Import Data ke R Markdown
Rima Nurfitriana
2025-08-11
BAB I CSV
Import data csv
Untuk mengimport data csv ke RMarkdown menggunakan kode berikut
data1 <- read.csv("C:/Users/H P/OneDrive/Dokumen/KOMPUTASI STATISTIK (sem 3)/Indian Liver Patient Dataset (ILPD).csv")Eksplorasi Struktur Data
Data Awal
‘head’ digunakan untuk melihat 6 baris pertama
head(data1)## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## 2 62 Male 10.9 5.5 699
## 3 62 Male 7.3 4.1 490
## 4 58 Male 1.0 0.4 182
## 5 72 Male 3.9 2.0 195
## 6 46 Male 1.8 0.7 208
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## 2 64 100 7.5 3.2
## 3 60 68 7.0 3.3
## 4 14 20 6.8 3.4
## 5 27 59 7.3 2.4
## 6 19 14 7.6 4.4
## AlbuminandGlobulinRatio LiverPatient
## 1 0.90 1
## 2 0.74 1
## 3 0.89 1
## 4 1.00 1
## 5 0.40 1
## 6 1.30 1Data Akhir
‘tail’ digunakan untuk melihat beberapa baris terakhir di data, banyaknya baris tergantung angka yang diinput
tail(data1, 10)## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 574 32 Male 3.7 1.6 612
## 575 32 Male 12.1 6.0 515
## 576 32 Male 25.0 13.7 560
## 577 32 Male 15.0 8.2 289
## 578 32 Male 12.7 8.4 190
## 579 60 Male 0.5 0.1 500
## 580 40 Male 0.6 0.1 98
## 581 52 Male 0.8 0.2 245
## 582 31 Male 1.3 0.5 184
## 583 38 Male 1.0 0.3 216
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 574 50 88 6.2 1.9
## 575 48 92 6.6 2.4
## 576 41 88 7.9 2.5
## 577 58 80 5.3 2.2
## 578 28 47 5.4 2.6
## 579 20 34 5.9 1.6
## 580 35 31 6.0 3.2
## 581 48 49 6.4 3.2
## 582 29 32 6.8 3.4
## 583 21 24 7.3 4.4
## AlbuminandGlobulinRatio LiverPatient
## 574 0.40 1
## 575 0.50 1
## 576 2.50 1
## 577 0.70 1
## 578 0.90 1
## 579 0.37 2
## 580 1.10 1
## 581 1.00 1
## 582 1.00 1
## 583 1.50 2Struktur dan Tipe Data
Struktur Data
‘str’ digunakan untuk melihat struktur data
str(data1)## 'data.frame': 583 obs. of 11 variables:
## $ Age : int 65 62 62 58 72 46 26 29 17 55 ...
## $ Gender : chr "Female" "Male" "Male" "Male" ...
## $ TotalBilirubin : num 0.7 10.9 7.3 1 3.9 1.8 0.9 0.9 0.9 0.7 ...
## $ DirectBilirubin : num 0.1 5.5 4.1 0.4 2 0.7 0.2 0.3 0.3 0.2 ...
## $ AlkalinePhosphotase : int 187 699 490 182 195 208 154 202 202 290 ...
## $ AlamineAminotransfera : int 16 64 60 14 27 19 16 14 22 53 ...
## $ ApsartateAminotransferase: int 18 100 68 20 59 14 12 11 19 58 ...
## $ TotalProtiens : num 6.8 7.5 7 6.8 7.3 7.6 7 6.7 7.4 6.8 ...
## $ Albumin : num 3.3 3.2 3.3 3.4 2.4 4.4 3.5 3.6 4.1 3.4 ...
## $ AlbuminandGlobulinRatio : num 0.9 0.74 0.89 1 0.4 1.3 1 1.1 1.2 1 ...
## $ LiverPatient : int 1 1 1 1 1 1 1 1 2 1 ...Tipe Data
‘typeof’ digunakan untuk melihat tipe data di kolom tertentu, misal kita ingin melihat type data di kolom “Age”
typeof(data1$Age)## [1] "integer"Class Data
‘class’ digunakan untuk melihat tipe obje data
class(data1)## [1] "data.frame"Ukuran dan Nama
‘nrow’ digunakan untuk melihat banyak baris di data tersebut
nrow(data1)## [1] 583‘ncol’ digunakan untuk melihat banyak kolom di data tersebut
ncol(data1)## [1] 11‘dim’ digunakan untuk melihat banyak baris dan kolom sekaligus, fungsi ‘dim’ merpakan gabungan dari ‘nrow’ dan ‘ncol’ sekaligus
dim(data1)## [1] 583 11‘colnames’ digunakan untuk menampilkan nama-nama kolom pada data yang sudah diimport sebelumnya
colnames(data1)## [1] "Age" "Gender"
## [3] "TotalBilirubin" "DirectBilirubin"
## [5] "AlkalinePhosphotase" "AlamineAminotransfera"
## [7] "ApsartateAminotransferase" "TotalProtiens"
## [9] "Albumin" "AlbuminandGlobulinRatio"
## [11] "LiverPatient"‘rownames’ digunakan untuk melihat nama atau nomor baris pada data yang diimport, terlihat pada syntax di bawah bahwa terdapat 583 baris data
rownames(data1)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156"
## [157] "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168"
## [169] "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190" "191" "192"
## [193] "193" "194" "195" "196" "197" "198" "199" "200" "201" "202" "203" "204"
## [205] "205" "206" "207" "208" "209" "210" "211" "212" "213" "214" "215" "216"
## [217] "217" "218" "219" "220" "221" "222" "223" "224" "225" "226" "227" "228"
## [229] "229" "230" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250" "251" "252"
## [253] "253" "254" "255" "256" "257" "258" "259" "260" "261" "262" "263" "264"
## [265] "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275" "276"
## [277] "277" "278" "279" "280" "281" "282" "283" "284" "285" "286" "287" "288"
## [289] "289" "290" "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310" "311" "312"
## [313] "313" "314" "315" "316" "317" "318" "319" "320" "321" "322" "323" "324"
## [325] "325" "326" "327" "328" "329" "330" "331" "332" "333" "334" "335" "336"
## [337] "337" "338" "339" "340" "341" "342" "343" "344" "345" "346" "347" "348"
## [349] "349" "350" "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370" "371" "372"
## [373] "373" "374" "375" "376" "377" "378" "379" "380" "381" "382" "383" "384"
## [385] "385" "386" "387" "388" "389" "390" "391" "392" "393" "394" "395" "396"
## [397] "397" "398" "399" "400" "401" "402" "403" "404" "405" "406" "407" "408"
## [409] "409" "410" "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430" "431" "432"
## [433] "433" "434" "435" "436" "437" "438" "439" "440" "441" "442" "443" "444"
## [445] "445" "446" "447" "448" "449" "450" "451" "452" "453" "454" "455" "456"
## [457] "457" "458" "459" "460" "461" "462" "463" "464" "465" "466" "467" "468"
## [469] "469" "470" "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490" "491" "492"
## [493] "493" "494" "495" "496" "497" "498" "499" "500" "501" "502" "503" "504"
## [505] "505" "506" "507" "508" "509" "510" "511" "512" "513" "514" "515" "516"
## [517] "517" "518" "519" "520" "521" "522" "523" "524" "525" "526" "527" "528"
## [529] "529" "530" "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550" "551" "552"
## [553] "553" "554" "555" "556" "557" "558" "559" "560" "561" "562" "563" "564"
## [565] "565" "566" "567" "568" "569" "570" "571" "572" "573" "574" "575" "576"
## [577] "577" "578" "579" "580" "581" "582" "583"Indexing dan Subsetting
Baris
Kode dibawah digunakan untuk melihat baris tertentu, misal ingin melihat baris ke 3 maka:
data1[3, ]## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 3 62 Male 7.3 4.1 490
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 3 60 68 7 3.3
## AlbuminandGlobulinRatio LiverPatient
## 3 0.89 1Kolom
Kode dibawah digunakan untuk melihat kolom tertentu, jika ingin melihat kolom ke-5, maka:
data1[, 5]## [1] 187 699 490 182 195 208 154 202 202 290 210 260 310 214 145
