# Membaca file dataset
df_hospital <- read.csv("hospital_dataset (3).csv")
head(df_hospital, 20)## Nama Tanggal_Lahir Tensi Skin_Stiffness_N_per_mm
## 1 Michael Anderson 01/04/1957 112/67 0.69
## 2 N/A 20/09/1975 140 / 91 1.50
## 3 Tan Wei Ming 12/04/1965 134/72 0.76
## 4 Shen Yi-Ching 11/09/1980 120/79 1.92
## 5 Kung Mei-Lin 22/08/1985 99/77 0.81
## 6 Ho Chuan-Wei 10/08/1962 149/65 0.61
## 7 18/01/1994 110/71 1.04
## 8 Betty Lewis 02/08/1982 108/67 2.24
## 9 Joseph Garcia 06/12/1982 0.18
## 10 Ong Lay Kheng 26/02/1951 128/78 NA
## 11 Lin Mei-Ling 16/02/1944 113/75 0.25
## 12 Tan Ah Kow 113/68 0.87
## 13 Tan Wei Ming 03/10/1946 105/90 1.92
## 14 N/A 02/11/1957 128/62 1.07
## 15 Hsu Kuo-Chang 18/03/1973 102/80 0.38
## 16 Lee Siew Eng 04/07/1964 135/64 0.42
## 17 John Smith 1967 106/67 0.83
## 18 Karen Thompson 08/02/1988 121/91 0.71
## 19 Chou Mei-Yu 02/05/1996 106/83 2.13
## 20 Ho Chuan-Wei 24/02/1988 103/83 0.23
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit
## 1 42.0 37.6 Non-Diabetic
## 2 41.9 36.5°C Non-Diabetic
## 3 26.3 37.5 Non-Diabetic
## 4 NA 37.0 Diabetic
## 5 25.5 36.0 Diabetic
## 6 42.2 36.8 Non-Diabetic
## 7 2.0 36.3 Diabetic
## 8 9.5 36.4 Diabetic
## 9 24.8 36.9 Non-Diabetic
## 10 40.9 36.6 Non-Diabetic
## 11 44.0 37.2celcius Non-Diabetic
## 12 23.1 36.4 Diabetic
## 13 6.5 37.1 Diabetic
## 14 20.0 37.1 Diabetic
## 15 53.5 36.5
## 16 31.9 36.6 Non-Diabetic
## 17 49.5 36.4 Non-Diabetic
## 18 40.8 37.0 Non-Diabetic
## 19 21.1 36.6 Diabetic
## 20 22.0 36.5 Non-Diabetic
## Peak_Plantar_Pressure_kPa
## 1 294.0
## 2 NA
## 3 431.8
## 4 577.5
## 5 502.3
## 6 201.4
## 7 512.8
## 8 327.7
## 9 NA
## 10 308.9
## 11 NA
## 12 327.8
## 13 623.0
## 14 513.7
## 15 254.2
## 16 -100.0
## 17 284.9
## 18 294.9
## 19 536.5
## 20 338.6library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'readr' was built under R version 4.5.3## Warning: package 'forcats' was built under R version 4.5.3## Warning: package 'lubridate' was built under R version 4.5.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── 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# Melihat struktur data
glimpse(df_hospital)## Rows: 700
## Columns: 8
## $ Nama <chr> "Michael Anderson", "N/A", "Tan Wei Ming", "…
## $ Tanggal_Lahir <chr> "01/04/1957", "20/09/1975", "12/04/1965", "1…
## $ Tensi <chr> "112/67", "140 / 91", "134/72", "120/79", "9…
## $ Skin_Stiffness_N_per_mm <dbl> 0.69, 1.50, 0.76, 1.92, 0.81, 0.61, 1.04, 2.…
## $ Microcirculation_PU <dbl> 42.0, 41.9, 26.3, NA, 25.5, 42.2, 2.0, 9.5, …
## $ Suhu_Tubuh_Celcius <chr> "37.6", "36.5°C", "37.5", "37.0", "36.0", "3…
## $ Penyakit <chr> "Non-Diabetic", "Non-Diabetic", "Non-Diabeti…
## $ Peak_Plantar_Pressure_kPa <dbl> 294.0, NA, 431.8, 577.5, 502.3, 201.4, 512.8…# Melihat statistik deskriptif
summary(df_hospital)## Nama Tanggal_Lahir Tensi
## Length:700 Length:700 Length:700
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Skin_Stiffness_N_per_mm Microcirculation_PU Suhu_Tubuh_Celcius
## Min. : -2.180 Min. : -32.50 Length:700
## 1st Qu.: 0.700 1st Qu.: 18.00 Class :character
## Median : 1.100 Median : 27.70 Mode :character
## Mean : 1.342 Mean : 35.58
## 3rd Qu.: 1.595 3rd Qu.: 39.00
## Max. :150.000 Max. :5000.00
## NA's :37 NA's :50
## Penyakit Peak_Plantar_Pressure_kPa
## Length:700 Min. : -100.0
## Class :character 1st Qu.: 268.6
## Mode :character Median : 384.3
## Mean : 991.9
## 3rd Qu.: 508.5
## Max. :99999.0
## NA's :43# Menghitung jumlah data yang kosong (NA) per kolom
colSums(is.na(df_hospital))## Nama Tanggal_Lahir Tensi
## 0 0 0
## Skin_Stiffness_N_per_mm Microcirculation_PU Suhu_Tubuh_Celcius
## 37 50 0
## Penyakit Peak_Plantar_Pressure_kPa
## 0 43# Melihat unique value untuk mendeteksi adanya inkonsistensi penulisan data
lapply(df_hospital, unique)## $Nama
## [1] "Michael Anderson" "N/A" "Tan Wei Ming" "Shen Yi-Ching"
## [5] "Kung Mei-Lin" "Ho Chuan-Wei" "" "Betty Lewis"
## [9] "Joseph Garcia" "Ong Lay Kheng" "Lin Mei-Ling" "Tan Ah Kow"
## [13] "Hsu Kuo-Chang" "Lee Siew Eng" "John Smith" "Karen Thompson"
## [17] "Chou Mei-Yu" "Barbara Taylor" "Cheng Shu-Fen" "Yen Kuo-Jung"
## [21] "Charles Clark" "Chang Chung-Wei" "Joseph Walker" "William Thomas"
## [25] "Fang Shu-Chen" "Tseng Wen-Liang" "Tung Li-Fang" "Hsieh Shu-Hui"
## [29] "Robert Wilson" "Pasien" "UNKNOWN" "Linda Martinez"
## [33] "Richard Martin" "Huang Li-Chen" "Nancy Robinson" "Jessica White"
## [37] "Helen Hall" "Susan Jackson" "Lu Hsiang-Ling" "???"
## [41] "Ng Boon Hua" "Wu Ming-Hui" "Tsai Chin-Lung" "Yang Hsiu-Mei"
## [45] "James Brown" "Patricia Davis" "Liao Chih-Cheng" "Wang Jie"
## [49] "Liu Hsiao-Fen" "Chiu Yu-Chin" "Pan Mei-Hsuan" "Mary Johnson"
## [53] "David Harris" "Chen Wei" "123456" "Kao Chin-Feng"
## [57] "unknown" "NULL" "."
##
## $Tanggal_Lahir
## [1] "01/04/1957" "20/09/1975" "12/04/1965"
## [4] "11/09/1980" "22/08/1985" "10/08/1962"
## [7] "18/01/1994" "02/08/1982" "06/12/1982"
## [10] "26/02/1951" "16/02/1944" ""
## [13] "03/10/1946" "02/11/1957" "18/03/1973"
## [16] "04/07/1964" "1967" "08/02/1988"
## [19] "02/05/1996" "24/02/1988" "25/08/1987"
## [22] "19/11/1946" "07/11/1977" "06/03/1982"
## [25] "19/02/1969" "05/11/1965" "07/07/1985"
## [28] "22/09/2001" "29/12/2001" "05/11/2001"
## [31] "30/04/1989" "30/11/1944" "10/05/1946"
## [34] "17/11/1942" "17/09/1972" "18/08/1971"
## [37] "19/05/1988" "01/02/1951" "08/06/1951"
## [40] "12/11/1940" "03/05/1993" "16/05/1970"
## [43] "26/09/1951" "12/05/1992" "29/07/1942"
## [46] "11/07/1989" "16/09/1967" "11/01/1958"
## [49] "05/10/1970" "03/08/1942" "16/03/1947"
## [52] "03/10/1989" "04/03/1992" "23/04/1952"
## [55] "27/11/2005" "10/11/1959" "1977"
## [58] "17/03/1966" "10/01/1992" "1980"
## [61] "20-02-2003" "16/03/1995" "31/10/1971"
## [64] "27/07/1989" "29/04/1990" "06/11/1991"
## [67] "05/12/1964" "26/01/2001" "26/05/1992"
## [70] "17/02/1954" "23/12/1943" "06/09/1941"
## [73] "20/11/1952" "08/05/1959" "17/05/1973"
## [76] "30/10/1983" "01/12/1991" "22/04/1999"
## [79] "07/06/1982" "01/10/1985" "27/07/1978"
## [82] "01/10/1989" "22/04/1962" "21/06/1978"
## [85] "29/10/1995" "19/11/1990" "11/10/1994"
## [88] "27/04/1984" "19/09/1974" "21/06/1981"
## [91] "23/03/1955" "02/09/1976" "18/04/1954"
## [94] "24/08/1968" "24/03/1962" "13/11/1962"
## [97] "16/08/1975" "26/03/1988" "05/08/1993"
## [100] "01/01/1995" "04/03/1946" "15/01/1967"
## [103] "04/05/1963" "29/04/1999" "28/03/1940"
## [106] "06/02/2000" "29/04/1951" "09/12/1960"
## [109] "28/03/1953" "08/06/1969" "09/05/1963"
## [112] "05/07/1967" "29/09/1978" "17/11/1976"
## [115] "12/11/1953" "14/04/1949" "24/11/1996"
## [118] "06/03/1970" "26/03/1995" "03/02/1992"
## [121] "31/05/2005" "17/06/1989" "17/11/1952"
## [124] "14/11/1957" "20/08/1979" "23/07/1985"
## [127] "13/04/1967" "11/11/1944" "29/05/1992"
## [130] "07/04/1979" "09/09/1956" "29/06/1941"
## [133] "April 10, 1989" "06/01/1985" "05/03/1952"
## [136] "04/06/1961" "20/05/1953" "22/03/1949"
## [139] "28/01/1979" "24/04/1947" "30/07/1982"
## [142] "18/09/1948" "11/04/1981" "13/03/1970"
## [145] "16/06/1961" "17/06/1999" "02/09/1998"
## [148] "24/10/1972" "14/09/1980" "18/12/1996"
## [151] "01/02/1992" "20/03/1980" "09/03/2005"
## [154] "25/06/1965" "14-12-1963" "13/04/1954"
## [157] "06/11/1953" "03/09/1948" "1945"
## [160] "23/09/1953" "23-08-1998" "07/02/1945"
## [163] "26/11/1957" "15/02/1990" "25/04/1992"
## [166] "21/01/1947" "09/11/1951" "07/04/2005"
## [169] "19/01/1999" "17/03/1954" "19/07/1941"
## [172] "27/06/1962" "22/09/1949" "09/09/1999"
## [175] "26/01/1998" "13/08/1946" "22/10/1996"
## [178] "24/12/1952" "12/08/1944" "09/11/1998"
## [181] "08/05/1972" "06/01/1973" "11/09/1943"
## [184] "02/21/1995" "06/06/1995" "11/06/1992"
## [187] "12/04/1970" "22/11/1999" "25/05/1998"
## [190] "12/05/1990" "12/09/1957" "17/09/1982"
## [193] "24/11/2005" "12/01/1968" "28/08/1979"
## [196] "11/07/1947" "27/03/1986" "21/06/1998"
## [199] "20/10/1955" "29/04/1997" "21/01/1993"
## [202] "12/11/1957" "07/03/1951" "19/04/1980"
## [205] "03/02/1973" "22/10/1990" "20/01/2003"
## [208] "02/06/1951" "05-05-1994" "04/10/1959"
## [211] "04/04/2002" "23/04/1991" "11/04/1994"
## [214] "February 16, 1961" "19/12/1967" "29/11/1992"
## [217] "19/08/1955" "08/09/1961" "15/04/2004"
## [220] "26/11/1997" "21/08/1978" "04/12/1954"
## [223] "20/04/1959" "29/07/1990" "13/08/2001"
## [226] "29/11/1958" "16/06/1941" "06/06/1973"
## [229] "30/11/1957" "19/04/2003" "18/07/1985"
## [232] "13/05/1968" "17/05/1972" "20/09/1979"
## [235] "29/10/1941" "13/12/1946" "20/03/1971"
## [238] "02/07/1990" "05/11/1958" "14/04/1986"
## [241] "18/09/1995" "02/24/2002" "05/07/1980"
## [244] "31/05/1959" "26/07/1949" "02/12/1972"
## [247] "11/02/1980" "06/07/1977" "12/07/1940"
## [250] "14/05/1953" "23/05/1988" "07/05/1950"
## [253] "07/03/1977" "22/09/1940" "11/11/1987"
## [256] "19/11/1955" "22/09/1952" "10/04/1980"
## [259] "03/02/1956" "04/12/1988" "28/05/1948"
## [262] "11/02/1984" "13/12/1975" "19/10/1981"
## [265] "15/09/1955" "04/10/1956" "14/06/2001"
## [268] "08/12/1942" "26/08/1981" "24/06/1994"
## [271] "19/07/2002" "17/11/1981" "12/01/1992"
## [274] "07 Nov 2004" "25/03/1996" "18/06/1961"
## [277] "10/11/2000" "23/02/1946" "24/11/1984"
## [280] "08/09/1959" "15/08/1969" "16/12/2002"
## [283] "14/08/1984" "03/07/1981" "16/12/1963"
## [286] "02/01/1980" "20/07/1985" "23/02/1944"
## [289] "11/12/1949" "21/10/1965" "28/08/1991"
## [292] "06/12/1950" "10/09/1954" "27/08/1987"
## [295] "30/12/1963" "17/05/1965" "16/09/1949"
## [298] "02/06/1981" "01/01/1969" "15/04/1977"
## [301] "17/11/1997" "19/03/1970" "07/11/1967"
## [304] "27/03/1974" "23/10/1988" "27/04/1966"
## [307] "06/04/1950" "28/04/1947" "03/05/1985"
## [310] "29/10/1961" "20/06/1969" "13/01/1998"
## [313] "12/01/1987" "20/10/1968" "25/09/1988"
## [316] "17/05/1945" "24/04/2000" "07/01/2000"
## [319] "04/03/1959" "11/01/1956" "19/12/1950"
## [322] "08/03/1956" "03/08/1969" "02 Feb 1967"
## [325] "27/11/1962" "05/03/1991" "29/10/1992"
## [328] "08/03/1948" "31/12/1973" "17/08/1997"
## [331] "23/01/1961" "13/12/1998" "14/02/1942"
## [334] "15/04/1991" "05/02/1957" "02/09/1964"
## [337] "12/03/1959" "22/12/1972" "27/07/1983"
## [340] "26/10/1977" "01/06/1997" "24/09/1951"
## [343] "03/07/2003" "01/02/1968" "16/12/1950"
## [346] "10/11/1982" "04/07/1998" "03/03/1971"
## [349] "24/01/1992" "08/11/1975" "30/03/1986"
## [352] "16/07/1950" "13/10/1959" "14/09/1989"
## [355] "02/01/2005" "01/05/1941" "21/08/1952"
## [358] "30/10/1963" "28/05/1957" "07/12/1970"
## [361] "19/05/1945" "25/11/1947" "02/09/1978"
## [364] "18/12/1985" "21/04/1958" "24/03/1941"
## [367] "02/12/1969" "02/06/1994" "10/03/1999"
## [370] "02/03/2001" "12/12/1982" "10/07/1966"
## [373] "13/10/1942" "16/07/1956" "23/01/1994"
## [376] "14/04/2003" "03/01/1990" "28/04/1954"
## [379] "11/02/1988" "15/11/1977" "31/05/1953"
## [382] "06/09/1972" "25/08/1991" "30 Jan 1980"
## [385] "13/10/1964" "21/05/1999" "11/05/2005"
## [388] "20/10/1998" "07/03/1979" "10/05/1950"
## [391] "08/09/1980" "26/02/1970" "05/08/1955"
## [394] "19/07/1940" "09/12/1975" "18/10/1973"
## [397] "12/02/1993" "30/09/1967" "22/03/1956"
## [400] "19/07/1979" "09/08/1999" "15/08/1996"
## [403] "18/06/1962" "03/11/1994" "20/01/1963"
## [406] "19/08/1984" "20/03/1947" "01/05/1970"
## [409] "31/05/1957" "17/04/1952" "15/02/2000"
## [412] "31/01/1955" "14/04/1947" "06 May 1947"
## [415] "10/12/1952" "17/07/1999" "25/11/1990"
## [418] "20/12/1944" "08/29/1959" "17/06/1948"
## [421] "21/01/2003" "05/12/1975" "11/10/1944"
## [424] "02/11/1966" "24/12/1975" "01/02/1979"
## [427] "24/03/1948" "06/03/1950" "19/05/1967"
## [430] "22/10/1954" "16/05/1951" "10/02/1997"
## [433] "05/12/1963" "22/04/1979" "14/09/1941"
## [436] "15/01/1984" "09-01-1941" "07/11/1999"
## [439] "23/08/1960" "21/07/2001" "26/01/1961"
## [442] "09/11/1970" "06/01/1999" "28/05/1946"
## [445] "02/12/1990" "28/08/1970" "03/02/1988"
## [448] "22/05/1994" "13/04/1976" "13 Jan 2001"
## [451] "17/12/1965" "09/02/1948" "05/04/1976"
## [454] "18/11/1959" "06/10/1953" "31/03/1942"
## [457] "22/05/1970" "17/06/1970" "31/07/1987"
## [460] "22/04/1953" "22/05/1990" "24/03/1960"
## [463] "15/01/1965" "28/04/1960" "27/01/1996"
## [466] "14/07/1953" "21/05/1982" "17/05/1969"
## [469] "10/05/1947" "21/08/1993" "28/04/2001"
## [472] "26/10/1967" "10/11/1944" "22/10/1952"
## [475] "28/06/1955" "18/08/1979" "17/05/1988"
## [478] "24/07/1984" "06/12/1958" "04/04/2000"
## [481] "13/03/1991" "12/04/1988" "22/06/1943"
## [484] "15/12/1943" "09/08/1944" "15/01/2001"
## [487] "26/09/2004" "06/04/1996" "16/03/1961"
## [490] "14/05/1991" "27/09/1965" "13/01/1950"
## [493] "01/04/1941" "23/05/1985" "28/10/1969"
## [496] "31/03/1945" "26/07/1965" "26/07/1976"
## [499] "13/07/1954" "20/09/1967" "07/10/1986"
## [502] "05/04/1993" "09/05/1985" "23/08/1990"
## [505] "15/03/1992" "30/07/1969" "21/06/1982"
## [508] "05/12/1942" "03/07/1997" "14/06/1979"
## [511] "23/06/1966" "10/01/1972" "24/02/2000"
## [514] "18/09/1953" "12/09/1941" "14/03/1951"
## [517] "24/04/1956" "12/11/1993" "17/04/1949"
## [520] "03/05/1977" "31/10/1967" "17/05/1964"
## [523] "23/01/1980" "06/01/1987" "09/09/1981"
## [526] "31/01/1978" "18/08/1976" "13/02/1954"
## [529] "26/08/1990" "21/02/1940" "25/11/1948"
## [532] "28/06/1995" "23/09/1991" "22/11/1978"
## [535] "21/04/1998" "22/02/2002" "03/04/1975"
## [538] "01/12/1998" "27/04/1987" "06/05/1971"
## [541] "15/02/1946" "17/07/2000" "28/06/1989"
## [544] "17/05/1959" "31/08/1977" "13/09/1985"
## [547] "15/06/1972" "26/08/1961" "15/07/1960"
## [550] "06/08/1967" "1988" "26/03/1943"
## [553] "08/01/1964" "29/05/1970" "10/09/1978"
## [556] "23/12/1980" "18/11/1986" "09/01/1962"
## [559] "05/03/1976" "05/09/1944" "18/11/2001"
## [562] "08/07/1952" "27/12/1974" "13/01/1966"
## [565] "18/05/1989" "31/01/1944" "10/09/1989"
## [568] "14/03/1985" "07/05/1974" "19/02/1968"
## [571] "29/08/1978" "04/09/1980" "12/03/1984"
## [574] "13/06/1969" "02/07/1979" "03/09/1985"
## [577] "10/10/1966" "13/07/1953" "19/08/1989"
## [580] "22/07/1958" "07/06/1986" "12/02/1960"
## [583] "12/04/1948" "25/03/1990" "26/06/2002"
## [586] "27/12/1958" "13/06/1973" "03 Jan 1947"
## [589] "17/10/1963" "28/11/1940" "16/12/1953"
## [592] "11/01/1986" "22/08/1979" "08/03/1947"
## [595] "15/09/1979" "12/07/1942" "28/03/1960"
## [598] "31/10/1975" "12/12/1992" "29/04/1966"
## [601] "24/11/1946" "04/06/1981" "15/08/1984"
## [604] "25/12/1943" "24/09/1972" "18/04/1993"
## [607] "1985" "19/06/2005" "22/11/1994"
## [610] "28/12/1964" "23/07/1986" "15/11/1987"
## [613] "13/02/2004" "07/11/1976" "03/12/1946"
## [616] "28/12/1980" "02/09/1997" "27/02/1960"
## [619] "15/05/1981" "24/07/1949" "15/07/1972"
## [622] "04/09/1988" "07/04/1962" "26/06/1962"
## [625] "06/02/1976" "13/11/1968" "24/09/1980"
## [628] "02/10/2002" "16/04/1996" "19/07/1973"
## [631] "26/07/1947" "18/09/1997" "28/08/2004"
## [634] "29/04/1958" "04/06/2001" "06/03/2001"
##
## $Tensi
## [1] "112/67" "140 / 91" "134/72" "120/79"
## [5] "99/77" "149/65" "110/71" "108/67"
## [9] "" "128/78" "113/75" "113/68"
## [13] "105/90" "128/62" "102/80" "135/64"
## [17] "106/67" "121/91" "106/83" "103/83"
## [21] "129/66" "93/70" "111/67" "127/85"
## [25] "121/92" "119/86" "135/78" "129/69"
## [29] "118/92" "87/76" "103/76" "105/88"
## [33] "134/73" "129/90" "115/74" "126/65"
## [37] "122/77" "129/80" "153/68 mmHg" "99/97"
## [41] "134/66" "114/60" "128/85" "101 / 96"
## [45] "122/69" "117/83" "93/78" "132/74"
## [49] "101/78" "124/93" "122/62" "132/66"
## [53] "121/60" "119/83" "143/93" "106/74"
## [57] "108/68" "133/85" "121/70" "121/71"
## [61] "123/65" "132/76" "102/94" "111/72"
## [65] "114/79" "136/75" "140/80" "105/81"
## [69] "121/55" "126/84" "131/93" "150"
## [73] "112/86" "125/77" "136/69" "110/70"
## [77] "117/75" "96/81" "113/49" "108/71"
## [81] "98/82" "123/94" "120/72" "128/69"
## [85] "128/95" "125/86" "128/91" "158 / 62"
## [89] "115/84" "85/84" "132/73" "125/47"
## [93] "134/101" "117/80" "139/94" "109/77"
## [97] "114/81" "119/93" "96/73" "122/74"
## [101] "128/81" "121/73" "111/83" "109/82"
## [105] "97/77" "128/74" "143/77" "98/62"
## [109] "143/68" "127/96" "117/78" "108/65"
## [113] "117/82" "122/78" "134/92" "102 / 71"
## [117] "136/85" "127/111" "152/86" "142/90"
## [121] "119/67" "123/82" "113/69" "100/77"
## [125] "143-71" "140/76" "116/80" "121/78"
## [129] "108/76" "131/86" "118/96" "132/83"
## [133] "118/79" "143/72" "112/61" "141/105"
## [137] "101/88" "135/68" "128/79" "105/84"
## [141] "86/92" "123/62" "126/63 mmHg" "123/69"
## [145] "132/92" "116/78" "131/60" "115/78"
## [149] "120/78" "129/93" "119/78" "149/83"
## [153] "118/83" "125/75" "110/78" "123/97"
## [157] "133/81" "130/81" "130/69" "142/89"
## [161] "107/78" "134/70" "109/94" "131/70"
## [165] "106/71" "130/84" "115/80" "145/84"
## [169] "138/85" "110/81" "116/72" "130/72"
## [173] "125/66" "124/76" "138/96" "103mmHg/81"
## [177] "118/62" "113/98" "124/85" "119/85"
## [181] "136/94" "151 / 80" "115/87" "122/85"
## [185] "139/67" "142/75" "119/68" "135/58"
## [189] "94/83" "128/71" "117/95" "157/67"
## [193] "135/80" "127/75" "110/88" "101/86"
## [197] "154/113" "125/110" "104/80" "111/86"
## [201] "146/75" "143/86" "104/87" "139/84"
## [205] "121/61" "92/90" "106/78" "111/69"
## [209] "115/60" "123/75" "111/73" "94/67"
## [213] "108/86" "129/82" "116/85" "129/78"
## [217] "119/71" "159/78" "147/88" "129/59"
## [221] "125/87" "122/113" "127/60" "144/67"
## [225] "103/72" "143/70" "132/84" "149/90"
## [229] "121/96" "111/77" "124/92" "129/106"
## [233] "126/76" "108/87" "116/83" "114/91"
## [237] "118/84" "107/84" "118/88" "91/80"
## [241] "94/74" "124/82" "113/82" "144/97"
