,6.7] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (6.7,7.9]
## [127] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9] (6.7,7.9] (6.7,7.9] (5.5,6.7]
## [134] (5.5,6.7] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7] (6.7,7.9]
## [141] (5.5,6.7] (6.7,7.9] (5.5,6.7] (6.7,7.9] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## [148] (5.5,6.7] (5.5,6.7] (5.5,6.7]
## Levels: (4.3,5.5] (5.5,6.7] (6.7,7.9]
table(cut(L$Sepal.Length,3))
## 
## (4.3,5.5] (5.5,6.7] (6.7,7.9] 
##        59        71        20

Lập bảng tần số 2 biến

M <- iris
M$sl.c <- cut(M$Sepal.Length,3, labels = c('ngắn','tb','dài'))
tnt1 <- table(M$Species,M$sl.c)
tnt1
##             
##              ngắn tb dài
##   setosa       47  3   0
##   versicolor   11 36   3
##   virginica     1 32  17

Hoặc chúng ta có cách khác

tnt2 <- M %>% group_by(Species,sl.c) %>% summarise(n= n())
## `summarise()` has grouped output by 'Species'. You can override using the
## `.groups` argument.
tnt2
## # A tibble: 8 × 3
## # Groups:   Species [3]
##   Species    sl.c      n
##   <fct>      <fct> <int>
## 1 setosa     ngắn     47
## 2 setosa     tb        3
## 3 versicolor ngắn     11
## 4 versicolor tb       36
## 5 versicolor dài       3
## 6 virginica  ngắn      1
## 7 virginica  tb       32
## 8 virginica  dài      17

Lập biểu đồ nhánh và lá

stem(M$Petal.Length)
## 
##   The decimal point is at the |
## 
##   1 | 012233333334444444444444
##   1 | 55555555555556666666777799
##   2 | 
##   2 | 
##   3 | 033
##   3 | 55678999
##   4 | 000001112222334444
##   4 | 5555555566677777888899999
##   5 | 000011111111223344
##   5 | 55566666677788899
##   6 | 0011134
##   6 | 6779
stem(M$Sepal.Length,scale = .5)
## 
##   The decimal point is at the |
## 
##   4 | 3444
##   4 | 566667788888999999
##   5 | 000000000011111111122223444444
##   5 | 5555555666666777777778888888999
##   6 | 00000011111122223333333334444444
##   6 | 5555566777777778889999
##   7 | 0122234
##   7 | 677779

Các tính toán đặc trưng đo lường

tnt2 <- diamonds
summary(tnt2$carat)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2000  0.4000  0.7000  0.7979  1.0400  5.0100
sum(tnt2$carat)
## [1] 43040.87
mean(tnt2$carat,na.rm = T)
## [1] 0.7979397
length(tnt2$carat)
## [1] 53940
var(tnt2$carat)
## [1] 0.2246867
sd(tnt2$carat)
## [1] 0.4740112
median(tnt2$carat)
## [1] 0.7
quantile(tnt2$carat, probs = c(.25,.5,.75))
##  25%  50%  75% 
## 0.40 0.70 1.04

Các tính toán đo lường theo nhóm

tnt3 <- diamonds
nt <- tnt3 %>% group_by(color) %>% summarise(mean_of_carat = mean(carat))
nt
## # A tibble: 7 × 2
##   color mean_of_carat
##   <ord>         <dbl>
## 1 D             0.658
## 2 E             0.658
## 3 F             0.737
## 4 G             0.771
## 5 H             0.912
## 6 I             1.03 
## 7 J             1.16
nt <- tnt3 %>% group_by(color) %>% summarise(n = n(),mean_of_carat = mean(carat))
nt
## # A tibble: 7 × 3
##   color     n mean_of_carat
##   <ord> <int>         <dbl>
## 1 D      6775         0.658
## 2 E      9797         0.658
## 3 F      9542         0.737
## 4 G     11292         0.771
## 5 H      8304         0.912
## 6 I      5422         1.03 
## 7 J      2808         1.16
nt1 <- tnt3 %>% group_by(color) %>% summarise(med_of_carat = median(carat))
nt1
## # A tibble: 7 × 2
##   color med_of_carat
##   <ord>        <dbl>
## 1 D             0.53
## 2 E             0.53
## 3 F             0.7 
## 4 G             0.7 
## 5 H             0.9 
## 6 I             1   
## 7 J             1.11
nt2 <- tnt3 %>% group_by(cut) %>% summarise(mean_of_carat = mean(carat))
nt2
## # A tibble: 5 × 2
##   cut       mean_of_carat
##   <ord>             <dbl>
## 1 Fair              1.05 
## 2 Good              0.849
## 3 Very Good         0.806
## 4 Premium           0.892
## 5 Ideal             0.703
nt2 <- tnt3 %>% group_by(color,cut) %>% summarise(n = n(),mean_of_carat = mean(carat),.groups = 'drop')
nt2
## # A tibble: 35 × 4
##    color cut           n mean_of_carat
##    <ord> <ord>     <int>         <dbl>
##  1 D     Fair        163         0.920
##  2 D     Good        662         0.745
##  3 D     Very Good  1513         0.696
##  4 D     Premium    1603         0.722
##  5 D     Ideal      2834         0.566
##  6 E     Fair        224         0.857
##  7 E     Good        933         0.745
##  8 E     Very Good  2400         0.676
##  9 E     Premium    2337         0.718
## 10 E     Ideal      3903         0.578
## # ℹ 25 more rows

Nhiệm vụ 2.2

##Đọc dữ liệu và gán vào object “mt”

