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| reviewers: | ||
| # The default reviewers | ||
| defaults: | ||
| - repository-owners # GitHub team | ||
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| # Reviewer groups each of which has a list of GitHub usernames | ||
| groups: | ||
| repository-owners: | ||
| - Jacob953 | ||
| - DevRickLin | ||
| core-manager: | ||
| - jzndd | ||
| science: | ||
| - DevRickLin # username | ||
| - HanX-gthb # username | ||
| bigdata: | ||
| - tangent-heng # username | ||
| - Yemonade # username | ||
| - zhangchenqi123 # username | ||
| security: | ||
| - Gstalker # username | ||
| - the-fall-moon # username | ||
| - wbfwonderful # username | ||
| automation: | ||
| - jzndd | ||
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| files: | ||
| # Keys are glob expressions. | ||
| # You can assign groups defined above as well as GitHub usernames. | ||
| '**/science/**': | ||
| - science # group | ||
| '**/bigdata/**': | ||
| - bigdata # group | ||
| '**/security/**': | ||
| - security # group | ||
| '**': | ||
| - repository-owners # group | ||
| - core-manager # group | ||
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| options: | ||
| ignore_draft: false | ||
| ignored_keywords: | ||
| - DO NOT REVIEW | ||
| enable_group_assignment: false |
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| - [ ] checkout 1 | ||
| - [ ] checkout 2 | ||
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| ## Screenshoot | ||
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| @\<evalcsu/your_manager> | ||
| ## Screenshoot | ||
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| name: Auto Request Review | ||
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| on: | ||
| pull_request_target: | ||
| types: [opened, ready_for_review, reopened] | ||
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| jobs: | ||
| auto-request-review: | ||
| name: Auto Request Review | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - name: Request review based on files changes and/or groups the author belongs to | ||
| uses: necojackarc/[email protected] | ||
| with: | ||
| token: ${{ secrets.GITHUB_TOKEN }} |
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| .* | ||
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| !.gitignore | ||
| !.gitignore | ||
| !.github |
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| #1. 假设有300 名学生，起始学号为210222001 | ||
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| xuehao <- c(210222001:210222300) | ||
| xuehao | ||
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| mySeed <- 20 | ||
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| #2. 生成各科成绩 | ||
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| set.seed(mySeed) | ||
| gaoshu <- rnorm(300,70, sqrt(10)); | ||
| gaoshu <- round(gaoshu); | ||
| gaoshu[gaoshu > 100] <- 100; | ||
| gaoshu[gaoshu < 0] <- 0; | ||
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| set.seed(mySeed) | ||
| xiandai <- rnorm(300,0, sqrt(2)); | ||
| xiandai <- round(xiandai); | ||
| xiandai <- xiandai + gaoshu; | ||
| xiandai[xiandai > 100] <- 100; | ||
| xiandai[xiandai < 0] <- 0; | ||
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| set.seed(mySeed) | ||
| yingyu <- runif(300,56,99) | ||
| yingyu <- round(yingyu) | ||
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| set.seed(mySeed); | ||
| chengshe <- rnorm(300,85,sqrt(12)); | ||
| chengshe <- round(chengshe); | ||
| chengshe[chengshe > 100] <- 100; | ||
| chengshe[chengshe < 0] <- 0; | ||
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| #3.把上述信息组合成数据框，并写到文本文件中 | ||
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| #生成数据框 | ||
| classscore <- data.frame(xuehao,gaoshu,xiandai,yingyu,chengshe) | ||
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| #写入文本文件 | ||
| write.table(classscore,file = 'classscore.txt') | ||
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| classscore | ||
| #4. 计算各科平均分，求出每人的平均成绩，总成绩 | ||
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| #各科的平均成绩 | ||
| subjectMean <- apply(classscore[,2:5],2,mean) | ||
| subjectMean | ||
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| #5.计算求各种指标 | ||
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| #每人的平均成绩 | ||
| studentMean <- apply(classscore[,2:5],1,mean) | ||
| studentMean | ||
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| #每人的最高分 | ||
| studentMax <- apply(classscore[,2:5],1,max); | ||
| studentMax | ||
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| #每人的最低分 | ||
| studentMin <- apply(classscore[,2:5],1,min); | ||
| studentMin | ||
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| #每人的总成绩 | ||
| studentSum <- apply(classscore[,2:5],1,sum) | ||
| studentSum | ||
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| #给每个人分数评级 | ||