## [245] "119/73" "122/56" "125/83" "155/71"
## [249] "113/77" "133/100" "129/70" "109/76"
## [253] "143/69" "136/92" "97/65" "124/78"
## [257] "113/89" "108/72" "117/85" "105/68"
## [261] "114/63" "139/74" "143/78" "143/73"
## [265] "155/83" "94/89" "121/77" "126/97"
## [269] "151/74" "131/73" "134/76" "118/81"
## [273] "132/96" "116/71" "131/89" "136/77"
## [277] "97/71" "115/100" "119/81" "102/88"
## [281] "103/74" "154mmHg/89" "114/67" "112/76"
## [285] "113/83" "112/62" "124/91" "121/86"
## [289] "137/70" "126/88" "97/76" "92/67"
## [293] "137/81" "136/65" "110/63" "111/71"
## [297] "138/77" "124/75" "142/81" "133/93"
## [301] "105/83" "148/102" "114/77" "126/77"
## [305] "121/65" "133/79" "114/68" "120/89"
## [309] "78/76" "131/81" "115/82" "123/76"
## [313] "129/75" "128/76" "115/85" "100/74"
## [317] "101/100" "117/88" "119/61" "126/96"
## [321] "139/72" "156/76" "128/64" "100/84"
## [325] "133/64" "118/80" "123/72" "143/79"
## [329] "82/71" "115/68" "164/70" "150/67"
## [333] "128/82" "88/84" "112/74" "107/63"
## [337] "142/72" "117/77" "138/62" "126/72"
## [341] "154/75" "123/58" "129/81" "156/92"
## [345] "105/69" "115/81" "116/68" "121/83"
## [349] "125/81" "119/46" "102/83" "118/64"
## [353] "115/79" "125/85" "117/79" "149/95"
## [357] "84/54" "109/85" "127/81" "125/84"
## [361] "117/64" "121/102" "98/86" "110"
## [365] "131/62" "91/97" "139/77" "116/61"
## [369] "132/80" "129/74" "111/88" "129/76"
## [373] "130/63" "131/63" "108/57" "94/78"
## [377] "132/82" "104/82" "100/86" "142/73"
## [381] "101/46" "80/73" "113/86" "119/74"
## [385] "92/69" "97/89" "117/68" "132/75"
## [389] "110/86" "101/72" "145/73" "128/80"
## [393] "122/67" "110/68" "120/95" "127/95"
## [397] "Sys:131 Dia:65" "102/77" "117/87" "124/97"
## [401] "108/88" "141|87" "115/75" "146/100"
## [405] "106/86" "116/84" "122/72" "103/80"
## [409] "94/80" "134/78" "124/74" "127/90"
## [413] "135/96" "107/77" "142/86" "109/69"
## [417] "120/76" "122/57" "130/76" "125/68"
## [421] "124/68" "130/107" "128/92" "137/82"
## [425] "108/73" "116/65" "135/90" "139/87"
## [429] "109/70" "126/69" "129/94" "121/72"
## [433] "122-71" "138/82" "105/72" "102/87"
## [437] "127/88" "127/98" "114|71" "127/69"
## [441] "117/66" "132/72" "120/81" "147/76"
## [445] "123/66" "108/75" "121/63" "Sys:90 Dia:76"
## [449] "122/76" "137/78" "137/98" "105/70"
## [453] "107/57" "114/73" "122/58" "141/80"
## [457] "141/86" "92/75" "132/88" "136/76"
## [461] "106/62" "109/79" "130/85" "146/66"
## [465] "123/80" "124/84" "92/89" "99/69"
## [469] "121/56" "89/72" "148/79" "120/86"
## [473] "96/95" "113/79" "106/69" "116/70"
## [477] "105/79" "102/89" "97/83" "140/93"
## [481] "127/86" "104/88" "125/82" "130/79"
## [485] "133/103" "129/83" "95/88" "117/74"
## [489] "114/69" "148/89" "109/65" "125/71"
## [493] "116/88" "109/86" "155/99" "131/68"
## [497] "90/67" "127-62" "116/90" "145/88"
## [501] "127/83" "137/90" "105/97" "118/76"
## [505] "156/75" "133|78" "142/83" "120/58"
## [509] "148/91" "107/74" "150/82" "120/62"
## [513] "114mmHg/67" "114/85" "149/91" "107|60"
## [517] "107/82" "86/80" "109/74" "118/87"
## [521] "123/93" "115/97" "127/87" "119/56"
## [525] "109/52" "110/85" "105/63" "122/90"
## [529] "88/83" "100/47" "141/76" "131/82"
## [533] "97/66" "89/78" "141/71" "99/76"
## [537] "98/94" "124/73" "117/73" "96/53"
## [541] "122/89" "147/79" "103/92" "123/60"
## [545] "133/75" "146/67" "114/82"
##
## $Skin_Stiffness_N_per_mm
## [1] 0.69 1.50 0.76 1.92 0.81 0.61 1.04 2.24 0.18 NA
## [11] 0.25 0.87 1.07 0.38 0.42 0.83 0.71 2.13 0.23 1.10
## [21] 0.46 1.60 0.78 1.67 1.26 1.17 0.50 0.65 1.63 1.99
## [31] 0.89 0.74 -2.18 1.64 0.72 -1.50 1.12 1.31 0.79 0.47
## [41] 0.67 1.76 2.26 0.49 0.95 1.65 0.94 2.03 0.88 1.21
## [51] 0.56 1.68 0.10 1.53 0.84 1.24 2.07 1.13 2.90 0.41
## [61] 0.97 1.05 0.36 0.33 1.78 0.64 1.80 0.51 0.55 0.66
## [71] 0.80 1.11 1.01 0.13 1.25 0.54 1.89 1.18 1.43 1.81
## [81] 0.63 0.31 1.47 0.85 0.52 1.46 1.29 1.42 0.30 1.75
## [91] 0.39 1.72 1.00 1.94 1.98 1.22 0.73 1.56 1.73 1.74
## [101] 1.06 2.08 1.16 1.66 1.33 0.53 1.39 1.71 1.36 1.45
## [111] 1.88 -1.21 1.02 0.70 1.61 1.15 -0.53 1.52 0.17 2.27
## [121] 2.00 1.28 1.90 0.32 2.22 1.41 1.32 2.16 0.99 0.58
## [131] 1.55 0.14 0.16 0.86 2.02 1.59 1.84 0.93 2.35 1.85
## [141] 0.57 0.29 1.69 0.62 1.23 0.59 1.83 0.98 1.49 0.75
## [151] 1.37 1.34 0.91 1.14 1.57 1.87 2.58 2.05 0.21 1.58
## [161] 1.40 1.03 2.28 1.09 2.51 0.68 0.12 0.19 1.44 2.12
## [171] 1.93 2.42 0.77 2.20 0.96 1.19 0.48 2.19 0.27 1.82
## [181] 1.77 1.38 1.48 2.21 1.35 0.92 2.11 0.82 1.20 0.34
## [191] 2.06 1.62 1.27 1.08 0.26 0.40 1.97 0.28 0.37 2.59
## [201] 2.10 0.44 150.00 0.35 0.60 2.30 -0.46 2.01 1.95 0.90
## [211] 2.57 0.43 2.25 2.31 1.54
##
## $Microcirculation_PU
## [1] 42.0 41.9 26.3 NA 25.5 42.2 2.0 9.5 24.8 40.9
## [11] 44.0 23.1 6.5 20.0 53.5 31.9 49.5 40.8 21.1 22.0
## [21] 51.6 45.2 15.7 1.0 56.2 25.1 22.3 28.5 39.9 21.3
## [31] 25.6 8.6 22.8 4.3 56.8 36.3 21.4 13.7 35.8 -20.0
## [41] 35.3 52.8 34.7 -19.3 21.6 10.4 42.3 49.4 19.5 37.6
## [51] 13.3 46.3 28.1 14.5 28.3 34.6 48.7 43.8 33.2 19.9
## [61] 34.3 41.1 34.9 22.1 36.5 32.5 48.6 10.9 19.3 45.1
## [71] 45.0 43.4 32.6 15.0 14.4 37.9 37.4 29.4 40.6 34.5
## [81] 21.9 29.0 23.9 45.6 22.2 53.0 35.1 26.7 14.2 36.0
## [91] 17.5 6.7 32.9 -1.0 36.9 9.0 17.7 35.5 23.2 10.0
## [101] 24.2 48.4 10.3 30.7 5.0 46.8 41.8 11.7 14.9 7.0
## [111] 24.6 21.5 43.2 10.2 53.7 33.0 35.9 40.2 8.2 20.1
## [121] 22.6 28.7 15.6 21.0 22.9 8.8 47.3 47.0 20.6 19.4
## [131] 6.0 11.9 33.9 37.0 49.7 24.4 20.3 26.5 43.5 27.7
## [141] 43.7 52.9 39.0 4.7 53.2 49.9 15.9 17.9 29.3 27.0
## [151] 30.0 16.0 31.6 8.5 45.7 41.7 6.9 38.2 41.4 45.5
## [161] 24.1 42.9 48.9 18.0 15.5 25.3 27.3 18.1 19.2 26.8
## [171] 21.8 14.6 14.1 23.5 16.2 28.6 47.4 5.6 42.4 36.2
## [181] 22.7 20.5 35.2 40.4 8.7 37.2 34.0 -32.5 10.5 28.0
## [191] 10.6 40.1 54.3 9.1 38.8 44.7 12.7 77.3 28.8 46.9
## [201] 32.3 12.4 35.0 -15.4 50.5 23.8 25.7 7.8 32.1 58.9
## [211] 29.2 27.5 29.8 38.0 23.7 13.1 24.0 19.8 43.0 36.6
## [221] 3.3 41.2 34.1 14.0 30.4 17.4 37.3 55.1 7.4 32.7
## [231] 39.8 48.5 60.3 23.0 20.2 11.8 54.2 32.4 42.6 46.1
## [241] 30.1 23.4 29.6 12.0 43.6 18.7 40.0 21.2 3.9 31.3
## [251] 37.8 38.7 3.0 12.3 32.0 45.3 26.2 5000.0 16.4 8.9
## [261] 13.2 19.6 21.7 50.2 25.4 4.5 57.4 15.3 55.8 46.5
## [271] 4.4 51.1 15.2 51.7 58.4 32.8 31.0 57.6 28.2 56.5
## [281] 39.4 51.0 26.6 44.9 28.4 35.6 40.5 33.8 34.2 6.3
## [291] 20.4 41.6 25.9 53.6 46.4 50.8 16.8 26.0 17.3 45.4
## [301] 33.6 10.7 44.8 33.5 18.9 60.9 51.4 44.1 31.1 7.6
## [311] 47.2 13.5 14.3 41.3 36.8 46.2 12.1 14.7 30.2 35.7
## [321] 25.8 50.4 34.8 15.8 42.5 -13.9 29.7 59.6 13.9 12.8
## [331] 13.6 63.6 27.2 2.6 27.9 5.7 49.1 18.2 44.2 7.9
## [341] 13.4 29.9 20.7 8.1 25.0 47.1 42.7 47.6 18.8 43.1
## [351] 7.5 11.5 57.0 31.2 39.2 38.5 35.4 49.3 50.9
##
## $Suhu_Tubuh_Celcius
## [1] "37.6" "36.5°C" "37.5" "37.0" "36.0"
## [6] "36.8" "36.3" "36.4" "36.9" "36.6"
## [11] "37.2celcius" "37.1" "36.5" "36.9 C" "36.7"
## [16] "37.4" "37.2" "35.7" "36.2" ""
## [21] "37.3" "36.1" "36.5 derajat" "37.8" "42.5"
## [26] "35.9" "36.9°C" "37.7" "35.5" "37.0 derajat"
## [31] "35.6" "99.9" "36.1 derajat" "36.7 derajat" "38.0"
## [36] "-1.0" "35.8" "37.4°C" "37.1celcius" "37.2°C"
## [41] "36.6 derajat"
##
## $Penyakit
## [1] "Non-Diabetic" "Diabetic" "" "Sehat" "Sakit"
## [6] "Tidak" "Yes" "No" "NON-DIABETIC" "Normal"
## [11] "DIABETIC" "DM" "diabetic" "1" "non-diabetic"
## [16] "Healthy"
##
## $Peak_Plantar_Pressure_kPa
## [1] 294.000 NA 431.800 577.500 502.300 201.400 512.800
## [8] 327.700 308.900 327.800 623.000 513.700 254.200 -100.000
## [15] 284.900 294.900 536.500 338.600 430.100 340.600 386.000
## [22] 677.000 324.200 206.500 602.300 173.300 554.500 174.400
## [29] 337.300 267.600 415.300 557.100 612.700 566.300 233.800
## [36] 250.400 667.400 296.600 367.600 396.200 549.700 275.700
## [43] 162.900 348.000 404.400 497.500 387.500 340.300 495.400
## [50] 373.200 606.100 309.900 351.000 481.800 253.200 410.000
## [57] 476.400 354.100 320.400 518.500 352.300 203.200 208.400
## [64] 538.700 206.000 491.500 358.000 456.800 218.000 254.800
## [71] 437.300 229.900 310.400 289.800 337.500 563.100 389.300
## [78] 559.200 508.500 238.800 213.500 261.200 255.700 356.500
## [85] 512.300 607.700 449.600 311.100 651.700 390.400 263.100
## [92] 319.500 237.000 551.700 220.300 253.100 539.100 398.500
## [99] 548.700 475.200 393.200 499.300 697.100 544.100 223.000
## [106] 423.700 375.400 501.700 404.800 53.400 598.400 390.000
## [113] 514.500 398.400 319.400 504.100 710.600 212.500 538.600
## [120] 643.900 563.000 302.300 503.800 392.100 570.900 495.600
## [127] 302.800 530.200 457.400 513.800 320.000 235.100 228.000
## [134] 514.900 408.900 99999.000 139.100 401.500 520.600 593.000
## [141] 511.500 410.600 608.500 627.100 332.100 311.900 276.500
## [148] 436.300 330.400 442.900 274.700 571.200 259.600 232.800
## [155] 172.600 660.800 213.700 502.600 640.200 475.600 550.800
## [162] 422.500 237.500 512.600 529.100 348.400 426.100 473.100
## [169] 342.300 290.800 258.300 553.500 239.800 254.300 591.000
## [176] 304.000 632.400 602.000 125.800 488.800 517.000 384.300
## [183] 280.300 0.001 323.500 509.400 264.700 468.600 193.800
## [190] 167.400 265.000 601.800 536.300 537.100 350.600 428.300
## [197] 433.800 511.700 520.800 416.300 295.600 496.200 477.300
## [204] 482.000 551.900 241.800 129.300 372.900 276.200 364.600
## [211] 303.900 474.500 506.200 354.000 553.000 336.900 609.100
## [218] 571.000 561.800 480.600 153.300 311.800 522.200 198.300
## [225] 385.700 528.400 328.400 292.600 260.700 370.200 432.100
## [232] 195.200 595.600 169.100 114.100 313.900 344.600 546.300
## [239] 198.800 257.500 409.900 458.200 482.900 416.800 595.300
## [246] 407.900 591.900 300.000 517.500 255.200 192.700 263.700
## [253] 607.600 442.500 475.800 224.000 534.400 250.200 455.600
## [260] 418.000 166.900 177.900 317.700 525.100 476.700 253.300
## [267] 462.000 374.900 273.100 319.300 205.900 445.800 411.700
## [274] 238.100 366.500 536.200 294.800 348.300 536.000 558.100
## [281] 210.700 202.700 532.600 281.200 350.000 397.400 216.700
## [288] 290.500 502.700 479.900 538.000 422.300 563.300 185.600
## [295] 271.700 186.600 359.400 252.500 305.900 514.000 545.600
## [302] 329.800 433.900 260.200 150.900 400.900 402.300 179.700
## [309] 407.200 162.100 302.900 320.100 640.400 442.700 516.500
## [316] 483.300 203.400 436.600 276.900 367.900 213.400 233.200
## [323] 462.200 232.600 354.200 305.600 444.200 304.900 196.600
## [330] 473.900 565.100 598.300 593.500 575.000 547.400 589.700
## [337] 570.800 503.200 236.900 487.500 122.000 307.300 241.500
## [344] 389.600 248.300 286.900 453.800 395.800 432.900 120.100
## [351] 446.300 484.500 263.000 366.400 555.800 351.400 268.500
## [358] 246.900 268.000 715.400 537.400 386.200 521.600 325.700
## [365] 255.400 640.700 237.900 539.800 203.700 292.700 245.500
## [372] 379.100 425.300 375.000 328.900 560.400 443.400 386.500
## [379] 585.100 631.100 246.800 342.200 349.800 291.500 421.700
## [386] 239.300 371.800 240.000 187.700 510.100 585.800 282.200
## [393] 433.400 589.000 265.400 225.800 547.100 147.000 568.000
## [400] 285.500 236.000 353.500 505.400 212.300 422.900 403.200
## [407] 356.200 582.000 217.900 267.000 487.200 268.400 369.600
## [414] 319.700 300.500 399.400 317.000 245.400 602.400 457.300
## [421] 151.100 251.300 264.500 311.500 217.200 247.000 541.000
## [428] 523.000 490.600 416.200 287.500 170.800 227.200 269.200
## [435] 52.100 297.200 288.900 350.100 305.700 136.600 560.200
## [442] 227.000 495.900 477.500 347.400 537.800 387.200 325.800
## [449] 550.300 402.600 374.300 393.600 283.700 362.200 298.500
## [456] 509.800 429.000 633.500 307.000 303.800 487.300 264.800
## [463] 579.700 529.000 537.000 282.800 471.400 390.500 554.200
## [470] 288.500 301.300 643.600 357.800 541.100 214.000 339.600
## [477] 353.100 436.200 463.300 299.500 473.300 328.100 212.000
## [484] 583.900 444.600 262.100 148.400 204.100 241.200 487.900
## [491] 364.900 293.400 273.400 219.200 615.900 342.500 421.600
## [498] 369.500 404.500 321.100 330.500 233.400 265.300 492.800
## [505] 566.800 391.400 254.700 388.400 463.700 396.300 427.900
## [512] 449.900 519.100 246.300 615.500 232.000 324.600 630.100
## [519] 290.600 497.000 386.700 606.400 222.700 273.000 516.900
## [526] 463.400 600.600 297.500 641.800 482.800 501.500 438.700
## [533] 463.000 348.500 445.500 138.500 479.200 362.900 610.900
## [540] 166.200 462.700 218.400 157.800 675.700 707.200 609.500
## [547] 550.100 526.700 530.700 346.400 434.600 467.700 525.600
## [554] 578.600 313.100 268.600 262.900 379.600 502.400 224.600
## [561] 471.900 152.400 576.400 262.800 336.100 267.200 527.200
## [568] 227.900 310.500 326.600 536.100 592.300 535.800 598.900
## [575] 238.500 314.300 458.900 523.800 498.800 273.900 305.100
## [582] 262.300 511.100 454.100 588.200 355.000 532.300 653.100
## [589] 255.300 610.000 439.700 202.000 549.400 333.700 646.100
## [596] 573.400 445.400 373.800 255.800 377.200 345.400 191.500
## [603] 608.200 191.800# Mengubah string kosong menjadi NA agar seragam
df_hospital[df_hospital == ""] <- NAMissing value pada beberapa kolom bertipe character tidak terbaca sebagai missing value karena masih dianggap sebagai string kosong (““). Oleh karena itu, perlu dilakukan konversi string kosong menjadi NA agar seluruh data yang hilang memiliki format yang konsisten dan dapat terdeteksi dengan baik.
# Standarisasi nilai missing pada kolom Nama yang berisi simbol atau kata tidak valid
df_hospital$Nama[df_hospital$Nama %in% c("N/A","NULL","unknown","UNKNOWN","???",".","123456","Pasien",""
)] <- NA# Mengisi Nama yang kosong dengan label "Unknown"
df_hospital$Nama[is.na(df_hospital$Nama)] <- "Unknown"Kolom Nama yang memiliki missing value diimputasi dengan “Unknown” agar data pasien tetap dapat digunakan tanpa perlu menghapus barisnya. Hal ini dilakukan karena penghapusan baris berpotensi menghilangkan informasi medis lain yang masih valid dan penting untuk dianalisis.
# mengatasi inkonsistensi kolom tensi
df_hospital$Tensi <- gsub(" ", "", df_hospital$Tensi)
df_hospital$Tensi <- gsub("mmHg", "", df_hospital$Tensi)
df_hospital$Tensi <- gsub("-", "/", df_hospital$Tensi)
df_hospital$Tensi <- gsub("\\|", "/", df_hospital$Tensi)
df_hospital$Tensi <- gsub("Sys:(\\d+)Dia:(\\d+)", "\\1/\\2", df_hospital$Tensi)# Mengubah data yang formatnya tetap tidak sesuai menjadi NA
df_hospital$Tensi[!grepl("^\\d+/\\d+$", df_hospital$Tensi)] <- NAData Tensi yang hanya berisi satu nilai dianggap tidak lengkap karena tidak memenuhi format systolic/diastolic, sehingga diubah menjadi NA.
library(tidyr)
# Memisahkan kolom Tensi menjadi dua kolom terpisah
df_hospital <- separate(df_hospital, Tensi, into = c("Systolic","Diastolic"), sep = "/")# Mengubah tipe data menjadi numerik
df_hospital$Systolic <- as.numeric(df_hospital$Systolic)
df_hospital$Diastolic <- as.numeric(df_hospital$Diastolic)# Imputasi missing value pada Tensi menggunakan median
df_hospital$Systolic[is.na(df_hospital$Systolic)] <- median(df_hospital$Systolic, na.rm = TRUE)
df_hospital$Diastolic[is.na(df_hospital$Diastolic)] <- median(df_hospital$Diastolic, na.rm = TRUE)# Ubah nilai yg minus atau sangat ekstrim pada skin stiffness menjadi NA
df_hospital$Skin_Stiffness_N_per_mm[
df_hospital$Skin_Stiffness_N_per_mm < 0 |
df_hospital$Skin_Stiffness_N_per_mm > 10
] <- NANilai pada kolom Skin Stiffness yang berada di luar rentang wajar (negatif atau >10 N/mm) diubah menjadi NA. Hal ini dilakukan karena kulit manusia tidak mungkin memiliki nilai kekakuan negatif, dan nilai yang terlalu ekstrem biasanya mengindikasikan adanya kesalahan input data atau error pada alat sensor saat pengambilan sampel.
# Imputasi missing value pada skin stiffness menggunakan median
df_hospital$Skin_Stiffness_N_per_mm [
is.na(df_hospital$Skin_Stiffness_N_per_mm)
] <- median(df_hospital$Skin_Stiffness_N_per_mm, na.rm = TRUE)# Mengatasi inkonsistensi pada variabel suhu tubuh
df_hospital$Suhu_Tubuh_Celcius <- gsub("[^0-9.-]", "", df_hospital$Suhu_Tubuh_Celcius)
# Ubah ke numerik agar bisa dihitung secara statistik
df_hospital$Suhu_Tubuh_Celcius <- as.numeric(df_hospital$Suhu_Tubuh_Celcius)# Menghapus suhu yang tidak masuk akal (di bawah 34 atau di atas 42 derajat) dan ubah jadi NA
df_hospital$Suhu_Tubuh_Celcius[
df_hospital$Suhu_Tubuh_Celcius < 34 | df_hospital$Suhu_Tubuh_Celcius > 42
] <- NA# Imputasi missing value pada suhu tubuh menggunakan median
df_hospital$Suhu_Tubuh_Celcius[is.na(df_hospital$Suhu_Tubuh_Celcius)] <- median(df_hospital$Suhu_Tubuh_Celcius, na.rm = TRUE)# Ubah nilai yg ekstrem atau tidak wajar secara medis pada peak plantar pleasure menjadi NA
df_hospital$Peak_Plantar_Pressure_kPa[
df_hospital$Peak_Plantar_Pressure_kPa < 50 |
df_hospital$Peak_Plantar_Pressure_kPa > 2000
] <- NA# Imputasi missing value pada peak plantar pressure menggunakan median
df_hospital$Peak_Plantar_Pressure_kPa[is.na(df_hospital$Peak_Plantar_Pressure_kPa)] <-
median(df_hospital$Peak_Plantar_Pressure_kPa, na.rm = TRUE)# Mengubah semua teks menjadi huruf kecil
df_hospital$Penyakit <- tolower(df_hospital$Penyakit)# Menyeragamkan berbagai variasi penulisan status penyakit untuk menghindari inkonsistensi data
# DIABETES
df_hospital$Penyakit[df_hospital$Penyakit %in% c("diabetic", "dm", "yes", "sakit", "1")] <- "diabetic"
# NON-DIABETES
df_hospital$Penyakit[df_hospital$Penyakit %in% c("non-diabetic", "sehat", "healthy", "normal", "no", "tidak")] <- "non-diabetic"
# Nilai yang berisi missing value dilabeli sebagai 'unknown' agar tidak menghilangkan informasi lain dalam baris tersebut.