library(xlsx)
mt <- read.xlsx("D:/naaaaaa/NNLT_ST2/file_example_XLSX_1000.xlsx", sheetIndex = 1, header = T)
table <- knitr::kable(mt, format= "markdown")
table
NA. First.Name Last.Name Gender Country Age Date Id
1 Dulce Abril Female United States 32 15/10/2017 1562
2 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
3 Philip Gent Male France 36 21/05/2015 2587
4 Kathleen Hanner Female United States 25 15/10/2017 3549
5 Nereida Magwood Female United States 58 16/08/2016 2468
6 Gaston Brumm Male United States 24 21/05/2015 2554
7 Etta Hurn Female Great Britain 56 15/10/2017 3598
8 Earlean Melgar Female United States 27 16/08/2016 2456
9 Vincenza Weiland Female United States 40 21/05/2015 6548
10 Fallon Winward Female Great Britain 28 16/08/2016 5486
11 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
12 Franklyn Unknow Male France 38 15/10/2017 2579
13 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
14 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
15 Kina Hazelton Female Great Britain 31 16/08/2016 3259
16 Shavonne Pia Female France 24 21/05/2015 1546
17 Shavon Benito Female France 39 15/10/2017 3579
18 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
19 Loreta Curren Female France 26 21/05/2015 9654
20 Teresa Strawn Female France 46 21/05/2015 3569
21 Belinda Partain Female United States 37 15/10/2017 2564
22 Holly Eudy Female United States 52 16/08/2016 8561
23 Many Cuccia Female Great Britain 46 21/05/2015 5489
24 Libbie Dalby Female France 42 21/05/2015 5489
25 Lester Prothro Male France 21 15/10/2017 6574
26 Marvel Hail Female Great Britain 28 16/08/2016 5555
27 Angelyn Vong Female United States 29 21/05/2015 6125
28 Francesca Beaudreau Female France 23 15/10/2017 5412
29 Garth Gangi Male United States 41 16/08/2016 3256
30 Carla Trumbull Female Great Britain 28 21/05/2015 3264
31 Veta Muntz Female Great Britain 37 15/10/2017 4569
32 Stasia Becker Female Great Britain 34 16/08/2016 7521
33 Jona Grindle Female Great Britain 26 21/05/2015 6458
34 Judie Claywell Female France 35 16/08/2016 7569
35 Dewitt Borger Male United States 36 21/05/2015 8514
36 Nena Hacker Female United States 29 15/10/2017 8563
37 Kelsie Wachtel Female France 27 16/08/2016 8642
38 Sau Pfau Female United States 25 21/05/2015 9536
39 Shanice Mccrystal Female United States 36 21/05/2015 2567
40 Chase Karner Male United States 37 15/10/2017 2154
41 Tommie Underdahl Male United States 26 16/08/2016 3265
42 Dorcas Darity Female United States 37 21/05/2015 8765
43 Angel Sanor Male France 24 15/10/2017 3259
44 Willodean Harn Female United States 39 16/08/2016 3567
45 Weston Martina Male United States 26 21/05/2015 6540
46 Roma Lafollette Female United States 34 15/10/2017 2654
47 Felisa Cail Female United States 28 16/08/2016 6525
48 Demetria Abbey Female United States 32 21/05/2015 3265
49 Jeromy Danz Male United States 39 15/10/2017 3265
50 Rasheeda Alkire Female United States 29 16/08/2016 6125
51 Dulce Abril Female United States 32 15/10/2017 1562
52 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
53 Philip Gent Male France 36 21/05/2015 2587
54 Kathleen Hanner Female United States 25 15/10/2017 3549
55 Nereida Magwood Female United States 58 16/08/2016 2468
56 Gaston Brumm Male United States 24 21/05/2015 2554
57 Etta Hurn Female Great Britain 56 15/10/2017 3598
58 Earlean Melgar Female United States 27 16/08/2016 2456
59 Vincenza Weiland Female United States 40 21/05/2015 6548
60 Fallon Winward Female Great Britain 28 16/08/2016 5486
61 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
62 Franklyn Unknow Male France 38 15/10/2017 2579
63 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
64 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
65 Kina Hazelton Female Great Britain 31 16/08/2016 3259
66 Shavonne Pia Female France 24 21/05/2015 1546
67 Shavon Benito Female France 39 15/10/2017 3579
68 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
69 Loreta Curren Female France 26 21/05/2015 9654
70 Teresa Strawn Female France 46 21/05/2015 3569
71 Belinda Partain Female United States 37 15/10/2017 2564
72 Holly Eudy Female United States 52 16/08/2016 8561
73 Many Cuccia Female Great Britain 46 21/05/2015 5489
74 Libbie Dalby Female France 42 21/05/2015 5489
75 Lester Prothro Male France 21 15/10/2017 6574
76 Marvel Hail Female Great Britain 28 16/08/2016 5555
77 Angelyn Vong Female United States 29 21/05/2015 6125
78 Francesca Beaudreau Female France 23 15/10/2017 5412
79 Garth Gangi Male United States 41 16/08/2016 3256
80 Carla Trumbull Female Great Britain 28 21/05/2015 3264
81 Veta Muntz Female Great Britain 37 15/10/2017 4569
82 Stasia Becker Female Great Britain 34 16/08/2016 7521
83 Jona Grindle Female Great Britain 26 21/05/2015 6458
84 Judie Claywell Female France 35 16/08/2016 7569
85 Dewitt Borger Male United States 36 21/05/2015 8514
86 Nena Hacker Female United States 29 15/10/2017 8563
87 Kelsie Wachtel Female France 27 16/08/2016 8642
88 Sau Pfau Female United States 25 21/05/2015 9536
89 Shanice Mccrystal Female United States 36 21/05/2015 2567
90 Chase Karner Male United States 37 15/10/2017 2154
91 Tommie Underdahl Male United States 26 16/08/2016 3265
92 Dorcas Darity Female United States 37 21/05/2015 8765
93 Angel Sanor Male France 24 15/10/2017 3259
94 Willodean Harn Female United States 39 16/08/2016 3567
95 Weston Martina Male United States 26 21/05/2015 6540
96 Roma Lafollette Female United States 34 15/10/2017 2654
97 Felisa Cail Female United States 28 16/08/2016 6525
98 Demetria Abbey Female United States 32 21/05/2015 3265
99 Jeromy Danz Male United States 39 15/10/2017 3265
100 Rasheeda Alkire Female United States 29 16/08/2016 6125
101 Dulce Abril Female United States 32 15/10/2017 1562
102 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
103 Philip Gent Male France 36 21/05/2015 2587
104 Kathleen Hanner Female United States 25 15/10/2017 3549
105 Nereida Magwood Female United States 58 16/08/2016 2468
106 Gaston Brumm Male United States 24 21/05/2015 2554
107 Etta Hurn Female Great Britain 56 15/10/2017 3598
108 Earlean Melgar Female United States 27 16/08/2016 2456
109 Vincenza Weiland Female United States 40 21/05/2015 6548
110 Fallon Winward Female Great Britain 28 16/08/2016 5486
111 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
112 Franklyn Unknow Male France 38 15/10/2017 2579
113 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
114 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
115 Kina Hazelton Female Great Britain 31 16/08/2016 3259
116 Shavonne Pia Female France 24 21/05/2015 1546
117 Shavon Benito Female France 39 15/10/2017 3579
118 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
119 Loreta Curren Female France 26 21/05/2015 9654
120 Teresa Strawn Female France 46 21/05/2015 3569
121 Belinda Partain Female United States 37 15/10/2017 2564
122 Holly Eudy Female United States 52 16/08/2016 8561
123 Many Cuccia Female Great Britain 46 21/05/2015 5489
124 Libbie Dalby Female France 42 21/05/2015 5489
125 Lester Prothro Male France 21 15/10/2017 6574
126 Marvel Hail Female Great Britain 28 16/08/2016 5555
127 Angelyn Vong Female United States 29 21/05/2015 6125
128 Francesca Beaudreau Female France 23 15/10/2017 5412
129 Garth Gangi Male United States 41 16/08/2016 3256
130 Carla Trumbull Female Great Britain 28 21/05/2015 3264
131 Veta Muntz Female Great Britain 37 15/10/2017 4569
132 Stasia Becker Female Great Britain 34 16/08/2016 7521
133 Jona Grindle Female Great Britain 26 21/05/2015 6458
134 Judie Claywell Female France 35 16/08/2016 7569
135 Dewitt Borger Male United States 36 21/05/2015 8514
136 Nena Hacker Female United States 29 15/10/2017 8563
137 Kelsie Wachtel Female France 27 16/08/2016 8642
138 Sau Pfau Female United States 25 21/05/2015 9536
139 Shanice Mccrystal Female United States 36 21/05/2015 2567
140 Chase Karner Male United States 37 15/10/2017 2154
141 Tommie Underdahl Male United States 26 16/08/2016 3265
142 Dorcas Darity Female United States 37 21/05/2015 8765
143 Angel Sanor Male France 24 15/10/2017 3259
144 Willodean Harn Female United States 39 16/08/2016 3567
145 Weston Martina Male United States 26 21/05/2015 6540
146 Roma Lafollette Female United States 34 15/10/2017 2654
147 Felisa Cail Female United States 28 16/08/2016 6525
148 Demetria Abbey Female United States 32 21/05/2015 3265
149 Jeromy Danz Male United States 39 15/10/2017 3265
150 Rasheeda Alkire Female United States 29 16/08/2016 6125
151 Dulce Abril Female United States 32 15/10/2017 1562
152 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
153 Philip Gent Male France 36 21/05/2015 2587
154 Kathleen Hanner Female United States 25 15/10/2017 3549
155 Nereida Magwood Female United States 58 16/08/2016 2468
156 Gaston Brumm Male United States 24 21/05/2015 2554
157 Etta Hurn Female Great Britain 56 15/10/2017 3598
158 Earlean Melgar Female United States 27 16/08/2016 2456
159 Vincenza Weiland Female United States 40 21/05/2015 6548
160 Fallon Winward Female Great Britain 28 16/08/2016 5486
161 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
162 Franklyn Unknow Male France 38 15/10/2017 2579
163 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
164 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
165 Kina Hazelton Female Great Britain 31 16/08/2016 3259
