> ggplot(data, aes(x = rownames(data), y = `旅客运输平均运距(公里)`)) + + geom_line() + + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") Error in `geom_line()`: ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error: ! object '旅客运输平均运距(公里)' not found Run `rlang::last_trace()` to see where the error occurred. > ggplot(data, aes(x = rownames(data), y = `旅客运输平均运距(公里)`, group = 1)) + + geom_line(aes(color = "总体平均运距")) + + geom_line(aes(y = `铁路旅客运输平均运距(公里)`, color = "铁路")) + + geom_line(aes(y = `公路旅客运输平均运距(公里)`, color = "公路")) + + geom_line(aes(y = `水运旅客运输平均运距(公里)`, color = "水运")) + + geom_line(aes(y = `民用航空旅客运输平均运距(公里)`, color = "民航")) + + labs(title = "各种交通方式的平均运距折线图", x = "年份", y = "旅客运输平均运距(公里)", color = "交通方式") + + scale_color_manual(values = c("总体平均运距" = "black", "铁路" = "red", "公路" = "blue", "水运" = "green", "民航" = "purple")) Error in `geom_line()`: ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error: ! object '旅客运输平均运距(公里)' not found Run `rlang::last_trace()` to see where the error occurred.修改错误

ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距(公里))) + geom_line() + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距(公里), group = 1)) + geom_line(aes(color = "总体平均运距")) + geom_line(aes(y = 铁路旅客运输平均运距(公里), color = "铁路")) + geom_line(aes(y = 公路旅客运输平均运距(公里), color = "公路")) + geom_line(aes(y = 水运旅客运输平均运距(公里), color = "水运")) + geom_line(aes(y = 民用航空旅客运输平均运距(公里), color = "民航")) + labs(title = "各种交通方式的平均运距折线图", x = "年份", y = "旅客运输平均运距(公里)", color = "交通方式") + scale_color_manual(values = c("总体平均运距" = "black", "铁路" = "red", "公路" = "blue", "水运" = "green", "民航" = "purple"))这里哪里不对，改一下

ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距(公里))) + geom_line() + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") ggplot(data, aes(x = ...

> ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距.公里.)) + + geom_line() + + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") Error in geom_line(): ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error: ! object '旅客运输平均运距.公里.' not found Run rlang::last_trace() to see where the error occurred.这个错在哪里，改一下

ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距.公里.)) + geom_line() + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") 注意到列名中的点...

library(ggplot2) library(gridExtra) p1 <- ggplot(primates,aes(x = Bodywt, y = Brainwt,label = rownames(primates))) + geom_point()+ geom_text(hjust = -0.1) + scale_y_continuous(breaks = seq(0, 1000, by = 200)) p2 <- ggplot(primates,aes(x = Bodywt, y = Brainwt,label = rownames(primates))) + geom_point()+ geom_text(hjust = -0.1) + xlab("Body weight (kg)") + ylab("Brain weight (g)") + xlim(0,250)+ scale_y_continuous(breaks = seq(0, 1400, by = 200)) grid.arrange(p1, p2, nrow=1) 将p1图中实心圆点改为空心

p1 <- ggplot(primates,aes(x = Bodywt, y = Brainwt,label = rownames(primates))) + geom_point(shape = 1) + geom_text(hjust = -0.1) + scale_y_continuous(breaks = seq(0, 1000, by = 200)) p2 <- ...

plot4 <-ggplot(sums, aes(x = rownames(sums), y = sum(element_counts1,element_counts2,element_counts3,element_counts4))) + geom_bar(stat = "identity", fill = "pink") + geom_text(aes(label = Totol), vjust = -0.5) + xlab("feeling") + ylim(200,700)+ ylab("counting") + ggtitle("Feeling-Counting_1") + theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(hjust = 0.5))

plot4 <- ggplot(sums, aes(x = rownames, y = sum(element_counts1, element_counts2, element_counts3, element_counts4))) + geom_bar(stat = "identity", fill = "pink") + geom_text(aes(label = Total), ...

下列代码的各个参数是什么同时我该怎么修改纵坐标的值plot4 <-ggplot(sums, aes(x = rownames(sums), y = sum(element_counts1,element_counts2,element_counts3,element_counts4))) + geom_bar(stat = "identity", fill = "pink") + geom_text(aes(label = Totol), vjust = -0.5) + xlab("feeling") + ylim(200,700)+ ylab("counting") + ggtitle("Feeling-Counting_1") + theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(hjust = 0.5))

- x：x轴的值，使用rownames(sums)函数获取数据框的行名作为x轴标签 - y：y轴的值，使用sum(element_counts1, element_counts2, element_counts3, element_counts4)对四个元素的计数求和作为y轴的值 - geom...

