bw <- read.csv("D:/1. KOREAMARCH/LOPR/bai tap/birthwt.csv")
bw$ethnicity <- factor(bw$race, levels = c(1, 2, 3),
                     labels = c("White", "Black", "Others"))
bw$smoking <- factor(bw$smoke, levels = c(1, 0),
                     labels = c("Yes", "No"))
bw$low.bw <- factor(bw$low, levels = c(0, 1),
                    labels = c("Normal", "Low BW"))
bw$mother.wt <- bw$lwt * 0.45
if (!require(table1)) install.packages("table1")
## Loading required package: table1
## 
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
## 
##     units, units<-
library(table1)
table1(~ age + ethnicity + smoking + mother.wt + bwt | low.bw, data = bw)
Normal
(N=130)		 Low BW
(N=59)		 Overall
(N=189)
age
Mean (SD) 23.7 (5.58) 22.3 (4.51) 23.2 (5.30)
Median [Min, Max] 23.0 [14.0, 45.0] 22.0 [14.0, 34.0] 23.0 [14.0, 45.0]
ethnicity
White 73 (56.2%) 23 (39.0%) 96 (50.8%)
Black 15 (11.5%) 11 (18.6%) 26 (13.8%)
Others 42 (32.3%) 25 (42.4%) 67 (35.4%)
smoking
Yes 44 (33.8%) 30 (50.8%) 74 (39.2%)
No 86 (66.2%) 29 (49.2%) 115 (60.8%)
mother.wt
Mean (SD) 60.0 (14.3) 55.0 (12.0) 58.4 (13.8)
Median [Min, Max] 55.6 [38.3, 113] 54.0 [36.0, 90.0] 54.5 [36.0, 113]
bwt
Mean (SD) 3330 (478) 2100 (391) 2940 (729)
Median [Min, Max] 3270 [2520, 4990] 2210 [709, 2500] 2980 [709, 4990] 
if (!require(lessR)) install.packages("lessR")
## Loading required package: lessR
## 
## lessR 4.4.3                         feedback: gerbing@pdx.edu 
## --------------------------------------------------------------
## > d <- Read("")  Read data file, many formats available, e.g., Excel
##   d is default data frame, data= in analysis routines optional
## 
## Many examples of reading, writing, and manipulating data, 
## graphics, testing means and proportions, regression, factor analysis,
## customization, forecasting, and aggregation from pivot tables
##   Enter: browseVignettes("lessR")
## 
## View lessR updates, now including time series forecasting
##   Enter: news(package="lessR")
## 
## Interactive data analysis
##   Enter: interact()
## 
## Attaching package: 'lessR'
## The following object is masked from 'package:table1':
## 
##     label
## The following object is masked from 'package:base':
## 
##     sort_by
library(lessR)
Y <- bw$bwt
Histogram(Y, main = "Histogram of Birth Weight")
## >>> Note: Y is not in a data frame (table)
## >>> Note: Y is not in a data frame (table)

## >>> Suggestions 
## bin_width: set the width of each bin 
## bin_start: set the start of the first bin 
## bin_end: set the end of the last bin 
## Histogram(Y, density=TRUE)  # smoothed curve + histogram 
## Plot(Y)  # Violin/Box/Scatterplot (VBS) plot 
## 
## --- Y --- 
##  
##       n   miss       mean         sd        min        mdn        max 
##      189      0    2944.59     729.21     709.00    2977.00    4990.00 
##  
## 
##   
## --- Outliers ---     from the box plot: 1 
##  
## Small        Large 
## -----        ----- 
##  709.0            
## 
## 
## Bin Width: 500 
## Number of Bins: 9 
##  
##          Bin  Midpnt  Count    Prop  Cumul.c  Cumul.p 
## ----------------------------------------------------- 
##   500 > 1000     750      1    0.01        1     0.01 
##  1000 > 1500    1250      4    0.02        5     0.03 
##  1500 > 2000    1750     14    0.07       19     0.10 
##  2000 > 2500    2250     40    0.21       59     0.31 
##  2500 > 3000    2750     38    0.20       97     0.51 
##  3000 > 3500    3250     45    0.24      142     0.75 
##  3500 > 4000    3750     38    0.20      180     0.95 
##  4000 > 4500    4250      7    0.04      187     0.99 
##  4500 > 5000    4750      2    0.01      189     1.00 
## 
E <- bw$ethnicity
BarChart(E, main = "Ethnicity Distribution")
## >>> Note: E is not in a data frame (table)

## >>> Suggestions
## BarChart(E, horiz=TRUE)  # horizontal bar chart
## BarChart(E, fill="reds")  # red bars of varying lightness
## PieChart(E)  # doughnut (ring) chart
## Plot(E)  # bubble plot
## Plot(E, stat="count")  # lollipop plot 
## 
## --- E --- 
## 
## Missing Values: 0 
## 
##                White  Black  Others     Total 
## Frequencies:      96     26      67       189 
## Proportions:   0.508  0.138   0.354     1.000 
## 
## Chi-squared test of null hypothesis of equal probabilities 
##   Chisq = 39.270, df = 2, p-value = 0.000
X <- bw$mother.wt
Plot(Y, X, main = "Scatterplot: Mother's Weight vs Baby's Weight")
## >>> Note: Y is not in a data frame (table)
## >>> Note: X is not in a data frame (table)

## 
## >>> Suggestions  or  enter: style(suggest=FALSE)
## Plot(Y, X, enhance=TRUE)  # many options
## Plot(Y, X, fill="skyblue")  # interior fill color of points
## Plot(Y, X, fit="lm", fit_se=c(.90,.99))  # fit line, stnd errors
## Plot(Y, X, MD_cut=6)  # Mahalanobis distance from center > 6 is an outlier 
## 
## 
## >>> Pearson's product-moment correlation 
##  
## Number of paired values with neither missing, n = 189 
## Sample Correlation of Y and X: r = 0.186 
##   
## Hypothesis Test of 0 Correlation:  t = 2.585,  df = 187,  p-value = 0.011 
## 95% Confidence Interval for Correlation:  0.044 to 0.320 
##