166 Shavonne Pia Female France 24 21/05/2015 1546
167 Shavon Benito Female France 39 15/10/2017 3579
168 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
169 Loreta Curren Female France 26 21/05/2015 9654
170 Teresa Strawn Female France 46 21/05/2015 3569
171 Belinda Partain Female United States 37 15/10/2017 2564
172 Holly Eudy Female United States 52 16/08/2016 8561
173 Many Cuccia Female Great Britain 46 21/05/2015 5489
174 Libbie Dalby Female France 42 21/05/2015 5489
175 Lester Prothro Male France 21 15/10/2017 6574
176 Marvel Hail Female Great Britain 28 16/08/2016 5555
177 Angelyn Vong Female United States 29 21/05/2015 6125
178 Francesca Beaudreau Female France 23 15/10/2017 5412
179 Garth Gangi Male United States 41 16/08/2016 3256
180 Carla Trumbull Female Great Britain 28 21/05/2015 3264
181 Veta Muntz Female Great Britain 37 15/10/2017 4569
182 Stasia Becker Female Great Britain 34 16/08/2016 7521
183 Jona Grindle Female Great Britain 26 21/05/2015 6458
184 Judie Claywell Female France 35 16/08/2016 7569
185 Dewitt Borger Male United States 36 21/05/2015 8514
186 Nena Hacker Female United States 29 15/10/2017 8563
187 Kelsie Wachtel Female France 27 16/08/2016 8642
188 Sau Pfau Female United States 25 21/05/2015 9536
189 Shanice Mccrystal Female United States 36 21/05/2015 2567
190 Chase Karner Male United States 37 15/10/2017 2154
191 Tommie Underdahl Male United States 26 16/08/2016 3265
192 Dorcas Darity Female United States 37 21/05/2015 8765
193 Angel Sanor Male France 24 15/10/2017 3259
194 Willodean Harn Female United States 39 16/08/2016 3567
195 Weston Martina Male United States 26 21/05/2015 6540
196 Roma Lafollette Female United States 34 15/10/2017 2654
197 Felisa Cail Female United States 28 16/08/2016 6525
198 Demetria Abbey Female United States 32 21/05/2015 3265
199 Jeromy Danz Male United States 39 15/10/2017 3265
200 Rasheeda Alkire Female United States 29 16/08/2016 6125
201 Dulce Abril Female United States 32 15/10/2017 1562
202 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
203 Philip Gent Male France 36 21/05/2015 2587
204 Kathleen Hanner Female United States 25 15/10/2017 3549
205 Nereida Magwood Female United States 58 16/08/2016 2468
206 Gaston Brumm Male United States 24 21/05/2015 2554
207 Etta Hurn Female Great Britain 56 15/10/2017 3598
208 Earlean Melgar Female United States 27 16/08/2016 2456
209 Vincenza Weiland Female United States 40 21/05/2015 6548
210 Fallon Winward Female Great Britain 28 16/08/2016 5486
211 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
212 Franklyn Unknow Male France 38 15/10/2017 2579
213 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
214 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
215 Kina Hazelton Female Great Britain 31 16/08/2016 3259
216 Shavonne Pia Female France 24 21/05/2015 1546
217 Shavon Benito Female France 39 15/10/2017 3579
218 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
219 Loreta Curren Female France 26 21/05/2015 9654
220 Teresa Strawn Female France 46 21/05/2015 3569
221 Belinda Partain Female United States 37 15/10/2017 2564
222 Holly Eudy Female United States 52 16/08/2016 8561
223 Many Cuccia Female Great Britain 46 21/05/2015 5489
224 Libbie Dalby Female France 42 21/05/2015 5489
225 Lester Prothro Male France 21 15/10/2017 6574
226 Marvel Hail Female Great Britain 28 16/08/2016 5555
227 Angelyn Vong Female United States 29 21/05/2015 6125
228 Francesca Beaudreau Female France 23 15/10/2017 5412
229 Garth Gangi Male United States 41 16/08/2016 3256
230 Carla Trumbull Female Great Britain 28 21/05/2015 3264
231 Veta Muntz Female Great Britain 37 15/10/2017 4569
232 Stasia Becker Female Great Britain 34 16/08/2016 7521
233 Jona Grindle Female Great Britain 26 21/05/2015 6458
234 Judie Claywell Female France 35 16/08/2016 7569
235 Dewitt Borger Male United States 36 21/05/2015 8514
236 Nena Hacker Female United States 29 15/10/2017 8563
237 Kelsie Wachtel Female France 27 16/08/2016 8642
238 Sau Pfau Female United States 25 21/05/2015 9536
239 Shanice Mccrystal Female United States 36 21/05/2015 2567
240 Chase Karner Male United States 37 15/10/2017 2154
241 Tommie Underdahl Male United States 26 16/08/2016 3265
242 Dorcas Darity Female United States 37 21/05/2015 8765
243 Angel Sanor Male France 24 15/10/2017 3259
244 Willodean Harn Female United States 39 16/08/2016 3567
245 Weston Martina Male United States 26 21/05/2015 6540
246 Roma Lafollette Female United States 34 15/10/2017 2654
247 Felisa Cail Female United States 28 16/08/2016 6525
248 Demetria Abbey Female United States 32 21/05/2015 3265
249 Jeromy Danz Male United States 39 15/10/2017 3265
250 Rasheeda Alkire Female United States 29 16/08/2016 6125
251 Dulce Abril Female United States 32 15/10/2017 1562
252 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
253 Philip Gent Male France 36 21/05/2015 2587
254 Kathleen Hanner Female United States 25 15/10/2017 3549
255 Nereida Magwood Female United States 58 16/08/2016 2468
256 Gaston Brumm Male United States 24 21/05/2015 2554
257 Etta Hurn Female Great Britain 56 15/10/2017 3598
258 Earlean Melgar Female United States 27 16/08/2016 2456
259 Vincenza Weiland Female United States 40 21/05/2015 6548
260 Fallon Winward Female Great Britain 28 16/08/2016 5486
261 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
262 Franklyn Unknow Male France 38 15/10/2017 2579
263 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
264 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
265 Kina Hazelton Female Great Britain 31 16/08/2016 3259
266 Shavonne Pia Female France 24 21/05/2015 1546
267 Shavon Benito Female France 39 15/10/2017 3579
268 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
269 Loreta Curren Female France 26 21/05/2015 9654
270 Teresa Strawn Female France 46 21/05/2015 3569
271 Belinda Partain Female United States 37 15/10/2017 2564
272 Holly Eudy Female United States 52 16/08/2016 8561
273 Many Cuccia Female Great Britain 46 21/05/2015 5489
274 Libbie Dalby Female France 42 21/05/2015 5489
275 Lester Prothro Male France 21 15/10/2017 6574
276 Marvel Hail Female Great Britain 28 16/08/2016 5555
277 Angelyn Vong Female United States 29 21/05/2015 6125
278 Francesca Beaudreau Female France 23 15/10/2017 5412
279 Garth Gangi Male United States 41 16/08/2016 3256
280 Carla Trumbull Female Great Britain 28 21/05/2015 3264
281 Veta Muntz Female Great Britain 37 15/10/2017 4569
282 Stasia Becker Female Great Britain 34 16/08/2016 7521
283 Jona Grindle Female Great Britain 26 21/05/2015 6458
284 Judie Claywell Female France 35 16/08/2016 7569
285 Dewitt Borger Male United States 36 21/05/2015 8514
286 Nena Hacker Female United States 29 15/10/2017 8563
287 Kelsie Wachtel Female France 27 16/08/2016 8642
288 Sau Pfau Female United States 25 21/05/2015 9536
289 Shanice Mccrystal Female United States 36 21/05/2015 2567
290 Chase Karner Male United States 37 15/10/2017 2154
291 Tommie Underdahl Male United States 26 16/08/2016 3265
292 Dorcas Darity Female United States 37 21/05/2015 8765
293 Angel Sanor Male France 24 15/10/2017 3259
294 Willodean Harn Female United States 39 16/08/2016 3567
295 Weston Martina Male United States 26 21/05/2015 6540
296 Roma Lafollette Female United States 34 15/10/2017 2654
297 Felisa Cail Female United States 28 16/08/2016 6525
298 Demetria Abbey Female United States 32 21/05/2015 3265
299 Jeromy Danz Male United States 39 15/10/2017 3265
300 Rasheeda Alkire Female United States 29 16/08/2016 6125
301 Dulce Abril Female United States 32 15/10/2017 1562
302 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
303 Philip Gent Male France 36 21/05/2015 2587
304 Kathleen Hanner Female United States 25 15/10/2017 3549
305 Nereida Magwood Female United States 58 16/08/2016 2468
306 Gaston Brumm Male United States 24 21/05/2015 2554
307 Etta Hurn Female Great Britain 56 15/10/2017 3598
308 Earlean Melgar Female United States 27 16/08/2016 2456
309 Vincenza Weiland Female United States 40 21/05/2015 6548
310 Fallon Winward Female Great Britain 28 16/08/2016 5486
311 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
312 Franklyn Unknow Male France 38 15/10/2017 2579
313 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
314 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
315 Kina Hazelton Female Great Britain 31 16/08/2016 3259
316 Shavonne Pia Female France 24 21/05/2015 1546
317 Shavon Benito Female France 39 15/10/2017 3579
318 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
319 Loreta Curren Female France 26 21/05/2015 9654
320 Teresa Strawn Female France 46 21/05/2015 3569
321 Belinda Partain Female United States 37 15/10/2017 2564
322 Holly Eudy Female United States 52 16/08/2016 8561
323 Many Cuccia Female Great Britain 46 21/05/2015 5489
324 Libbie Dalby Female France 42 21/05/2015 5489
325 Lester Prothro Male France 21 15/10/2017 6574
326 Marvel Hail Female Great Britain 28 16/08/2016 5555
327 Angelyn Vong Female United States 29 21/05/2015 6125
328 Francesca Beaudreau Female France 23 15/10/2017 5412
329 Garth Gangi Male United States 41 16/08/2016 3256
330 Carla Trumbull Female Great Britain 28 21/05/2015 3264
331 Veta Muntz Female Great Britain 37 15/10/2017 4569
332 Stasia Becker Female Great Britain 34 16/08/2016 7521