# MISSING
df_hospital$Penyakit[is.na(df_hospital$Penyakit)] <- "unknown"missing value pada kolom Penyakit diimputasi dengan label “unknown” karena jika baris data tersebut dihapus, kita akan kehilangan banyak informasi medis lainnya yang masih valid dan sangat berharga untuk dianalisis. Pemberian label “unknown” dilakukan karena kita tidak bisa menebak secara sembarangan apakah seorang pasien menderita diabetes atau tidak tanpa adanya catatan medis yang jelas.
# Ubah data negatif atau yang melebihi batas alat (1000 PU) pada microcirculation PU menjadi NA
df_hospital$Microcirculation_PU[
df_hospital$Microcirculation_PU < 0 |
df_hospital$Microcirculation_PU > 1000
] <- NA# Hitung median berdasarkan masing-masing kelompok penyakit
med_micro_diabetic <- median(df_hospital$Microcirculation_PU[df_hospital$Penyakit == "diabetic"], na.rm = TRUE)
med_micro_non <- median(df_hospital$Microcirculation_PU[df_hospital$Penyakit == "non-diabetic"], na.rm = TRUE)
med_micro_unk <- median(df_hospital$Microcirculation_PU[df_hospital$Penyakit == "unknown"], na.rm = TRUE)
# Imputasi missing value dengan median
df_hospital$Microcirculation_PU[
is.na(df_hospital$Microcirculation_PU) & df_hospital$Penyakit == "diabetic"
] <- med_micro_diabetic
df_hospital$Microcirculation_PU[
is.na(df_hospital$Microcirculation_PU) & df_hospital$Penyakit == "non-diabetic"
] <- med_micro_non
df_hospital$Microcirculation_PU[
is.na(df_hospital$Microcirculation_PU) & df_hospital$Penyakit == "unknown"
] <- med_micro_unklibrary(lubridate)
library(stringr)
# Menyamakan format pemisah tanggal
tgl <- df_hospital$Tanggal_Lahir %>%
str_replace_all("-", "/") %>%
str_trim() %>%
str_squish()
# Memisahkan data yang menggunakan nama bulan dengan yang hanya angka
is_text <- str_detect(tgl, "[A-Za-z]")
is_text[is.na(is_text)] <- FALSE
parsed <- rep(as.POSIXct(NA), length(tgl))
# Mengubah tanggal dengan nama bulan menjadi bentuk tanggal yang seragam
parsed[is_text] <- parse_date_time(
tgl[is_text],
orders = c("d b Y", "d B Y", "B d, Y")
)
# Mengubah tanggal numerik (angka saja) menjadi bentuk tanggal yang seragam
parsed[!is_text] <- parse_date_time(
tgl[!is_text],
orders = c("dmy", "mdy", "Y")
)
# Mengubah hasil menjadi tipe Date dan menyimpannya ke kolom baru
df_hospital$tgl_lahir_clean <- as.Date(parsed)# Menampilkan data dengan tanggal lahir yang tidak wajar
df_hospital[
!is.na(df_hospital$tgl_lahir_clean) &
df_hospital$tgl_lahir_clean > Sys.Date(),
]## [1] Nama Tanggal_Lahir
## [3] Systolic Diastolic
## [5] Skin_Stiffness_N_per_mm Microcirculation_PU
## [7] Suhu_Tubuh_Celcius Penyakit
## [9] Peak_Plantar_Pressure_kPa tgl_lahir_clean
## <0 rows> (or 0-length row.names)tidak ditemukan data dengan tanggal lahir yang melebihi tanggal saat ini, sehingga data dapat dianggap valid.
# Menghitung umur
df_hospital$umur <- floor(
time_length(
interval(df_hospital$tgl_lahir_clean, Sys.Date()),
"years"
)
)# Hitung semua median per kelompok penyakit
med_U_diabetic <- median(df_hospital$umur[df_hospital$Penyakit == "diabetic"], na.rm = TRUE)
med_U_non <- median(df_hospital$umur[df_hospital$Penyakit == "non-diabetic"], na.rm = TRUE)
med_U_unk <- median(df_hospital$umur[df_hospital$Penyakit == "unknown"], na.rm = TRUE)
# Imputasi missing value pada umur berdasarkan kelompoknya
df_hospital$umur[is.na(df_hospital$umur) & df_hospital$Penyakit == "diabetic"] <- med_U_diabetic
df_hospital$umur[is.na(df_hospital$umur) & df_hospital$Penyakit == "non-diabetic"] <- med_U_non
df_hospital$umur[is.na(df_hospital$umur) & df_hospital$Penyakit == "unknown"] <- med_U_unk # Cek unique value apakah sudah bersih (tidak ada inkonsistensi data dan missing value)
lapply(df_hospital, unique)## $Nama
## [1] "Michael Anderson" "Unknown" "Tan Wei Ming" "Shen Yi-Ching"
## [5] "Kung Mei-Lin" "Ho Chuan-Wei" "Betty Lewis" "Joseph Garcia"
## [9] "Ong Lay Kheng" "Lin Mei-Ling" "Tan Ah Kow" "Hsu Kuo-Chang"
## [13] "Lee Siew Eng" "John Smith" "Karen Thompson" "Chou Mei-Yu"
## [17] "Barbara Taylor" "Cheng Shu-Fen" "Yen Kuo-Jung" "Charles Clark"
## [21] "Chang Chung-Wei" "Joseph Walker" "William Thomas" "Fang Shu-Chen"
## [25] "Tseng Wen-Liang" "Tung Li-Fang" "Hsieh Shu-Hui" "Robert Wilson"
## [29] "Linda Martinez" "Richard Martin" "Huang Li-Chen" "Nancy Robinson"
## [33] "Jessica White" "Helen Hall" "Susan Jackson" "Lu Hsiang-Ling"
## [37] "Ng Boon Hua" "Wu Ming-Hui" "Tsai Chin-Lung" "Yang Hsiu-Mei"
## [41] "James Brown" "Patricia Davis" "Liao Chih-Cheng" "Wang Jie"
## [45] "Liu Hsiao-Fen" "Chiu Yu-Chin" "Pan Mei-Hsuan" "Mary Johnson"
## [49] "David Harris" "Chen Wei" "Kao Chin-Feng"
##
## $Tanggal_Lahir
## [1] "01/04/1957" "20/09/1975" "12/04/1965"
## [4] "11/09/1980" "22/08/1985" "10/08/1962"
## [7] "18/01/1994" "02/08/1982" "06/12/1982"
## [10] "26/02/1951" "16/02/1944" NA
## [13] "03/10/1946" "02/11/1957" "18/03/1973"
## [16] "04/07/1964" "1967" "08/02/1988"
## [19] "02/05/1996" "24/02/1988" "25/08/1987"
## [22] "19/11/1946" "07/11/1977" "06/03/1982"
## [25] "19/02/1969" "05/11/1965" "07/07/1985"
## [28] "22/09/2001" "29/12/2001" "05/11/2001"
## [31] "30/04/1989" "30/11/1944" "10/05/1946"
## [34] "17/11/1942" "17/09/1972" "18/08/1971"
## [37] "19/05/1988" "01/02/1951" "08/06/1951"
## [40] "12/11/1940" "03/05/1993" "16/05/1970"
## [43] "26/09/1951" "12/05/1992" "29/07/1942"
## [46] "11/07/1989" "16/09/1967" "11/01/1958"
## [49] "05/10/1970" "03/08/1942" "16/03/1947"
## [52] "03/10/1989" "04/03/1992" "23/04/1952"
## [55] "27/11/2005" "10/11/1959" "1977"
## [58] "17/03/1966" "10/01/1992" "1980"
## [61] "20-02-2003" "16/03/1995" "31/10/1971"
## [64] "27/07/1989" "29/04/1990" "06/11/1991"
## [67] "05/12/1964" "26/01/2001" "26/05/1992"
## [70] "17/02/1954" "23/12/1943" "06/09/1941"
## [73] "20/11/1952" "08/05/1959" "17/05/1973"
## [76] "30/10/1983" "01/12/1991" "22/04/1999"
## [79] "07/06/1982" "01/10/1985" "27/07/1978"
## [82] "01/10/1989" "22/04/1962" "21/06/1978"
## [85] "29/10/1995" "19/11/1990" "11/10/1994"
## [88] "27/04/1984" "19/09/1974" "21/06/1981"
## [91] "23/03/1955" "02/09/1976" "18/04/1954"
## [94] "24/08/1968" "24/03/1962" "13/11/1962"
## [97] "16/08/1975" "26/03/1988" "05/08/1993"
## [100] "01/01/1995" "04/03/1946" "15/01/1967"
## [103] "04/05/1963" "29/04/1999" "28/03/1940"
## [106] "06/02/2000" "29/04/1951" "09/12/1960"
## [109] "28/03/1953" "08/06/1969" "09/05/1963"
## [112] "05/07/1967" "29/09/1978" "17/11/1976"
## [115] "12/11/1953" "14/04/1949" "24/11/1996"
## [118] "06/03/1970" "26/03/1995" "03/02/1992"
## [121] "31/05/2005" "17/06/1989" "17/11/1952"
## [124] "14/11/1957" "20/08/1979" "23/07/1985"
## [127] "13/04/1967" "11/11/1944" "29/05/1992"
## [130] "07/04/1979" "09/09/1956" "29/06/1941"
## [133] "April 10, 1989" "06/01/1985" "05/03/1952"
## [136] "04/06/1961" "20/05/1953" "22/03/1949"
## [139] "28/01/1979" "24/04/1947" "30/07/1982"
## [142] "18/09/1948" "11/04/1981" "13/03/1970"
## [145] "16/06/1961" "17/06/1999" "02/09/1998"
## [148] "24/10/1972" "14/09/1980" "18/12/1996"
## [151] "01/02/1992" "20/03/1980" "09/03/2005"
## [154] "25/06/1965" "14-12-1963" "13/04/1954"
## [157] "06/11/1953" "03/09/1948" "1945"
## [160] "23/09/1953" "23-08-1998" "07/02/1945"
## [163] "26/11/1957" "15/02/1990" "25/04/1992"
## [166] "21/01/1947" "09/11/1951" "07/04/2005"
## [169] "19/01/1999" "17/03/1954" "19/07/1941"
## [172] "27/06/1962" "22/09/1949" "09/09/1999"
## [175] "26/01/1998" "13/08/1946" "22/10/1996"
## [178] "24/12/1952" "12/08/1944" "09/11/1998"
## [181] "08/05/1972" "06/01/1973" "11/09/1943"
## [184] "02/21/1995" "06/06/1995" "11/06/1992"
## [187] "12/04/1970" "22/11/1999" "25/05/1998"
## [190] "12/05/1990" "12/09/1957" "17/09/1982"
## [193] "24/11/2005" "12/01/1968" "28/08/1979"
## [196] "11/07/1947" "27/03/1986" "21/06/1998"
## [199] "20/10/1955" "29/04/1997" "21/01/1993"
## [202] "12/11/1957" "07/03/1951" "19/04/1980"
## [205] "03/02/1973" "22/10/1990" "20/01/2003"
## [208] "02/06/1951" "05-05-1994" "04/10/1959"
## [211] "04/04/2002" "23/04/1991" "11/04/1994"
## [214] "February 16, 1961" "19/12/1967" "29/11/1992"
## [217] "19/08/1955" "08/09/1961" "15/04/2004"
## [220] "26/11/1997" "21/08/1978" "04/12/1954"
## [223] "20/04/1959" "29/07/1990" "13/08/2001"
## [226] "29/11/1958" "16/06/1941" "06/06/1973"
## [229] "30/11/1957" "19/04/2003" "18/07/1985"
## [232] "13/05/1968" "17/05/1972" "20/09/1979"
## [235] "29/10/1941" "13/12/1946" "20/03/1971"
## [238] "02/07/1990" "05/11/1958" "14/04/1986"
## [241] "18/09/1995" "02/24/2002" "05/07/1980"
## [244] "31/05/1959" "26/07/1949" "02/12/1972"
## [247] "11/02/1980" "06/07/1977" "12/07/1940"
## [250] "14/05/1953" "23/05/1988" "07/05/1950"
## [253] "07/03/1977" "22/09/1940" "11/11/1987"
## [256] "19/11/1955" "22/09/1952" "10/04/1980"
## [259] "03/02/1956" "04/12/1988" "28/05/1948"
## [262] "11/02/1984" "13/12/1975" "19/10/1981"
## [265] "15/09/1955" "04/10/1956" "14/06/2001"
## [268] "08/12/1942" "26/08/1981" "24/06/1994"
## [271] "19/07/2002" "17/11/1981" "12/01/1992"
## [274] "07 Nov 2004" "25/03/1996" "18/06/1961"
## [277] "10/11/2000" "23/02/1946" "24/11/1984"
## [280] "08/09/1959" "15/08/1969" "16/12/2002"
## [283] "14/08/1984" "03/07/1981" "16/12/1963"
## [286] "02/01/1980" "20/07/1985" "23/02/1944"
## [289] "11/12/1949" "21/10/1965" "28/08/1991"
## [292] "06/12/1950" "10/09/1954" "27/08/1987"
## [295] "30/12/1963" "17/05/1965" "16/09/1949"
## [298] "02/06/1981" "01/01/1969" "15/04/1977"
## [301] "17/11/1997" "19/03/1970" "07/11/1967"
## [304] "27/03/1974" "23/10/1988" "27/04/1966"
## [307] "06/04/1950" "28/04/1947" "03/05/1985"
## [310] "29/10/1961" "20/06/1969" "13/01/1998"
## [313] "12/01/1987" "20/10/1968" "25/09/1988"
## [316] "17/05/1945" "24/04/2000" "07/01/2000"
## [319] "04/03/1959" "11/01/1956" "19/12/1950"
## [322] "08/03/1956" "03/08/1969" "02 Feb 1967"
## [325] "27/11/1962" "05/03/1991" "29/10/1992"
## [328] "08/03/1948" "31/12/1973" "17/08/1997"
## [331] "23/01/1961" "13/12/1998" "14/02/1942"
## [334] "15/04/1991" "05/02/1957" "02/09/1964"
## [337] "12/03/1959" "22/12/1972" "27/07/1983"
## [340] "26/10/1977" "01/06/1997" "24/09/1951"
## [343] "03/07/2003" "01/02/1968" "16/12/1950"
## [346] "10/11/1982" "04/07/1998" "03/03/1971"
## [349] "24/01/1992" "08/11/1975" "30/03/1986"
## [352] "16/07/1950" "13/10/1959" "14/09/1989"
## [355] "02/01/2005" "01/05/1941" "21/08/1952"
## [358] "30/10/1963" "28/05/1957" "07/12/1970"
## [361] "19/05/1945" "25/11/1947" "02/09/1978"
## [364] "18/12/1985" "21/04/1958" "24/03/1941"
## [367] "02/12/1969" "02/06/1994" "10/03/1999"
## [370] "02/03/2001" "12/12/1982" "10/07/1966"
## [373] "13/10/1942" "16/07/1956" "23/01/1994"
## [376] "14/04/2003" "03/01/1990" "28/04/1954"
## [379] "11/02/1988" "15/11/1977" "31/05/1953"
## [382] "06/09/1972" "25/08/1991" "30 Jan 1980"
## [385] "13/10/1964" "21/05/1999" "11/05/2005"
## [388] "20/10/1998" "07/03/1979" "10/05/1950"
## [391] "08/09/1980" "26/02/1970" "05/08/1955"
## [394] "19/07/1940" "09/12/1975" "18/10/1973"
## [397] "12/02/1993" "30/09/1967" "22/03/1956"
## [400] "19/07/1979" "09/08/1999" "15/08/1996"
## [403] "18/06/1962" "03/11/1994" "20/01/1963"
## [406] "19/08/1984" "20/03/1947" "01/05/1970"
## [409] "31/05/1957" "17/04/1952" "15/02/2000"
## [412] "31/01/1955" "14/04/1947" "06 May 1947"
## [415] "10/12/1952" "17/07/1999" "25/11/1990"
## [418] "20/12/1944" "08/29/1959" "17/06/1948"
## [421] "21/01/2003" "05/12/1975" "11/10/1944"
## [424] "02/11/1966" "24/12/1975" "01/02/1979"
## [427] "24/03/1948" "06/03/1950" "19/05/1967"
## [430] "22/10/1954" "16/05/1951" "10/02/1997"
## [433] "05/12/1963" "22/04/1979" "14/09/1941"
## [436] "15/01/1984" "09-01-1941" "07/11/1999"
## [439] "23/08/1960" "21/07/2001" "26/01/1961"
## [442] "09/11/1970" "06/01/1999" "28/05/1946"
## [445] "02/12/1990" "28/08/1970" "03/02/1988"
## [448] "22/05/1994" "13/04/1976" "13 Jan 2001"
## [451] "17/12/1965" "09/02/1948" "05/04/1976"
## [454] "18/11/1959" "06/10/1953" "31/03/1942"
## [457] "22/05/1970" "17/06/1970" "31/07/1987"
## [460] "22/04/1953" "22/05/1990" "24/03/1960"
## [463] "15/01/1965" "28/04/1960" "27/01/1996"
## [466] "14/07/1953" "21/05/1982" "17/05/1969"
## [469] "10/05/1947" "21/08/1993" "28/04/2001"
## [472] "26/10/1967" "10/11/1944" "22/10/1952"
## [475] "28/06/1955" "18/08/1979" "17/05/1988"
## [478] "24/07/1984" "06/12/1958" "04/04/2000"
## [481] "13/03/1991" "12/04/1988" "22/06/1943"
## [484] "15/12/1943" "09/08/1944" "15/01/2001"
## [487] "26/09/2004" "06/04/1996" "16/03/1961"
## [490] "14/05/1991" "27/09/1965" "13/01/1950"
## [493] "01/04/1941" "23/05/1985" "28/10/1969"
## [496] "31/03/1945" "26/07/1965" "26/07/1976"
## [499] "13/07/1954" "20/09/1967" "07/10/1986"
## [502] "05/04/1993" "09/05/1985" "23/08/1990"
## [505] "15/03/1992" "30/07/1969" "21/06/1982"
## [508] "05/12/1942" "03/07/1997" "14/06/1979"
## [511] "23/06/1966" "10/01/1972" "24/02/2000"
## [514] "18/09/1953" "12/09/1941" "14/03/1951"
## [517] "24/04/1956" "12/11/1993" "17/04/1949"
## [520] "03/05/1977" "31/10/1967" "17/05/1964"
## [523] "23/01/1980" "06/01/1987" "09/09/1981"
## [526] "31/01/1978" "18/08/1976" "13/02/1954"
## [529] "26/08/1990" "21/02/1940" "25/11/1948"
## [532] "28/06/1995" "23/09/1991" "22/11/1978"
## [535] "21/04/1998" "22/02/2002" "03/04/1975"
## [538] "01/12/1998" "27/04/1987" "06/05/1971"
## [541] "15/02/1946" "17/07/2000" "28/06/1989"
## [544] "17/05/1959" "31/08/1977" "13/09/1985"
## [547] "15/06/1972" "26/08/1961" "15/07/1960"
## [550] "06/08/1967" "1988" "26/03/1943"
## [553] "08/01/1964" "29/05/1970" "10/09/1978"
## [556] "23/12/1980" "18/11/1986" "09/01/1962"
## [559] "05/03/1976" "05/09/1944" "18/11/2001"
## [562] "08/07/1952" "27/12/1974" "13/01/1966"
## [565] "18/05/1989" "31/01/1944" "10/09/1989"
## [568] "14/03/1985" "07/05/1974" "19/02/1968"
## [571] "29/08/1978" "04/09/1980" "12/03/1984"
## [574] "13/06/1969" "02/07/1979" "03/09/1985"
## [577] "10/10/1966" "13/07/1953" "19/08/1989"
## [580] "22/07/1958" "07/06/1986" "12/02/1960"
## [583] "12/04/1948" "25/03/1990" "26/06/2002"
## [586] "27/12/1958" "13/06/1973" "03 Jan 1947"
## [589] "17/10/1963" "28/11/1940" "16/12/1953"
## [592] "11/01/1986" "22/08/1979" "08/03/1947"
## [595] "15/09/1979" "12/07/1942" "28/03/1960"
## [598] "31/10/1975" "12/12/1992" "29/04/1966"
## [601] "24/11/1946" "04/06/1981" "15/08/1984"
## [604] "25/12/1943" "24/09/1972" "18/04/1993"
## [607] "1985" "19/06/2005" "22/11/1994"
## [610] "28/12/1964" "23/07/1986" "15/11/1987"
## [613] "13/02/2004" "07/11/1976" "03/12/1946"
## [616] "28/12/1980" "02/09/1997" "27/02/1960"
## [619] "15/05/1981" "24/07/1949" "15/07/1972"
## [622] "04/09/1988" "07/04/1962" "26/06/1962"
## [625] "06/02/1976" "13/11/1968" "24/09/1980"
## [628] "02/10/2002" "16/04/1996" "19/07/1973"
## [631] "26/07/1947" "18/09/1997" "28/08/2004"
## [634] "29/04/1958" "04/06/2001" "06/03/2001"
##
## $Systolic
## [1] 112 140 134 120 99 149 110 108 121 128 113 105 102 135 106 103 129 93 111
## [20] 127 119 118 87 115 126 122 153 114 101 117 132 124 143 133 123 136 131 125
## [39] 96 98 158 85 139 109 97 152 142 100 116 141 86 130 107 145 138 151 94
## [58] 157 154 104 146 92 159 147 144 91 155 137 148 78 156 82 164 150 88 84
## [77] 80 90 89 95
##
## $Diastolic
## [1] 67 91 72 79 77 65 71 78 75 68 90 62 80 64 83 66 70 85 92
## [20] 86 69 76 88 73 74 97 60 96 93 94 81 55 84 49 82 95 47 101
## [39] 111 61 105 63 89 98 87 58 113 110 59 106 56 100 102 46 54 57 107
## [58] 103 99 52 53
##
## $Skin_Stiffness_N_per_mm
## [1] 0.69 1.50 0.76 1.92 0.81 0.61 1.04 2.24 0.18 1.10 0.25 0.87 1.07 0.38 0.42
## [16] 0.83 0.71 2.13 0.23 0.46 1.60 0.78 1.67 1.26 1.17 0.50 0.65 1.63 1.99 0.89
## [31] 0.74 1.64 0.72 1.12 1.31 0.79 0.47 0.67 1.76 2.26 0.49 0.95 1.65 0.94 2.03
## [46] 0.88 1.21 0.56 1.68 0.10 1.53 0.84 1.24 2.07 1.13 2.90 0.41 0.97 1.05 0.36
## [61] 0.33 1.78 0.64 1.80 0.51 0.55 0.66 0.80 1.11 1.01 0.13 1.25 0.54 1.89 1.18
## [76] 1.43 1.81 0.63 0.31 1.47 0.85 0.52 1.46 1.29 1.42 0.30 1.75 0.39 1.72 1.00
## [91] 1.94 1.98 1.22 0.73 1.56 1.73 1.74 1.06 2.08 1.16 1.66 1.33 0.53 1.39 1.71
## [106] 1.36 1.45 1.88 1.02 0.70 1.61 1.15 1.52 0.17 2.27 2.00 1.28 1.90 0.32 2.22
## [121] 1.41 1.32 2.16 0.99 0.58 1.55 0.14 0.16 0.86 2.02 1.59 1.84 0.93 2.35 1.85
## [136] 0.57 0.29 1.69 0.62 1.23 0.59 1.83 0.98 1.49 0.75 1.37 1.34 0.91 1.14 1.57
## [151] 1.87 2.58 2.05 0.21 1.58 1.40 1.03 2.28 1.09 2.51 0.68 0.12 0.19 1.44 2.12
## [166] 1.93 2.42 0.77 2.20 0.96 1.19 0.48 2.19 0.27 1.82 1.77 1.38 1.48 2.21 1.35
## [181] 0.92 2.11 0.82 1.20 0.34 2.06 1.62 1.27 1.08 0.26 0.40 1.97 0.28 0.37 2.59
## [196] 2.10 0.44 0.35 0.60 2.30 2.01 1.95 0.90 2.57 0.43 2.25 2.31 1.54
##
## $Microcirculation_PU
## [1] 42.0 41.9 26.3 18.8 25.5 42.2 2.0 9.5 24.8 40.9 44.0 23.1 6.5 20.0 53.5
## [16] 31.9 49.5 40.8 21.1 22.0 51.6 45.2 15.7 1.0 56.2 25.1 22.3 37.0 28.5 39.9
## [31] 21.3 25.6 8.6 22.8 4.3 56.8 36.3 21.4 13.7 35.8 35.3 52.8 34.7 21.6 10.4
## [46] 42.3 49.4 19.5 37.6 13.3 46.3 28.1 14.5 28.3 34.6 48.7 43.8 33.2 19.9 34.3
## [61] 41.1 34.9 22.1 36.5 32.5 48.6 10.9 19.3 45.1 45.0 43.4 32.6 15.0 14.4 37.9
## [76] 37.4 29.4 40.6 34.5 21.9 29.0 23.9 45.6 22.2 53.0 35.1 26.7 14.2 36.0 17.5
## [91] 6.7 32.9 36.9 9.0 17.7 35.5 23.2 10.0 24.2 48.4 10.3 30.7 5.0 46.8 41.8
## [106] 11.7 14.9 7.0 24.6 21.5 43.2 10.2 53.7 33.0 35.9 40.2 8.2 20.1 22.6 28.7
## [121] 15.6 21.0 22.9 8.8 47.3 47.0 20.6 19.4 6.0 11.9 33.9 49.7 24.4 20.3 26.5
## [136] 43.5 27.7 43.7 52.9 39.0 4.7 53.2 49.9 15.9 17.9 29.3 27.0 30.0 16.0 31.6
## [151] 8.5 45.7 41.7 6.9 38.2 41.4 45.5 24.1 42.9 48.9 18.0 15.5 25.3 27.3 18.1
## [166] 19.2 26.8 21.8 14.6 14.1 23.5 16.2 28.6 47.4 5.6 42.4 36.2 22.7 20.5 35.2
## [181] 40.4 8.7 37.2 34.0 10.5 28.0 10.6 40.1 54.3 9.1 38.8 44.7 12.7 77.3 28.8
## [196] 46.9 32.3 12.4 35.0 50.5 23.8 25.7 7.8 32.1 58.9 29.2 27.5 29.8 38.0 23.7
## [211] 13.1 24.0 19.8 43.0 36.6 3.3 41.2 34.1 14.0 30.4 17.4 37.3 55.1 7.4 32.7
## [226] 39.8 48.5 60.3 23.0 20.2 11.8 54.2 32.4 42.6 46.1 30.1 23.4 29.6 12.0 43.6
## [241] 18.7 40.0 21.2 3.9 31.3 37.8 38.7 3.0 12.3 32.0 45.3 26.2 16.4 8.9 13.2
## [256] 19.6 21.7 50.2 25.4 4.5 57.4 15.3 55.8 46.5 4.4 51.1 15.2 51.7 58.4 32.8
## [271] 31.0 57.6 28.2 56.5 39.4 51.0 26.6 44.9 28.4 35.6 40.5 33.8 34.2 6.3 20.4
## [286] 41.6 25.9 53.6 46.4 50.8 16.8 26.0 17.3 45.4 33.6 10.7 44.8 33.5 18.9 60.9
## [301] 51.4 44.1 31.1 7.6 47.2 13.5 14.3 41.3 36.8 46.2 12.1 14.7 30.2 35.7 25.8
## [316] 50.4 34.8 15.8 42.5 29.7 59.6 13.9 12.8 13.6 63.6 27.2 2.6 27.9 5.7 49.1
## [331] 18.2 44.2 7.9 13.4 29.9 20.7 8.1 25.0 47.1 42.7 47.6 43.1 7.5 11.5 57.0
## [346] 31.2 39.2 38.5 35.4 49.3 50.9
##
## $Suhu_Tubuh_Celcius
## [1] 37.6 36.5 37.5 37.0 36.0 36.8 36.3 36.4 36.9 36.6 37.2 37.1 36.7 37.4 35.7
## [16] 36.2 37.3 36.1 37.8 35.9 37.7 35.5 35.6 38.0 35.8
##
## $Penyakit
## [1] "non-diabetic" "diabetic" "unknown"
##
## $Peak_Plantar_Pressure_kPa
## [1] 