## [16] 183 342 165 293 293 610 482 542 231 194 289 289 240 128 188
## [31] 190 156 187 410 410 482 145 374 263 275 168 160 630 415 208
## [46] 275 150 230 176 206 170 161 253 198 272 272 198 175 367 145
## [61] 158 158 158 208 259 470 195 215 239 215 186 188 205 171 145
## [76] 162 518 1620 146 670 915 75 148 258 237 269 320 298 538 238
## [91] 214 308 298 204 168 282 298 215 265 312 161 243 224 225 170
## [106] 145 145 158 158 486 188 257 179 272 661 1580 1630 194 280 298
## [121] 300 290 188 178 177 201 802 248 1896 263 512 237 199 238 178
## [136] 1110 310 282 282 380 186 159 332 332 189 201 168 392 202 286
## [151] 180 218 182 178 290 298 462 196 196 282 750 1050 599 180 180
## [166] 282 332 292 962 950 200 298 750 175 175 198 482 1020 562 386
## [181] 250 218 170 171 201 298 750 191 614 218 314 257 272 206 209
## [196] 1124 664 142 169 1420 218 218 145 142 135 163 285 350 220 189
## [211] 190 219 160 401 180 100 116 159 289 125 147 192 265 175 400
## [226] 120 173 186 202 290 196 282 157 2110 285 360 300 158 190 196
## [241] 165 230 205 316 218 290 272 190 202 498 480 680 258 180 152
## [256] 859 901 335 182 285 245 505 228 185 247 348 195 140 358 110
## [271] 235 460 380 262 196 180 190 190 209 144 123 192 188 316 300
## [286] 575 192 155 239 315 250 174 245 191 340 202 234 159 190 195
## [301] 180 280 430 206 155 195 588 174 165 527 175 574 106 158 195
## [316] 179 182 198 216 310 63 198 205 302 171 158 358 174 192 211
## [331] 157 210 258 152 350 182 458 375 405 215 206 650 198 198 195
## [346] 230 115 216 358 158 145 195 144 621 150 178 256 205 176 146
## [361] 218 182 215 165 183 176 418 271 182 130 558 135 326 140 145
## [376] 206 168 202 192 185 331 188 172 159 490 152 105 160 160 102
## [391] 148 162 149 580 310 140 175 152 208 205 162 92 162 199 198
## [406] 215 180 719 554 555 215 509 190 208 260 690 862 592 450 1350
## [421] 1350 163 246 178 240 100 166 170 194 1750 182 236 165 201 194
## [436] 206 212 157 162 168 198 292 298 152 163 279 181 1550 142 173
## [451] 282 279 1100 224 159 186 189 192 140 686 215 309 110 130 164
## [466] 270 137 90 190 165 167 185 197 154 226 310 310 220 196 186
## [481] 352 282 92 182 103 850 195 276 171 146 193 180 805 265 185
## [496] 165 189 198 151 349 365 305 127 238 218 219 239 194 450 254
## [511] 205 320 195 215 230 189 168 215 210 108 224 230 185 137 156
## [526] 210 268 298 315 214 138 285 162 298 230 466 227 395 97 406
## [541] 114 198 173 204 350 153 188 380 214 768 172 160 196 232 220
## [556] 290 180 189 275 390 356 315 388 298 165 143 191 251 200 268
## [571] 236 215 134 612 515 560 289 190 500 98 245 184 216Baris dan Kolom
Kode dibawah digunakan untuk melihat beberapa baris dan kolom sekaligus, jika ingin melihat data dari baris 1-3 dan kolom 2-4, maka:
data1[1:3, 2:4]## Gender TotalBilirubin DirectBilirubin
## 1 Female 0.7 0.1
## 2 Male 10.9 5.5
## 3 Male 7.3 4.1Kolom “Gender”
Untuk melihat kolom tertentu kita bisa menggunakan kode ‘data$nama kolom’ contoh:
data1$Gender## [1] "Female" "Male" "Male" "Male" "Male" "Male" "Female" "Female"
## [9] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [17] "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [25] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [33] "Male" "Female" "Female" "Male" "Female" "Female" "Male" "Male"
## [41] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Male"
## [49] "Female" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [57] "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Male"
## [65] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Female"
## [73] "Female" "Male" "Male" "Female" "Male" "Female" "Male" "Female"
## [81] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [89] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [97] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [105] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [113] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [121] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [129] "Female" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [137] "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [145] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [153] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [161] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [169] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [177] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [185] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [193] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Female"
## [201] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [209] "Female" "Female" "Male" "Male" "Male" "Female" "Male" "Male"
## [217] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [225] "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [233] "Male" "Male" "Female" "Male" "Male" "Female" "Female" "Male"