333 Jona Grindle Female Great Britain 26 21/05/2015 6458
334 Judie Claywell Female France 35 16/08/2016 7569
335 Dewitt Borger Male United States 36 21/05/2015 8514
336 Nena Hacker Female United States 29 15/10/2017 8563
337 Kelsie Wachtel Female France 27 16/08/2016 8642
338 Sau Pfau Female United States 25 21/05/2015 9536
339 Shanice Mccrystal Female United States 36 21/05/2015 2567
340 Chase Karner Male United States 37 15/10/2017 2154
341 Tommie Underdahl Male United States 26 16/08/2016 3265
342 Dorcas Darity Female United States 37 21/05/2015 8765
343 Angel Sanor Male France 24 15/10/2017 3259
344 Willodean Harn Female United States 39 16/08/2016 3567
345 Weston Martina Male United States 26 21/05/2015 6540
346 Roma Lafollette Female United States 34 15/10/2017 2654
347 Felisa Cail Female United States 28 16/08/2016 6525
348 Demetria Abbey Female United States 32 21/05/2015 3265
349 Jeromy Danz Male United States 39 15/10/2017 3265
350 Rasheeda Alkire Female United States 29 16/08/2016 6125
351 Dulce Abril Female United States 32 15/10/2017 1562
352 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
353 Philip Gent Male France 36 21/05/2015 2587
354 Kathleen Hanner Female United States 25 15/10/2017 3549
355 Nereida Magwood Female United States 58 16/08/2016 2468
356 Gaston Brumm Male United States 24 21/05/2015 2554
357 Etta Hurn Female Great Britain 56 15/10/2017 3598
358 Earlean Melgar Female United States 27 16/08/2016 2456
359 Vincenza Weiland Female United States 40 21/05/2015 6548
360 Fallon Winward Female Great Britain 28 16/08/2016 5486
361 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
362 Franklyn Unknow Male France 38 15/10/2017 2579
363 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
364 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
365 Kina Hazelton Female Great Britain 31 16/08/2016 3259
366 Shavonne Pia Female France 24 21/05/2015 1546
367 Shavon Benito Female France 39 15/10/2017 3579
368 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
369 Loreta Curren Female France 26 21/05/2015 9654
370 Teresa Strawn Female France 46 21/05/2015 3569
371 Belinda Partain Female United States 37 15/10/2017 2564
372 Holly Eudy Female United States 52 16/08/2016 8561
373 Many Cuccia Female Great Britain 46 21/05/2015 5489
374 Libbie Dalby Female France 42 21/05/2015 5489
375 Lester Prothro Male France 21 15/10/2017 6574
376 Marvel Hail Female Great Britain 28 16/08/2016 5555
377 Angelyn Vong Female United States 29 21/05/2015 6125
378 Francesca Beaudreau Female France 23 15/10/2017 5412
379 Garth Gangi Male United States 41 16/08/2016 3256
380 Carla Trumbull Female Great Britain 28 21/05/2015 3264
381 Veta Muntz Female Great Britain 37 15/10/2017 4569
382 Stasia Becker Female Great Britain 34 16/08/2016 7521
383 Jona Grindle Female Great Britain 26 21/05/2015 6458
384 Judie Claywell Female France 35 16/08/2016 7569
385 Dewitt Borger Male United States 36 21/05/2015 8514
386 Nena Hacker Female United States 29 15/10/2017 8563
387 Kelsie Wachtel Female France 27 16/08/2016 8642
388 Sau Pfau Female United States 25 21/05/2015 9536
389 Shanice Mccrystal Female United States 36 21/05/2015 2567
390 Chase Karner Male United States 37 15/10/2017 2154
391 Tommie Underdahl Male United States 26 16/08/2016 3265
392 Dorcas Darity Female United States 37 21/05/2015 8765
393 Angel Sanor Male France 24 15/10/2017 3259
394 Willodean Harn Female United States 39 16/08/2016 3567
395 Weston Martina Male United States 26 21/05/2015 6540
396 Roma Lafollette Female United States 34 15/10/2017 2654
397 Felisa Cail Female United States 28 16/08/2016 6525
398 Demetria Abbey Female United States 32 21/05/2015 3265
399 Jeromy Danz Male United States 39 15/10/2017 3265
400 Rasheeda Alkire Female United States 29 16/08/2016 6125
401 Dulce Abril Female United States 32 15/10/2017 1562
402 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
403 Philip Gent Male France 36 21/05/2015 2587
404 Kathleen Hanner Female United States 25 15/10/2017 3549
405 Nereida Magwood Female United States 58 16/08/2016 2468
406 Gaston Brumm Male United States 24 21/05/2015 2554
407 Etta Hurn Female Great Britain 56 15/10/2017 3598
408 Earlean Melgar Female United States 27 16/08/2016 2456
409 Vincenza Weiland Female United States 40 21/05/2015 6548
410 Fallon Winward Female Great Britain 28 16/08/2016 5486
411 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
412 Franklyn Unknow Male France 38 15/10/2017 2579
413 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
414 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
415 Kina Hazelton Female Great Britain 31 16/08/2016 3259
416 Shavonne Pia Female France 24 21/05/2015 1546
417 Shavon Benito Female France 39 15/10/2017 3579
418 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
419 Loreta Curren Female France 26 21/05/2015 9654
420 Teresa Strawn Female France 46 21/05/2015 3569
421 Belinda Partain Female United States 37 15/10/2017 2564
422 Holly Eudy Female United States 52 16/08/2016 8561
423 Many Cuccia Female Great Britain 46 21/05/2015 5489
424 Libbie Dalby Female France 42 21/05/2015 5489
425 Lester Prothro Male France 21 15/10/2017 6574
426 Marvel Hail Female Great Britain 28 16/08/2016 5555
427 Angelyn Vong Female United States 29 21/05/2015 6125
428 Francesca Beaudreau Female France 23 15/10/2017 5412
429 Garth Gangi Male United States 41 16/08/2016 3256
430 Carla Trumbull Female Great Britain 28 21/05/2015 3264
431 Veta Muntz Female Great Britain 37 15/10/2017 4569
432 Stasia Becker Female Great Britain 34 16/08/2016 7521
433 Jona Grindle Female Great Britain 26 21/05/2015 6458
434 Judie Claywell Female France 35 16/08/2016 7569
435 Dewitt Borger Male United States 36 21/05/2015 8514
436 Nena Hacker Female United States 29 15/10/2017 8563
437 Kelsie Wachtel Female France 27 16/08/2016 8642
438 Sau Pfau Female United States 25 21/05/2015 9536
439 Shanice Mccrystal Female United States 36 21/05/2015 2567
440 Chase Karner Male United States 37 15/10/2017 2154
441 Tommie Underdahl Male United States 26 16/08/2016 3265
442 Dorcas Darity Female United States 37 21/05/2015 8765
443 Angel Sanor Male France 24 15/10/2017 3259
444 Willodean Harn Female United States 39 16/08/2016 3567
445 Weston Martina Male United States 26 21/05/2015 6540
446 Roma Lafollette Female United States 34 15/10/2017 2654
447 Felisa Cail Female United States 28 16/08/2016 6525
448 Demetria Abbey Female United States 32 21/05/2015 3265
449 Jeromy Danz Male United States 39 15/10/2017 3265
450 Rasheeda Alkire Female United States 29 16/08/2016 6125
451 Dulce Abril Female United States 32 15/10/2017 1562
452 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
453 Philip Gent Male France 36 21/05/2015 2587
454 Kathleen Hanner Female United States 25 15/10/2017 3549
455 Nereida Magwood Female United States 58 16/08/2016 2468
456 Gaston Brumm Male United States 24 21/05/2015 2554
457 Etta Hurn Female Great Britain 56 15/10/2017 3598
458 Earlean Melgar Female United States 27 16/08/2016 2456
459 Vincenza Weiland Female United States 40 21/05/2015 6548
460 Fallon Winward Female Great Britain 28 16/08/2016 5486
461 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
462 Franklyn Unknow Male France 38 15/10/2017 2579
463 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
464 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
465 Kina Hazelton Female Great Britain 31 16/08/2016 3259
466 Shavonne Pia Female France 24 21/05/2015 1546
467 Shavon Benito Female France 39 15/10/2017 3579
468 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
469 Loreta Curren Female France 26 21/05/2015 9654
470 Teresa Strawn Female France 46 21/05/2015 3569
471 Belinda Partain Female United States 37 15/10/2017 2564
472 Holly Eudy Female United States 52 16/08/2016 8561
473 Many Cuccia Female Great Britain 46 21/05/2015 5489
474 Libbie Dalby Female France 42 21/05/2015 5489
475 Lester Prothro Male France 21 15/10/2017 6574
476 Marvel Hail Female Great Britain 28 16/08/2016 5555
477 Angelyn Vong Female United States 29 21/05/2015 6125
478 Francesca Beaudreau Female France 23 15/10/2017 5412
479 Garth Gangi Male United States 41 16/08/2016 3256
480 Carla Trumbull Female Great Britain 28 21/05/2015 3264
481 Veta Muntz Female Great Britain 37 15/10/2017 4569
482 Stasia Becker Female Great Britain 34 16/08/2016 7521
483 Jona Grindle Female Great Britain 26 21/05/2015 6458
484 Judie Claywell Female France 35 16/08/2016 7569
485 Dewitt Borger Male United States 36 21/05/2015 8514
486 Nena Hacker Female United States 29 15/10/2017 8563
487 Kelsie Wachtel Female France 27 16/08/2016 8642
488 Sau Pfau Female United States 25 21/05/2015 9536
489 Shanice Mccrystal Female United States 36 21/05/2015 2567
490 Chase Karner Male United States 37 15/10/2017 2154
491 Tommie Underdahl Male United States 26 16/08/2016 3265
492 Dorcas Darity Female United States 37 21/05/2015 8765
493 Angel Sanor Male France 24 15/10/2017 3259
494 Willodean Harn Female United States 39 16/08/2016 3567
495 Weston Martina Male United States 26 21/05/2015 6540
496 Roma Lafollette Female United States 34 15/10/2017 2654
497 Felisa Cail Female United States 28 16/08/2016 6525
498 Demetria Abbey Female United States 32 21/05/2015 3265
499 Jeromy Danz Male United States 39 15/10/2017 3265