294.0 385.7 431.8 577.5 502.3 201.4 512.8 327.7 308.9 327.8 623.0 513.7
## [13] 254.2 284.9 294.9 536.5 338.6 430.1 340.6 386.0 677.0 324.2 206.5 602.3
## [25] 173.3 554.5 174.4 337.3 267.6 415.3 557.1 612.7 566.3 233.8 250.4 667.4
## [37] 296.6 367.6 396.2 549.7 275.7 162.9 348.0 404.4 497.5 387.5 340.3 495.4
## [49] 373.2 606.1 309.9 351.0 481.8 253.2 410.0 476.4 354.1 320.4 518.5 352.3
## [61] 203.2 208.4 538.7 206.0 491.5 358.0 456.8 218.0 254.8 437.3 229.9 310.4
## [73] 289.8 337.5 563.1 389.3 559.2 508.5 238.8 213.5 261.2 255.7 356.5 512.3
## [85] 607.7 449.6 311.1 651.7 390.4 263.1 319.5 237.0 551.7 220.3 253.1 539.1
## [97] 398.5 548.7 475.2 393.2 499.3 697.1 544.1 223.0 423.7 375.4 501.7 404.8
## [109] 53.4 598.4 390.0 514.5 398.4 319.4 504.1 710.6 212.5 538.6 643.9 563.0
## [121] 302.3 503.8 392.1 570.9 495.6 302.8 530.2 457.4 513.8 320.0 235.1 228.0
## [133] 514.9 408.9 139.1 401.5 520.6 593.0 511.5 410.6 608.5 627.1 332.1 311.9
## [145] 276.5 436.3 330.4 442.9 274.7 571.2 259.6 232.8 172.6 660.8 213.7 502.6
## [157] 640.2 475.6 550.8 422.5 237.5 512.6 529.1 348.4 426.1 473.1 342.3 290.8
## [169] 258.3 553.5 239.8 254.3 591.0 304.0 632.4 602.0 125.8 488.8 517.0 384.3
## [181] 280.3 323.5 509.4 264.7 468.6 193.8 167.4 265.0 601.8 536.3 537.1 350.6
## [193] 428.3 433.8 511.7 520.8 416.3 295.6 496.2 477.3 482.0 551.9 241.8 129.3
## [205] 372.9 276.2 364.6 303.9 474.5 506.2 354.0 553.0 336.9 609.1 571.0 561.8
## [217] 480.6 153.3 311.8 522.2 198.3 528.4 328.4 292.6 260.7 370.2 432.1 195.2
## [229] 595.6 169.1 114.1 313.9 344.6 546.3 198.8 257.5 409.9 458.2 482.9 416.8
## [241] 595.3 407.9 591.9 300.0 517.5 255.2 192.7 263.7 607.6 442.5 475.8 224.0
## [253] 534.4 250.2 455.6 418.0 166.9 177.9 317.7 525.1 476.7 253.3 462.0 374.9
## [265] 273.1 319.3 205.9 445.8 411.7 238.1 366.5 536.2 294.8 348.3 536.0 558.1
## [277] 210.7 202.7 532.6 281.2 350.0 397.4 216.7 290.5 502.7 479.9 538.0 422.3
## [289] 563.3 185.6 271.7 186.6 359.4 252.5 305.9 514.0 545.6 329.8 433.9 260.2
## [301] 150.9 400.9 402.3 179.7 407.2 162.1 302.9 320.1 640.4 442.7 516.5 483.3
## [313] 203.4 436.6 276.9 367.9 213.4 233.2 462.2 232.6 354.2 305.6 444.2 304.9
## [325] 196.6 473.9 565.1 598.3 593.5 575.0 547.4 589.7 570.8 503.2 236.9 487.5
## [337] 122.0 307.3 241.5 389.6 248.3 286.9 453.8 395.8 432.9 120.1 446.3 484.5
## [349] 263.0 366.4 555.8 351.4 268.5 246.9 268.0 715.4 537.4 386.2 521.6 325.7
## [361] 255.4 640.7 237.9 539.8 203.7 292.7 245.5 379.1 425.3 375.0 328.9 560.4
## [373] 443.4 386.5 585.1 631.1 246.8 342.2 349.8 291.5 421.7 239.3 371.8 240.0
## [385] 187.7 510.1 585.8 282.2 433.4 589.0 265.4 225.8 547.1 147.0 568.0 285.5
## [397] 236.0 353.5 505.4 212.3 422.9 403.2 356.2 582.0 217.9 267.0 487.2 268.4
## [409] 369.6 319.7 300.5 399.4 317.0 245.4 602.4 457.3 151.1 251.3 264.5 311.5
## [421] 217.2 247.0 541.0 523.0 490.6 416.2 287.5 170.8 227.2 269.2 52.1 297.2
## [433] 288.9 350.1 305.7 136.6 560.2 227.0 495.9 477.5 347.4 537.8 387.2 325.8
## [445] 550.3 402.6 374.3 393.6 283.7 362.2 298.5 509.8 429.0 633.5 307.0 303.8
## [457] 487.3 264.8 579.7 529.0 537.0 282.8 471.4 390.5 554.2 288.5 301.3 643.6
## [469] 357.8 541.1 214.0 339.6 353.1 436.2 463.3 299.5 473.3 328.1 212.0 583.9
## [481] 444.6 262.1 148.4 204.1 241.2 487.9 364.9 293.4 273.4 219.2 615.9 342.5
## [493] 421.6 369.5 404.5 321.1 330.5 233.4 265.3 492.8 566.8 391.4 254.7 388.4
## [505] 463.7 396.3 427.9 449.9 519.1 246.3 615.5 232.0 324.6 630.1 290.6 497.0
## [517] 386.7 606.4 222.7 273.0 516.9 463.4 600.6 297.5 641.8 482.8 501.5 438.7
## [529] 463.0 348.5 445.5 138.5 479.2 362.9 610.9 166.2 462.7 218.4 157.8 675.7
## [541] 707.2 609.5 550.1 526.7 530.7 346.4 434.6 467.7 525.6 578.6 313.1 268.6
## [553] 262.9 379.6 502.4 224.6 471.9 152.4 576.4 262.8 336.1 267.2 527.2 227.9
## [565] 310.5 326.6 536.1 592.3 535.8 598.9 238.5 314.3 458.9 523.8 498.8 273.9
## [577] 305.1 262.3 511.1 454.1 588.2 355.0 532.3 653.1 255.3 610.0 439.7 202.0
## [589] 549.4 333.7 646.1 573.4 445.4 373.8 255.8 377.2 345.4 191.5 608.2 191.8
##
## $tgl_lahir_clean
## [1] "1957-04-01" "1975-09-20" "1965-04-12" "1980-09-11" "1985-08-22"
## [6] "1962-08-10" "1994-01-18" "1982-08-02" "1982-12-06" "1951-02-26"
## [11] "1944-02-16" NA "1946-10-03" "1957-11-02" "1973-03-18"
## [16] "1964-07-04" "1967-01-01" "1988-02-08" "1996-05-02" "1988-02-24"
## [21] "1987-08-25" "1946-11-19" "1977-11-07" "1982-03-06" "1969-02-19"
## [26] "1965-11-05" "1985-07-07" "2001-09-22" "2001-12-29" "2001-11-05"
## [31] "1989-04-30" "1944-11-30" "1946-05-10" "1942-11-17" "1972-09-17"
## [36] "1971-08-18" "1988-05-19" "1951-02-01" "1951-06-08" "1940-11-12"
## [41] "1993-05-03" "1970-05-16" "1951-09-26" "1992-05-12" "1942-07-29"
## [46] "1989-07-11" "1967-09-16" "1958-01-11" "1970-10-05" "1942-08-03"
## [51] "1947-03-16" "1989-10-03" "1992-03-04" "1952-04-23" "2005-11-27"
## [56] "1959-11-10" "1977-01-01" "1966-03-17" "1992-01-10" "1980-01-01"
## [61] "2003-02-20" "1995-03-16" "1971-10-31" "1989-07-27" "1990-04-29"
## [66] "1991-11-06" "1964-12-05" "2001-01-26" "1992-05-26" "1954-02-17"
## [71] "1943-12-23" "1941-09-06" "1952-11-20" "1959-05-08" "1973-05-17"
## [76] "1983-10-30" "1991-12-01" "1999-04-22" "1982-06-07" "1985-10-01"
## [81] "1978-07-27" "1989-10-01" "1962-04-22" "1978-06-21" "1995-10-29"
## [86] "1990-11-19" "1994-10-11" "1984-04-27" "1974-09-19" "1981-06-21"
## [91] "1955-03-23" "1976-09-02" "1954-04-18" "1968-08-24" "1962-03-24"
## [96] "1962-11-13" "1975-08-16" "1988-03-26" "1993-08-05" "1995-01-01"
## [101] "1946-03-04" "1967-01-15" "1963-05-04" "1999-04-29" "1940-03-28"
## [106] "2000-02-06" "1951-04-29" "1960-12-09" "1953-03-28" "1969-06-08"
## [111] "1963-05-09" "1967-07-05" "1978-09-29" "1976-11-17" "1953-11-12"
## [116] "1949-04-14" "1996-11-24" "1970-03-06" "1995-03-26" "1992-02-03"
## [121] "2005-05-31" "1989-06-17" "1952-11-17" "1957-11-14" "1979-08-20"
## [126] "1985-07-23" "1967-04-13" "1944-11-11" "1992-05-29" "1979-04-07"
## [131] "1956-09-09" "1941-06-29" "1989-04-10" "1985-01-06" "1952-03-05"
## [136] "1961-06-04" "1953-05-20" "1949-03-22" "1979-01-28" "1947-04-24"
## [141] "1982-07-30" "1948-09-18" "1981-04-11" "1970-03-13" "1961-06-16"
## [146] "1999-06-17" "1998-09-02" "1972-10-24" "1980-09-14" "1996-12-18"
## [151] "1992-02-01" "1980-03-20" "2005-03-09" "1965-06-25" "1963-12-14"
## [156] "1954-04-13" "1953-11-06" "1948-09-03" "1945-01-01" "1953-09-23"
## [161] "1998-08-23" "1945-02-07" "1957-11-26" "1990-02-15" "1992-04-25"
## [166] "1947-01-21" "1951-11-09" "2005-04-07" "1999-01-19" "1954-03-17"
## [171] "1941-07-19" "1962-06-27" "1949-09-22" "1999-09-09" "1998-01-26"
## [176] "1946-08-13" "1996-10-22" "1952-12-24" "1944-08-12" "1998-11-09"
## [181] "1972-05-08" "1973-01-06" "1943-09-11" "1995-02-21" "1995-06-06"
## [186] "1992-06-11" "1970-04-12" "1999-11-22" "1998-05-25" "1990-05-12"
## [191] "1957-09-12" "1982-09-17" "2005-11-24" "1968-01-12" "1979-08-28"
## [196] "1947-07-11" "1986-03-27" "1998-06-21" "1955-10-20" "1997-04-29"
## [201] "1993-01-21" "1957-11-12" "1951-03-07" "1980-04-19" "1973-02-03"
## [206] "1990-10-22" "2003-01-20" "1951-06-02" "1994-05-05" "1959-10-04"
## [211] "2002-04-04" "1991-04-23" "1994-04-11" "1961-02-16" "1967-12-19"
## [216] "1992-11-29" "1955-08-19" "1961-09-08" "2004-04-15" "1997-11-26"
## [221] "1978-08-21" "1954-12-04" "1959-04-20" "1990-07-29" "2001-08-13"
## [226] "1958-11-29" "1941-06-16" "1973-06-06" "1957-11-30" "2003-04-19"
## [231] "1985-07-18" "1968-05-13" "1972-05-17" "1979-09-20" "1941-10-29"
## [236] "1946-12-13" "1971-03-20" "1990-07-02" "1958-11-05" "1986-04-14"
## [241] "1995-09-18" "2002-02-24" "1980-07-05" "1959-05-31" "1949-07-26"
## [246] "1972-12-02" "1980-02-11" "1977-07-06" "1940-07-12" "1953-05-14"
## [251] "1988-05-23" "1950-05-07" "1977-03-07" "1940-09-22" "1987-11-11"
## [256] "1955-11-19" "1952-09-22" "1980-04-10" "1956-02-03" "1988-12-04"
## [261] "1948-05-28" "1984-02-11" "1975-12-13" "1981-10-19" "1955-09-15"
## [266] "1956-10-04" "2001-06-14" "1942-12-08" "1981-08-26" "1994-06-24"
## [271] "2002-07-19" "1981-11-17" "1992-01-12" "2004-11-07" "1996-03-25"
## [276] "1961-06-18" "2000-11-10" "1946-02-23" "1984-11-24" "1959-09-08"
## [281] "1969-08-15" "2002-12-16" "1984-08-14" "1981-07-03" "1963-12-16"
## [286] "1980-01-02" "1985-07-20" "1944-02-23" "1949-12-11" "1965-10-21"
## [291] "1991-08-28" "1950-12-06" "1954-09-10" "1987-08-27" "1963-12-30"
## [296] "1965-05-17" "1949-09-16" "1981-06-02" "1969-01-01" "1977-04-15"
## [301] "1997-11-17" "1970-03-19" "1967-11-07" "1974-03-27" "1988-10-23"
## [306] "1966-04-27" "1950-04-06" "1947-04-28" "1985-05-03" "1961-10-29"
## [311] "1969-06-20" "1998-01-13" "1987-01-12" "1968-10-20" "1988-09-25"
## [316] "1945-05-17" "2000-04-24" "2000-01-07" "1959-03-04" "1956-01-11"
## [321] "1950-12-19" "1956-03-08" "1969-08-03" "1967-02-02" "1962-11-27"
## [326] "1991-03-05" "1992-10-29" "1948-03-08" "1973-12-31" "1997-08-17"
## [331] "1961-01-23" "1998-12-13" "1942-02-14" "1991-04-15" "1957-02-05"
## [336] "1964-09-02" "1959-03-12" "1972-12-22" "1983-07-27" "1977-10-26"
## [341] "1997-06-01" "1951-09-24" "2003-07-03" "1968-02-01" "1950-12-16"
## [346] "1982-11-10" "1998-07-04" "1971-03-03" "1992-01-24" "1975-11-08"
## [351] "1986-03-30" "1950-07-16" "1959-10-13" "1989-09-14" "2005-01-02"
## [356] "1941-05-01" "1952-08-21" "1963-10-30" "1957-05-28" "1970-12-07"
## [361] "1945-05-19" "1947-11-25" "1978-09-02" "1985-12-18" "1958-04-21"
## [366] "1941-03-24" "1969-12-02" "1994-06-02" "1999-03-10" "2001-03-02"
## [371] "1982-12-12" "1966-07-10" "1942-10-13" "1956-07-16" "1994-01-23"
## [376] "2003-04-14" "1990-01-03" "1954-04-28" "1988-02-11" "1977-11-15"
## [381] "1953-05-31" "1972-09-06" "1991-08-25" "1980-01-30" "1964-10-13"
## [386] "1999-05-21" "2005-05-11" "1998-10-20" "1979-03-07" "1950-05-10"
## [391] "1980-09-08" "1970-02-26" "1955-08-05" "1940-07-19" "1975-12-09"
## [396] "1973-10-18" "1993-02-12" "1967-09-30" "1956-03-22" "1979-07-19"
## [401] "1999-08-09" "1996-08-15" "1962-06-18" "1994-11-03" "1963-01-20"
## [406] "1984-08-19" "1947-03-20" "1970-05-01" "1957-05-31" "1952-04-17"
## [411] "2000-02-15" "1955-01-31" "1947-04-14" "1947-05-06" "1952-12-10"
## [416] "1999-07-17" "1990-11-25" "1944-12-20" "1959-08-29" "1948-06-17"
## [421] "2003-01-21" "1975-12-05" "1944-10-11" "1966-11-02" "1975-12-24"
## [426] "1979-02-01" "1948-03-24" "1950-03-06" "1967-05-19" "1954-10-22"
## [431] "1951-05-16" "1997-02-10" "1963-12-05" "1979-04-22" "1941-09-14"
## [436] "1984-01-15" "1941-01-09" "1999-11-07" "1960-08-23" "2001-07-21"
## [441] "1961-01-26" "1970-11-09" "1999-01-06" "1946-05-28" "1990-12-02"
## [446] "1970-08-28" "1988-02-03" "1994-05-22" "1976-04-13" "2001-01-13"
## [451] "1965-12-17" "1948-02-09" "1976-04-05" "1959-11-18" "1953-10-06"
## [456] "1942-03-31" "1970-05-22" "1970-06-17" "1987-07-31" "1953-04-22"
## [461] "1990-05-22" "1960-03-24" "1965-01-15" "1960-04-28" "1996-01-27"
## [466] "1953-07-14" "1982-05-21" "1969-05-17" "1947-05-10" "1993-08-21"
## [471] "2001-04-28" "1967-10-26" "1944-11-10" "1952-10-22" "1955-06-28"
## [476] "1979-08-18" "1988-05-17" "1984-07-24" "1958-12-06" "2000-04-04"
## [481] "1991-03-13" "1988-04-12" "1943-06-22" "1943-12-15" "1944-08-09"
## [486] "2001-01-15" "2004-09-26" "1996-04-06" "1961-03-16" "1991-05-14"
## [491] "1965-09-27" "1950-01-13" "1941-04-01" "1985-05-23" "1969-10-28"
## [496] "1945-03-31" "1965-07-26" "1976-07-26" "1954-07-13" "1967-09-20"
## [501] "1986-10-07" "1993-04-05" "1985-05-09" "1990-08-23" "1992-03-15"
## [506] "1969-07-30" "1982-06-21" "1942-12-05" "1997-07-03" "1979-06-14"
## [511] "1966-06-23" "1972-01-10" "2000-02-24" "1953-09-18" "1941-09-12"
## [516] "1951-03-14" "1956-04-24" "1993-11-12" "1949-04-17" "1977-05-03"
## [521] "1967-10-31" "1964-05-17" "1980-01-23" "1987-01-06" "1981-09-09"
## [526] "1978-01-31" "1976-08-18" "1954-02-13" "1990-08-26" "1940-02-21"
## [531] "1948-11-25" "1995-06-28" "1991-09-23" "1978-11-22" "1998-04-21"
## [536] "2002-02-22" "1975-04-03" "1998-12-01" "1987-04-27" "1971-05-06"
## [541] "1946-02-15" "2000-07-17" "1989-06-28" "1959-05-17" "1977-08-31"
## [546] "1985-09-13" "1972-06-15" "1961-08-26" "1960-07-15" "1967-08-06"
## [551] "1988-01-01" "1943-03-26" "1964-01-08" "1970-05-29" "1978-09-10"
## [556] "1980-12-23" "1986-11-18" "1962-01-09" "1976-03-05" "1944-09-05"
## [561] "2001-11-18" "1952-07-08" "1974-12-27" "1966-01-13" "1989-05-18"
## [566] "1944-01-31" "1989-09-10" "1985-03-14" "1974-05-07" "1968-02-19"
## [571] "1978-08-29" "1980-09-04" "1984-03-12" "1969-06-13" "1979-07-02"
## [576] "1985-09-03" "1966-10-10" "1953-07-13" "1989-08-19" "1958-07-22"
## [581] "1986-06-07" "1960-02-12" "1948-04-12" "1990-03-25" "2002-06-26"
## [586] "1958-12-27" "1973-06-13" "1947-01-03" "1963-10-17" "1940-11-28"
## [591] "1953-12-16" "1986-01-11" "1979-08-22" "1947-03-08" "1979-09-15"
## [596] "1942-07-12" "1960-03-28" "1975-10-31" "1992-12-12" "1966-04-29"
## [601] "1946-11-24" "1981-06-04" "1984-08-15" "1943-12-25" "1972-09-24"
## [606] "1993-04-18" "1985-01-01" "2005-06-19" "1994-11-22" "1964-12-28"
## [611] "1986-07-23" "1987-11-15" "2004-02-13" "1976-11-07" "1946-12-03"
## [616] "1980-12-28" "1997-09-02" "1960-02-27" "1981-05-15" "1949-07-24"
## [621] "1972-07-15" "1988-09-04" "1962-04-07" "1962-06-26" "1976-02-06"
## [626] "1968-11-13" "1980-09-24" "2002-10-02" "1996-04-16" "1973-07-19"
## [631] "1947-07-26" "1997-09-18" "2004-08-28" "1958-04-29" "2001-06-04"
## [636] "2001-03-06"
##
## $umur
## [1] 69 50 61 45 40 63 32 43 75 82 53 79 68 59 38 30 48 44 57 60 24 37 81 80 83
## [26] 54 74 85 33 55 36 58 34 20 66 49 46 23 31 25 72 84 73 67 52 42 27 47 64 35
## [51] 51 71 86 26 65 56 77 29 41 21 62 76 28 78 70 22 39# Cek missing value setelah dilakukan cleaning
colSums(is.na(df_hospital))## Nama Tanggal_Lahir Systolic
## 0 42 0
## Diastolic Skin_Stiffness_N_per_mm Microcirculation_PU
## 0 0 0
## Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 0 0 0
## tgl_lahir_clean umur
## 42 0library(dplyr)
# Memindahkan kolom umur ke posisi setelah Nama
df_hospital <- df_hospital %>%
relocate(umur, .after = Nama)# Menghapus kolom Tanggal_Lahir dan tgl_lahir_clean yang sudah tidak terpakai
df_hospital$Tanggal_Lahir <- NULL
df_hospital$tgl_lahir_clean <- NULL# Cek kembali apakah kolomnya sudah terhapus
colSums(is.na(df_hospital))## Nama umur Systolic
## 0 0 0
## Diastolic Skin_Stiffness_N_per_mm Microcirculation_PU
## 0 0 0
## Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 0 0 0# Cek apakah ada duplikasi
sum(duplicated(df_hospital))## [1] 4library(dplyr)
#Mengecek baris yang mengandung duplikasi
df_hospital %>%
group_by(across(everything())) %>%
filter(n() > 1)## # A tibble: 8 × 9
## # Groups: Nama, umur, Systolic, Diastolic, Skin_Stiffness_N_per_mm,
## # Microcirculation_PU, Suhu_Tubuh_Celcius, Penyakit,
## # Peak_Plantar_Pressure_kPa [4]
## Nama umur Systolic Diastolic Skin_Stiffness_N_per…¹ Microcirculation_PU
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Hsieh Shu… 59 96 73 1.43 36.9
## 2 Betty Lew… 37 121 96 1.66 14.6
## 3 Tung Li-F… 34 139 74 1.03 33
## 4 Betty Lew… 37 121 96 1.66 14.6
## 5 Tung Li-F… 34 139 74 1.03 33
## 6 Hsieh Shu… 59 96 73 1.43 36.9
## 7 Fang Shu-… 72 109 79 0.46 45.6
## 8 Fang Shu-… 72 109 79 0.46 45.6
## # ℹ abbreviated name: ¹Skin_Stiffness_N_per_mm
## # ℹ 3 more variables: Suhu_Tubuh_Celcius <dbl>, Penyakit <chr>,
## # Peak_Plantar_Pressure_kPa <dbl>library(dplyr)
# Menghapus baris yang seluruh kolomnya sama persis
df_hospital <- df_hospital %>%
distinct()# Cek apakah masih ada duplikasi
sum(duplicated(df_hospital))## [1] 0# Mengecek outlier dengan IQR pada kolom Systolic
Q1_sys <- quantile(df_hospital$Systolic, 0.25, na.rm = TRUE)
Q3_sys <- quantile(df_hospital$Systolic, 0.75, na.rm = TRUE)
IQR_sys <- Q3_sys - Q1_sys
lower_sys <- Q1_sys - 1.5 * IQR_sys
upper_sys <- Q3_sys + 1.5 * IQR_sys
df_hospital %>%
filter(Systolic < lower_sys | Systolic > upper_sys)## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Joseph Walker 37 87 76 0.65
## 2 Hsu Kuo-Chang 44 158 62 1.99
## 3 Shen Yi-Ching 49 85 84 0.13
## 4 Susan Jackson 27 86 92 0.83
## 5 Susan Jackson 53 157 67 0.86
## 6 Yen Kuo-Jung 67 159 78 1.42
## 7 Liao Chih-Cheng 75 78 76 0.88
## 8 Tan Wei Ming 68 82 71 1.19
## 9 Liu Hsiao-Fen 56 164 70 2.19
## 10 Unknown 85 84 54 0.51
## 11 Richard Martin 50 80 73 1.19
## 12 Liao Chih-Cheng 75 78 76 0.88
## 13 James Brown 33 86 80 2.57
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit
## 1 21.3 37.0 non-diabetic
## 2 22.1 37.1 diabetic
## 3 53.0 36.7 non-diabetic
## 4 37.0 36.6 non-diabetic
## 5 18.8 36.8 diabetic
## 6 18.8 37.4 diabetic
## 7 37.0 36.4 non-diabetic
## 8 22.3 35.6 diabetic
## 9 28.0 36.9 diabetic
## 10 42.6 35.7 non-diabetic
## 11 57.6 36.3 non-diabetic
## 12 47.2 36.4 non-diabetic
## 13 25.8 36.8 diabetic
## Peak_Plantar_Pressure_kPa
## 1 337.3
## 2 651.7
## 3 263.1
## 4 311.9
## 5 474.5
## 6 458.2
## 7 286.9
## 8 325.7
## 9 640.7
## 10 240.0
## 11 319.7
## 12 286.9
## 13 385.7# Mengecek outlier dengan IQR pada kolom Diastolic
Q1_dia <- quantile(df_hospital$Diastolic, 0.25, na.rm = TRUE)
Q3_dia <- quantile(df_hospital$Diastolic, 0.75, na.rm = TRUE)
IQR_dia <- Q3_dia - Q1_dia
lower_dia <- Q1_dia - 1.5 * IQR_dia
upper_dia <- Q3_dia + 1.5 * IQR_dia
df_hospital %>%
filter(Diastolic < lower_dia | Diastolic > upper_dia)## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Kung Mei-Lin 36 113 49 1.10
## 2 Nancy Robinson 57 125 47 0.41
## 3 Mary Johnson 31 127 111 1.75
## 4 Fang Shu-Chen 79 141 105 1.68
## 5 Shen Yi-Ching 35 154 113 1.84
## 6 Kao Chin-Feng 32 125 110 0.93
## 7 Unknown 66 122 113 1.72
## 8 Hsu Kuo-Chang 70 129 106 1.34
## 9 Ong Lay Kheng 26 119 46 0.68
## 10 Yen Kuo-Jung 23 101 46 0.92
## 11 Ho Chuan-Wei 43 130 107 0.86
## 12 Hsieh Shu-Hui 31 109 52 0.59
## 13 Patricia Davis 49 100 47 0.43
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit
## 1 37.4 36.4 non-diabetic
## 2 26.7 36.7 non-diabetic
## 3 14.9 37.2 diabetic
## 4 9.0 36.2 diabetic
## 5 16.2 37.0 diabetic
## 6 28.6 36.7 diabetic
## 7 10.5 36.6 diabetic
## 8 12.7 36.6 diabetic
## 9 21.7 37.3 non-diabetic
## 10 47.0 36.8 non-diabetic
## 11 18.8 37.1 diabetic
## 12 47.1 37.0 non-diabetic
## 13 47.6 37.0 non-diabetic
## Peak_Plantar_Pressure_kPa
## 1 238.8
## 2 237.0
## 3 538.6
## 4 520.6
## 5 571.0
## 6 561.8
## 7 591.9
## 8 455.6
## 9 342.2
## 10 385.7
## 11 509.8
## 12 314.3
## 13 262.3Berdasarkan hasil deteksi outlier menggunakan metode IQR, ditemukan beberapa data tidak wajar, yaitu nilai diastolik yang lebih besar dari sistolik. Kondisi ini secara medis tidak mungkin terjadi, sehingga data tersebut diindikasikan sebagai kesalahan input atau gangguan pengukuran. Oleh karena itu, nilai pada baris tersebut diubah menjadi NA.