## [241] "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [249] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [257] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [265] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [273] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [281] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [289] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [297] "Female" "Female" "Female" "Female" "Male" "Female" "Female" "Male"
## [305] "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Female"
## [313] "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Male"
## [321] "Female" "Female" "Male" "Male" "Male" "Male" "Female" "Male"
## [329] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Female"
## [337] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [345] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [353] "Female" "Female" "Female" "Male" "Male" "Male" "Male" "Female"
## [361] "Male" "Female" "Male" "Female" "Male" "Male" "Male" "Male"
## [369] "Female" "Female" "Female" "Male" "Male" "Female" "Female" "Male"
## [377] "Male" "Female" "Female" "Male" "Male" "Male" "Female" "Female"
## [385] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [393] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [401] "Female" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [409] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [417] "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [425] "Female" "Male" "Male" "Male" "Female" "Male" "Female" "Female"
## [433] "Male" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [441] "Female" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [449] "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Female"
## [457] "Male" "Male" "Male" "Male" "Female" "Female" "Male" "Male"
## [465] "Female" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [473] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [481] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [489] "Male" "Male" "Female" "Male" "Female" "Male" "Male" "Male"
## [497] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [505] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [513] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [521] "Male" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [529] "Male" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [537] "Male" "Female" "Male" "Female" "Male" "Male" "Male" "Male"
## [545] "Female" "Female" "Male" "Female" "Male" "Female" "Male" "Male"
## [553] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [561] "Male" "Male" "Male" "Male" "Female" "Male" "Female" "Male"
## [569] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [577] "Male" "Male" "Male" "Male" "Male" "Male" "Male"Kolom “Age”
Untuk melihat kolom tertentu kita bisa menggunakan kode ‘data$nama kolom’ Contoh:
data1[["Age"]]## [1] 65 62 62 58 72 46 26 29 17 55 57 72 64 74 61 25 38 33 40 40 51 51 62 40 63
## [26] 34 34 34 20 84 57 52 57 38 38 30 17 46 48 47 45 62 42 50 85 35 21 40 32 55
## [51] 45 34 38 38 42 42 33 48 51 64 31 58 58 57 57 57 54 37 66 60 19 75 75 52 68
## [76] 29 31 68 70 58 58 29 49 33 32 14 13 58 18 60 60 60 60 60 60 75 39 39 18 18
## [101] 27 27 17 55 63 36 36 36 36 36 24 48 27 74 50 50 48 32 32 32 32 32 58 64 28
## [126] 60 48 64 58 45 45 70 18 53 18 66 46 18 18 15 60 66 30 30 45 65 66 65 50 60
## [151] 56 50 46 52 34 34 32 72 72 50 60 60 60 39 39 48 55 47 60 60 72 44 55 31 31
## [176] 31 55 75 75 75 75 75 65 40 64 38 60 60 60 48 60 60 60 49 49 60 60 26 41 7
## [201] 49 49 38 21 21 45 40 40 70 45 28 42 22 8 38 66 55 49 6 37 37 47 47 50 70
## [226] 26 26 68 65 46 61 61 50 33 40 60 22 35 35 40 48 51 29 28 54 54 55 55 40 33
## [251] 33 33 65 35 38 38 50 44 36 42 42 33 18 38 38 4 62 43 40 26 37 4 21 30 33
## [276] 26 35 60 45 48 58 50 50 18 18 13 34 43 50 57 45 60 45 23 22 22 74 25 31 24
## [301] 58 51 50 50 55 54 48 30 45 48 51 54 27 30 26 22 44 35 38 14 30 30 36 12 60
## [326] 42 36 24 43 21 26 26 26 36 13 13 75 75 75 75 75 36 35 70 37 60 46 38 70 49
## [351] 37 37 26 48 48 19 33 33 37 69 24 65 55 42 21 40 16 60 42 58 54 33 48 25 56
## [376] 47 33 20 50 72 50 39 58 60 34 50 38 51 46 72 72 75 41 41 48 45 74 78 38 27
## [401] 66 50 42 65 22 31 45 12 48 48 18 23 65 48 65 70 70 11 50 55 55 26 41 53 32
## [426] 58 45 65 52 73 53 47 29 41 30 17 23 35 65 42 49 42 42 42 61 17 54 45 48 48
## [451] 65 35 58 46 28 21 32 61 26 65 22 28 38 25 45 45 28 28 66 66 66 49 42 42 35
## [476] 38 38 55 33 33 7 45 45 30 62 22 42 32 60 65 53 27 35 65 25 32 24 67 68 55
## [501] 70 36 42 53 32 32 56 50 46 46 37 45 56 69 49 49 60 28 45 35 62 55 46 50 29
## [526] 53 46 40 45 55 22 40 62 46 39 60 46 10 52 65 42 42 62 40 54 45 45 50 42 40
## [551] 46 29 45 46 73 55 51 51 51 26 66 66 66 64 38 43 50 52 20 16 16 90 32 32 32
## [576] 32 32 32 60 40 52 31 38Missing Value
Mengecek Missing value
‘is.na’ digunakan untuk melihat apakah ada NA atau missing value di dalam data Output FALSE berarti data tersebut tidak mengandung nilai NA (missing value) Output TRUE berarti data tersebut mengandung nilai NA missing value