500 Rasheeda Alkire Female United States 29 16/08/2016 6125
501 Dulce Abril Female United States 32 15/10/2017 1562
502 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
503 Philip Gent Male France 36 21/05/2015 2587
504 Kathleen Hanner Female United States 25 15/10/2017 3549
505 Nereida Magwood Female United States 58 16/08/2016 2468
506 Gaston Brumm Male United States 24 21/05/2015 2554
507 Etta Hurn Female Great Britain 56 15/10/2017 3598
508 Earlean Melgar Female United States 27 16/08/2016 2456
509 Vincenza Weiland Female United States 40 21/05/2015 6548
510 Fallon Winward Female Great Britain 28 16/08/2016 5486
511 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
512 Franklyn Unknow Male France 38 15/10/2017 2579
513 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
514 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
515 Kina Hazelton Female Great Britain 31 16/08/2016 3259
516 Shavonne Pia Female France 24 21/05/2015 1546
517 Shavon Benito Female France 39 15/10/2017 3579
518 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
519 Loreta Curren Female France 26 21/05/2015 9654
520 Teresa Strawn Female France 46 21/05/2015 3569
521 Belinda Partain Female United States 37 15/10/2017 2564
522 Holly Eudy Female United States 52 16/08/2016 8561
523 Many Cuccia Female Great Britain 46 21/05/2015 5489
524 Libbie Dalby Female France 42 21/05/2015 5489
525 Lester Prothro Male France 21 15/10/2017 6574
526 Marvel Hail Female Great Britain 28 16/08/2016 5555
527 Angelyn Vong Female United States 29 21/05/2015 6125
528 Francesca Beaudreau Female France 23 15/10/2017 5412
529 Garth Gangi Male United States 41 16/08/2016 3256
530 Carla Trumbull Female Great Britain 28 21/05/2015 3264
531 Veta Muntz Female Great Britain 37 15/10/2017 4569
532 Stasia Becker Female Great Britain 34 16/08/2016 7521
533 Jona Grindle Female Great Britain 26 21/05/2015 6458
534 Judie Claywell Female France 35 16/08/2016 7569
535 Dewitt Borger Male United States 36 21/05/2015 8514
536 Nena Hacker Female United States 29 15/10/2017 8563
537 Kelsie Wachtel Female France 27 16/08/2016 8642
538 Sau Pfau Female United States 25 21/05/2015 9536
539 Shanice Mccrystal Female United States 36 21/05/2015 2567
540 Chase Karner Male United States 37 15/10/2017 2154
541 Tommie Underdahl Male United States 26 16/08/2016 3265
542 Dorcas Darity Female United States 37 21/05/2015 8765
543 Angel Sanor Male France 24 15/10/2017 3259
544 Willodean Harn Female United States 39 16/08/2016 3567
545 Weston Martina Male United States 26 21/05/2015 6540
546 Roma Lafollette Female United States 34 15/10/2017 2654
547 Felisa Cail Female United States 28 16/08/2016 6525
548 Demetria Abbey Female United States 32 21/05/2015 3265
549 Jeromy Danz Male United States 39 15/10/2017 3265
550 Rasheeda Alkire Female United States 29 16/08/2016 6125
551 Dulce Abril Female United States 32 15/10/2017 1562
552 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
553 Philip Gent Male France 36 21/05/2015 2587
554 Kathleen Hanner Female United States 25 15/10/2017 3549
555 Nereida Magwood Female United States 58 16/08/2016 2468
556 Gaston Brumm Male United States 24 21/05/2015 2554
557 Etta Hurn Female Great Britain 56 15/10/2017 3598
558 Earlean Melgar Female United States 27 16/08/2016 2456
559 Vincenza Weiland Female United States 40 21/05/2015 6548
560 Fallon Winward Female Great Britain 28 16/08/2016 5486
561 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
562 Franklyn Unknow Male France 38 15/10/2017 2579
563 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
564 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
565 Kina Hazelton Female Great Britain 31 16/08/2016 3259
566 Shavonne Pia Female France 24 21/05/2015 1546
567 Shavon Benito Female France 39 15/10/2017 3579
568 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
569 Loreta Curren Female France 26 21/05/2015 9654
570 Teresa Strawn Female France 46 21/05/2015 3569
571 Belinda Partain Female United States 37 15/10/2017 2564
572 Holly Eudy Female United States 52 16/08/2016 8561
573 Many Cuccia Female Great Britain 46 21/05/2015 5489
574 Libbie Dalby Female France 42 21/05/2015 5489
575 Lester Prothro Male France 21 15/10/2017 6574
576 Marvel Hail Female Great Britain 28 16/08/2016 5555
577 Angelyn Vong Female United States 29 21/05/2015 6125
578 Francesca Beaudreau Female France 23 15/10/2017 5412
579 Garth Gangi Male United States 41 16/08/2016 3256
580 Carla Trumbull Female Great Britain 28 21/05/2015 3264
581 Veta Muntz Female Great Britain 37 15/10/2017 4569
582 Stasia Becker Female Great Britain 34 16/08/2016 7521
583 Jona Grindle Female Great Britain 26 21/05/2015 6458
584 Judie Claywell Female France 35 16/08/2016 7569
585 Dewitt Borger Male United States 36 21/05/2015 8514
586 Nena Hacker Female United States 29 15/10/2017 8563
587 Kelsie Wachtel Female France 27 16/08/2016 8642
588 Sau Pfau Female United States 25 21/05/2015 9536
589 Shanice Mccrystal Female United States 36 21/05/2015 2567
590 Chase Karner Male United States 37 15/10/2017 2154
591 Tommie Underdahl Male United States 26 16/08/2016 3265
592 Dorcas Darity Female United States 37 21/05/2015 8765
593 Angel Sanor Male France 24 15/10/2017 3259
594 Willodean Harn Female United States 39 16/08/2016 3567
595 Weston Martina Male United States 26 21/05/2015 6540
596 Roma Lafollette Female United States 34 15/10/2017 2654
597 Felisa Cail Female United States 28 16/08/2016 6525
598 Demetria Abbey Female United States 32 21/05/2015 3265
599 Jeromy Danz Male United States 39 15/10/2017 3265
600 Rasheeda Alkire Female United States 29 16/08/2016 6125
601 Dulce Abril Female United States 32 15/10/2017 1562
602 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
603 Philip Gent Male France 36 21/05/2015 2587
604 Kathleen Hanner Female United States 25 15/10/2017 3549
605 Nereida Magwood Female United States 58 16/08/2016 2468
606 Gaston Brumm Male United States 24 21/05/2015 2554
607 Etta Hurn Female Great Britain 56 15/10/2017 3598
608 Earlean Melgar Female United States 27 16/08/2016 2456
609 Vincenza Weiland Female United States 40 21/05/2015 6548
610 Fallon Winward Female Great Britain 28 16/08/2016 5486
611 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
612 Franklyn Unknow Male France 38 15/10/2017 2579
613 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
614 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
615 Kina Hazelton Female Great Britain 31 16/08/2016 3259
616 Shavonne Pia Female France 24 21/05/2015 1546
617 Shavon Benito Female France 39 15/10/2017 3579
618 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
619 Loreta Curren Female France 26 21/05/2015 9654
620 Teresa Strawn Female France 46 21/05/2015 3569
621 Belinda Partain Female United States 37 15/10/2017 2564
622 Holly Eudy Female United States 52 16/08/2016 8561
623 Many Cuccia Female Great Britain 46 21/05/2015 5489
624 Libbie Dalby Female France 42 21/05/2015 5489
625 Lester Prothro Male France 21 15/10/2017 6574
626 Marvel Hail Female Great Britain 28 16/08/2016 5555
627 Angelyn Vong Female United States 29 21/05/2015 6125
628 Francesca Beaudreau Female France 23 15/10/2017 5412
629 Garth Gangi Male United States 41 16/08/2016 3256
630 Carla Trumbull Female Great Britain 28 21/05/2015 3264
631 Veta Muntz Female Great Britain 37 15/10/2017 4569
632 Stasia Becker Female Great Britain 34 16/08/2016 7521
633 Jona Grindle Female Great Britain 26 21/05/2015 6458
634 Judie Claywell Female France 35 16/08/2016 7569
635 Dewitt Borger Male United States 36 21/05/2015 8514
636 Nena Hacker Female United States 29 15/10/2017 8563
637 Kelsie Wachtel Female France 27 16/08/2016 8642
638 Sau Pfau Female United States 25 21/05/2015 9536
639 Shanice Mccrystal Female United States 36 21/05/2015 2567
640 Chase Karner Male United States 37 15/10/2017 2154
641 Tommie Underdahl Male United States 26 16/08/2016 3265
642 Dorcas Darity Female United States 37 21/05/2015 8765
643 Angel Sanor Male France 24 15/10/2017 3259
644 Willodean Harn Female United States 39 16/08/2016 3567
645 Weston Martina Male United States 26 21/05/2015 6540
646 Roma Lafollette Female United States 34 15/10/2017 2654
647 Felisa Cail Female United States 28 16/08/2016 6525
648 Demetria Abbey Female United States 32 21/05/2015 3265
649 Jeromy Danz Male United States 39 15/10/2017 3265
650 Rasheeda Alkire Female United States 29 16/08/2016 6125
651 Dulce Abril Female United States 32 15/10/2017 1562
652 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
653 Philip Gent Male France 36 21/05/2015 2587
654 Kathleen Hanner Female United States 25 15/10/2017 3549
655 Nereida Magwood Female United States 58 16/08/2016 2468
656 Gaston Brumm Male United States 24 21/05/2015 2554
657 Etta Hurn Female Great Britain 56 15/10/2017 3598
658 Earlean Melgar Female United States 27 16/08/2016 2456
659 Vincenza Weiland Female United States 40 21/05/2015 6548
660 Fallon Winward Female Great Britain 28 16/08/2016 5486
661 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
662 Franklyn Unknow Male France 38 15/10/2017 2579
663 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
664 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
665 Kina Hazelton Female Great Britain 31 16/08/2016 3259
666 Shavonne Pia Female France 24 21/05/2015 1546
