Modul 7

cancer <- read.csv("C:\\Users\\Lenovo\\Downloads\\breast_cancer_survival.csv",
          header = TRUE, stringsAsFactors = TRUE, na.strings = "NA")
head(cancer)

##   Age Gender Protein1 Protein2   Protein3  Protein4 Tumour_Stage
## 1  42 FEMALE  0.95256  2.15000  0.0079716 -0.048340           II
## 2  54 FEMALE  0.00000  1.38020 -0.4980300 -0.507320           II
## 3  63 FEMALE -0.52303  1.76400 -0.3701900  0.010815           II
## 4  78 FEMALE -0.87618  0.12943 -0.3703800  0.132190            I
## 5  42 FEMALE  0.22611  1.74910 -0.5439700 -0.390210           II
## 6  80 FEMALE  0.46647  2.57970 -1.2537000  0.151540          III
##                       Histology ER.status PR.status HER2.status
## 1 Infiltrating Ductal Carcinoma  Positive  Positive    Negative
## 2 Infiltrating Ductal Carcinoma  Positive  Positive    Negative
## 3 Infiltrating Ductal Carcinoma  Positive  Positive    Negative
## 4 Infiltrating Ductal Carcinoma  Positive  Positive    Negative
## 5 Infiltrating Ductal Carcinoma  Positive  Positive    Positive
## 6 Infiltrating Ductal Carcinoma  Positive  Positive    Negative
##                  Surgery_type Date_of_Surgery Date_of_Last_Visit Patient_Status
## 1                       Other       20-May-18          26-Aug-18          Alive
## 2                       Other       26-Apr-18          25-Jan-19           Dead
## 3                  Lumpectomy       24-Aug-18          08-Apr-20          Alive
## 4                       Other       16-Nov-18          28-Jul-20          Alive
## 5                  Lumpectomy       12-Dec-18          05-Jan-19          Alive
## 6 Modified Radical Mastectomy       25-Jun-18          16-Feb-19          Alive

EVAL TRUE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

EVAL FALSE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

Include TRUE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Include FALSE

ECHO TRUE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

ECHO FALSE

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Warning TRUE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Warning FALSE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Cache TRUE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Cache FALSE

by(data = cancer$Protein1, INDICES = cancer$Tumour_Stage, FUN = shapiro.test)

## cancer$Tumour_Stage: I
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.91562, p-value = 0.0003278
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: II
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.99281, p-value = 0.4809
## 
## ------------------------------------------------------------ 
## cancer$Tumour_Stage: III
## 
##  Shapiro-Wilk normality test
## 
## data:  dd[x, ]
## W = 0.94323, p-value = 0.001329

Visualisasi

plot(pressure)

Ini adalah grafik tekanan

Text

Yokoso

Hyperlink

Google

Gambar

Gambar

Italic

normal normal

Bold

normal

penulisan senyawa kimia

h-2-0

penulisan pangkat

10²

Tabel 1

Nama	Umur	Kota
Lelou	20	Jakarta
Budi	22	Bandung
Siti	21	Surabaya

Tabel 2

library(knitr)
data <- data.frame(
  Nama = c("Lelou", "Budi", "Siti"),
  Umur = c(20, 22, 21),
  Kota = c("Jakarta", "Bandung", "Surabaya")
)
kable(data)

## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")

## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")

Nama	Umur	Kota
Lelou	20	Jakarta
Budi	22	Bandung
Siti	21	Surabaya

Persamaan di dalam kalimat

persamaan linear dasar dituliskan sebagai \(y = mx + b\) dimana m adalah gradien garis dan b adalah intersep pada sumbu y.

Integral

\[ \int_0^1 x^2 \, dx = \left[ \frac{x^3}{3} \right]_0^1 = \frac{1}{3} \]

Matriks

\[ \begin{pmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \end{pmatrix} \]

Pecahan dan eksponen

\[ \frac{a}{b} \cdot x^2 \]

Citation

Huang et al. (2021)

Huang, Yanqian, Meng Zhu, Mengmeng Ji, Jingyi Fan, Junxing Xie, Xiaoxia Wei, Xiangxiang Jiang, et al. 2021. “Air pollution, genetic factors, and the risk of lung cancer. A prospective study in the UK biobank.” American Journal of Respiratory and Critical Care Medicine 204 (7): 817–25. https://doi.org/10.1164/rccm.202011-4063OC.