is.na(data1)## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE FALSE FALSE
## [8,] FALSE FALSE FALSE FALSE FALSE
## [9,] FALSE FALSE FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE FALSE FALSE
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## [583,] FALSE FALSE FALSE FALSE FALSE
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE
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## AlbuminandGlobulinRatio LiverPatient
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
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## [5,] FALSE FALSE
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## [314,] FALSE FALSE
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## [583,] FALSE FALSEJumlah Missing Value
‘sum(is.na)’ digunakan untuk melihat banyak elemen data yang mengandung nilai NA (missing value)
sum(is.na(data1))## [1] 4Menghitung Missing Value di Tiap KOlom
‘colSums’ digunakan untuk menghitung jumlah NA perkolom pada data
colSums(is.na(data1))## Age Gender TotalBilirubin
## 0 0 0
## DirectBilirubin AlkalinePhosphotase AlamineAminotransfera
## 0 0 0
## ApsartateAminotransferase TotalProtiens Albumin
## 0 0 0
## AlbuminandGlobulinRatio LiverPatient
## 4 0Menemukan Missing Value di Baris
Indeks baris NA digunakan untuk menemukan di baris mana yang mengandung NA pada kolom AlbuminandGlobulinRatio
which(is.na(data1$lbuminandGlobulinRatio))## integer(0)Mengapus Missing Value
Untuk menghapus semua baris yang mengandung NA gunakan kode berikut:
data1 <- na.omit(data1)Statistik Deskriptif Awal
Summary Seluruh Data
‘summary’ digunakan untuk melihat statistik deskriptif data perkolom pada data
summary(data1)## Age Gender TotalBilirubin DirectBilirubin
## Min. : 4.00 Length:579 Min. : 0.400 Min. : 0.100
## 1st Qu.:33.00 Class :character 1st Qu.: 0.800 1st Qu.: 0.200
## Median :45.00 Mode :character Median : 1.000 Median : 0.300
## Mean :44.78 Mean : 3.315 Mean : 1.494
## 3rd Qu.:58.00 3rd Qu.: 2.600 3rd Qu.: 1.300
## Max. :90.00 Max. :75.000 Max. :19.700
## AlkalinePhosphotase AlamineAminotransfera ApsartateAminotransferase
## Min. : 63.0 Min. : 10.00 Min. : 10.0
## 1st Qu.: 175.5 1st Qu.: 23.00 1st Qu.: 25.0
## Median : 208.0 Median : 35.00 Median : 42.0
## Mean : 291.4 Mean : 81.13 Mean : 110.4
## 3rd Qu.: 298.0 3rd Qu.: 61.00 3rd Qu.: 87.0
## Max. :2110.0 Max. :2000.00 Max. :4929.0
## TotalProtiens Albumin AlbuminandGlobulinRatio LiverPatient
## Min. :2.700 Min. :0.900 Min. :0.3000 Min. :1.000
## 1st Qu.:5.800 1st Qu.:2.600 1st Qu.:0.7000 1st Qu.:1.000
## Median :6.600 Median :3.100 Median :0.9300 Median :1.000
## Mean :6.482 Mean :3.139 Mean :0.9471 Mean :1.285
## 3rd Qu.:7.200 3rd Qu.:3.800 3rd Qu.:1.1000 3rd Qu.:2.000
## Max. :9.600 Max. :5.500 Max. :2.8000 Max. :2.000Summary Kolom Tertentu
‘summary(data$nama kolom)’ digunakan untuk melihat statistik deskriptif pada kolom tertentu
summary(data1$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 33.00 45.00 44.78 58.00 90.00Rata-rata Suatu Kolom
‘mean’ digunakan untuk melihat rata-rata pada kolom tertentu
mean(data1$Age, na.rm = TRUE)## [1] 44.78238Nilai Minimal Suatu Kolom
‘min’ digunakan untuk melhat nilai minimum data pada kolom tertentu
min(data1$Albumin, na.rm = TRUE)## [1] 0.9Outlier Sederhana (Boxplot)
Menampilkan Bloxpot
‘boxplot’ digunakan untuk menampilkan diagram batang, disini kita bisa memilih elemen data mana saja yang ingin dibut boxplotnya. Fungsi ‘main’ digunakankan untuk memberi nama pada boxplot. Fungsi ‘col’ digunakan untuk memberi warna pada boxplot
boxplot(data1$Age, main = "Boxplot Usia", col = "skyblue" )
BAB II EXCEL
Import Data
`
library(readxl)## Warning: package 'readxl' was built under R version 4.5.1data_excel <- read_xlsx("Heart Failure Clinical Records.xlsx")Eksplorasi Struktur Data
Data Awal
head(data_excel)## # A tibble: 6 × 13
## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 75 0 582 0 20
## 2 55 0 7861 0 38
## 3 65 0 146 0 20
## 4 50 1 111 0 20
## 5 65 1 160 1 20
## 6 90 1 47 0 40
## # ℹ 8 more variables: high_blood_pressure <dbl>, platelets <dbl>,
## # serum_creatinine <dbl>, serum_sodium <dbl>, sex <chr>, smoking <dbl>,
## # time <dbl>, DEATH_EVENT <dbl>Data Akhir
tail(data_excel, 10)## # A tibble: 10 × 13
## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 90 1 337 0 38
## 2 45 0 615 1 55
## 3 60 0 320 0 35
## 4 52 0 190 1 38
## 5 63 1 103 1 35
## 6 62 0 61 1 38
## 7 55 0 1820 0 38
## 8 45 0 2060 1 60
## 9 45 0 2413 0 38
## 10 50 0 196 0 45
## # ℹ 8 more variables: high_blood_pressure <dbl>, platelets <dbl>,
## # serum_creatinine <dbl>, serum_sodium <dbl>, sex <chr>, smoking <dbl>,
## # time <dbl>, DEATH_EVENT <dbl>Struktur dan Tipe Data
Struktur Data
‘str’ digunakan untuk melihat struktur data
str(data_excel)## tibble [299 × 13] (S3: tbl_df/tbl/data.frame)