667 Shavon Benito Female France 39 15/10/2017 3579
668 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
669 Loreta Curren Female France 26 21/05/2015 9654
670 Teresa Strawn Female France 46 21/05/2015 3569
671 Belinda Partain Female United States 37 15/10/2017 2564
672 Holly Eudy Female United States 52 16/08/2016 8561
673 Many Cuccia Female Great Britain 46 21/05/2015 5489
674 Libbie Dalby Female France 42 21/05/2015 5489
675 Lester Prothro Male France 21 15/10/2017 6574
676 Marvel Hail Female Great Britain 28 16/08/2016 5555
677 Angelyn Vong Female United States 29 21/05/2015 6125
678 Francesca Beaudreau Female France 23 15/10/2017 5412
679 Garth Gangi Male United States 41 16/08/2016 3256
680 Carla Trumbull Female Great Britain 28 21/05/2015 3264
681 Veta Muntz Female Great Britain 37 15/10/2017 4569
682 Stasia Becker Female Great Britain 34 16/08/2016 7521
683 Jona Grindle Female Great Britain 26 21/05/2015 6458
684 Judie Claywell Female France 35 16/08/2016 7569
685 Dewitt Borger Male United States 36 21/05/2015 8514
686 Nena Hacker Female United States 29 15/10/2017 8563
687 Kelsie Wachtel Female France 27 16/08/2016 8642
688 Sau Pfau Female United States 25 21/05/2015 9536
689 Shanice Mccrystal Female United States 36 21/05/2015 2567
690 Chase Karner Male United States 37 15/10/2017 2154
691 Tommie Underdahl Male United States 26 16/08/2016 3265
692 Dorcas Darity Female United States 37 21/05/2015 8765
693 Angel Sanor Male France 24 15/10/2017 3259
694 Willodean Harn Female United States 39 16/08/2016 3567
695 Weston Martina Male United States 26 21/05/2015 6540
696 Roma Lafollette Female United States 34 15/10/2017 2654
697 Felisa Cail Female United States 28 16/08/2016 6525
698 Demetria Abbey Female United States 32 21/05/2015 3265
699 Jeromy Danz Male United States 39 15/10/2017 3265
700 Rasheeda Alkire Female United States 29 16/08/2016 6125
701 Dulce Abril Female United States 32 15/10/2017 1562
702 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
703 Philip Gent Male France 36 21/05/2015 2587
704 Kathleen Hanner Female United States 25 15/10/2017 3549
705 Nereida Magwood Female United States 58 16/08/2016 2468
706 Gaston Brumm Male United States 24 21/05/2015 2554
707 Etta Hurn Female Great Britain 56 15/10/2017 3598
708 Earlean Melgar Female United States 27 16/08/2016 2456
709 Vincenza Weiland Female United States 40 21/05/2015 6548
710 Fallon Winward Female Great Britain 28 16/08/2016 5486
711 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
712 Franklyn Unknow Male France 38 15/10/2017 2579
713 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
714 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
715 Kina Hazelton Female Great Britain 31 16/08/2016 3259
716 Shavonne Pia Female France 24 21/05/2015 1546
717 Shavon Benito Female France 39 15/10/2017 3579
718 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
719 Loreta Curren Female France 26 21/05/2015 9654
720 Teresa Strawn Female France 46 21/05/2015 3569
721 Belinda Partain Female United States 37 15/10/2017 2564
722 Holly Eudy Female United States 52 16/08/2016 8561
723 Many Cuccia Female Great Britain 46 21/05/2015 5489
724 Libbie Dalby Female France 42 21/05/2015 5489
725 Lester Prothro Male France 21 15/10/2017 6574
726 Marvel Hail Female Great Britain 28 16/08/2016 5555
727 Angelyn Vong Female United States 29 21/05/2015 6125
728 Francesca Beaudreau Female France 23 15/10/2017 5412
729 Garth Gangi Male United States 41 16/08/2016 3256
730 Carla Trumbull Female Great Britain 28 21/05/2015 3264
731 Veta Muntz Female Great Britain 37 15/10/2017 4569
732 Stasia Becker Female Great Britain 34 16/08/2016 7521
733 Jona Grindle Female Great Britain 26 21/05/2015 6458
734 Judie Claywell Female France 35 16/08/2016 7569
735 Dewitt Borger Male United States 36 21/05/2015 8514
736 Nena Hacker Female United States 29 15/10/2017 8563
737 Kelsie Wachtel Female France 27 16/08/2016 8642
738 Sau Pfau Female United States 25 21/05/2015 9536
739 Shanice Mccrystal Female United States 36 21/05/2015 2567
740 Chase Karner Male United States 37 15/10/2017 2154
741 Tommie Underdahl Male United States 26 16/08/2016 3265
742 Dorcas Darity Female United States 37 21/05/2015 8765
743 Angel Sanor Male France 24 15/10/2017 3259
744 Willodean Harn Female United States 39 16/08/2016 3567
745 Weston Martina Male United States 26 21/05/2015 6540
746 Roma Lafollette Female United States 34 15/10/2017 2654
747 Felisa Cail Female United States 28 16/08/2016 6525
748 Demetria Abbey Female United States 32 21/05/2015 3265
749 Jeromy Danz Male United States 39 15/10/2017 3265
750 Rasheeda Alkire Female United States 29 16/08/2016 6125
751 Dulce Abril Female United States 32 15/10/2017 1562
752 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
753 Philip Gent Male France 36 21/05/2015 2587
754 Kathleen Hanner Female United States 25 15/10/2017 3549
755 Nereida Magwood Female United States 58 16/08/2016 2468
756 Gaston Brumm Male United States 24 21/05/2015 2554
757 Etta Hurn Female Great Britain 56 15/10/2017 3598
758 Earlean Melgar Female United States 27 16/08/2016 2456
759 Vincenza Weiland Female United States 40 21/05/2015 6548
760 Fallon Winward Female Great Britain 28 16/08/2016 5486
761 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
762 Franklyn Unknow Male France 38 15/10/2017 2579
763 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
764 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
765 Kina Hazelton Female Great Britain 31 16/08/2016 3259
766 Shavonne Pia Female France 24 21/05/2015 1546
767 Shavon Benito Female France 39 15/10/2017 3579
768 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
769 Loreta Curren Female France 26 21/05/2015 9654
770 Teresa Strawn Female France 46 21/05/2015 3569
771 Belinda Partain Female United States 37 15/10/2017 2564
772 Holly Eudy Female United States 52 16/08/2016 8561
773 Many Cuccia Female Great Britain 46 21/05/2015 5489
774 Libbie Dalby Female France 42 21/05/2015 5489
775 Lester Prothro Male France 21 15/10/2017 6574
776 Marvel Hail Female Great Britain 28 16/08/2016 5555
777 Angelyn Vong Female United States 29 21/05/2015 6125
778 Francesca Beaudreau Female France 23 15/10/2017 5412
779 Garth Gangi Male United States 41 16/08/2016 3256
780 Carla Trumbull Female Great Britain 28 21/05/2015 3264
781 Veta Muntz Female Great Britain 37 15/10/2017 4569
782 Stasia Becker Female Great Britain 34 16/08/2016 7521
783 Jona Grindle Female Great Britain 26 21/05/2015 6458
784 Judie Claywell Female France 35 16/08/2016 7569
785 Dewitt Borger Male United States 36 21/05/2015 8514
786 Nena Hacker Female United States 29 15/10/2017 8563
787 Kelsie Wachtel Female France 27 16/08/2016 8642
788 Sau Pfau Female United States 25 21/05/2015 9536
789 Shanice Mccrystal Female United States 36 21/05/2015 2567
790 Chase Karner Male United States 37 15/10/2017 2154
791 Tommie Underdahl Male United States 26 16/08/2016 3265
792 Dorcas Darity Female United States 37 21/05/2015 8765
793 Angel Sanor Male France 24 15/10/2017 3259
794 Willodean Harn Female United States 39 16/08/2016 3567
795 Weston Martina Male United States 26 21/05/2015 6540
796 Roma Lafollette Female United States 34 15/10/2017 2654
797 Felisa Cail Female United States 28 16/08/2016 6525
798 Demetria Abbey Female United States 32 21/05/2015 3265
799 Jeromy Danz Male United States 39 15/10/2017 3265
800 Rasheeda Alkire Female United States 29 16/08/2016 6125
801 Dulce Abril Female United States 32 15/10/2017 1562
802 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
803 Philip Gent Male France 36 21/05/2015 2587
804 Kathleen Hanner Female United States 25 15/10/2017 3549
805 Nereida Magwood Female United States 58 16/08/2016 2468
806 Gaston Brumm Male United States 24 21/05/2015 2554
807 Etta Hurn Female Great Britain 56 15/10/2017 3598
808 Earlean Melgar Female United States 27 16/08/2016 2456
809 Vincenza Weiland Female United States 40 21/05/2015 6548
810 Fallon Winward Female Great Britain 28 16/08/2016 5486
811 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
812 Franklyn Unknow Male France 38 15/10/2017 2579
813 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
814 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
815 Kina Hazelton Female Great Britain 31 16/08/2016 3259
816 Shavonne Pia Female France 24 21/05/2015 1546
817 Shavon Benito Female France 39 15/10/2017 3579
818 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
819 Loreta Curren Female France 26 21/05/2015 9654
820 Teresa Strawn Female France 46 21/05/2015 3569
821 Belinda Partain Female United States 37 15/10/2017 2564
822 Holly Eudy Female United States 52 16/08/2016 8561
823 Many Cuccia Female Great Britain 46 21/05/2015 5489
824 Libbie Dalby Female France 42 21/05/2015 5489
825 Lester Prothro Male France 21 15/10/2017 6574
826 Marvel Hail Female Great Britain 28 16/08/2016 5555
827 Angelyn Vong Female United States 29 21/05/2015 6125
828 Francesca Beaudreau Female France 23 15/10/2017 5412
829 Garth Gangi Male United States 41 16/08/2016 3256
830 Carla Trumbull Female Great Britain 28 21/05/2015 3264
831 Veta Muntz Female Great Britain 37 15/10/2017 4569
832 Stasia Becker Female Great Britain 34 16/08/2016 7521
833 Jona Grindle Female Great Britain 26 21/05/2015 6458