# Menampilkan data dengan kondisi tidak wajar (Diastolic ≥ Systolic)
df_hospital %>%
filter(
Diastolic >= Systolic
)## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Susan Jackson 27 86 92 0.83
## 2 Lu Hsiang-Ling 29 91 97 1.10
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 1 37.0 36.6 non-diabetic 311.9
## 2 15.3 36.6 diabetic 547.1# Mengubah outlier yang tidak wajar menjadi NA
df_hospital$Systolic[df_hospital$Diastolic >= df_hospital$Systolic] <- NA
df_hospital$Diastolic[df_hospital$Diastolic >= df_hospital$Systolic] <- NA# Imputasi nilai NA pada Systolic dengan median berdasarkan kelompok penyakit
df_hospital$Systolic[
is.na(df_hospital$Systolic) & df_hospital$Penyakit == "diabetic"
] <- median(df_hospital$Systolic[df_hospital$Penyakit == "diabetic"], na.rm = TRUE)
df_hospital$Systolic[
is.na(df_hospital$Systolic) & df_hospital$Penyakit == "non-diabetic"
] <- median(df_hospital$Systolic[df_hospital$Penyakit == "non-diabetic"], na.rm = TRUE)
# Imputasi nilai NA pada Diastolic dengan median berdasarkan kelompok penyakit
df_hospital$Diastolic[
is.na(df_hospital$Diastolic) & df_hospital$Penyakit == "diabetic"
] <- median(df_hospital$Diastolic[df_hospital$Penyakit == "diabetic"], na.rm = TRUE)
df_hospital$Diastolic[
is.na(df_hospital$Diastolic) & df_hospital$Penyakit == "non-diabetic"
] <- median(df_hospital$Diastolic[df_hospital$Penyakit == "non-diabetic"], na.rm = TRUE)Outlier pada systolic dan diastolic yang tersisa dipertahankan karena masih mencerminkan perbedaan kondisi antar pasien. Pada pasien diabetes cenderung memiliki tekanan darah lebih tinggi (hipertensi), sedangkan non-diabetes menunjukkan variasi yang lebih normal. Oleh karena itu, nilai ini dianggap masih valid secara medis.
# Mengecek outlier dengan IQR pada kolom Skin Stiffness
Q1_SS <- quantile(df_hospital$Skin_Stiffness_N_per_mm, 0.25, na.rm = TRUE)
Q3_SS <- quantile(df_hospital$Skin_Stiffness_N_per_mm, 0.75, na.rm = TRUE)
IQR_SS <- Q3_SS - Q1_SS
lower_SS <- Q1_SS - 1.5 * IQR_SS
upper_SS <- Q3_SS + 1.5 * IQR_SS
df_hospital %>%
dplyr::filter(
Skin_Stiffness_N_per_mm < lower_SS |
Skin_Stiffness_N_per_mm > upper_SS
)## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Tsai Chin-Lung 53 140 80 2.9
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 1 41.1 36.7 diabetic 491.5Outlier pada Skin Stiffness tetap dipertahankan karena dapat terjadi pada pasien diabetes akibat peningkatan kekakuan jaringan, sehingga masih dianggap valid secara medis.
# Hitung Mean dan SD dari data yang sudah bersih
mean_suhu <- mean(df_hospital$Suhu_Tubuh_Celcius, na.rm = TRUE)
sd_suhu <- sd(df_hospital$Suhu_Tubuh_Celcius, na.rm = TRUE)
# Cari data yang menjauh lebih dari 3 standar deviasi
outlier_suhu <- df_hospital %>%
filter(abs((Suhu_Tubuh_Celcius - mean_suhu) / sd_suhu) > 3)
outlier_suhu## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Shen Yi-Ching 52 131 89 1.23
## 2 Karen Thompson 35 110 63 0.70
## 3 Tan Wei Ming 68 82 71 1.19
## 4 Liao Chih-Cheng 77 142 73 0.98
## 5 Richard Martin 23 113 69 0.32
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 1 17.4 35.5 unknown 418.0
## 2 32.4 35.6 non-diabetic 444.2
## 3 22.3 35.6 diabetic 325.7
## 4 41.1 38.0 non-diabetic 369.6
## 5 37.0 38.0 non-diabetic 379.6summary(df_hospital$Suhu_Tubuh_Celcius)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 35.50 36.50 36.80 36.78 37.00 38.00Outlier dibiarkan karena nilai suhu masih wajar secara medis, namun terdeteksi sebagai pencilan akibat data yang cenderung homogen dengan varians yang kecil.
# Mengecek outlier dengan IQR pada kolom Peak Plantar Pressure
Q1_ppp <- quantile(df_hospital$Peak_Plantar_Pressure_kPa, 0.25, na.rm = TRUE)
Q3_ppp <- quantile(df_hospital$Peak_Plantar_Pressure_kPa, 0.75, na.rm = TRUE)
IQR_ppp <- Q3_ppp - Q1_ppp
lower_ppp <- Q1_ppp - 1.5 * IQR_ppp
upper_ppp <- Q3_ppp + 1.5 * IQR_ppp
df_hospital %>%
filter(
Peak_Plantar_Pressure_kPa < lower_ppp |
Peak_Plantar_Pressure_kPa > upper_ppp)## [1] Nama umur
## [3] Systolic Diastolic
## [5] Skin_Stiffness_N_per_mm Microcirculation_PU
## [7] Suhu_Tubuh_Celcius Penyakit
## [9] Peak_Plantar_Pressure_kPa
## <0 rows> (or 0-length row.names)Tidak ada outlier yang terdeteksi pada peak plantar pressure
# Mengecek outlier dengan IQR pada kolom Microcirculation PU
Q1_micro <- quantile(df_hospital$Microcirculation_PU, 0.25, na.rm = TRUE)
Q3_micro <- quantile(df_hospital$Microcirculation_PU, 0.75, na.rm = TRUE)
IQR_micro <- Q3_micro - Q1_micro
lower_micro <- Q1_micro - 1.5 * IQR_micro
upper_micro <- Q3_micro + 1.5 * IQR_micro
df_hospital %>%
filter(
Microcirculation_PU < lower_micro |
Microcirculation_PU > upper_micro)## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Chang Chung-Wei 73 129 78 0.95
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit Peak_Plantar_Pressure_kPa
## 1 77.3 36 non-diabetic 418Outlier Microcirculation (77.3) tetap dipertahankan karena pasien ini termasuk kategori non-diabetic. Secara medis, orang tanpa diabetes cenderung memiliki aliran darah yang lebih lancar sehingga nilai yang lebih tinggi masih mungkin terjadi dibanding pasien diabetes. Karena angka ini masih masuk akal secara medis, maka tidak dianggap sebagai kesalahan input data.
# Mengecek outlier dengan IQR pada kolom umur
Q1_umur <- quantile(df_hospital$umur, 0.25, na.rm = TRUE)
Q3_umur <- quantile(df_hospital$umur, 0.75, na.rm = TRUE)
IQR_umur <- Q3_umur - Q1_umur
lower_umur <- Q1_umur - 1.5 * IQR_umur
upper_umur <- Q3_umur + 1.5 * IQR_umur
df_hospital %>%
filter(
umur < lower_umur |
umur > upper_umur)## [1] Nama umur
## [3] Systolic Diastolic
## [5] Skin_Stiffness_N_per_mm Microcirculation_PU
## [7] Suhu_Tubuh_Celcius Penyakit
## [9] Peak_Plantar_Pressure_kPa
## <0 rows> (or 0-length row.names)Tidak ada outlier yang terdeteksi pada umur
# Mengecek kembali struktur data untuk memastikan tipe data, nama kolom, dan isi data sudah sesuai
glimpse(df_hospital)## Rows: 696
## Columns: 9
## $ Nama <chr> "Michael Anderson", "Unknown", "Tan Wei Ming…
## $ umur <dbl> 69, 50, 61, 45, 40, 63, 32, 43, 43, 75, 82, …
## $ Systolic <dbl> 112, 140, 134, 120, 99, 149, 110, 108, 121, …
## $ Diastolic <dbl> 67, 91, 72, 79, 77, 65, 71, 67, 78, 78, 75, …
## $ Skin_Stiffness_N_per_mm <dbl> 0.69, 1.50, 0.76, 1.92, 0.81, 0.61, 1.04, 2.…
## $ Microcirculation_PU <dbl> 42.0, 41.9, 26.3, 18.8, 25.5, 42.2, 2.0, 9.5…
## $ Suhu_Tubuh_Celcius <dbl> 37.6, 36.5, 37.5, 37.0, 36.0, 36.8, 36.3, 36…
## $ Penyakit <chr> "non-diabetic", "non-diabetic", "non-diabeti…
## $ Peak_Plantar_Pressure_kPa <dbl> 294.0, 385.7, 431.8, 577.5, 502.3, 201.4, 51…# Menampilkan keseluruhan data setelah dilakukan proses pembersihan
df_hospital## Nama umur Systolic Diastolic Skin_Stiffness_N_per_mm
## 1 Michael Anderson 69 112 67 0.69
## 2 Unknown 50 140 91 1.50
## 3 Tan Wei Ming 61 134 72 0.76
## 4 Shen Yi-Ching 45 120 79 1.92
## 5 Kung Mei-Lin 40 99 77 0.81
## 6 Ho Chuan-Wei 63 149 65 0.61
## 7 Unknown 32 110 71 1.04
## 8 Betty Lewis 43 108 67 2.24
## 9 Joseph Garcia 43 121 78 0.18
## 10 Ong Lay Kheng 75 128 78 1.10
## 11 Lin Mei-Ling 82 113 75 0.25
## 12 Tan Ah Kow 53 113 68 0.87
## 13 Tan Wei Ming 79 105 90 1.92
## 14 Unknown 68 128 62 1.07
## 15 Hsu Kuo-Chang 53 102 80 0.38
## 16 Lee Siew Eng 61 135 64 0.42
## 17 John Smith 59 106 67 0.83
## 18 Karen Thompson 38 121 91 0.71
## 19 Chou Mei-Yu 30 106 83 2.13
## 20 Ho Chuan-Wei 38 103 83 0.23
## 21 Unknown 38 129 66 1.10
## 22 Ho Chuan-Wei 79 93 70 0.46
## 23 Barbara Taylor 48 111 67 1.10
## 24 Shen Yi-Ching 44 127 85 1.60
## 25 Lin Mei-Ling 57 121 92 0.46
## 26 Cheng Shu-Fen 60 121 78 0.78
## 27 Yen Kuo-Jung 40 119 86 1.67
## 28 Charles Clark 24 135 78 1.26
## 29 Chang Chung-Wei 24 129 69 1.17
## 30 Ong Lay Kheng 24 118 92 0.50
## 31 Joseph Walker 37 87 76 0.65
## 32 William Thomas 81 103 76 1.10
## 33 Fang Shu-Chen 80 105 88 1.10
## 34 Tseng Wen-Liang 83 134 73 1.17
## 35 Tung Li-Fang 53 129 90 1.63
## 36 Charles Clark 54 115 74 1.99
## 37 Hsieh Shu-Hui 37 121 78 0.89
## 38 Robert Wilson 75 126 65 0.74
## 39 Unknown 74 122 77 1.10
## 40 Unknown 53 129 80 1.64
## 41 Linda Martinez 85 153 68 0.72
## 42 Barbara Taylor 33 99 97 1.10
## 43 Robert Wilson 55 121 78 1.12
## 44 Fang Shu-Chen 74 134 66 1.31
## 45 Richard Martin 33 114 60 0.65
## 46 Fang Shu-Chen 83 128 85 0.79
## 47 Huang Li-Chen 36 101 96 0.47
## 48 Unknown 53 122 69 0.67
## 49 Kung Mei-Lin 58 117 83 1.76
## 50 Cheng Shu-Fen 68 93 78 2.26
## 51 Nancy Robinson 55 132 74 1.67
## 52 Cheng Shu-Fen 83 121 78 0.49
## 53 Jessica White 79 101 78 0.95
## 54 Richard Martin 36 124 93 1.65
## 55 Richard Martin 34 122 62 0.94
## 56 Helen Hall 74 132 66 2.03
## 57 Robert Wilson 20 121 60 0.88
## 58 Susan Jackson 66 119 83 0.94
## 59 Lu Hsiang-Ling 53 143 93 1.21
## 60 Huang Li-Chen 53 106 74 0.95
## 61 Tseng Wen-Liang 49 108 68 0.56
## 62 Linda Martinez 60 133 85 1.68
## 63 Barbara Taylor 34 121 70 0.10
## 64 Lee Siew Eng 46 121 71 1.07
## 65 John Smith 23 123 65 1.53
## 66 Ong Lay Kheng 31 132 76 0.84
## 67 Unknown 54 102 94 1.24
## 68 Barbara Taylor 36 111 72 0.84
## 69 Ng Boon Hua 36 114 79 2.07
## 70 Wu Ming-Hui 34 136 75 1.13
## 71 Tsai Chin-Lung 53 140 80 2.90
## 72 Unknown 61 133 85 0.41
## 73 Yang Hsiu-Mei 25 105 81 0.74
## 74 Shen Yi-Ching 33 115 74 1.10
## 75 Tung Li-Fang 72 121 55 0.97
## 76 Tsai Chin-Lung 82 126 84 1.24
## 77 Ng Boon Hua 84 131 93 1.05
## 78 William Thomas 73 121 78 0.36
## 79 Kung Mei-Lin 67 117 83 1.76
## 80 Kung Mei-Lin 52 112 86 1.10
## 81 James Brown 42 125 77 0.41
## 82 Wu Ming-Hui 34 136 69 0.33
## 83 Cheng Shu-Fen 27 121 78 1.78
## 84 Lu Hsiang-Ling 43 110 70 0.64
## 85 Patricia Davis 40 117 75 1.80
## 86 Yen Kuo-Jung 47 96 81 1.05
## 87 Kung Mei-Lin 36 113 49 1.10
## 88 Wu Ming-Hui 64 108 71 0.51
## 89 Barbara Taylor 53 98 82 0.55
## 90 Barbara Taylor 47 123 94 0.66
## 91 Tsai Chin-Lung 30 120 72 0.80
## 92 Hsieh Shu-Hui 35 128 69 1.67
## 93 Ong Lay Kheng 31 128 95 1.78
## 94 Lu Hsiang-Ling 42 125 86 1.05
## 95 Patricia Davis 51 128 91 1.11
## 96 Hsu Kuo-Chang 44 158 62 1.99
## 97 Betty Lewis 71 115 84 1.01
## 98 Shen Yi-Ching 49 85 84 0.13
## 99 Liao Chih-Cheng 72 132 73 1.05
## 100 Nancy Robinson 57 125 47 0.41
## 101 Wang Jie 64 134 101 1.25
## 102 Richard Martin 63 117 80 0.54
## 103 Chang Chung-Wei 50 121 78 1.04
## 104 Chang Chung-Wei 38 139 94 1.89
## 105 Shen Yi-Ching 32 109 77 1.53
## 106 Unknown 31 114 81 1.18
## 107 Yen Kuo-Jung 80 119 93 1.17
## 108 Hsieh Shu-Hui 59 96 73 1.43
## 109 Liu Hsiao-Fen 63 122 74 1.81
## 110 Wang Jie 27 128 81 0.83
## 111 Hsu Kuo-Chang 86 121 73 1.10
## 112 Jessica White 53 111 83 0.63
## 113 Lee Siew Eng 26 109 82 0.83
## 114 Wang Jie 75 97 77 0.31
## 115 Karen Thompson 65 128 74 1.47
## 116 Fang Shu-Chen 73 143 77 0.85
## 117 Ho Chuan-Wei 56 98 62 0.52
## 118 Chiu Yu-Chin 63 143 68 1.46
## 119 Pan Mei-Hsuan 58 127 96 0.76
## 120 Huang Li-Chen 47 117 78 1.29
## 121 Ng Boon Hua 49 108 65 1.42
## 122 Susan Jackson 72 117 82 0.30
## 123 Pan Mei-Hsuan 53 122 78 2.07
## 124 Ong Lay Kheng 77 134 92 1.75
## 125 Karen Thompson 29 102 71 1.10
## 126 Barbara Taylor 56 136 85 0.39
## 127 Mary Johnson 31 127 111 1.75
## 128 Barbara Taylor 34 121 78 1.72
## 129 Robert Wilson 20 152 86 1.12
## 130 Tsai Chin-Lung 36 121 78 1.13
## 131 Nancy Robinson 73 142 90 1.05
## 132 Tung Li-Fang 68 119 67 0.42
## 133 Chiu Yu-Chin 53 123 82 1.00
## 134 Tan Wei Ming 46 123 82 0.56
## 135 Liu Hsiao-Fen 40 113 69 1.10
## 136 Unknown 59 100 77 0.74
## 137 Lee Siew Eng 81 143 71 1.17
## 138 Liu Hsiao-Fen 33 140 76 1.10
## 139 Charles Clark 47 116 80 1.94
## 140 Unknown 69 121 78 1.98
## 141 John Smith 84 109 82 1.22
## 142 Chang Chung-Wei 37 108 76 0.73
## 143 Patricia Davis 41 131 86 0.10
## 144 Cheng Shu-Fen 74 121 78 1.56
## 145 Joseph Garcia 64 118 96 1.73
## 146 Yang Hsiu-Mei 72 132 83 1.74
## 147 Richard Martin 77 118 79 1.06
## 148 Tan Wei Ming 47 143 72 0.84
## 149 Patricia Davis 53 112 61 0.76
## 150 Fang Shu-Chen 79 141 105 1.68
## 151 William Thomas 43 101 88 2.08
## 152 David Harris 77 135 68 2.08
## 153 Susan Jackson 45 128 79 2.08
## 154 Fang Shu-Chen 61 133 85 0.41
## 155 Hsieh Shu-Hui 56 117 80 1.16
## 156 Robert Wilson 64 105 84 1.66
## 157 Unknown 26 121 78 0.39
## 158 Susan Jackson 27 120 92 0.83
## 159 Linda Martinez 53 123 62 1.17
## 160 Unknown 53 126 63 0.10
## 161 Hsu Kuo-Chang 45 123 69 1.11
## 162 William Thomas 29 128 85 1.33
## 163 Chou Mei-Yu 34 121 78 0.53
## 164 John Smith 46 132 92 1.10
## 165 Barbara Taylor 21 121 78 0.94
## 166 Unknown 60 116 78 1.24
## 167 Tsai Chin-Lung 62 131 60 1.39
## 168 Pan Mei-Hsuan 72 115 78 1.71
## 169 Jessica White 72 120 78 0.66
## 170 Tsai Chin-Lung 77 129 93 1.36
## 171 Chen Wei 53 119 78 1.04
## 172 Unknown 81 149 83 1.24
## 173 Patricia Davis 72 118 83 1.45
## 174 Lee Siew Eng 27 125 75 1.88
## 175 Wang Jie 81 135 64 1.42
## 176 Tseng Wen-Liang 68 110 78 0.74
## 177 James Brown 36 119 83 1.10
## 178 Mary Johnson 34 123 97 1.02
## 179 Patricia Davis 79 133 81 0.70
## 180 Liao Chih-Cheng 74 130 81 1.61
## 181 Chiu Yu-Chin 21 118 92 1.15
## 182 Lin Mei-Ling 27 130 69 1.10
## 183 Nancy Robinson 72 142 89 1.74
## 184 Kung Mei-Lin 53 107 78 0.10
## 185 Ong Lay Kheng 84 114 79 1.12
## 186 Robert Wilson 63 134 70 1.52
## 187 Unknown 76 109 94 0.17
## 188 Yen Kuo-Jung 26 131 70 0.70
## 189 David Harris 28 106 71 2.27
## 190 Michael Anderson 79 121 78 0.83
## 191 James Brown 29 130 84 1.52
## 192 Pan Mei-Hsuan 73 115 80 1.36
## 193 Charles Clark 81 145 84 0.94
## 194 Yang Hsiu-Mei 27 138 85 2.00
## 195 Patricia Davis 54 110 81 1.28
## 196 Cheng Shu-Fen 53 105 84 0.84
## 197 Michael Anderson 82 116 72 0.73
## 198 Kao Chin-Feng 43 130 72 1.25
## 199 Tsai Chin-Lung 53 125 66 0.79
## 200 Unknown 31 121 78 1.90
## 201 Wu Ming-Hui 53 124 76 0.49
## 202 Joseph Walker 30 138 96 1.36
## 203 Betty Lewis 33 103 81 0.56
## 204 Tung Li-Fang 53 121 70 0.56
## 205 Kao Chin-Feng 56 118 62 0.32
## 206 Hsu Kuo-Chang 53 113 98 1.50
## 207 Tan Wei Ming 26 124 85 1.73
## 208 Jessica White 27 119 85 2.22
## 209 Lu Hsiang-Ling 35 121 78 1.41
## 210 Chiu Yu-Chin 68 131 86 1.32
## 211 Joseph Garcia 43 136 94 1.56
## 212 Ng Boon Hua 53 121 78 0.97
## 213 Ng Boon Hua 20 151 80 2.16
## 214 William Thomas 58 121 78 0.73
## 215 David Harris 46 115 87 0.99
## 216 Ho Chuan-Wei 78 132 76 1.80
## 217 David Harris 40 122 85 1.88
## 218 Helen Hall 27 139 67 0.58
## 219 Ng Boon Hua 70 142 75 1.55
## 220 Susan Jackson 29 119 68 1.15
## 221 Pan Mei-Hsuan 33 121 78 0.14
## 222 Betty Lewis 69 121 78 1.98
## 223 Tseng Wen-Liang 68 135 58 0.61
## 224 Richard Martin 75 94 83 1.06
## 225 Tseng Wen-Liang 46 116 72 0.16
## 226 Helen Hall 53 128 71 1.61
## 227 Yang Hsiu-Mei 35 117 95 1.45
## 228 Susan Jackson 53 157 67 0.86
## 229 Richard Martin 23 135 80 1.39
## 230 Linda Martinez 74 121 78 0.52
## 231 Karen Thompson 32 127 75 1.63
## 232 Barbara Taylor 66 110 88 2.02
## 233 Ng Boon Hua 84 131 93 1.05
## 234 Unknown 24 101 86 1.59
## 235 Shen