## $ age : num [1:299] 75 55 65 50 65 90 75 60 65 80 ...
## $ anaemia : num [1:299] 0 0 0 1 1 1 1 1 0 1 ...
## $ creatinine_phosphokinase: num [1:299] 582 7861 146 111 160 ...
## $ diabetes : num [1:299] 0 0 0 0 1 0 0 1 0 0 ...
## $ ejection_fraction : num [1:299] 20 38 20 20 20 40 15 60 65 35 ...
## $ high_blood_pressure : num [1:299] 1 0 0 0 0 1 0 0 0 1 ...
## $ platelets : num [1:299] 265000 263358 162000 210000 327000 ...
## $ serum_creatinine : num [1:299] 1.9 1.1 1.3 1.9 2.7 2.1 1.2 1.1 1.5 9.4 ...
## $ serum_sodium : num [1:299] 130 136 129 137 116 132 137 131 138 133 ...
## $ sex : chr [1:299] "M" "M" "M" "M" ...
## $ smoking : num [1:299] 0 0 1 0 0 1 0 1 0 1 ...
## $ time : num [1:299] 4 6 7 7 8 8 10 10 10 10 ...
## $ DEATH_EVENT : num [1:299] 1 1 1 1 1 1 1 1 1 1 ...Tipe Data
‘typeof’ digunakan untuk melihat tipe data di kolom tertentu
typeof(data_excel$Age)## Warning: Unknown or uninitialised column: `Age`.## [1] "NULL"Class Data
‘class’ digunakan untuk melihat class atau tipe objek data
class(data_excel)## [1] "tbl_df" "tbl" "data.frame"Ukuran dan Nama
‘nrow’ digunakan untuk melihat banyak baris di data tersebut
nrow(data_excel)## [1] 299‘ncol’ digunakan untuk melihat banyak kolom di data tersebut
ncol(data_excel)## [1] 13‘dim’ digunakan untuk melihat banyak baris dan kolom sekaligus, fungsi ‘dim’ merpakan gabungan dari ‘nrow’ dan ‘ncol’ sekaligus
dim(data_excel)## [1] 299 13‘colnames’ digunakan untuk menampilkan nama-nama kolom pada data yang sudah diimport sebelumnya
colnames(data_excel)## [1] "age" "anaemia"
## [3] "creatinine_phosphokinase" "diabetes"
## [5] "ejection_fraction" "high_blood_pressure"
## [7] "platelets" "serum_creatinine"
## [9] "serum_sodium" "sex"
## [11] "smoking" "time"
## [13] "DEATH_EVENT"‘rownames’ digunakan untuk melihat nama atau nomor baris pada data yang diimport, terlihat pada syntax di bawah bahwa terdapat 583 baris data
rownames(data_excel)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156"
## [157] "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168"
## [169] "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190" "191" "192"
## [193] "193" "194" "195" "196" "197" "198" "199" "200" "201" "202" "203" "204"
## [205] "205" "206" "207" "208" "209" "210" "211" "212" "213" "214" "215" "216"
## [217] "217" "218" "219" "220" "221" "222" "223" "224" "225" "226" "227" "228"
## [229] "229" "230" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250" "251" "252"
## [253] "253" "254" "255" "256" "257" "258" "259" "260" "261" "262" "263" "264"
## [265] "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275" "276"
## [277] "277" "278" "279" "280" "281" "282" "283" "284" "285" "286" "287" "288"
## [289] "289" "290" "291" "292" "293" "294" "295" "296" "297" "298" "299"Indexing dan Subsetting
Baris
Kode dibawah digunakan untuk melihat baris tertentu, misal ingin melihat baris ke 4 maka:
data_excel[4, ]## # A tibble: 1 × 13
## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 50 1 111 0 20
## # ℹ 8 more variables: high_blood_pressure <dbl>, platelets <dbl>,
## # serum_creatinine <dbl>, serum_sodium <dbl>, sex <chr>, smoking <dbl>,
## # time <dbl>, DEATH_EVENT <dbl>Kolom
Kode dibawah digunakan untuk melihat kolom tertentu, jika ingin melihat kolom ke-5, maka:
data_excel[, 5]## # A tibble: 299 × 1
## ejection_fraction
## <dbl>
## 1 20
## 2 38
## 3 20
## 4 20
## 5 20
## 6 40
## 7 15
## 8 60
## 9 65
## 10 35
## # ℹ 289 more rowsBaris dan Kolom
Kode dibawah digunakan untuk melihat beberapa baris dan kolom sekaligus, jika ingin melihat data dari baris 1-3 dan kolom 2-4, maka:
data_excel[1:3, 2:4]## # A tibble: 3 × 3
## anaemia creatinine_phosphokinase diabetes
## <dbl> <dbl> <dbl>
## 1 0 582 0
## 2 0 7861 0
## 3 0 146 0Kolom ‘age’
Untuk melihat kolom tertentu kita bisa menggunakan kode ‘data$nama kolom’ contoh:
data_excel$age## [1] 75.000 55.000 65.000 50.000 65.000 90.000 75.000 60.000 65.000 80.000
## [11] 75.000 62.000 45.000 50.000 49.000 82.000 87.000 45.000 70.000 48.000
## [21] 65.000 65.000 68.000 53.000 75.000 80.000 95.000 70.000 58.000 82.000
## [31] 94.000 85.000 50.000 50.000 65.000 69.000 90.000 82.000 60.000 60.000
## [41] 70.000 50.000 70.000 72.000 60.000 50.000 51.000 60.000 80.000 57.000
## [51] 68.000 53.000 60.000 70.000 60.000 95.000 70.000 60.000 49.000 72.000
## [61] 45.000 50.000 55.000 45.000 45.000 60.000 42.000 72.000 70.000 65.000
## [71] 41.000 58.000 85.000 65.000 69.000 60.000 70.000 42.000 75.000 55.000
## [81] 70.000 67.000 60.000 79.000 59.000 51.000 55.000 65.000 44.000 57.000
## [91] 70.000 60.000 42.000 60.000 58.000 58.000 63.000 70.000 60.000 63.000
## [101] 65.000 75.000 80.000 42.000 60.000 72.000 55.000 45.000 63.000 45.000
## [111] 85.000 55.000 50.000 70.000 60.000 58.000 60.000 85.000 65.000 86.000
## [121] 60.000 66.000 60.000 60.000 60.000 43.000 46.000 58.000 61.000 53.000
## [131] 53.000 60.000 46.000 63.000 81.000 75.000 65.000 68.000 62.000 50.000
## [141] 80.000 46.000 50.000 61.000 72.000 50.000 52.000 64.000 75.000 60.000
## [151] 72.000 62.000 50.000 50.000 65.000 60.000 52.000 50.000 85.000 59.000
## [161] 66.000 45.000 63.000 50.000 45.000 80.000 53.000 59.000 65.000 70.000
## [171] 51.000 52.000 70.000 50.000 65.000 60.000 69.000 49.000 63.000 55.000
## [181] 40.000 59.000 65.000 75.000 58.000 60.667 50.000 60.000 60.667 40.000
## [191] 80.000 64.000 50.000 73.000 45.000 77.000 45.000 65.000 50.000 60.000
## [201] 63.000 45.000 70.000 60.000 78.000 50.000 40.000 85.000 60.000 49.000
## [211] 70.000 50.000 78.000 48.000 65.000 73.000 70.000 54.000 68.000 55.000
## [221] 73.000 65.000 42.000 47.000 58.000 75.000 58.000 55.000 65.000 72.000
## [231] 60.000 70.000 40.000 53.000 53.000 77.000 75.000 70.000 65.000 55.000
## [241] 70.000 65.000 40.000 73.000 54.000 61.000 55.000 64.000 40.000 53.000
## [251] 50.000 55.000 50.000 70.000 53.000 52.000 65.000 58.000 45.000 53.000
## [261] 55.000 62.000 65.000 68.000 61.000 50.000 55.000 56.000 45.000 40.000
## [271] 44.000 51.000 67.000 42.000 60.000 45.000 70.000 70.000 50.000 55.000
## [281] 70.000 70.000 42.000 65.000 50.000 55.000 60.000 45.000 65.000 90.000
## [291] 45.000 60.000 52.000 63.000 62.000 55.000 45.000 45.000 50.000Kolom ‘anemia’
Untuk melihat kolom tertentu kita bisa menggunakan kode ‘data$nama kolom’ contoh:
data_excel[["anemia"]]## NULLMissing Value
Mengecek Missing Value
‘is.na’ digunakan untuk melihat apakah ada NA atau missing value di dalam data Output FALSE berarti data tersebut tidak mengandung nilai NA (missing value) Output TRUE berarti data tersebut mengandung nilai NA missing value
is.na(data_excel)## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
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## high_blood_pressure platelets serum_creatinine serum_sodium sex
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## smoking time DEATH_EVENT