| y <- quantile(studentSum, c(.8,.6,.4,.2)) | ||
| classscore$grade[studentSum >= y[1]] <- "A" | ||
| classscore$grade[studentSum >= y[2] & studentSum < y[1]] <- "B" | ||
| classscore$grade[studentSum >= y[3] & studentSum < y[2]] <- "C" | ||
| classscore$grade[studentSum < y[3]] <- "D" | ||
| classscore | ||
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| #6. 求总分最高的同学的学号 | ||
| classscore$xuehao[studentSum == max(studentSum)] | ||
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| #7.绘高等数学成绩直方图、柱状图丶饼图；画高数和线代，高数和英语的散点图；画各科成绩的箱尾图 | ||
| #直方图 | ||
| hist(classscore$gaoshu,breaks = 9,main = '高等数学成绩直方图',xlab = '数学成绩') | ||
| #饼图??? | ||
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| y <- quantile(classscore$gaoshu, c(.8,.6,.4,.2)) | ||
| gaoshuGrade <- c(1:300) | ||
| gaoshuGrade[classscore$gaoshu >= y[1]] <- "A" | ||
| gaoshuGrade[classscore$gaoshu >= y[2] & classscore$gaoshu < y[1]] <- "B" | ||
| gaoshuGrade[classscore$gaoshu >= y[3] & classscore$gaoshu < y[2]] <- "C" | ||
| gaoshuGrade[classscore$gaoshu < y[3]] <- "D" | ||
| gaoshuGrade | ||
| gaoshuTable <- table(gaoshuGrade) | ||
| gaoshuTable | ||
| pie(gaoshuTable) | ||
| barplot(gaoshuTable) | ||
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| pie(classscore$gaoshu) | ||
| #柱状图??? | ||
| barplot(classscore$gaoshu) | ||
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| #画高数和线代散点图 | ||
| plot(classscore$gaoshu,classscore$xiandai,pch = 20,xlab = '高数',ylab = '线代',main = '高数和线代') | ||
| classscore$gaoshu | ||
| classscore$xiandai | ||
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| classscore$gaoshu == classscore$xiandai | ||
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| #画高数和英语散点图 | ||
| plot(classscore$gaoshu,classscore$yingyu,xlab = '高数',ylab = '英语',main = '高数和英语') | ||
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| #画各科成绩的箱尾图 | ||
| boxplot(classscore$gaoshu,classscore$xiandai,classscore$yingyu,classscore$chengshe,main = '高数，线代，英语，程设成绩的箱尾图') | ||
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| #星象图 | ||
| stars(classscore[,2:5],full = TRUE,draw.segments = TRUE,key.loc=c(30,1.5)) | ||
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| #绘制高等数学与线性代数关系图 | ||
| fit <- lm(classscore$xiandai ~ classscore$gaoshu,data = classscore) | ||
| plot(classscore$gaoshu,classscore$xiandai,xlab = '高数',ylab = '线代',main = '高数和线代') | ||
| abline(fit) | ||
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| #install.packages("ggplot2") | ||
| library(ggplot2) | ||
| ggplot(data=classscore, aes(x=gaoshu, y=xiandai)) + | ||
| geom_point(pch=17, color="blue", size=2) + | ||
| geom_smooth(method="lm", color="red", linetype=2) + | ||
| labs(title="gaoshu&xiandai", x="gaoshu", y="xiandai") | ||
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| #绘制高等数学与英语关系图 | ||
| library(ggplot2) | ||
| ggplot(data=classscore, aes(x=gaoshu, y=yingyu)) + | ||
| geom_point(pch=17, color="blue", size=2) + | ||
| geom_smooth(method="lm", color="red", linetype=2) + | ||
| labs(title="gaoshu&yingyu", x="gaoshu", y="yingyu") | ||
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| #10. 生成社会实践课成绩（A，B，C，D，E）并将其加入到classscore数据框 | ||
| level<- c("A","B","C","D","E") | ||
| shijian<- sample(level,300, replace = TRUE) | ||
| classscore$shijian <- shijian | ||
| classscore | ||
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| #15 | ||
| shijianTable <- table(classscore$shijian) | ||
| shijianTable | ||
| plot(shijianTable) | ||
| pie(shijianTable) |
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| install.packages("caret") | ||
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| data(iris) | ||
| iris | ||
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| train_list <- sample(nrow(iris), 0.7*nrow(iris)) | ||
| train <- iris[train_list,] | ||
| test <- iris[-train_list,] | ||
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| dim(iris) | ||
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| sapply(iris, class) | ||
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| head(iris) | ||
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| a <- factor(iris$Species) | ||
| levels(a) | ||
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| percentage <- prop.table(table(iris$Species)) * 100 | ||
| percentage | ||
| cbind(freq=table(iris$Species), percentage=percentage) | ||
| freq=table(iris$Species) | ||
| iris$Species | ||
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| summary(iris) | ||
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| x <- iris[,1:4] | ||
| y <- iris[,5] | ||
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| par(mfrow=c(1,4)) | ||
| for(i in 1:4) { | ||
| boxplot(x[,i], main=names(iris)[i]) | ||
| } | ||
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| plot(y) |
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