ggplot(plotdata,aes(x=RDA1,y=RDA2,color=plotdata$Group))+ geom_point(size=4,aes(color = plotdata$Group,shape=plotdata$Treatment))+ stat_ellipse(aes(fill=plotdata$Group),geom = "polygon",linewidth=0.6,level = 0.95,alpha = 0.1)+ geom_segment(data=RDAE,aes(x = 0, y = 0, xend = RDAE[,1]3.5, yend = RDAE[,2]3.5), arrow = arrow(length = unit(0.03, 'npc')),linewidth =1,color="gray2")+ geom_text(data = RDAE,aes(RDA1 * 4,RDA2 * 4,label = rownames(RDAE)),color = 'gray2',size = 4)+ xlab(paste("RDA1 (",rda1,"%",")"))+ylab(paste("RDA2 (",rda2,"%",")"))+ theme_bw(base_line_size = 1.05,base_rect_size = 1.05)+ scale_color_manual(values=c("#3FBDA7","#0172B6","#BD3C29","#F0965D","#BEBADA"))+ scale_shape_manual(values = c(1,2,3))+ theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+ geom_hline(aes(yintercept=0), colour="gray45",size=0.8, linetype="dashed")+ geom_vline(aes(xintercept=0), colour="gray45",size=0.8, linetype="dashed") 请修改R脚本，将图例名修改为Bac,同时将填充图例与颜色图例合并表示

geom_segment(data=RDAE,aes(x = 0, y = 0, xend = RDAE[,1]*3.5, yend = RDAE[,2]*3.5), arrow = arrow(length = unit(0.03, 'npc')),linewidth =1,color="gray2")+ geom_text(data = RDAE,aes(RDA1 * 4,RDA2 * ...

指标 2021年 2020年 2019年 2018年 2017年 2016年 2015年 2014年 2013年 2012年旅客运输平均运距(公里) 237.98 199.18 200.8 190.76 177.5 164.5 154.68 140.96 129.87 87.76 铁路旅客运输平均运距(公里) 366.34 375.14 401.82 419.16 436.38 447.02 471.85 487.8 503.12 518.25 公路旅客运输平均运距(公里) 71.31 67.32 68.07 67.88 67.03 66.3 66.35 63.34 60.7 51.92 水运旅客运输平均运距(公里) 20.27 22.01 29.42 28.44 27.44 26.56 27 28.27 29.03 30.09 民用航空旅客运输平均运距(公里) 1482.14 1510.68 1773.71 1751.13 1724.75 1716.97 1669.62 1616.08 1598.11 1573.69 用R语言对该csv文件进行数据分析，并绘图，要求不低于五个绘图,该csv文件的名字是sos.csv

ggplot(data, aes(x = rownames(data), y = 旅客运输平均运距.公里.)) + geom_line() + labs(title = "旅客运输平均运距与年份的折线图", x = "年份", y = "旅客运输平均运距(公里)") 2. 各种交通方式的...

ggplot(plotdata,aes(x=RDA1,y=RDA2,color=plotdata$Group))+ #geom_point(size=4,aes(color = plotdata$Group,shape=plotdata$Treatment))+ # #guides(color=guide_legend(title = "Bac"),shape=guide_legend(title = ""))+ #改图例名称 #stat_ellipse(geom = "polygon",linewidth=0.5,level = 0.95,alpha = 0.1)+ #圈 geom_segment(data=RDAE,aes(x = 0, y = 0, xend = RDAE[,1]3.5, yend = RDAE[,2]3.5), arrow = arrow(length = unit(0.03, 'npc')),linewidth =0.4,color="gray2",size = 3)+ #箭头 geom_text(data = RDAE,aes(RDA1 * 4,RDA2 * 4,label = rownames(RDAE)),color = 'gray2',size = 4)+ xlab(paste("RDA1 (",rda1,"%",")"))+ylab(paste("RDA2 (",rda2,"%",")"))+ theme_bw(base_line_size = 1.05,base_rect_size = 1.05)+ scale_color_manual(values=c("#3FBDA7","#0172B6","#BD3C29","#F0965D","#BEBADA"))+ scale_shape_manual(values = c(1,2,3))+ theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(), legend.position = c(0.97, 0.85), #修改图例位置 legend.background = element_blank(), # 设置图例背景为透明 )+ geom_hline(aes(yintercept=0), colour="gray45",size=0.5, linetype="dashed")+ geom_vline(aes(xintercept=0), colour="gray45",size=0.5, linetype="dashed") 请调整箭头与文本的位置

geom_segment(data=RDAE,aes(x = 0, y = 0, xend = RDAE[,1]*3.5, yend = RDAE[,2]*3.5),arrow = arrow(length = unit(0.03, 'npc')),linewidth =0.4,color="gray2",size = 3)+ #箭头 geom_text(data = RDAE,aes...