834 Judie Claywell Female France 35 16/08/2016 7569
835 Dewitt Borger Male United States 36 21/05/2015 8514
836 Nena Hacker Female United States 29 15/10/2017 8563
837 Kelsie Wachtel Female France 27 16/08/2016 8642
838 Sau Pfau Female United States 25 21/05/2015 9536
839 Shanice Mccrystal Female United States 36 21/05/2015 2567
840 Chase Karner Male United States 37 15/10/2017 2154
841 Tommie Underdahl Male United States 26 16/08/2016 3265
842 Dorcas Darity Female United States 37 21/05/2015 8765
843 Angel Sanor Male France 24 15/10/2017 3259
844 Willodean Harn Female United States 39 16/08/2016 3567
845 Weston Martina Male United States 26 21/05/2015 6540
846 Roma Lafollette Female United States 34 15/10/2017 2654
847 Felisa Cail Female United States 28 16/08/2016 6525
848 Demetria Abbey Female United States 32 21/05/2015 3265
849 Jeromy Danz Male United States 39 15/10/2017 3265
850 Rasheeda Alkire Female United States 29 16/08/2016 6125
851 Dulce Abril Female United States 32 15/10/2017 1562
852 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
853 Philip Gent Male France 36 21/05/2015 2587
854 Kathleen Hanner Female United States 25 15/10/2017 3549
855 Nereida Magwood Female United States 58 16/08/2016 2468
856 Gaston Brumm Male United States 24 21/05/2015 2554
857 Etta Hurn Female Great Britain 56 15/10/2017 3598
858 Earlean Melgar Female United States 27 16/08/2016 2456
859 Vincenza Weiland Female United States 40 21/05/2015 6548
860 Fallon Winward Female Great Britain 28 16/08/2016 5486
861 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
862 Franklyn Unknow Male France 38 15/10/2017 2579
863 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
864 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
865 Kina Hazelton Female Great Britain 31 16/08/2016 3259
866 Shavonne Pia Female France 24 21/05/2015 1546
867 Shavon Benito Female France 39 15/10/2017 3579
868 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
869 Loreta Curren Female France 26 21/05/2015 9654
870 Teresa Strawn Female France 46 21/05/2015 3569
871 Belinda Partain Female United States 37 15/10/2017 2564
872 Holly Eudy Female United States 52 16/08/2016 8561
873 Many Cuccia Female Great Britain 46 21/05/2015 5489
874 Libbie Dalby Female France 42 21/05/2015 5489
875 Lester Prothro Male France 21 15/10/2017 6574
876 Marvel Hail Female Great Britain 28 16/08/2016 5555
877 Angelyn Vong Female United States 29 21/05/2015 6125
878 Francesca Beaudreau Female France 23 15/10/2017 5412
879 Garth Gangi Male United States 41 16/08/2016 3256
880 Carla Trumbull Female Great Britain 28 21/05/2015 3264
881 Veta Muntz Female Great Britain 37 15/10/2017 4569
882 Stasia Becker Female Great Britain 34 16/08/2016 7521
883 Jona Grindle Female Great Britain 26 21/05/2015 6458
884 Judie Claywell Female France 35 16/08/2016 7569
885 Dewitt Borger Male United States 36 21/05/2015 8514
886 Nena Hacker Female United States 29 15/10/2017 8563
887 Kelsie Wachtel Female France 27 16/08/2016 8642
888 Sau Pfau Female United States 25 21/05/2015 9536
889 Shanice Mccrystal Female United States 36 21/05/2015 2567
890 Chase Karner Male United States 37 15/10/2017 2154
891 Tommie Underdahl Male United States 26 16/08/2016 3265
892 Dorcas Darity Female United States 37 21/05/2015 8765
893 Angel Sanor Male France 24 15/10/2017 3259
894 Willodean Harn Female United States 39 16/08/2016 3567
895 Weston Martina Male United States 26 21/05/2015 6540
896 Roma Lafollette Female United States 34 15/10/2017 2654
897 Felisa Cail Female United States 28 16/08/2016 6525
898 Demetria Abbey Female United States 32 21/05/2015 3265
899 Jeromy Danz Male United States 39 15/10/2017 3265
900 Rasheeda Alkire Female United States 29 16/08/2016 6125
901 Dulce Abril Female United States 32 15/10/2017 1562
902 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
903 Philip Gent Male France 36 21/05/2015 2587
904 Kathleen Hanner Female United States 25 15/10/2017 3549
905 Nereida Magwood Female United States 58 16/08/2016 2468
906 Gaston Brumm Male United States 24 21/05/2015 2554
907 Etta Hurn Female Great Britain 56 15/10/2017 3598
908 Earlean Melgar Female United States 27 16/08/2016 2456
909 Vincenza Weiland Female United States 40 21/05/2015 6548
910 Fallon Winward Female Great Britain 28 16/08/2016 5486
911 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
912 Franklyn Unknow Male France 38 15/10/2017 2579
913 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
914 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
915 Kina Hazelton Female Great Britain 31 16/08/2016 3259
916 Shavonne Pia Female France 24 21/05/2015 1546
917 Shavon Benito Female France 39 15/10/2017 3579
918 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
919 Loreta Curren Female France 26 21/05/2015 9654
920 Teresa Strawn Female France 46 21/05/2015 3569
921 Belinda Partain Female United States 37 15/10/2017 2564
922 Holly Eudy Female United States 52 16/08/2016 8561
923 Many Cuccia Female Great Britain 46 21/05/2015 5489
924 Libbie Dalby Female France 42 21/05/2015 5489
925 Lester Prothro Male France 21 15/10/2017 6574
926 Marvel Hail Female Great Britain 28 16/08/2016 5555
927 Angelyn Vong Female United States 29 21/05/2015 6125
928 Francesca Beaudreau Female France 23 15/10/2017 5412
929 Garth Gangi Male United States 41 16/08/2016 3256
930 Carla Trumbull Female Great Britain 28 21/05/2015 3264
931 Veta Muntz Female Great Britain 37 15/10/2017 4569
932 Stasia Becker Female Great Britain 34 16/08/2016 7521
933 Jona Grindle Female Great Britain 26 21/05/2015 6458
934 Judie Claywell Female France 35 16/08/2016 7569
935 Dewitt Borger Male United States 36 21/05/2015 8514
936 Nena Hacker Female United States 29 15/10/2017 8563
937 Kelsie Wachtel Female France 27 16/08/2016 8642
938 Sau Pfau Female United States 25 21/05/2015 9536
939 Shanice Mccrystal Female United States 36 21/05/2015 2567
940 Chase Karner Male United States 37 15/10/2017 2154
941 Tommie Underdahl Male United States 26 16/08/2016 3265
942 Dorcas Darity Female United States 37 21/05/2015 8765
943 Angel Sanor Male France 24 15/10/2017 3259
944 Willodean Harn Female United States 39 16/08/2016 3567
945 Weston Martina Male United States 26 21/05/2015 6540
946 Roma Lafollette Female United States 34 15/10/2017 2654
947 Felisa Cail Female United States 28 16/08/2016 6525
948 Demetria Abbey Female United States 32 21/05/2015 3265
949 Jeromy Danz Male United States 39 15/10/2017 3265
950 Rasheeda Alkire Female United States 29 16/08/2016 6125
951 Dulce Abril Female United States 32 15/10/2017 1562
952 Mara Hashimoto Female Great Britain 25 16/08/2016 1582
953 Philip Gent Male France 36 21/05/2015 2587
954 Kathleen Hanner Female United States 25 15/10/2017 3549
955 Nereida Magwood Female United States 58 16/08/2016 2468
956 Gaston Brumm Male United States 24 21/05/2015 2554
957 Etta Hurn Female Great Britain 56 15/10/2017 3598
958 Earlean Melgar Female United States 27 16/08/2016 2456
959 Vincenza Weiland Female United States 40 21/05/2015 6548
960 Fallon Winward Female Great Britain 28 16/08/2016 5486
961 Arcelia Bouska Female Great Britain 39 21/05/2015 1258
962 Franklyn Unknow Male France 38 15/10/2017 2579
963 Sherron Ascencio Female Great Britain 32 16/08/2016 3256
964 Marcel Zabriskie Male Great Britain 26 21/05/2015 2587
965 Kina Hazelton Female Great Britain 31 16/08/2016 3259
966 Shavonne Pia Female France 24 21/05/2015 1546
967 Shavon Benito Female France 39 15/10/2017 3579
968 Lauralee Perrine Female Great Britain 28 16/08/2016 6597
969 Loreta Curren Female France 26 21/05/2015 9654
970 Teresa Strawn Female France 46 21/05/2015 3569
971 Belinda Partain Female United States 37 15/10/2017 2564
972 Holly Eudy Female United States 52 16/08/2016 8561
973 Many Cuccia Female Great Britain 46 21/05/2015 5489
974 Libbie Dalby Female France 42 21/05/2015 5489
975 Lester Prothro Male France 21 15/10/2017 6574
976 Marvel Hail Female Great Britain 28 16/08/2016 5555
977 Angelyn Vong Female United States 29 21/05/2015 6125
978 Francesca Beaudreau Female France 23 15/10/2017 5412
979 Garth Gangi Male United States 41 16/08/2016 3256
980 Carla Trumbull Female Great Britain 28 21/05/2015 3264
981 Veta Muntz Female Great Britain 37 15/10/2017 4569
982 Stasia Becker Female Great Britain 34 16/08/2016 7521
983 Jona Grindle Female Great Britain 26 21/05/2015 6458
984 Judie Claywell Female France 35 16/08/2016 7569
985 Dewitt Borger Male United States 36 21/05/2015 8514
986 Nena Hacker Female United States 29 15/10/2017 8563
987 Kelsie Wachtel Female France 27 16/08/2016 8642
988 Sau Pfau Female United States 25 21/05/2015 9536
989 Shanice Mccrystal Female United States 36 21/05/2015 2567
990 Chase Karner Male United States 37 15/10/2017 2154
991 Tommie Underdahl Male United States 26 16/08/2016 3265
992 Dorcas Darity Female United States 37 21/05/2015 8765
993 Angel Sanor Male France 24 15/10/2017 3259
994 Willodean Harn Female United States 39 16/08/2016 3567
995 Weston Martina Male United States 26 21/05/2015 6540
996 Roma Lafollette Female United States 34 15/10/2017 2654
997 Felisa Cail Female United States 28 16/08/2016 6525
998 Demetria Abbey Female United States 32 21/05/2015 3265
999 Jeromy Danz Male United States 39 15/10/2017 3265
1000 Rasheeda Alkire Female United States 29 16/08/2016 6125

