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# 读取数据，从csv为例子

alldata <- read.csv("datas.csv")
#对数据的读取情况基本查看
print(is.data.frame(alldata)) # 是否存在
print(ncol(alldata))   #多少列
print(nrow(alldata))    #多少行
print(summary(alldata))

duizao30 <- alldata[7]
shiyan30 <- alldata[4]
#install.packages("carData",repos = "https://mirrors.ustc.edu.cn/CRAN/") 
library(carData)
#install.packages("car",repos = "https://mirrors.ustc.edu.cn/CRAN/") 
library(car)
?t.test
if(FALSE){
  ## Default S3 method:
#t.test(x, y = NULL,
    #   alternative = c("two.sided"单侧双侧, "less"上限, "greater"下限),
     "  mu = 0总体均值, paired = FALSE配对否, var.equal = FALSE方差等否,
       conf.level = 0.95, ...)

## S3 method for class 'formula'
t.test(formula, data, subset, na.action, ...)
"}
#
#danyanbtjianyan
datadan <- as.matrix(as.vector(shiyan30))
t.test(datadan,alternative = "two.sided",mu = 8)
if(false){"
  	One Sample t-test

data:  datadan
t = 5.6545, df = 23'就是v自由度', p-value = 9.342e-06‘p值小于0.05接受H1’
alternative hypothesis: true mean is not equal to 8
95 percent confidence interval:
 20.03088 33.91212这个区间内是95%置信区间，该区间内认为和均值没有显著差异。
sample estimates:
mean of x 
  26.9715 在置信区间之内故没有差异。
  "}

#
#
#两独立样本（随机分为两组一组给药，一组不给药）
dataliang1 <- as.matrix(as.vector(shiyan30))
dataliang2 <- as.matrix(as.vector(duizao30))
shiyanzu <- dataliang1
duizaozu <- dataliang2
t.test(shiyanzu,duizaozu,var.equal = TRUE)#假设方差相等（方差同了再比较均值）
#结论p>0.05接受原假设，有证据显著表明没有差异。
if(false){"
  Two Sample t-test

data:  shiyanzu and duizaozu
t = 4.1118, df = 46, p-value = 0.0001603
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  7.293327 21.282173
sample estimates:
mean of x mean of y 
 26.97150  12.68375 
  "}
###
##
#配对样本t检验(一个人培训前培训后，一只老鼠给药前后)
datapn1 <- as.matrix(as.vector(alldata[5]))
datapn2 <- as.matrix(as.vector(alldata[7]))
t.test(datapn1,datapn2,paired = TRUE)
# =
t.test(datap1-datap2,mu = 0)
#p<0.05拒绝原假设，数据有差异
if(FALSE){"
  Paired t-test

data:  datap1 and datap2
t = 5.0135, df = 23, p-value =
4.511e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  9.142484 21.987266
sample estimates:
mean of the differences 
               15.56487 

  ###
  One Sample t-test(p1-p2)

data:  datap1 - datap2
t = 5.0135, df = 23, p-value =
4.511e-05
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
  9.142484 21.987266
sample estimates:
mean of x 
 15.56487 
"}