Yi-Ching 35 154 113 1.84
## 236 Kao Chin-Feng 32 125 110 0.93
## 237 Tung Li-Fang 65 104 80 1.80
## 238 Nancy Robinson 58 111 86 1.07
## 239 David Harris 33 146 75 0.47
## 240 Lin Mei-Ling 70 143 86 0.83
## 241 Kao Chin-Feng 64 104 87 2.35
## 242 Barbara Taylor 34 139 84 1.72
## 243 Mary Johnson 22 121 61 1.85
## 244 Charles Clark 28 92 90 1.10
## 245 Kung Mei-Lin 47 121 78 1.05
## 246 Ong Lay Kheng 71 121 78 1.22
## 247 Lu Hsiang-Ling 67 106 78 0.57
## 248 Tan Wei Ming 35 111 69 0.29
## 249 Yang Hsiu-Mei 24 115 60 0.55
## 250 Unknown 67 123 75 0.54
## 251 Chiu Yu-Chin 84 128 81 0.69
## 252 Betty Lewis 52 121 78 1.43
## 253 Unknown 68 111 73 0.71
## 254 Joseph Walker 23 123 69 1.69
## 255 Wu Ming-Hui 40 94 67 0.72
## 256 Unknown 57 121 78 0.10
## 257 Tseng Wen-Liang 53 108 86 0.62
## 258 Ong Lay Kheng 46 129 82 0.38
## 259 Liao Chih-Cheng 84 116 85 1.01
## 260 Jessica White 79 101 78 0.95
## 261 Ong Lay Kheng 79 129 78 0.79
## 262 John Smith 55 121 78 1.56
## 263 Nancy Robinson 35 119 71 0.10
## 264 Yen Kuo-Jung 67 159 78 1.42
## 265 Nancy Robinson 40 147 88 1.23
## 266 Michael Anderson 30 129 59 0.33
## 267 Yen Kuo-Jung 24 119 83 1.66
## 268 Ng Boon Hua 45 125 87 0.59
## 269 Unknown 66 122 113 1.72
## 270 John Smith 76 127 60 1.17
## 271 Patricia Davis 53 144 67 1.83
## 272 Yen Kuo-Jung 46 103 72 1.25
## 273 Susan Jackson 48 125 75 1.10
## 274 John Smith 85 143 70 0.98
## 275 Karen Thompson 53 132 84 1.10
## 276 Jessica White 72 149 90 1.49
## 277 Betty Lewis 37 121 96 1.66
## 278 Lin Mei-Ling 76 111 77 1.10
## 279 Tan Wei Ming 49 119 78 1.47
## 280 Pan Mei-Hsuan 85 124 92 0.75
## 281 Susan Jackson 38 135 68 1.37
## 282 Hsu Kuo-Chang 70 129 106 1.34
## 283 Chang Chung-Wei 73 129 78 0.95
## 284 Ng Boon Hua 53 126 76 0.97
## 285 Unknown 46 125 75 1.66
## 286 Lin Mei-Ling 70 108 87 1.32
## 287 Unknown 37 128 62 0.91
## 288 Unknown 77 116 83 1.14
## 289 David Harris 42 114 91 0.97
## 290 Chang Chung-Wei 50 118 84 1.71
## 291 Robert Wilson 44 107 84 0.63
## 292 Hsu Kuo-Chang 70 118 88 1.64
## 293 Unknown 69 91 80 0.10
## 294 Lu Hsiang-Ling 24 94 74 0.56
## 295 Unknown 83 124 82 1.57
## 296 David Harris 44 113 82 1.87
## 297 William Thomas 31 144 97 1.61
## 298 Wu Ming-Hui 23 119 73 1.18
## 299 James Brown 44 122 56 0.30
## 300 Ng Boon Hua 34 125 83 1.85
## 301 Tung Li-Fang 21 155 71 1.61
## 302 Kao Chin-Feng 30 113 77 0.42
## 303 David Harris 64 133 100 1.10
## 304 Linda Martinez 25 121 78 1.10
## 305 Chou Mei-Yu 80 129 70 2.58
## 306 Pan Mei-Hsuan 41 109 76 0.81
## 307 Mary Johnson 66 143 69 2.27
## 308 Richard Martin 56 121 78 1.42
## 309 Wu Ming-Hui 23 136 92 0.98
## 310 Tung Li-Fang 41 97 65 0.10
## 311 Pan Mei-Hsuan 44 115 87 1.29
## 312 Hsieh Shu-Hui 62 124 78 2.05
## 313 Linda Martinez 46 113 89 0.69
## 314 Fang Shu-Chen 40 108 72 0.21
## 315 Lin Mei-Ling 82 117 85 1.58
## 316 Chang Chung-Wei 76 105 68 1.57
## 317 Patricia Davis 53 114 63 1.40
## 318 Tan Wei Ming 60 121 78 1.74
## 319 Tung Li-Fang 34 139 74 1.03
## 320 Tung Li-Fang 75 143 78 2.03
## 321 Ong Lay Kheng 53 143 73 2.28
## 322 Helen Hall 71 155 83 1.45
## 323 Lu Hsiang-Ling 38 146 75 0.85
## 324 Cheng Shu-Fen 62 94 89 1.10
## 325 Chen Wei 60 121 77 1.09
## 326 Charles Clark 51 126 97 1.10
## 327 Tseng Wen-Liang 76 151 74 0.87
## 328 Unknown 44 131 73 0.86
## 329 Unknown 57 134 76 0.87
## 330 Ong Lay Kheng 49 118 81 1.76
## 331 Lu Hsiang-Ling 28 139 84 0.99
## 332 Richard Martin 56 132 96 2.51
## 333 Tan Ah Kow 58 116 71 0.83
## 334 Shen Yi-Ching 52 131 89 1.23
## 335 Patricia Davis 37 136 77 1.76
## 336 Liu Hsiao-Fen 60 110 78 0.58
## 337 Richard Martin 76 121 78 0.68
## 338 Shen Yi-Ching 79 97 71 1.10
## 339 Tseng Wen-Liang 41 115 100 1.59
## 340 Unknown 64 119 81 0.12
## 341 Jessica White 56 102 88 0.36
## 342 Wang Jie 28 103 74 0.10
## 343 Hsu Kuo-Chang 39 111 83 0.19
## 344 Wang Jie 28 154 89 0.10
## 345 Fang Shu-Chen 57 100 77 1.44
## 346 Ong Lay Kheng 37 114 67 0.98
## 347 Joseph Garcia 80 112 76 1.41
## 348 Ong Lay Kheng 53 113 83 1.78
## 349 Joseph Walker 26 121 78 2.12
## 350 Betty Lewis 53 112 62 0.58
## 351 Pan Mei-Hsuan 26 124 91 1.21
## 352 Karen Thompson 67 121 86 1.42
## 353 Lu Hsiang-Ling 28 139 84 0.99
## 354 James Brown 70 137 70 0.97
## 355 Richard Martin 75 123 65 0.71
## 356 Lin Mei-Ling 70 126 88 0.50
## 357 Hsu Kuo-Chang 56 97 76 1.28
## 358 Michael Anderson 59 92 67 1.14
## 359 Tan Ah Kow 53 137 81 0.93
## 360 Tan Wei Ming 63 136 65 1.02
## 361 Karen Thompson 35 110 63 0.70
## 362 Lu Hsiang-Ling 33 111 71 1.10
## 363 Unknown 78 117 80 1.10
## 364 Hsu Kuo-Chang 52 121 78 1.72
## 365 Kao Chin-Feng 28 138 77 1.88
## 366 Charles Clark 24 135 78 1.26
## 367 Wu Ming-Hui 65 124 75 1.40
## 368 Charles Clark 27 142 81 1.15
## 369 Tseng Wen-Liang 84 133 93 1.93
## 370 Ong Lay Kheng 35 105 83 2.00
## 371 Pan Mei-Hsuan 53 148 102 2.42
## 372 Shen Yi-Ching 69 114 77 1.39
## 373 Tsai Chin-Lung 61 121 78 1.75
## 374 Nancy Robinson 67 126 77 1.65
## 375 Linda Martinez 53 121 65 0.51
## 376 Lee Siew Eng 42 133 79 1.75
## 377 Pan Mei-Hsuan 48 118 81 0.72
## 378 Pan Mei-Hsuan 28 114 68 0.39
## 379 Tan Ah Kow 74 121 78 0.77
## 380 Chang Chung-Wei 22 120 89 2.20
## 381 Chang Chung-Wei 58 144 67 0.53
## 382 Liao Chih-Cheng 75 78 76 0.88
## 383 Unknown 43 131 81 1.24
## 384 Liu Hsiao-Fen 27 115 82 1.26
## 385 Richard Martin 55 123 76 0.89
## 386 Tan Wei Ming 34 129 75 0.58
## 387 Mary Johnson 50 128 76 1.04
## 388 Ho Chuan-Wei 40 115 85 1.10
## 389 Tan Wei Ming 75 100 74 1.29
## 390 Wang Jie 66 101 100 1.36
## 391 Kung Mei-Lin 36 117 88 1.61
## 392 Pan Mei-Hsuan 21 119 61 1.12
## 393 Tsai Chin-Lung 85 126 96 1.76
## 394 Lu Hsiang-Ling 73 139 72 1.33
## 395 Lu Hsiang-Ling 62 156 76 0.76
## 396 Unknown 68 128 64 0.71
## 397 Chang Chung-Wei 55 100 84 0.72
## 398 Linda Martinez 80 133 64 1.46
## 399 Tan Wei Ming 78 118 80 1.10
## 400 Richard Martin 47 123 72 0.96
## 401 Joseph Walker 40 143 79 1.74
## 402 Tan Wei Ming 68 82 71 1.19
## 403 Linda Martinez 85 115 68 0.48
## 404 Liu Hsiao-Fen 56 164 70 2.19
## 405 Patricia Davis 31 150 67 0.41
## 406 Unknown 27 128 82 1.64
## 407 Michael Anderson 25 88 84 0.27
## 408 John Smith 43 130 81 0.65
## 409 Richard Martin 59 112 74 0.59
## 410 Joseph Walker 83 107 63 1.82
## 411 Fang Shu-Chen 69 142 72 1.24
## 412 Ong Lay Kheng 32 117 77 0.48
## 413 James Brown 23 138 62 0.10
## 414 Lu Hsiang-Ling 75 126 72 2.08
## 415 Liu Hsiao-Fen 36 154 75 0.83
## 416 Chen Wei 72 123 58 1.10
## 417 Liao Chih-Cheng 38 129 81 1.50
## 418 Karen Thompson 48 156 92 0.99
## 419 Hsieh Shu-Hui 72 105 69 0.42
## 420 Lee Siew Eng 53 115 81 1.77
## 421 Joseph Walker 34 116 68 1.56
## 422 Yang Hsiu-Mei 46 121 83 0.16
## 423 Chang Chung-Wei 61 125 81 1.47
## 424 Ong Lay Kheng 26 119 46 0.68
## 425 Yen Kuo-Jung 20 102 83 0.85
## 426 Hsieh Shu-Hui 27 118 64 0.89
## 427 Huang Li-Chen 47 115 79 0.73
## 428 Chiu Yu-Chin 76 125 85 1.38
## 429 Tung Li-Fang 45 117 79 1.48
## 430 Shen Yi-Ching 56 123 72 1.31
## 431 Lu Hsiang-Ling 70 149 95 2.26
## 432 Unknown 85 84 54 0.51
## 433 Wang Jie 50 109 85 1.43
## 434 William Thomas 52 127 81 1.77
## 435 Chang Chung-Wei 33 125 84 2.21
## 436 James Brown 58 117 64 1.35
## 437 Hsu Kuo-Chang 70 121 102 1.33
## 438 Tung Li-Fang 46 98 86 1.19
## 439 Chou Mei-Yu 26 121 78 0.70
## 440 Chang Chung-Wei 53 131 62 0.80
## 441 Lu Hsiang-Ling 29 123 97 1.10
## 442 William Thomas 63 115 84 1.07
## 443 John Smith 31 139 77 0.98
## 444 Hsu Kuo-Chang 63 116 61 0.38
## 445 Joseph Garcia 41 121 78 0.33
## 446 Ong Lay Kheng 79 132 80 0.49
## 447 Chang Chung-Wei 56 129 74 2.22
## 448 Wu Ming-Hui 68 111 88 0.78
## 449 Wang Jie 74 129 76 1.94
## 450 Hsieh Shu-Hui 26 130 63 0.84
## 451 Fang Shu-Chen 71 131 63 1.14
## 452 Richard Martin 79 108 57 0.95
## 453 Kung Mei-Lin 79 94 78 1.98
## 454 Joseph Walker 73 132 82 0.10
## 455 Shen Yi-Ching 26 104 82 0.62
## 456 Lu Hsiang-Ling 35 114 79 1.72
## 457 James Brown 81 100 86 0.98
## 458 Unknown 66 138 85 2.00
## 459 Liao Chih-Cheng 77 142 73 0.98
## 460 Yen Kuo-Jung 23 101 46 0.92
## 461 Richard Martin 50 80 73 1.19
## 462 Lee Siew Eng 81 113 86 2.11
## 463 David Harris 53 119 74 1.75
## 464 Barbara Taylor 59 92 69 1.23
## 465 Hsieh Shu-Hui 50 135 78 0.89
## 466 Lu Hsiang-Ling 47 97 89 1.56
## 467 Ng Boon Hua 78 117 68 1.73
## 468 Lu Hsiang-Ling 76 132 75 1.49
## 469 Yen Kuo-Jung 58 110 86 1.02
## 470 Fang Shu-Chen 83 128 85 1.10
## 471 Charles Clark 71 101 72 1.24
## 472 Liu Hsiao-Fen 74 145 73 0.41
## 473 Tseng Wen-Liang 29 128 80 0.95
## 474 Patricia Davis 62 122 67 0.82
## 475 Ho Chuan-Wei 47 110 68 1.35
## 476 Huang Li-Chen 84 120 95 1.73
## 477 David Harris 42 127 95 1.20
## 478 Ho Chuan-Wei 85 131 65 0.84
## 479 Patricia Davis 26 128 78 1.10
## 480 Mary Johnson 53 102 77 0.36
## 481 Helen Hall 65 121 78 0.95
## 482 Ng Boon Hua 24 129 82 0.34
## 483 Huang Li-Chen 65 117 87 0.97
## 484 Yen Kuo-Jung 55 124 97 0.55
## 485 William Thomas 27 108 88 1.76
## 486 Unknown 79 141 87 0.70
## 487 Helen Hall 35 115 75 0.79
## 488 Hsieh Shu-Hui 55 146 100 0.93
## 489 William Thomas 38 106 86 0.21
## 490 Yang Hsiu-Mei 31 116 84 0.70
## 491 Michael Anderson 50 122 72 0.71
## 492 Wu Ming-Hui 25 103 80 0.41
## 493 Helen Hall 60 122 85 1.59
## 494 Tseng Wen-Liang 78 94 80 1.56
## 495 Helen Hall 50 134 78 1.75
## 496 Ho Chuan-Wei 66 124 74 0.63
## 497 William Thomas 72 127 90 1.88
## 498 John Smith 84 135 96 1.10
## 499 Ho Chuan-Wei 55 107 77 0.53
## 500 Tung Li-Fang 55 142 86 0.46
## 501 Robert Wilson 38 109 69 2.06
## 502 William Thomas 73 120 76 0.61
## 503 James Brown 35 122 57 0.10
## 504 Nancy Robinson 66 130 76 1.21
## 505 Chang Chung-Wei 61 123 72 0.76
## 506 Liao Chih-Cheng 66 125 68 1.62
## 507 Unknown 30 117 78 0.63
## 508 James Brown 72 124 68 0.84
## 509 Ho Chuan-Wei 43 130 107 0.86
## 510 Hsu Kuo-Chang 56 128 92 1.27
## 511 Unknown 79 137 82 1.08
## 512 Karen Thompson 32 108 73 1.32
## 513 Joseph Garcia 25 121 83 0.26
## 514 Kao Chin-Feng 58 94 83 1.92
## 515 Charles Clark 81 119 67 1.10
## 516 Lin Mei-Ling 73 119 74 1.73
## 517 Unknown 70 121 78 1.62
## 518 Chang Chung-Wei 46 108 71 1.87
## 519 Hsieh Shu-Hui 37 116 65 0.53
## 520 William Thomas 41 135 90 1.48
## 521 Kung Mei-Lin 53 116 84 0.58
## 522 David Harris 67 139 87 1.68
## 523 Tsai Chin-Lung 26 109 70 0.74
## 524 Lu Hsiang-Ling 35 126 69 0.66
## 525 Susan Jackson 38 116 80 0.40
## 526 Shen Yi-Ching 82 122 74 1.05
## 527 Pan Mei-Hsuan 82 121 78 1.03
## 528 Charles Clark 81 129 94 2.12
## 529 Susan Jackson 25 121 72 0.99
## 530 Tung Li-Fang 21 115 82 1.10
## 531 Mary Johnson 30 115 82 1.61
## 532 Huang Li-Chen 65 137 70 1.81
## 533 Joseph Walker 34 122 71 1.56
## 534 Wang Jie 34 138 82 1.76
## 535 Tung Li-Fang 60 132 76 1.59
## 536 Lin Mei-Ling 76 105 72 0.92
## 537 Tan Ah Kow 85 102 87 1.83
## 538 Chang Chung-Wei 40 110 81 1.10
## 539 Yen Kuo-Jung 56 127 88 0.69
## 540 Hsieh Shu-Hui 81 127 98 0.82
## 541 Kung Mei-Lin 60 129 69 1.97
## 542 Unknown 49 114 71 1.04
## 543 Hsu Kuo-Chang 71 127 69 1.42
## 544 Unknown 58 117 66 0.38
## 545 Chiu Yu-Chin 57 134 76 0.87
## 546 Helen Hall 39 132 72 0.10
## 547 James Brown 33 120 81 0.28
## 548 Tan Wei Ming 41 147 76 1.15
## 549 Karen Thompson 35 116 71 0.37
## 550 Helen Hall 34 134 73 2.59
## 551 Chang Chung-Wei 56 123 66 0.84
## 552 Robert Wilson 43 112 62 0.46
## 553 Yen Kuo-Jung 47 121 78 1.10
## 554 Ho Chuan-Wei 83 108 75 0.58
## 555 James Brown 28 121 63 1.42
## 556 Unknown 46 90 76 1.15
## 557 Huang Li-Chen 59 122 76 1.66
## 558 Liao Chih-Cheng 75 78 76 0.88
## 559 Ho Chuan-Wei 54 116 85 2.10
## 560 Jessica White 26 116 80 1.63
## 561 Unknown 72 137 78 0.44
## 562 Liao Chih-Cheng 84 137 98 1.10
## 563 Unknown 75 105 70 1.12
## 564 Unknown 70 107 57 0.72
## 565 Karen Thompson 32 114 73 0.87
## 566 Richard Martin 77 122 58 0.21
## 567 Linda Martinez 49 141 80 0.73
## 568 Yen Kuo-Jung 58 141 86 1.60
## 569 Lu Hsiang-Ling 61 92 75 0.35
## 570 Unknown 46 132 88 0.79
## 571 Ong Lay Kheng 39 114 67 1.10
## 572 Joseph Walker 44 136 76 1.27
## 573 Charles Clark 48 128 79 1.60
## 574 Jessica White 49 106 62 0.87
## 575 Fang Shu-Chen 72 109 79 0.46
## 576 Tsai Chin-Lung 35 130 85 1.58
## 577 Ng Boon Hua 86 146 66 0.42
## 578 Susan Jackson 77 123 80 1.63
## 579 David Harris 30 122 78 0.75
## 580 Shen Yi-Ching 34 124 84 1.50
## 581 Wu Ming-Hui 47 137 82 0.69
## 582 Richard Martin 28 92 89 0.70
## 583 Susan Jackson 24 99 69 0.60
## 584 Hsu Kuo-Chang 51 121 56 0.61
## 585 Chou Mei-Yu 53 89 72 0.83
## 586 Unknown 27 148 79 1.50
## 587 James Brown 39 120 86 0.82
## 588 Unknown 55 96 95 1.63
## 589 Tung Li-Fang 50 113 79 2.24
## 590 Ong Lay Kheng 80 139 87 1.16
## 591 Wu Ming-Hui 25 106 69 1.10
## 592 Tan Wei Ming 36 116 70 0.56
## 593 Barbara Taylor 66 124 82 1.94
## 594 Unknown 53 105 79 1.28
## 595 Shen Yi-Ching 48 102 89 1.43
## 596 Jessica White 40 97 83 1.01
## 597 Patricia Davis 53 132 88 1.82
## 598 Huang Li-Chen 64 132 76 1.13
## 599 Huang Li-Chen 65 140 93 1.10
## 600 Hsu Kuo-Chang 58 127 86 1.16
## 601 Tsai Chin-Lung 38 104 88 1.36
## 602 Robert Wilson 83 125 82 1.98
## 603 Tan Ah Kow 62 125 75 0.41
## 604 Lee Siew Eng 55 130 79 2.30
## 605 Kung Mei-Lin 47 121 78 1.58
## 606 Lu Hsiang-Ling 45 133 103 0.91
## 607 Helen Hall 39 129 83 1.88
## 608 Kao Chin-Feng 64 95 88 0.53
## 609 Joseph Walker 50 117 74 1.24
## 610 Chiu Yu-Chin 81 114 69 1.99
## 611 Hsu Kuo-Chang 24 135 80 0.67
## 612 Tseng Wen-Liang 73 148 89 1.41
## 613 Tung Li-Fang 51 109 65 0.49
## 614 Liu Hsiao-Fen 53 125 71 0.16
## 615 Liu Hsiao-Fen 60 116 88 1.61
## 616 Ng Boon Hua 36 124 82 0.82
## 617 Pan Mei-Hsuan 82 109 86 1.53
## 618 Barbara Taylor 36 155 99 1.10
## 619 Ong Lay Kheng 41 131 68 1.94
## 620 Kung Mei-Lin 52 90 67 0.72
## 621 Betty Lewis 58 127 62 1.63
## 622 Wu Ming-Hui 47 122 77 1.62
## 623 Pan Mei-Hsuan 45 121 78 0.79
## 624 Tung Li-Fang 42 116 90 1.45
## 625 Tung Li-Fang 55 142 86 1.10
## 626 Tan Ah Kow 56 145 88 1.53
## 627 Wu Ming-Hui 46 121 78 0.28
## 628 William Thomas 40 127 83 1.33
## 629 David Harris 59 137 90 2.12
## 630 Tsai Chin-Lung 72 121 78 1.64
## 631 Betty Lewis 36 121 78 2.01
## 632 Liu Hsiao-Fen 67 105 97 0.27
## 633 Ong Lay Kheng 39 118 76 0.57
## 634 Yen Kuo-Jung 66 119 83 1.66
## 635 Unknown 78 156 75 1.21
## 636 Helen Hall 36 133 78 1.37
## 637 Richard Martin 23 113 69 0.32
## 638 Kao Chin-Feng 67 121 78 0.74
## 639 Kung Mei-Lin 52 142 83 1.63
## 640 Ng Boon Hua 79 127 75 1.49
## 641 Unknown 62 143 77 0.78
## 642 Linda Martinez 85 120 58 0.88
## 643 Liu Hsiao-Fen 72 148 91 1.95
## 644 Kung Mei-Lin 40 107 74 0.57
## 645 Tseng Wen-Liang 53 150 82 0.10
## 646 Hsu Kuo-Chang 46 120 62 0.90
## 647 Wu Ming-Hui 79 114 67 0.41
## 648 Joseph Walker 46 114 85 1.23
## 649 Tseng Wen-Liang 83 149 91 1.02
## 650 Unknown 66 107 60 2.01
## 651 Barbara Taylor 50 107 82 0.46
## 652 Chen Wei 53 121 102 0.98
## 653 James Brown 33 86 80 2.57
## 654 Wang Jie 60 121 78 0.10
## 655 Joseph Walker 79 109 74 1.42
## 656 Tung Li-Fang 44 121 78 1.29
