Pertemuan 1 Komstat D
Razha Irnanda Alika
2025-08-15
1 Import data di RMarkdown
Mengimpor data di RMarkdown sebenarnya sama seperti di R biasa, hanya saja kodenya ditulis di dalam code chunk agar bisa dieksekusi saat dokumen di-knit. Prosesnya dimulai dengan menempatkan file data (misalnya data.csv atau data.xlsx) di folder yang sama dengan file .Rmd, atau menggunakan path yang sesuai jika berada di lokasi berbeda. Setiap format file akan membutuhkan syntax yang berbeda juga :
-CSV → read.csv() atau readr::read_csv()
-Excel → readxl::read_xlsx()
-TXT/TSV → read.table() atau read_delim()
-RData/RDS → load() atau readRDS()
1.1 Import data dengan format xlsx
Mengimpor data berformat .xlsx di RMarkdown dilakukan dengan bantuan paket readxl. Sebelum membaca data, panggil dulu paket readxl dengan perintah library(readxl) agar fungsi-fungsi di dalamnya bisa digunakan. Nah berikut syntax untuk mengimport data dengan format Excel (xlsx) ya temen-temen:
## Warning: package 'readxl' was built under R version 4.4.3## # A tibble: 299 × 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
## 7 75 1 246 0 15
## 8 60 1 315 1 60
## 9 65 0 157 0 65
## 10 80 1 123 0 35
## # ℹ 289 more rows
## # 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>1.2 Import data dengan format CSV
Mengimpor data berformat .csv di RMarkdown dilakukan dengan fungsi bawaan R seperti read.csv() atau fungsi dari paket readr seperti read_csv(). Format CSV (Comma-Separated Values) adalah file teks sederhana yang memisahkan data menggunakan tanda koma, sehingga mudah dibaca oleh berbagai software termasuk R. Caranya masih sama seperti cara import data di file xlsx guys. Berikut syntax nya:
## # A tibble: 299 × 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
## 7 75 1 246 0 15
## 8 60 1 315 1 60
## 9 65 0 157 0 65
## 10 80 1 123 0 35
## # ℹ 289 more rows
## # 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>1.3 Eksplorasi Struktur Data
1.3.1 1. head(data)
Nah Syntax ini merupakan perintah untuk menampilkan 6 baris pertama secara default, kaau mau jumlah yang lain kita hanya menambahkan argumen kedua setelah nama variabel data yang kita masukin sebelumnya:
## # A tibble: 10 × 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
## 7 75 1 246 0 15
## 8 60 1 315 1 60
## 9 65 0 157 0 65
## 10 80 1 123 0 35
## # 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>1.3.2 2. tail(data)
Kalo syntax ini untuk nampilin baris terakhir datanya.
## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 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
## 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
## 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 21.3.3 3. str(data)
Syntax untuk menampilkan struktur dari objek data. Biasanya dipakai untuk melihat tipe objek, jumlah baris/kolom, nama kolom, tipe data, dan cuplikan isi data.
## 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 ...1.3.4 3. typeof(data$nama_kolom)
Untuk mengecek tipe dari isi kolom. Misal nya kita ingin mengetahui tipe data dari kolom anaemia di tabel data1
## [1] "double"1.3.5 4. class(data)
Untuk menunjukan kelas/jenis objek data yang kita import
## [1] "data.frame"1.3.8 7. dim(data)
Memberikan dimensi data dalam bentuk vektor (jumlah_baris, jumlah_kolom).
## [1] 299 131.3.9 8. colnames(data)
Menampilkan nama-nama kolom yang ada pada data.
## [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"1.3.10 9. rownames(data)
Sama seperti colnames tapi ini versi baris nya.
## [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"1.3.11 10. Indexing dan Subsetting
a.data[1,] : Mengambil baris pertama dari data
## # A tibble: 1 × 13
## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 75 0 582 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>b.data[,3] : Mengambil kolom ketiga dari data
## # A tibble: 299 × 1
## creatinine_phosphokinase
## <dbl>
## 1 582
## 2 7861
## 3 146
## 4 111
## 5 160
## 6 47
## 7 246
## 8 315
## 9 157
## 10 123
## # ℹ 289 more rowsc.data[1:3, 2:4] : Mengambil baris 1 sampai 3 dan kolom 2 sampai 4.
## # A tibble: 3 × 3
## anaemia creatinine_phosphokinase diabetes
## <dbl> <dbl> <dbl>
## 1 0 582 0
## 2 0 7861 0
## 3 0 146 0- data$Gender : menampilkan isi pada kolom Gender dari data. Hasilnya nanti akan berbentuk vektor temen-temen.
## [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"1.4 Pemeriksaan Data
2 Missing Value
- is.na(data) Mengecek apakah setiap elemen di data adalah NA (missing value).
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## high_blood_pressure platelets serum_creatinine serum_sodium sex
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## smoking time DEATH_EVENT
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## [298,] FALSE FALSE FALSE
## [299,] FALSE FALSE FALSE- sum(is.na(data)) Menghitung total jumlah NA di seluruh dataset.
## [1] 0- colSums(is.na(data)) Menghitung jumlah NA per kolom. Nanti hasilnya vektor dengan nama kolom dan jumlah NA nya masing-masing.
## 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
## 04.which(is.na(data$AlbuminandGlobulinRatio)) Mengecek indeks baris di mana kolom AlbuminandGlobulinRatio terdapat nilai NA.
## [1] 210 242 254 313- data <- na.omit(data) Menghapus semua baris yang memiliki minimal satu nilai NA di kolom manapun.
## # A tibble: 299 × 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
## 7 75 1 246 0 15
## 8 60 1 315 1 60
## 9 65 0 157 0 65
## 10 80 1 123 0 35
## # ℹ 289 more rows
## # 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>3 Statistik deskriptif awal
- summary(data) Memberikan ringkasan statistik untuk setiap kolom di data.
## 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. :1.0000- summary(data$age) Ringkasan statistik khusus untuk kolom Age saja.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 40.00 51.00 60.00 60.83 70.00 95.003.mean(data$age, na.rm = TRUE) Menghitung nilai rata-rata umur (age).
## [1] 60.833894.min(data$Albumin, na.rm = TRUE) Mengambil nilai terkecil dari kolom Albumin.
## [1] 0.9- boxplot(data$Age, main = “Boxplot Usia”) Perintah ini membuat boxplot (diagram kotak) untuk kolom Age di dataset data.
