> 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

ggplot2的相关矩阵：_R_下载.zip

ggplot(tidy_cor, aes(x = variable1, y = rownames(tidy_cor), fill = correlation)) + geom_tile() + scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) + theme_minimal() + ...

treemapify：:deciduous_tree:在ggplot2中绘制树图

- 例如：ggplot(data, aes(area = value, fill = category)) + geom_treemap() 4. **自定义样式**： - treemapify 支持 ggplot2 的各种主题、颜色和标签选项，可以调整边框、填充色、字体等。 - scale_...

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. > 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 = ...

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), ...

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 * ...

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))+ # #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...

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...

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...

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$date <- as.Date(my_data$date) 确保在运行 fit_mfgarch() 函数之前，你的数据集...

pear_year_total <- pear_year %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% pivot_longer(-1,names_to = "year",values_to = "pear_year") %>% mutate(pear_year = replace_na(pear_year, 0)) %>% group_by(age) %>% summarise (pear_year = sum(pear_year))

这段代码使用了R语言中的dplyr包，进行了一系列数据处理操作。具体来说，它的功能如下： 1. 将一个名为pear_year的数据框转换成数据框格式 2. 增加一个名为age的列，将行名作为age的值 3. 将数据框转换为tibble格式...

high_corr = np.where(corr_matrix > 0.9) high_corr = [(corr_matrix.index[x], corr_matrix.columns[y]) for x, y in zip(*high_corr) if x != y and x < y] for feat1, feat2 in high_corr: X = data[[feat1, feat2]] y = data['y'] model = LinearRegression().fit(X, y) print(f'{feat1} and {feat2}: R^2 = {model.score(X, y):.2f}')把这些python代码转换为matlab形式

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...

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...

翻译一下这段代码case_count <- case_count %>% t %>% t %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% mutate(case_count = V1)

首先，使用管道操作符 %>% 将数据框 case_count 传入管道中，然后使用两次 t() 函数将矩阵转置两次，将矩阵的行列位置对调，然后使用 as.data.frame() 函数将其转换为数据框格式。接着，使用 rownames_to_...

case_count <- case_count %>% t %>% t %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% mutate(case_count = V1) 这段代码有错误吗

2. rownames_to_column() 函数的作用是将数据框的行名转换为列，但是代码中没有指定行名的名称，可能会导致后续的数据分析和可视化操作出现问题。综上所述，建议对这段代码进行一些改进和优化，例如明确指定函数...

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...

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pear_year_total <- pear_year %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% pivot_longer(-1,names_to = "year",values_to = "pear_year") %>% mutate(pear_year = replace_na(pear_year, 0)) %>% group_by(age) %>% summarise (pear_year = sum(pear_year))

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

翻译一下这段代码case_count <- case_count %>% t %>% t %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% mutate(case_count = V1)

case_count <- case_count %>% t %>% t %>% as.data.frame() %>% rownames_to_column(., "age") %>% as_tibble() %>% mutate(case_count = V1) 这段代码有错误吗

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