2026_Học R_Ngày 2
Trần Thế Cường
2026-01-07
Việc 1: Đọc dữ liệu
df=read.csv("G:\\My Drive\\0. My Mac\\Participated Training\\2026_R\\DỮ LIỆU THỰC HÀNH (Gửi học viên)\\Bone data.csv")
dim(df) # trả về c(nrow, ncol) # View(df) để mở data## [1] 2162 9cat("So quan sat:", nrow(df), "\nSo bien:", ncol(df), "\n")## So quan sat: 2162
## So bien: 9head(df)## id sex age weight height prior.fx fnbmd smoking fx
## 1 1 Male 73 98 175 0 1.08 1 0
## 2 2 Female 68 72 166 0 0.97 0 0
## 3 3 Male 68 87 184 0 1.01 0 0
## 4 4 Female 62 72 173 0 0.84 1 0
## 5 5 Male 61 72 173 0 0.81 1 0
## 6 6 Female 76 57 156 0 0.74 0 0summary(df)## id sex age weight
## Min. : 1.0 Length:2162 Min. :57.00 Min. : 34.00
## 1st Qu.: 545.2 Class :character 1st Qu.:65.00 1st Qu.: 60.00
## Median :1093.5 Mode :character Median :69.00 Median : 69.00
## Mean :1094.7 Mean :70.65 Mean : 70.14
## 3rd Qu.:1639.8 3rd Qu.:75.00 3rd Qu.: 79.00
## Max. :2216.0 Max. :94.00 Max. :133.00
## NA's :1
## height prior.fx fnbmd smoking
## Min. :136.0 Min. :0.0000 Min. :0.2800 Min. :0.0000
## 1st Qu.:158.0 1st Qu.:0.0000 1st Qu.:0.7300 1st Qu.:0.0000
## Median :164.0 Median :0.0000 Median :0.8200 Median :0.0000
## Mean :164.9 Mean :0.1554 Mean :0.8287 Mean :0.4251
## 3rd Qu.:171.0 3rd Qu.:0.0000 3rd Qu.:0.9300 3rd Qu.:1.0000
## Max. :196.0 Max. :1.0000 Max. :1.5100 Max. :1.0000
## NA's :40
## fx
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.2521
## 3rd Qu.:1.0000
## Max. :1.0000
## Việc 2: So sánh mật độ xương giữa Nam và Nữ
2.1 Vẽ biểu đồ histogram đánh giá phân bố mật độ xương tại cổ xương đùi (fnbmd). Bạn đánh giá phân bố mật độ xương như thế nào?
#Gọi gói lessR
library(lessR)##
## lessR 4.5 feedback: gerbing@pdx.edu
## --------------------------------------------------------------
## > d <- Read("") Read data file, many formats available, e.g., Excel
## d is the 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 to pivot tables.
## Enter: browseVignettes("lessR")
##
## View lessR updates, now including modern time series forecasting
## and many, new Plotly interactive visualizations output. Most
## visualization functions are now reorganized to three functions:
## Chart(): type="bar", "pie", "radar", "bubble", "treemap", "icicle"
## X(): type="histogram", "density", "vbs" and more
## XY(): type="scatter" for a scatterplot, or "contour", "smooth"
## Most previous function calls still work, such as:
## BarChart(), Histogram, and Plot().
## Enter: news(package="lessR"), or ?Chart, ?X, or ?XY
## There is also Flows() for Sankey flow diagrams, see ?Flows
##
## Interactive data analysis for constructing visualizations.
## Enter: interact()#Vẽ historam
Histogram(
fnbmd,
data = df,
fill = "blue",
xlab = "Mật độ xương ở cổ xương đùi (g/cm2)",
ylab = "Số người",
main = "Phân bố mật độ xương"
)## lessR visualizations are now unified over just three core functions:
## - Chart() for pivot tables, such as bar charts. More info: ?Chart
## - X() for a single variable x, such as histograms. More info: ?X
## - XY() for scatterplots of two variables, x and y. More info: ?XY
##
## Histogram() is deprecated, though still working for now.
## Please use X(..., type = "histogram") going forward.## [Interactive plot from the Plotly R package (Sievert, 2020)]
## >>> 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
## X(fnbmd, type="density") # smoothed curve + histogram
## X(fnbmd, type="vbs") # Violin/Box/Scatterplot (VBS) plot
##
## --- fnbmd ---
##
## n miss mean sd min mdn max
## 2122 40 0.829 0.155 0.280 0.820 1.510
##
##
##
## --- Outliers --- from the box plot: 33
##
## Small Large
## ----- -----
## 0.3 1.5
## 0.3 1.5
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.4
## 0.4 1.3
## 0.4 1.3
## 0.4 1.3
## 1.3
## 1.3
## 1.3
## 1.3
## 1.2
## 1.2
## 1.2
##
## + 15 more outliers
##
##
## Bin Width: 0.1
## Number of Bins: 14
##
## Bin Midpnt Count Prop Cumul.c Cumul.p
## ---------------------------------------------------
## 0.2 > 0.3 0.25 1 0.00 1 0.00
## 0.3 > 0.4 0.35 9 0.00 10 0.00
## 0.4 > 0.5 0.45 15 0.01 25 0.01
## 0.5 > 0.6 0.55 103 0.05 128 0.06
## 0.6 > 0.7 0.65 306 0.14 434 0.20
## 0.7 > 0.8 0.75 522 0.25 956 0.45
## 0.8 > 0.9 0.85 534 0.25 1490 0.70
## 0.9 > 1.0 0.95 371 0.17 1861 0.88
## 1.0 > 1.1 1.05 183 0.09 2044 0.96
## 1.1 > 1.2 1.15 48 0.02 2092 0.99
## 1.2 > 1.3 1.25 21 0.01 2113 1.00
## 1.3 > 1.4 1.35 6 0.00 2119 1.00
## 1.4 > 1.5 1.45 2 0.00 2121 1.00
## 1.5 > 1.6 1.55 1 0.00 2122 1.00
## 2.2 Làm t test
ttest(fnbmd ~ sex, data=df)##
## Compare fnbmd across sex with levels Male and Female
## Grouping Variable: sex
## Response Variable: fnbmd
##
##
## ------ Describe ------
##
## fnbmd for sex Male: n.miss = 23, n = 822, mean = 0.910, sd = 0.153
## fnbmd for sex Female: n.miss = 17, n = 1300, mean = 0.778, sd = 0.132
##
## Mean Difference of fnbmd: 0.132
##
## Weighted Average Standard Deviation: 0.141
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of fnbmd.
## Group Male: Sample mean assumed normal because n > 30, so no test needed.
## Group Female: Sample mean assumed normal because n > 30, so no test needed.
##
## Null hypothesis is equal variances of fnbmd, homogeneous.
## Variance Ratio test: F = 0.023/0.018 = 1.336, df = 821;1299, p-value = 0.000
## Levene's test, Brown-Forsythe: t = 3.449, df = 2120, p-value = 0.001
##
##
## ------ Infer ------
##
## --- Assume equal population variances of fnbmd for each sex
##
## t-cutoff for 95% range of variation: tcut = 1.961
## Standard Error of Mean Difference: SE = 0.006
##
## Hypothesis Test of 0 Mean Diff: t-value = 21.080, df = 2120, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.012
## 95% Confidence Interval for Mean Difference: 0.120 to 0.144
##
##
## --- Do not assume equal population variances of fnbmd for each sex
##
## t-cutoff: tcut = 1.961
## Standard Error of Mean Difference: SE = 0.006
##
## Hypothesis Test of 0 Mean Diff: t = 20.407, df = 1560.981, p-value = 0.000
##
## Margin of Error for 95% Confidence Level: 0.013
## 95% Confidence Interval for Mean Difference: 0.119 to 0.145
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of fnbmd for each sex
##
## Standardized Mean Difference of fnbmd, Cohen's d: 0.939
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for sex Male: 0.044
## Density bandwidth for sex Female: 0.034
# Việc 3: So sánh sự khác biệt về chỉ số RER giữa 2 nhóm ## 3.1 Giả sử
bạn có dữ liệu về chỉ số RER của 2 nhóm chứng (placebo) và café như sau:
Nhóm chứng (n= 9): 105, 119, 100, 97, 96, 101, 94, 95, 98 Nhóm café (n=
9): 96, 99, 94, 89, 96, 93, 88, 105, 88 Nhập dữ liệu trên vào R.
placebo <- c(105, 119, 100, 97, 96, 101, 94, 95, 98) #nhập dạng vector
coffee <- c(96, 99, 94, 89, 96, 93, 88, 105, 88)3.2 Thực hiện phép kiểm t để đánh giá khác biệt về RER giữa 2 nhóm. Nhận xét kết quả.