PCA_Plot_3=function (data,Annotation,VAR,Color) { # logcountdata row:genes,column: samples pca <- prcomp(data) pca_out<-as.data.frame(pca$x) df_out<- pca_out %>%tibble::rownames_to_column(var=VAR) %>% left_join(., Annotation) #df_out<- merge (pca_out,Annotation,by.x=0,by.y=0) # label_color<- factor(df_out[,group]) ggplot(df_out,aes_string(x="PC1",y="PC2")) +geom_point(aes_string(colour = Color)) } Deseq2_Deseq_function_2=function (Countdata,Coldata) { dds_fil <- DESeq2:: DESeqDataSetFromMatrix(countData =Countdata, colData = Coldata, design = ~Group) dds_fil_Deg<- DESeq2::DESeq(dds_fil) return(dds_fil_Deg) } pheatmap_singscore=function (pathways,data,Annotation) { Gene_select_anno= data[,colnames(data) %in% pathways] %>%t()%>%.[,rownames(Annotation)] # return(Gene_select_anno) # Anno_expression_data=Gene_select_anno[,c("SYMBOL",Group_select)] %>% as.data.frame() %>% distinct() %>% na.omit() # rownames(Anno_expression_data)=Anno_expression_data[,"SYMBOL"] # Annotation=group_anno["Gene_type"] # input= Anno_expression_data[,Group_select] # F2_pheatmap <- pheatmap::pheatmap(input, cellwigermline calling GATKdth = 10, cellheight = 12, scale = "row", # treeheight_row = 5, # show_rownames = T,show_colnames = T, # annotation_col= Annotation, # # annotation_row=Annotation, # annotation_legend=Label_def, # cluster_rows = T, cluster_cols = F,clustering_distance_rows = "euclidean") pheatmap::pheatmap(Gene_select_anno, cellwigermline=5, cellheight = 10,cellwidth = 10, scale = "row", treeheight_row = 5, show_rownames = T,show_colnames = F, annotation_col= Annotation, # annotation_row=Annotation, #annotation_legend=Label_def, cluster_rows = T, cluster_cols = F,clustering_distance_rows = "euclidean") } matrix.please<-function(x) { m<-as.matrix(x[,-1]) rownames(m)<-x[,1] m } 这是r语言的代码，告诉我每一条代码的作用和意义

最后，它使用ggplot库绘制散点图，其中x轴表示PC1，y轴表示PC2，点的颜色由Color参数指定。 Deseq2_Deseq_function_2: 这个函数用于进行差异表达分析。它接受两个参数：Countdata（基因计数数据）和Coldata（样本...

model <- fit_mfgarch(data = data, y = y, x = x, low.freq = low.freq, K = K, gamma = TRUE) [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100." Error in fit_mfgarch(data = data, y = y, x = x, low.freq = low.freq, K = K, : No date column.

rownames(my_data) <- my_data$date my_data$date 或者，你可以使用以下代码将日期信息添加为一个新列： my_data$date (my_data$date) 确保在运行 fit_mfgarch() 函数之前，你的数据集包含日期列...

foldChange=1 > padj=0.05 > rt=as.matrix(rt) > rownames(rt)=rt[,1] > exp=rt[,2:ncol(rt)] Error in rt[, 2:ncol(rt)] : 下标出界怎么解决

在这个R代码片段中，rt是一个矩阵，rownames(rt) = rt[,1] 设置了行名为矩阵的第一列。然后尝试获取第二列到最后一列的内容赋值给exp。如果出现“下标出界”的错误，通常是由于以下原因： 1. rt 的实际行数...

解释这段代码：########################### Application (Table 3) ################################ # Data preparation yy=c(final$AY_GeneralHealth08); dd=final$AD_HealthInsurance06; mm=final$AM_RoutineCheckUp07 mis=rowSums(apply(X=cbind(yy,dd,mm), MARGIN=2, FUN=is.na)) y=yy[mis==0]; d=dd[mis==0]; m=mm[mis==0]; x=data.matrix(final[mis==0,c(5:ncol(final))]) # Estimation based on Theorem 1 (Table 3) output1=medDML(y, d, m, x, k = 4, trim = 0.02, order = 1, multmed = FALSE, fewsplits=FALSE, normalized = TRUE) part1 <- data.frame(round(output1$results,2),r = rownames(output1$results)) output1$ntrimmed # Estimation based on Theorem 2 (Table 3) output2=medDML(y, d, m, x, k = 4, trim = 0.02, order = 1, multmed = TRUE, fewsplits=FALSE, normalized = TRUE) part2 <- data.frame(round(output2$results,2),r = rownames(output2$results)) output2$ntrimmed print(xtable(merge(part1, part2, by = "r", all = TRUE, sort = FALSE)),include.rownames=FALSE, file = "output/Table3.tex")