## 657 Chen Wei 41 118 87 2.08
## 658 Chiu Yu-Chin 82 123 93 1.74
## 659 Barbara Taylor 53 115 97 1.39
## 660 Unknown 33 145 88 1.71
## 661 Robert Wilson 41 127 87 0.82
## 662 Wang Jie 20 119 56 0.66
## 663 Hsieh Shu-Hui 31 109 52 0.59
## 664 Unknown 61 116 90 1.16
## 665 Unknown 53 110 85 1.20
## 666 John Smith 39 124 84 1.28
## 667 Unknown 38 105 63 0.66
## 668 Wu Ming-Hui 22 122 90 1.10
## 669 Joseph Garcia 53 88 83 1.10
## 670 Patricia Davis 49 100 47 0.43
## 671 Unknown 79 141 76 1.27
## 672 Patricia Davis 45 131 82 1.87
## 673 Tan Ah Kow 28 97 66 0.96
## 674 Huang Li-Chen 66 89 78 1.10
## 675 Tseng Wen-Liang 44 138 77 0.56
## 676 Joseph Walker 76 141 71 1.85
## 677 Lu Hsiang-Ling 53 99 76 0.47
## 678 Richard Martin 37 98 94 1.24
## 679 Cheng Shu-Fen 53 134 70 2.25
## 680 Huang Li-Chen 64 124 73 1.02
## 681 Susan Jackson 63 117 73 1.88
## 682 Kao Chin-Feng 50 121 55 0.65
## 683 Tan Wei Ming 57 96 53 0.53
## 684 Joseph Garcia 45 122 89 0.10
## 685 Wang Jie 81 135 64 1.42
## 686 Tan Wei Ming 23 147 79 1.33
## 687 Yang Hsiu-Mei 30 117 75 2.31
## 688 James Brown 52 103 92 1.10
## 689 Nancy Robinson 78 135 90 0.65
## 690 Tan Wei Ming 28 123 60 0.79
## 691 Liu Hsiao-Fen 53 120 89 1.73
## 692 Tseng Wen-Liang 49 133 75 1.02
## 693 Karen Thompson 21 146 67 2.07
## 694 Chiu Yu-Chin 68 114 82 1.49
## 695 Helen Hall 24 111 73 1.54
## 696 Huang Li-Chen 25 121 78 1.56
## Microcirculation_PU Suhu_Tubuh_Celcius Penyakit
## 1 42.0 37.6 non-diabetic
## 2 41.9 36.5 non-diabetic
## 3 26.3 37.5 non-diabetic
## 4 18.8 37.0 diabetic
## 5 25.5 36.0 diabetic
## 6 42.2 36.8 non-diabetic
## 7 2.0 36.3 diabetic
## 8 9.5 36.4 diabetic
## 9 24.8 36.9 non-diabetic
## 10 40.9 36.6 non-diabetic
## 11 44.0 37.2 non-diabetic
## 12 23.1 36.4 diabetic
## 13 6.5 37.1 diabetic
## 14 20.0 37.1 diabetic
## 15 53.5 36.5 unknown
## 16 31.9 36.6 non-diabetic
## 17 49.5 36.4 non-diabetic
## 18 40.8 37.0 non-diabetic
## 19 21.1 36.6 diabetic
## 20 22.0 36.5 non-diabetic
## 21 51.6 37.0 unknown
## 22 45.2 36.4 non-diabetic
## 23 15.7 36.8 diabetic
## 24 1.0 36.9 diabetic
## 25 56.2 37.1 non-diabetic
## 26 25.1 36.5 non-diabetic
## 27 22.3 36.9 diabetic
## 28 37.0 36.3 non-diabetic
## 29 28.5 36.8 unknown
## 30 39.9 36.5 non-diabetic
## 31 21.3 37.0 non-diabetic
## 32 25.6 36.9 non-diabetic
## 33 8.6 36.6 diabetic
## 34 22.8 36.7 diabetic
## 35 18.8 36.6 diabetic
## 36 4.3 37.4 diabetic
## 37 56.8 37.2 diabetic
## 38 36.3 36.9 non-diabetic
## 39 18.8 37.2 diabetic
## 40 18.8 36.5 diabetic
## 41 21.4 37.0 non-diabetic
## 42 13.7 36.4 diabetic
## 43 35.8 37.2 non-diabetic
## 44 18.8 36.8 diabetic
## 45 35.3 35.7 non-diabetic
## 46 52.8 36.3 non-diabetic
## 47 34.7 36.6 non-diabetic
## 48 37.0 36.6 non-diabetic
## 49 18.8 36.8 diabetic
## 50 21.6 36.5 diabetic
## 51 10.4 36.6 diabetic
## 52 42.3 37.1 non-diabetic
## 53 49.4 37.4 non-diabetic
## 54 19.5 36.7 diabetic
## 55 37.6 36.2 non-diabetic
## 56 13.3 36.8 diabetic
## 57 46.3 36.0 non-diabetic
## 58 37.0 37.1 non-diabetic
## 59 28.1 36.9 diabetic
## 60 37.0 36.3 non-diabetic
## 61 28.5 36.8 non-diabetic
## 62 14.5 36.9 diabetic
## 63 37.0 37.4 non-diabetic
## 64 28.3 37.0 non-diabetic
## 65 34.6 36.8 diabetic
## 66 48.7 36.8 non-diabetic
## 67 43.8 36.7 non-diabetic
## 68 33.2 37.3 non-diabetic
## 69 19.9 36.8 diabetic
## 70 34.3 36.1 non-diabetic
## 71 41.1 36.7 diabetic
## 72 34.9 36.9 non-diabetic
## 73 22.1 37.0 non-diabetic
## 74 36.5 37.2 non-diabetic
## 75 32.5 37.6 non-diabetic
## 76 48.6 37.1 unknown
## 77 10.9 36.7 non-diabetic
## 78 28.3 36.8 diabetic
## 79 19.3 36.8 diabetic
## 80 45.1 37.0 non-diabetic
## 81 45.0 36.6 non-diabetic
## 82 43.4 36.8 non-diabetic
## 83 32.6 37.1 diabetic
## 84 15.0 36.2 non-diabetic
## 85 14.4 36.7 diabetic
## 86 37.9 37.2 diabetic
## 87 37.4 36.4 non-diabetic
## 88 29.4 36.8 non-diabetic
## 89 40.6 36.9 non-diabetic
## 90 34.5 36.8 non-diabetic
## 91 36.5 37.1 non-diabetic
## 92 21.9 36.4 diabetic
## 93 29.0 37.0 diabetic
## 94 23.9 35.7 diabetic
## 95 45.6 36.8 non-diabetic
## 96 22.1 37.1 diabetic
## 97 22.2 36.8 non-diabetic
## 98 53.0 36.7 non-diabetic
## 99 35.1 36.8 non-diabetic
## 100 26.7 36.7 non-diabetic
## 101 14.2 36.9 diabetic
## 102 36.0 36.7 non-diabetic
## 103 17.5 37.3 non-diabetic
## 104 6.7 36.6 diabetic
## 105 32.9 36.4 non-diabetic
## 106 18.8 36.9 diabetic
## 107 37.0 37.0 non-diabetic
## 108 36.9 37.0 diabetic
## 109 9.0 36.8 diabetic
## 110 17.7 36.8 diabetic
## 111 18.8 36.8 diabetic
## 112 35.5 37.2 non-diabetic
## 113 22.8 37.4 non-diabetic
## 114 23.2 36.5 non-diabetic
## 115 10.0 37.2 diabetic
## 116 24.2 36.5 diabetic
## 117 48.4 36.2 non-diabetic
## 118 10.3 37.0 diabetic
## 119 30.7 36.7 non-diabetic
## 120 5.0 36.6 diabetic
## 121 46.8 36.8 non-diabetic
## 122 41.8 36.9 non-diabetic
## 123 11.7 36.8 diabetic
## 124 19.5 36.4 diabetic
## 125 28.5 36.8 diabetic
## 126 26.7 36.9 non-diabetic
## 127 14.9 37.2 diabetic
## 128 7.0 36.4 diabetic
## 129 24.6 36.4 diabetic
## 130 37.0 37.1 non-diabetic
## 131 21.5 36.0 diabetic
## 132 43.2 37.0 non-diabetic
## 133 10.2 37.3 diabetic
## 134 53.7 36.3 non-diabetic
## 135 33.0 37.1 diabetic
## 136 35.9 36.7 non-diabetic
## 137 40.2 36.8 non-diabetic
## 138 8.2 36.6 diabetic
## 139 20.1 36.3 diabetic
## 140 22.6 36.3 non-diabetic
## 141 35.3 36.7 non-diabetic
## 142 22.2 36.4 unknown
## 143 28.7 36.2 non-diabetic
## 144 15.6 37.2 diabetic
## 145 21.0 37.0 diabetic
## 146 22.9 37.1 diabetic
## 147 8.8 36.8 diabetic
## 148 47.3 36.2 non-diabetic
## 149 47.0 36.4 non-diabetic
## 150 9.0 36.2 diabetic
## 151 36.5 37.1 diabetic
## 152 20.6 36.7 unknown
## 153 19.4 36.0 diabetic
## 154 34.9 36.9 non-diabetic
## 155 6.0 37.1 diabetic
## 156 11.9 36.9 diabetic
## 157 33.9 36.4 non-diabetic
## 158 37.0 36.6 non-diabetic
## 159 49.7 36.7 non-diabetic
## 160 24.4 36.5 non-diabetic
## 161 41.1 37.1 non-diabetic
## 162 20.3 36.9 diabetic
## 163 41.8 36.8 non-diabetic
## 164 26.5 36.4 diabetic
## 165 43.5 37.0 non-diabetic
## 166 27.7 37.0 non-diabetic
## 167 43.7 37.4 non-diabetic
## 168 21.3 37.1 unknown
## 169 52.9 36.2 non-diabetic
## 170 39.0 36.6 diabetic
## 171 10.2 36.4 diabetic
## 172 4.7 36.7 diabetic
## 173 1.0 36.6 diabetic
## 174 21.9 37.2 unknown
## 175 53.2 36.8 unknown
## 176 49.9 37.8 non-diabetic
## 177 15.9 37.0 diabetic
## 178 17.9 36.3 diabetic
## 179 43.8 36.5 non-diabetic
## 180 10.4 36.7 diabetic
## 181 29.3 36.9 diabetic
## 182 27.0 37.0 non-diabetic
## 183 28.7 36.8 diabetic
## 184 32.5 36.4 non-diabetic
## 185 30.0 37.4 non-diabetic
## 186 17.9 37.0 diabetic
## 187 16.0 36.8 non-diabetic
## 188 31.6 37.5 non-diabetic
## 189 8.5 36.8 diabetic
## 190 45.7 36.2 non-diabetic
## 191 10.4 37.2 diabetic
## 192 1.0 37.2 diabetic
## 193 41.7 36.8 non-diabetic
## 194 1.0 37.6 diabetic
## 195 6.9 36.8 diabetic
## 196 38.2 36.4 non-diabetic
## 197 41.4 37.2 non-diabetic
## 198 29.3 36.3 diabetic
## 199 45.5 36.1 non-diabetic
## 200 24.1 36.8 non-diabetic
## 201 42.9 37.5 non-diabetic
## 202 15.7 36.1 diabetic
## 203 48.9 36.8 non-diabetic
## 204 37.0 36.2 non-diabetic
## 205 34.9 36.6 non-diabetic
## 206 18.0 36.8 diabetic
## 207 22.2 37.4 diabetic
## 208 26.5 36.8 diabetic
## 209 18.8 36.8 diabetic
## 210 10.9 36.8 diabetic
## 211 16.0 36.4 diabetic
## 212 27.0 36.6 diabetic
## 213 15.5 36.9 diabetic
## 214 23.9 36.8 non-diabetic
## 215 8.6 37.1 unknown
## 216 37.4 37.1 diabetic
## 217 25.3 36.4 diabetic
## 218 27.3 36.3 diabetic
## 219 18.1 36.5 diabetic
## 220 35.8 37.0 non-diabetic
## 221 19.2 37.1 non-diabetic
## 222 22.6 36.3 diabetic
## 223 26.8 36.3 non-diabetic
## 224 37.0 36.4 non-diabetic
## 225 37.0 36.6 non-diabetic
## 226 31.9 37.1 non-diabetic
## 227 21.8 36.9 diabetic
## 228 18.8 36.8 diabetic
## 229 14.6 37.0 diabetic
## 230 43.7 37.4 unknown
## 231 14.1 36.8 non-diabetic
## 232 1.0 36.3 diabetic
## 233 10.9 36.7 diabetic
## 234 23.5 36.9 diabetic
## 235 16.2 37.0 diabetic
## 236 28.6 36.7 diabetic
## 237 36.0 37.1 diabetic
## 238 36.3 37.3 diabetic
## 239 22.9 36.1 non-diabetic
## 240 47.4 36.4 non-diabetic
## 241 22.2 36.9 diabetic
## 242 7.0 36.4 diabetic
## 243 5.6 36.8 diabetic
## 244 42.4 37.2 non-diabetic
## 245 36.2 36.5 unknown
## 246 22.7 37.4 diabetic
## 247 37.0 37.4 non-diabetic
## 248 20.5 36.6 non-diabetic
## 249 35.2 36.7 non-diabetic
## 250 40.4 36.8 non-diabetic
## 251 41.4 37.2 non-diabetic
## 252 8.7 36.8 diabetic
## 253 37.2 36.8 non-diabetic
## 254 10.4 35.9 diabetic
## 255 37.2 37.6 non-diabetic
## 256 37.0 37.3 non-diabetic
## 257 34.0 36.4 non-diabetic
## 258 48.9 36.6 non-diabetic
## 259 28.5 36.8 unknown
## 260 49.4 37.4 non-diabetic
## 261 37.4 36.5 non-diabetic
## 262 18.8 36.8 diabetic
## 263 15.7 36.9 unknown
## 264 18.8 37.4 diabetic
## 265 2.0 36.7 diabetic
## 266 27.3 37.2 non-diabetic
## 267 18.0 37.0 diabetic
## 268 15.0 36.9 diabetic
## 269 10.5 36.6 diabetic
## 270 28.0 36.6 non-diabetic
## 271 10.6 36.2 diabetic
## 272 41.8 36.4 non-diabetic
## 273 40.1 36.9 non-diabetic
## 274 54.3 36.6 non-diabetic
## 275 22.7 37.6 diabetic
## 276 9.1 35.9 diabetic
## 277 14.6 36.6 diabetic
## 278 27.7 36.9 non-diabetic
## 279 38.8 37.0 diabetic
## 280 44.7 36.4 non-diabetic
## 281 22.7 37.5 non-diabetic
## 282 12.7 36.6 diabetic
## 283 77.3 36.0 non-diabetic
## 284 28.8 36.8 diabetic
## 285 46.9 37.0 non-diabetic
## 286 32.3 36.5 non-diabetic
## 287 12.4 36.4 diabetic
## 288 21.8 36.6 unknown
## 289 35.0 37.1 non-diabetic
## 290 18.8 37.0 diabetic
## 291 50.5 36.5 non-diabetic
## 292 23.8 37.6 diabetic
## 293 25.7 36.0 non-diabetic
## 294 28.3 36.7 non-diabetic
## 295 37.0 37.0 non-diabetic
## 296 15.9 37.2 diabetic
## 297 10.5 37.0 unknown
## 298 1.0 37.6 diabetic
## 299 47.3 36.7 non-diabetic
## 300 7.8 36.7 diabetic
## 301 32.1 36.8 unknown
## 302 58.9 36.2 non-diabetic
## 303 37.0 37.0 non-diabetic
## 304 18.0 36.3 diabetic
## 305 19.3 36.8 diabetic
## 306 27.7 37.3 non-diabetic
## 307 22.2 36.5 diabetic
## 308 1.0 36.6 diabetic
## 309 29.2 37.6 non-diabetic
## 310 37.0 36.6 non-diabetic
## 311 1.0 36.8 unknown
## 312 27.5 36.5 diabetic
## 313 37.0 36.7 non-diabetic
## 314 29.8 36.6 non-diabetic
## 315 22.8 36.8 diabetic
## 316 38.0 37.1 non-diabetic
## 317 23.7 36.6 non-diabetic
## 318 13.1 36.8 diabetic
## 319 33.0 36.7 diabetic
## 320 24.0 37.7 diabetic
## 321 1.0 37.0 diabetic
## 322 19.8 36.3 diabetic
## 323 28.1 36.5 non-diabetic
## 324 43.0 36.2 non-diabetic
## 325 36.6 37.0 non-diabetic
## 326 3.3 36.9 unknown
## 327 22.8 37.4 non-diabetic
## 328 41.2 36.5 unknown
## 329 34.1 36.9 non-diabetic
## 330 20.6 37.3 diabetic
## 331 16.0 36.9 diabetic
## 332 14.0 37.0 diabetic
## 333 30.4 36.3 non-diabetic
## 334 17.4 35.5 unknown
## 335 23.5 37.0 diabetic
## 336 37.3 37.6 non-diabetic
## 337 55.1 36.8 non-diabetic
## 338 36.6 36.8 diabetic
## 339 7.4 37.1 diabetic
## 340 32.7 37.1 non-diabetic
## 341 54.3 36.7 non-diabetic
## 342 39.8 37.4 non-diabetic
## 343 48.5 36.3 non-diabetic
## 344 39.8 37.4 non-diabetic
## 345 60.3 37.1 non-diabetic
## 346 24.1 36.3 diabetic
## 347 21.6 37.7 diabetic
## 348 19.9 36.4 diabetic
## 349 18.8 36.6 diabetic
## 350 40.6 36.4 non-diabetic
## 351 23.0 36.6 diabetic
## 352 37.0 37.2 non-diabetic
## 353 16.0 36.8 diabetic
## 354 36.0 36.7 non-diabetic
## 355 20.2 36.8 non-diabetic
## 356 37.0 36.0 non-diabetic
## 357 11.8 36.7 diabetic
## 358 54.2 36.3 non-diabetic
## 359 37.0 36.5 non-diabetic
## 360 49.9 36.1 non-diabetic
## 361 32.4 35.6 non-diabetic
## 362 42.6 36.9 non-diabetic
## 363 37.0 36.3 non-diabetic
## 364 38.8 37.1 unknown
## 365 46.1 36.8 diabetic
## 366 40.9 36.3 non-diabetic
## 367 32.4 36.3 diabetic
## 368 14.1 36.9 diabetic
## 369 23.1 37.2 diabetic
## 370 18.8 36.8 diabetic
## 371 30.1 37.2 diabetic
## 372 19.5 36.5 diabetic
## 373 1.0 37.1 diabetic
## 374 23.4 36.8 diabetic
## 375 29.6 36.6 non-diabetic
## 376 12.0 36.8 diabetic
## 377 42.9 36.8 non-diabetic
## 378 29.8 36.8 non-diabetic
## 379 43.6 37.5 non-diabetic
## 380 18.7 37.5 diabetic
## 381 40.0 36.8 non-diabetic
## 382 37.0 36.4 non-diabetic
## 383 21.2 36.3 unknown
## 384 14.2 37.2 unknown
## 385 39.9 37.0 non-diabetic
## 386 17.7 36.4 non-diabetic
## 387 37.2 36.8 non-diabetic
## 388 20.6 36.6 diabetic
## 389 8.6 37.1 diabetic
## 390 44.0 36.8 non-diabetic
## 391 3.9 36.4 diabetic
## 392 31.3 37.5 non-diabetic
## 393 18.8 37.4 diabetic
## 394 37.2 36.1 non-diabetic
## 395 37.8 36.9 non-diabetic
## 396 30.0 36.6 non-diabetic
## 397 38.7 36.8 non-diabetic
## 398 21.0 36.9 diabetic
## 399 3.0 36.9 unknown
## 400 20.0 36.5 diabetic
## 401 12.3 36.1 diabetic
## 402 22.3 35.6 diabetic
## 403 36.6 36.5 non-diabetic
## 404 28.0 36.9 diabetic
## 405 32.0 36.0 non-diabetic
## 406 10.3 36.8 diabetic
## 407 45.3 36.8 non-diabetic
## 408 34.3 36.6 non-diabetic
## 409 26.2 36.6 non-diabetic
## 410 25.6 36.8 diabetic
## 411 53.2 37.2 non-diabetic
## 412 37.0 36.3 non-diabetic
## 413 39.8 37.5 non-diabetic
## 414 16.4 36.8 diabetic
## 415 48.5 37.2 non-diabetic
## 416 28.5 36.5 unknown
## 417 8.9 36.6 diabetic
## 418 13.2 37.3 diabetic
## 419 27.3 37.6 non-diabetic
## 420 22.2 36.1 diabetic
## 421 43.8 36.5 diabetic
## 422 49.4 36.9 diabetic
## 423 19.6 37.1 diabetic
## 424 21.7 37.3 non-diabetic
## 425 32.4 36.8 non-diabetic
## 426 37.0 37.2 non-diabetic
## 427 50.2 36.0 non-diabetic
## 428 10.2 36.9 diabetic
## 429 34.6 36.9 non-diabetic
## 430 18.8 37.2 diabetic
## 431 25.4 36.8 diabetic
## 432 42.6 35.7 non-diabetic
## 433 34.0 37.1 non-diabetic
## 434 1.0 37.1 diabetic
## 435 23.1 37.0 diabetic
## 436 46.8 36.7 non-diabetic
## 437 14.0 37.6 diabetic
## 438 4.5 36.2 diabetic
## 439 42.6 36.6 non-diabetic
## 440 57.4 36.4 non-diabetic
## 441 15.3 36.6 diabetic
## 442 20.5 36.8 non-diabetic
## 443 13.7 37.4 diabetic
## 444 28.0 36.9 non-diabetic
## 445 26.3 37.0 diabetic
## 446 55.8 36.3 unknown
## 447 19.2 37.3 diabetic
## 448 46.5 37.0 non-diabetic
## 449 4.4 36.4 diabetic
## 450 51.1 36.2 non-diabetic
## 451 15.2 36.7 diabetic
## 452 51.7 36.7 non-diabetic
## 453 19.9 37.6 diabetic
## 454 58.4 36.9 non-diabetic
## 455 44.0 36.6 non-diabetic
## 456 32.8 37.1 diabetic
## 457 31.0 36.2 non-diabetic
## 458 1.0 37.6 unknown
## 459 41.1 38.0 non-diabetic
## 460 47.0 36.8 non-diabetic
## 461 57.6 36.3 non-diabetic
## 462 1.0 37.0 diabetic
## 463 21.1 37.3 diabetic
## 464 28.2 36.6 non-diabetic
## 465 33.9 36.6 non-diabetic
## 466 22.2 36.5 diabetic
## 467 17.4 37.0 diabetic
## 468 37.0 36.7 non-diabetic
## 469 56.5 37.0 non-diabetic
## 470 52.8 36.3 non-diabetic
## 471 39.4 37.7 non-diabetic
## 472 37.0 36.4 non-diabetic
## 473 32.7 35.9 unknown
## 474 51.0 37.0 non-diabetic
## 475 26.6 37.2 diabetic
## 476 19.9 36.3 diabetic
## 477 29.4 37.3 diabetic
## 478 44.9 36.3 non-diabetic
## 479 28.4 36.8 non-diabetic
## 480 30.1 36.6 non-diabetic
## 481 37.0 36.8 non-diabetic
## 482 35.6 36.8 non-diabetic
## 483 34.3 37.1 non-diabetic
## 484 26.2 36.8 diabetic
## 485 18.8 36.5 diabetic