ttest(placebo, coffee)##
## Compare Y across X with levels Group1 and Group2
## Grouping Variable: X
## Response Variable: Y
##
##
## ------ Describe ------
##
## Y for X Group1: n.miss = 0, n = 9, mean = 100.556, sd = 7.699
## Y for X Group2: n.miss = 0, n = 9, mean = 94.222, sd = 5.608
##
## Mean Difference of Y: 6.333
##
## Weighted Average Standard Deviation: 6.735
##
##
## ------ Assumptions ------
##
## Note: These hypothesis tests can perform poorly, and the
## t-test is typically robust to violations of assumptions.
## Use as heuristic guides instead of interpreting literally.
##
## Null hypothesis, for each group, is a normal distribution of Y.
## Group Group1 Shapiro-Wilk normality test: W = 0.780, p-value = 0.012
## Group Group2 Shapiro-Wilk normality test: W = 0.924, p-value = 0.427
##
## Null hypothesis is equal variances of Y, homogeneous.
## Variance Ratio test: F = 59.278/31.444 = 1.885, df = 8;8, p-value = 0.389
## Levene's test, Brown-Forsythe: t = 0.230, df = 16, p-value = 0.821
##
##
## ------ Infer ------
##
## --- Assume equal population variances of Y for each X
##
## t-cutoff for 95% range of variation: tcut = 2.120
## Standard Error of Mean Difference: SE = 3.175
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.995, df = 16, p-value = 0.063
##
## Margin of Error for 95% Confidence Level: 6.731
## 95% Confidence Interval for Mean Difference: -0.397 to 13.064
##
##
## --- Do not assume equal population variances of Y for each X
##
## t-cutoff: tcut = 2.136
## Standard Error of Mean Difference: SE = 3.175
##
## Hypothesis Test of 0 Mean Diff: t = 1.995, df = 14.624, p-value = 0.065
##
## Margin of Error for 95% Confidence Level: 6.782
## 95% Confidence Interval for Mean Difference: -0.449 to 13.116
##
##
## ------ Effect Size ------
##
## --- Assume equal population variances of Y for each X
##
## Standardized Mean Difference of Y, Cohen's d: 0.940
##
##
## ------ Practical Importance ------
##
## Minimum Mean Difference of practical importance: mmd
## Minimum Standardized Mean Difference of practical importance: msmd
## Neither value specified, so no analysis
##
##
## ------ Graphics Smoothing Parameter ------
##
## Density bandwidth for X Group1: 5.642
## Density bandwidth for X Group2: 4.112
## 3.3 Đánh giá sự khác biệt về RER giữa 2 nhóm bằng bootstrap
library(simpleboot)## Simple Bootstrap Routines (1.1-8)library(boot)
set.seed(123) # câu lệnh giúp kết quả có thể tái lập được
boot_result <- two.boot(placebo, coffee, FUN = mean, R=10000)
boot.ci(boot_result, type = c("perc", "bca", "norm", "basic"))## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 10000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = boot_result, type = c("perc", "bca", "norm",
## "basic"))
##
## Intervals :
## Level Normal Basic
## 95% ( 0.383, 12.188 ) ( 0.111, 11.778 )
##
## Level Percentile BCa
## 95% ( 0.889, 12.556 ) ( 0.778, 12.444 )
## Calculations and Intervals on Original ScaleViệc 4 So sánh tỷ lệ gãy xương giữa 2 nhóm
4.1 So sánh tỷ lệ gãy xương giữa Nam và Nữ
library(table1)##
## Attaching package: 'table1'## The following object is masked from 'package:lessR':
##
## label## The following objects are masked from 'package:base':
##
## units, units<-table1(~fx + as.factor(fx)|sex, data=df)| Female (N=1317) |
Male (N=845) |
Overall (N=2162) |
|
|---|---|---|---|
| fx | |||
| Mean (SD) | 0.304 (0.460) | 0.170 (0.376) | 0.252 (0.434) |
| Median [Min, Max] | 0 [0, 1.00] | 0 [0, 1.00] | 0 [0, 1.00] |
| as.factor(fx) | |||
| 0 | 916 (69.6%) | 701 (83.0%) | 1617 (74.8%) |
| 1 | 401 (30.4%) | 144 (17.0%) | 545 (25.2%) |
chisq.test(df$fx, df$sex)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: df$fx and df$sex
## X-squared = 48.363, df = 1, p-value = 3.542e-12