Mô tả dữ liệu

names(mt) #Hiển thị tên của bảng dữ liệu
## [1] "NA."        "First.Name" "Last.Name"  "Gender"     "Country"   
## [6] "Age"        "Date"       "Id"
dim(mt) #Hiển thị số dòng và cột của dữ liệu
## [1] 1000    8
library(skimr)
skim(mt)
Data summary
Name mt
Number of rows 1000
Number of columns 8
_______________________
Column type frequency:
character 5
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
First.Name 0 1 3 9 0 50 0
Last.Name 0 1 3 10 0 50 0
Gender 0 1 4 6 0 2 0
Country 0 1 6 13 0 3 0
Date 0 1 10 10 0 3 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
NA. 0 1 500.50 288.82 1 250.75 500.5 750.25 1000 ▇▇▇▇▇
Age 0 1 33.26 8.35 21 26.00 32.0 38.00 58 ▇▃▆▁▁
Id 0 1 4717.72 2368.34 1258 2587.00 3574.0 6540.00 9654 ▇▇▃▅▃

Một số thông tin mở rộng

head(mt,10) #Hiển thị 10 dòng đầu của bảng dữ liệu
##    NA. First.Name Last.Name Gender       Country Age       Date   Id
## 1    1      Dulce     Abril Female United States  32 15/10/2017 1562
## 2    2       Mara Hashimoto Female Great Britain  25 16/08/2016 1582
## 3    3     Philip      Gent   Male        France  36 21/05/2015 2587
## 4    4   Kathleen    Hanner Female United States  25 15/10/2017 3549
## 5    5    Nereida   Magwood Female United States  58 16/08/2016 2468
## 6    6     Gaston     Brumm   Male United States  24 21/05/2015 2554
## 7    7       Etta      Hurn Female Great Britain  56 15/10/2017 3598
## 8    8    Earlean    Melgar Female United States  27 16/08/2016 2456
## 9    9   Vincenza   Weiland Female United States  40 21/05/2015 6548
## 10  10     Fallon   Winward Female Great Britain  28 16/08/2016 5486
is.na(mt) #Kiểm tra ô trống của dữ liệu, FALSE là ô có dữ liệu và ngược lại
##           NA. First.Name Last.Name Gender Country   Age  Date    Id
##    [1,] FALSE      FALSE     FALSE  FALSE   FALSE FALSE FALSE FALSE
##    [2,] FALSE      FALSE     FALSE  FALSE   FALSE FALSE FALSE FALSE
##    [3,] FALSE      FALSE     FALSE  FALSE   FALSE FALSE FALSE FALSE
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