## 486 38.8 36.7 unknown
## 487 40.5 36.8 non-diabetic
## 488 7.8 36.7 diabetic
## 489 33.8 36.8 non-diabetic
## 490 34.2 37.0 non-diabetic
## 491 32.7 36.2 non-diabetic
## 492 39.0 36.3 non-diabetic
## 493 6.3 36.8 diabetic
## 494 24.4 36.9 diabetic
## 495 17.7 37.3 diabetic
## 496 26.8 37.1 diabetic
## 497 23.8 36.5 diabetic
## 498 20.4 36.8 diabetic
## 499 35.3 37.5 non-diabetic
## 500 41.6 36.3 non-diabetic
## 501 25.9 37.0 diabetic
## 502 53.6 36.5 unknown
## 503 46.4 36.8 unknown
## 504 34.1 36.4 non-diabetic
## 505 50.8 36.9 non-diabetic
## 506 49.9 36.9 diabetic
## 507 51.6 36.8 non-diabetic
## 508 19.4 37.0 non-diabetic
## 509 18.8 37.1 diabetic
## 510 18.8 36.8 diabetic
## 511 5.0 36.7 diabetic
## 512 13.1 37.3 non-diabetic
## 513 24.8 37.0 non-diabetic
## 514 16.8 36.6 diabetic
## 515 43.4 36.4 non-diabetic
## 516 34.5 36.9 diabetic
## 517 25.7 36.6 diabetic
## 518 6.5 37.1 diabetic
## 519 26.0 37.6 non-diabetic
## 520 17.3 36.2 diabetic
## 521 37.0 36.8 non-diabetic
## 522 28.0 35.7 diabetic
## 523 32.6 36.5 non-diabetic
## 524 45.4 37.0 non-diabetic
## 525 33.6 37.1 non-diabetic
## 526 10.7 36.8 diabetic
## 527 32.4 37.1 non-diabetic
## 528 23.4 37.1 diabetic
## 529 44.8 36.9 non-diabetic
## 530 51.0 36.5 non-diabetic
## 531 22.8 36.7 diabetic
## 532 32.4 35.8 diabetic
## 533 43.8 36.5 diabetic
## 534 18.8 36.6 diabetic
## 535 39.4 37.5 unknown
## 536 32.4 36.5 non-diabetic
## 537 17.4 37.2 diabetic
## 538 38.2 36.9 non-diabetic
## 539 33.5 36.8 non-diabetic
## 540 40.5 36.6 non-diabetic
## 541 16.2 36.8 diabetic
## 542 35.5 36.3 diabetic
## 543 18.9 36.8 non-diabetic
## 544 31.3 36.9 non-diabetic
## 545 34.1 36.9 non-diabetic
## 546 60.9 36.2 non-diabetic
## 547 29.0 36.8 non-diabetic
## 548 51.4 36.5 non-diabetic
## 549 44.1 36.3 non-diabetic
## 550 20.6 37.3 diabetic
## 551 37.0 36.8 non-diabetic
## 552 43.2 36.9 non-diabetic
## 553 37.9 37.2 diabetic
## 554 31.1 36.2 unknown
## 555 7.6 36.6 diabetic
## 556 33.0 36.5 non-diabetic
## 557 18.8 37.2 diabetic
## 558 47.2 36.4 non-diabetic
## 559 13.5 36.5 diabetic
## 560 1.0 37.0 diabetic
## 561 14.3 36.4 diabetic
## 562 28.8 35.9 diabetic
## 563 41.3 36.6 non-diabetic
## 564 36.8 37.4 non-diabetic
## 565 46.2 37.1 non-diabetic
## 566 45.4 36.9 non-diabetic
## 567 26.5 36.6 unknown
## 568 12.1 36.4 diabetic
## 569 27.0 36.8 non-diabetic
## 570 40.0 36.6 unknown
## 571 45.2 36.8 non-diabetic
## 572 18.0 36.7 diabetic
## 573 14.7 36.7 unknown
## 574 37.0 36.7 non-diabetic
## 575 45.6 37.1 non-diabetic
## 576 30.2 37.2 unknown
## 577 45.6 36.8 non-diabetic
## 578 29.6 36.4 diabetic
## 579 35.7 36.2 non-diabetic
## 580 21.5 36.3 diabetic
## 581 25.8 36.7 non-diabetic
## 582 50.4 36.2 non-diabetic
## 583 34.8 37.3 non-diabetic
## 584 15.8 37.3 non-diabetic
## 585 42.5 36.6 non-diabetic
## 586 15.0 36.8 unknown
## 587 24.6 36.6 non-diabetic
## 588 18.8 36.8 diabetic
## 589 26.0 37.1 unknown
## 590 14.7 36.5 diabetic
## 591 29.7 36.8 non-diabetic
## 592 45.0 36.6 non-diabetic
## 593 1.0 36.6 diabetic
## 594 17.4 36.8 diabetic
## 595 6.7 36.6 diabetic
## 596 59.6 36.7 non-diabetic
## 597 18.9 36.7 diabetic
## 598 1.0 36.8 diabetic
## 599 13.9 36.8 diabetic
## 600 32.1 36.8 diabetic
## 601 35.2 37.6 diabetic
## 602 12.8 36.8 diabetic
## 603 37.0 36.6 non-diabetic
## 604 18.8 36.6 diabetic
## 605 18.8 36.9 diabetic
## 606 32.9 36.8 non-diabetic
## 607 13.6 37.0 diabetic
## 608 63.6 36.6 non-diabetic
## 609 27.2 36.5 non-diabetic
## 610 1.0 36.8 unknown
## 611 32.7 37.2 non-diabetic
## 612 23.4 36.6 diabetic
## 613 25.8 36.8 non-diabetic
## 614 35.9 37.3 non-diabetic
## 615 2.6 36.9 diabetic
## 616 34.3 36.5 non-diabetic
## 617 27.9 37.3 diabetic
## 618 27.7 37.1 diabetic
## 619 5.7 37.0 diabetic
## 620 43.4 35.9 non-diabetic
## 621 13.2 37.0 diabetic
## 622 1.0 37.0 diabetic
## 623 33.0 36.2 non-diabetic
## 624 17.5 36.8 diabetic
## 625 41.6 36.3 non-diabetic
## 626 19.5 37.3 diabetic
## 627 49.1 37.1 unknown
## 628 20.0 36.6 diabetic
## 629 18.2 37.1 diabetic
## 630 4.7 36.6 diabetic
## 631 22.7 36.9 diabetic
## 632 41.1 37.1 non-diabetic
## 633 44.2 36.4 non-diabetic
## 634 18.0 37.0 diabetic
## 635 31.0 36.8 non-diabetic
## 636 28.0 37.2 non-diabetic
## 637 37.0 38.0 non-diabetic
## 638 46.2 37.4 non-diabetic
## 639 23.5 36.5 diabetic
## 640 27.3 36.2 non-diabetic
## 641 7.9 36.1 diabetic
## 642 36.5 36.6 non-diabetic
## 643 13.4 36.4 diabetic
## 644 30.1 36.8 non-diabetic
## 645 39.9 36.8 non-diabetic
## 646 44.1 37.0 non-diabetic
## 647 39.0 36.3 non-diabetic
## 648 25.7 37.0 non-diabetic
## 649 29.9 36.8 non-diabetic
## 650 20.7 37.0 diabetic
## 651 40.2 36.8 non-diabetic
## 652 20.4 37.1 non-diabetic
## 653 25.8 36.8 diabetic
## 654 20.1 36.6 non-diabetic
## 655 28.6 37.4 unknown
## 656 26.3 37.2 diabetic
## 657 13.4 36.6 diabetic
## 658 22.6 37.0 diabetic
## 659 1.0 36.4 diabetic
## 660 8.1 36.5 diabetic
## 661 25.0 37.0 non-diabetic
## 662 37.0 36.8 non-diabetic
## 663 47.1 37.0 non-diabetic
## 664 20.2 37.3 diabetic
## 665 11.7 36.9 diabetic
## 666 22.2 36.6 diabetic
## 667 42.7 37.2 non-diabetic
## 668 27.2 36.8 non-diabetic
## 669 40.4 37.6 non-diabetic
## 670 47.6 37.0 non-diabetic
## 671 18.8 37.1 diabetic
## 672 23.9 36.7 diabetic
## 673 43.1 37.0 non-diabetic
## 674 29.8 37.1 non-diabetic
## 675 20.0 37.0 diabetic
## 676 7.5 36.6 diabetic
## 677 25.4 36.7 unknown
## 678 11.5 36.6 diabetic
## 679 6.5 36.9 diabetic
## 680 57.0 36.7 unknown
## 681 14.1 37.2 diabetic
## 682 31.2 36.4 non-diabetic
## 683 25.8 36.6 non-diabetic
## 684 39.2 36.8 non-diabetic
## 685 53.2 37.1 non-diabetic
## 686 28.4 36.6 diabetic
## 687 18.8 36.3 diabetic
## 688 26.3 36.7 non-diabetic
## 689 38.5 36.4 non-diabetic
## 690 35.4 37.1 non-diabetic
## 691 21.2 36.8 diabetic
## 692 27.3 36.2 non-diabetic
## 693 18.1 36.4 diabetic
## 694 49.3 36.7 non-diabetic
## 695 21.1 36.6 diabetic
## 696 50.9 36.6 non-diabetic
## Peak_Plantar_Pressure_kPa
## 1 294.0
## 2 385.7
## 3 431.8
## 4 577.5
## 5 502.3
## 6 201.4
## 7 512.8
## 8 327.7
## 9 385.7
## 10 308.9
## 11 385.7
## 12 327.8
## 13 623.0
## 14 513.7
## 15 254.2
## 16 385.7
## 17 284.9
## 18 294.9
## 19 536.5
## 20 338.6
## 21 430.1
## 22 340.6
## 23 386.0
## 24 677.0
## 25 324.2
## 26 206.5
## 27 602.3
## 28 173.3
## 29 554.5
## 30 174.4
## 31 337.3
## 32 267.6
## 33 415.3
## 34 557.1
## 35 612.7
## 36 566.3
## 37 233.8
## 38 250.4
## 39 385.7
## 40 667.4
## 41 296.6
## 42 367.6
## 43 396.2
## 44 549.7
## 45 275.7
## 46 385.7
## 47 162.9
## 48 348.0
## 49 404.4
## 50 497.5
## 51 387.5
## 52 340.3
## 53 385.7
## 54 495.4
## 55 373.2
## 56 606.1
## 57 309.9
## 58 351.0
## 59 481.8
## 60 253.2
## 61 410.0
## 62 476.4
## 63 354.1
## 64 320.4
## 65 518.5
## 66 352.3
## 67 203.2
## 68 208.4
## 69 538.7
## 70 206.0
## 71 491.5
## 72 358.0
## 73 233.8
## 74 456.8
## 75 218.0
## 76 254.8
## 77 437.3
## 78 229.9
## 79 385.7
## 80 310.4
## 81 289.8
## 82 337.5
## 83 563.1
## 84 389.3
## 85 559.2
## 86 508.5
## 87 238.8
## 88 213.5
## 89 261.2
## 90 255.7
## 91 356.5
## 92 512.3
## 93 607.7
## 94 449.6
## 95 311.1
## 96 651.7
## 97 390.4
## 98 263.1
## 99 319.5
## 100 237.0
## 101 551.7
## 102 220.3
## 103 253.1
## 104 539.1
## 105 398.5
## 106 548.7
## 107 475.2
## 108 393.2
## 109 499.3
## 110 697.1
## 111 544.1
## 112 223.0
## 113 423.7
## 114 375.4
## 115 501.7
## 116 404.8
## 117 53.4
## 118 598.4
## 119 390.0
## 120 514.5
## 121 398.4
## 122 319.4
## 123 504.1
## 124 385.7
## 125 710.6
## 126 212.5
## 127 538.6
## 128 643.9
## 129 563.0
## 130 302.3
## 131 503.8
## 132 392.1
## 133 570.9
## 134 162.9
## 135 495.6
## 136 385.7
## 137 302.8
## 138 530.2
## 139 457.4
## 140 513.8
## 141 320.0
## 142 235.1
## 143 228.0
## 144 514.9
## 145 408.9
## 146 385.7
## 147 385.7
## 148 139.1
## 149 401.5
## 150 520.6
## 151 593.0
## 152 511.5
## 153 410.6
## 154 358.0
## 155 608.5
## 156 627.1
## 157 332.1
## 158 311.9
## 159 276.5
## 160 436.3
## 161 330.4
## 162 442.9
## 163 274.7
## 164 571.2
## 165 259.6
## 166 232.8
## 167 172.6
## 168 660.8
## 169 213.7
## 170 502.6
## 171 640.2
## 172 385.7
## 173 475.6
## 174 550.8
## 175 422.5
## 176 237.5
## 177 512.6
## 178 529.1
## 179 348.4
## 180 426.1
## 181 473.1
## 182 342.3
## 183 385.7
## 184 290.8
## 185 258.3
## 186 553.5
## 187 239.8
## 188 254.3
## 189 591.0
## 190 304.0
## 191 632.4
## 192 602.0
## 193 125.8
## 194 488.8
## 195 517.0
## 196 384.3
## 197 280.3
## 198 385.7
## 199 323.5
## 200 509.4
## 201 264.7
## 202 468.6
## 203 193.8
## 204 167.4
## 205 265.0
## 206 601.8
## 207 536.3
## 208 537.1
## 209 350.6
## 210 428.3
## 211 433.8
## 212 511.7
## 213 520.8
## 214 416.3
## 215 295.6
## 216 496.2
## 217 477.3
## 218 482.0
## 219 551.9
## 220 241.8
## 221 129.3
## 222 513.8
## 223 372.9
## 224 276.2
## 225 364.6
## 226 303.9
## 227 385.7
## 228 474.5
## 229 506.2
## 230 354.0
## 231 553.0
## 232 336.9
## 233 437.3
## 234 609.1
## 235 571.0
## 236 561.8
## 237 480.6
## 238 385.7
## 239 153.3
## 240 311.8
## 241 385.7
## 242 385.7
## 243 522.2
## 244 198.3
## 245 385.7
## 246 528.4
## 247 328.4
## 248 292.6
## 249 260.7
## 250 370.2
## 251 385.7
## 252 432.1
## 253 195.2
## 254 595.6
## 255 169.1
## 256 114.1
## 257 313.9
## 258 344.6
## 259 546.3
## 260 198.8
## 261 257.5
## 262 409.9
## 263 235.1
## 264 458.2
## 265 482.9
## 266 416.8
## 267 595.3
## 268 407.9
## 269 591.9
## 270 300.0
## 271 517.5
## 272 255.2
## 273 192.7
## 274 263.7
## 275 607.6
## 276 442.5
## 277 475.8
## 278 224.0
## 279 534.4
## 280 385.7
## 281 250.2
## 282 455.6
## 283 418.0
## 284 166.9
## 285 177.9
## 286 317.7
## 287 525.1
## 288 476.7
## 289 253.3
## 290 462.0
## 291 302.3
## 292 374.9
## 293 273.1
## 294 319.3
## 295 205.9
## 296 385.7
## 297 445.8
## 298 411.7
## 299 238.1
## 300 366.5
## 301 536.2
## 302 294.8
## 303 348.3
## 304 536.0
## 305 558.1
## 306 210.7
## 307 385.7
## 308 385.7
## 309 385.7
## 310 202.7
## 311 503.8
## 312 532.6
## 313 281.2
## 314 350.0
## 315 397.4
## 316 216.7
## 317 290.5
## 318 502.7
## 319 479.9
## 320 385.7
## 321 538.0
## 322 422.3
## 323 563.3
## 324 185.6
## 325 271.7
## 326 504.1
## 327 186.6
## 328 359.4
## 329 252.5
## 330 305.9
## 331 514.0
## 332 545.6
## 333 329.8
## 334 418.0
## 335 433.9
## 336 260.2
## 337 150.9
## 338 400.9
## 339 402.3
## 340 179.7
## 341 407.2
## 342 162.1
## 343 302.9
## 344 162.1
## 345 320.1
## 346 640.4
## 347 442.7
## 348 516.5
## 349 483.3
## 350 203.4
## 351 436.6
## 352 276.9
## 353 514.0
## 354 367.9
## 355 213.4
## 356 233.2
## 357 462.2
## 358 232.6
## 359 354.2
## 360 305.6
## 361 444.2
## 362 304.9
## 363 196.6
## 364 473.9
## 365 385.7
## 366 173.3
## 367 565.1
## 368 598.3
## 369 593.5
## 370 575.0
## 371 547.4
## 372 589.7
## 373 570.8
## 374 503.2
## 375 236.9
## 376 487.5
## 377 122.0
## 378 307.3
## 379 241.5
## 380 389.6
## 381 248.3
## 382 286.9
## 383 385.7
## 384 453.8
## 385 395.8
## 386 432.9
## 387 120.1
## 388 446.3
## 389 484.5
## 390 263.0
## 391 385.7
## 392 366.4
## 393 555.8
## 394 351.4
## 395 268.5
## 396 246.9
## 397 268.0
## 398 715.4
## 399 537.4
## 400 386.2
## 401 521.6
## 402 325.7
## 403 255.4
## 404 640.7
## 405 237.9
## 406 539.8
## 407 203.7
## 408 385.7
## 409 385.7
## 410 292.7
## 411 245.5
## 412 385.7
## 413 379.1
## 414 425.3
## 415 375.0
## 416 328.9
## 417 560.4
## 418 443.4
## 419 386.5
## 420 585.1
## 421 631.1
## 422 246.8
## 423 385.7
## 424 342.2
## 425 349.8
## 426 385.7
## 427 291.5
## 428 421.7
## 429 239.3
## 430 548.7
## 431 371.8
## 432 240.0
## 433 187.7
## 434 510.1
## 435 585.8
## 436 282.2
## 437 433.4
## 438 589.0
## 439 265.4
## 440 225.8
## 441 547.1
## 442 147.0
## 443 568.0
## 444 285.5
## 445 236.0
## 446 353.5
## 447 505.4
## 448 212.3
## 449 422.9
## 450 294.8
## 451 403.2
## 452 356.2
## 453 582.0
## 454 217.9
## 455 267.0
## 456 487.2
## 457 268.4
## 458 488.8
## 459 369.6
## 460 385.7
## 461 319.7
## 462 300.5
## 463 399.4
## 464 317.0
## 465 245.4
## 466 602.4
## 467 457.3
## 468 151.1
## 469 251.3
## 470 264.5
## 471 311.5
## 472 217.2
## 473 385.7
## 474 247.0
## 475 541.0
## 476 523.0
## 477 490.6
## 478 416.2
## 479 287.5
## 480 170.8
## 481 227.2
## 482 269.2
## 483 52.1
## 484 385.7
## 485 385.7
## 486 228.0
## 487 297.2
## 488 385.7
## 489 288.9
## 490 350.1
## 491 305.7
## 492 136.6
## 493 560.2
## 494 227.0
## 495 495.9
## 496 477.5
## 497 347.4
## 498 537.8
## 499 387.2
## 500 325.8
## 501 550.3
## 502 402.6
## 503 374.3
## 504 393.6
## 505 283.7
## 506 362.2
## 507 298.5
## 508 303.9
## 509 509.8
## 510 429.0
## 511 633.5
## 512 307.0
## 513 303.8
## 514 487.3
## 515 264.8
## 516 579.7
## 517 529.0
## 518 537.0
## 519 282.8
## 520 471.4
## 521 390.5
## 522 554.2
## 523 288.5
## 524 301.3
## 525 250.2
## 526 643.6
## 527 357.8
## 528 541.1
## 529 214.0
## 530 339.6
## 531 353.1
## 532 436.2
## 533 385.7
## 534 463.3
## 535 385.7
## 536 299.5
## 537 473.3
## 538 416.2
## 539 328.1
## 540 212.0
## 541 583.9
## 542 444.6
## 543 262.1
## 544 265.0
## 545 385.7
## 546 148.4
## 547 204.1
## 548 241.2
## 549 385.7
## 550 487.9
## 551 364.9
## 552 293.4
## 553 508.5
## 554 273.4
## 555 385.7
## 556 219.2
## 557 615.9
## 558 286.9
## 559 342.5
## 560 421.6
## 561 369.5
## 562 404.5
## 563 321.1
## 564 330.5
## 565 233.4
## 566 265.3
## 567 492.8
## 568 566.8
## 569 391.4
## 570 254.7
## 571 388.4
## 572 463.7
## 573 517.0
## 574 396.3
## 575 427.9
## 576 449.9
## 577 385.7
## 578 519.1
## 579 246.3
## 580 615.5
## 581 212.0
## 582 232.0
## 583 324.6
## 584 385.7
## 585 282.8
## 586 630.1
## 587 290.6
## 588 497.0
## 589 386.7
## 590 606.4
## 591 222.7
## 592 273.0
## 593 516.9
## 594 463.4
## 595 600.6
## 596 297.5
## 597 487.9
## 598 641.8
## 599 482.8
## 600 501.5
## 601 438.7
## 602 463.0
## 603 348.5
## 604 487.9
## 605 445.5
## 606 138.5
## 607 479.2
## 608 385.7
## 609 362.9
## 610 610.9
## 611 166.2
## 612 462.7
## 613 218.4
## 614 157.8
## 615 675.7
## 616 286.9
## 617 707.2
## 618 609.5
## 619 550.1
## 620 267.0
## 621 526.7
## 622 530.7
## 623 285.5
## 624 346.4
## 625 325.8
## 626 434.6
## 627 337.5
## 628 467.7
## 629 475.2
## 630 525.6
## 631 578.6
## 632 313.1
## 633 268.6
## 634 595.3
## 635 421.7
## 636 262.9
## 637 379.6
## 638 324.6
## 639 502.4
## 640 224.6
## 641 471.9
## 642 152.4
## 643 576.4
## 644 262.8
## 645 385.7
## 646 366.5
## 647 136.6
## 648 336.1
## 649 267.2
## 650 527.2
## 651 227.9
## 652 310.5
## 653 385.7
## 654 326.6
## 655 254.2
## 656 536.1
## 657 592.3
## 658 535.8
## 659 598.9
## 660 385.7
## 661 385.7
## 662 238.5
## 663 314.3
## 664 458.9
## 665 523.8
## 666 498.8
## 667 273.9
## 668 305.1
## 669 385.7
## 670 262.3
## 671 511.1
## 672 454.1
## 673 588.2
## 674 355.0
## 675 532.3
## 676 653.1
## 677 255.3
## 678 610.0
## 679 439.7
## 680 202.0
## 681 549.4
## 682 257.5
## 683 385.7
## 684 333.7
## 685 422.5
## 686 646.1
## 687 573.4
## 688 445.4
## 689 373.8
## 690 255.8
## 691 377.2
## 692 345.4
## 693 537.0
## 694 191.5
## 695 608.2
## 696 191.8