代码调用了medDML函数，传入了y、d、m和x作为参数，并设置了一些参数选项（k=4，trim=0.02，order=1，multmed=FALSE等）。output1保存了medDML函数的输出结果，包括估计结果和其他统计信息。part1是一个数据框，保存...

table(row.names(ESTI_score) == rownames(phenotype))返回为FALS

intersect(row.names(ESTI_score), rownames(phenotype)) # 查看 ESTI_score 行名是否都存在于 phenotype 中 all(row.names(ESTI_score) %in% rownames(phenotype)) # 查看 phenotype 行名是否都存在于 ESTI_score...

control$ID=rownames(control)

This code creates a new variable called "control$ID" and assigns it the value of the row names of the "control" dataset. This can be useful if you want to have a unique identifier for each row in your...

high_corr = find(corr_matrix >0.9); [x, y] = ind2sub(size(corr_matrix), high_corr); high_corr = [corr_matrix.Properties.RowNames(x(x~=y & x<y)), corr_matrix.Properties.RowNames(y(x~=y & x<y))]; for i = 1:length(high_corr) feat1 = high_corr{i, 1}; feat2 = high_corr{i, 2}; X = data(:, {feat1, feat2}); y = data.y; model = fitlm(X, y); fprintf('%s and %s: R^2 = %.2f\n', feat1, feat2, model.Rsquared.Ordinary); end什么意思

data 是一个包含了特征和目标变量的 table 类型数据，其中 data(:, {feat1, feat2}) 表示选取数据中的 feat1 和 feat2 两个特征。fitlm 函数用于拟合线性回归模型，model.Rsquared.Ordinary 表示线性...

LogModel<-glm(y~.,data=newData,family=binomial(link="logit")) LogFit<-predict(object=LogModel,newdata=Data,type="response") Data$Log.scores<-LogFit library("ROCR") par(mfrow=c(2,2)) pd<-prediction(Data$Log.scores,Data$y) pf1<-performance(pd,measure="rec",x.measure="rpp") pf2<-performance(pd,measure="prec",x.measure="rec") plot(pf1,main="模式甄别的累计回溯精度曲线") plot(pf2,main="模式甄别的决策精度和回溯精度曲线") Data.Sort<-Data[order(x=Data$Log.scores,decreasing=TRUE),] P<-0.30 N<-length(Data[,1]) NoiseP<-head(Data.Sort,trunc(NP)) colP<-ifelse(1:N %in% rownames(NoiseP),2,1) plot(Data[,1:2],main="SMOTE处理后的模式甄别结果(30%)",xlab="x1",ylab="x2",pch=as.integer(as.vector(Data[,3]))+1,cex=0.8,col=colP) LogModel<-glm(y~.,data=Data,family=binomial(link="logit")) LogFit<-predict(object=LogModel,newdata=Data,type="response") Data$Log.scores<-LogFit Data.Sort<-Data[order(x=Data$Log.scores,decreasing=TRUE),] NoiseP<-head(Data.Sort,trunc(NP)) colP<-ifelse(1:N %in% rownames(NoiseP),2,1) plot(Data[,1:2],main="平衡化处理前的模式甄别结果(30%)",xlab="x1",ylab="x2",pch=as.integer(as.vector(Data[,3]))+1,cex=0.8,col=colP)

首先使用glm函数来创建逻辑回归模型(LogModel)，然后使用predict函数来预测数据集(Data)的输出(LogFit)，并将预测值保存在数据集(Data)的Log.scores列中。接下来使用ROCR包来创建累计回溯精度曲线(pf1)和决策精度和...

library(tidyverse) exp <- as.data.frame(exp) exp <- exp %>% mutate(probe_id=rownames(exp)) %>% inner_join(ids,by="probe_id") %>% select(probe_id, symbol, everything) exp <- exp[!duplicated(exp$symbol),] rownames(exp) <- exp$symbol exp <- exp[,-(1:2)]

这段代码使用了tidyverse包中的函数进行数据处理，将一个数据框exp中的行名转换为probe_id列，并与另一个数据框ids进行内连接，选取probe_id、symbol以及exp中的其他列，然后去除symbol重复的行，最后将symbol列作为...

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foldChange=1 > padj=0.05 > rt=as.matrix(rt) > rownames(rt)=rt[,1] > exp=rt[,2:ncol(rt)] Error in rt[, 2:ncol(rt)] : 下标出界怎么解决

table(row.names(ESTI_score) == rownames(phenotype))返回为FALS

control$ID=rownames(control)

library(tidyverse) exp <- as.data.frame(exp) exp <- exp %>% mutate(probe_id=rownames(exp)) %>% inner_join(ids,by="probe_id") %>% select(probe_id, symbol, everything) exp <- exp[!duplicated(exp$symbol),] rownames(exp) <- exp$symbol exp <- exp[,-(1:2)]

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