## [1,] FALSE FALSE FALSE
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## [299,] FALSE FALSE FALSEJumlah Missing Value
‘sum(is.na)’ digunakan untuk melihat banyak elemen data yang mengandung nilai NA (missing value)
sum(is.na(data_excel))## [1] 0Menghitung Missing Value di Tiap Kolom
‘colSums’ digunakan untuk menghitung jumlah NA perkolom pada data
colSums(is.na(data_excel))## age anaemia creatinine_phosphokinase
## 0 0 0
## diabetes ejection_fraction high_blood_pressure
## 0 0 0
## platelets serum_creatinine serum_sodium
## 0 0 0
## sex smoking time
## 0 0 0
## DEATH_EVENT
## 0Menemukan Missing Value di Baris
Indeks baris NA digunakan untuk menemukan di baris mana yang mengandung NA pada kolom AlbuminandGlobulinRatio
which(is.na(data_excel$lbuminandGlobulinRatio))## Warning: Unknown or uninitialised column: `lbuminandGlobulinRatio`.## integer(0)Menghapus Missing Value
Untuk menghapus semua baris yang mengandung NA gunakan kode berikut:
data1 <- na.omit(data_excel)Statistik Deskriptif Awal
Summary Seluruh Data
‘summary’ digunakan untuk melihat statistik deskriptif data perkolom pada data
summary(data_excel)## age anaemia creatinine_phosphokinase diabetes
## Min. :40.00 Min. :0.0000 Min. : 23.0 Min. :0.0000
## 1st Qu.:51.00 1st Qu.:0.0000 1st Qu.: 116.5 1st Qu.:0.0000
## Median :60.00 Median :0.0000 Median : 250.0 Median :0.0000
## Mean :60.83 Mean :0.4314 Mean : 581.8 Mean :0.4181
## 3rd Qu.:70.00 3rd Qu.:1.0000 3rd Qu.: 582.0 3rd Qu.:1.0000
## Max. :95.00 Max. :1.0000 Max. :7861.0 Max. :1.0000
## ejection_fraction high_blood_pressure platelets serum_creatinine
## Min. :14.00 Min. :0.0000 Min. : 25100 Min. :0.500
## 1st Qu.:30.00 1st Qu.:0.0000 1st Qu.:212500 1st Qu.:0.900
## Median :38.00 Median :0.0000 Median :262000 Median :1.100
## Mean :38.08 Mean :0.3512 Mean :263358 Mean :1.394
## 3rd Qu.:45.00 3rd Qu.:1.0000 3rd Qu.:303500 3rd Qu.:1.400
## Max. :80.00 Max. :1.0000 Max. :850000 Max. :9.400
## serum_sodium sex smoking time
## Min. :113.0 Length:299 Min. :0.0000 Min. : 4.0
## 1st Qu.:134.0 Class :character 1st Qu.:0.0000 1st Qu.: 73.0
## Median :137.0 Mode :character Median :0.0000 Median :115.0
## Mean :136.6 Mean :0.3211 Mean :130.3
## 3rd Qu.:140.0 3rd Qu.:1.0000 3rd Qu.:203.0
## Max. :148.0 Max. :1.0000 Max. :285.0
## DEATH_EVENT
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3211
## 3rd Qu.:1.0000
## Max. :1.0000Summary Kolom Tertentu
‘summary(data$nama kolom)’ digunakan untuk melihat statistik deskriptif pada kolom tertentu
summary(data_excel$Age)## Warning: Unknown or uninitialised column: `Age`.## Length Class Mode
## 0 NULL NULLRata-Rata Suatu Kolom
‘mean’ digunakan untuk melihat rata-rata pada kolom tertentu
mean(data_excel$Age, na.rm = TRUE)## Warning: Unknown or uninitialised column: `Age`.## Warning in mean.default(data_excel$Age, na.rm = TRUE): argument is not numeric
## or logical: returning NA## [1] NANilai Minimal Suatu Kolom
‘min’ digunakan untuk melihat nilai minimum data pada kolom tertentu
min(data_excel$age, na.rm = TRUE)## [1] 40Nilai Maksimal Suatu Kolom
‘max’ digunakan untuk melihat nilai maksimal
max(data_excel$age, na.rm = TRUE)## [1] 95Outlier Sederhana (Boxplot)
Menampilan Boxplot
‘boxplot` digunakan untuk menampilkan diagram batang, disini kita bisa memilih elemen data mana saja yang ingin dibut boxplotnya. Fungsi ’main’ digunakankan untuk memberi nama pada boxplot. Fungsi ‘col’ digunakan untuk memberi warna pada boxplot
boxplot(data_excel$age, main = "Boxplot Usia", col = "maroon" )