R GGPlot2:错误：长度必须为1或与数据（16）相同：x，y，组

这是我的代码，我以为这是一个简单的线图

ggplot(data=top15andAllDatasummary.df, aes(x=years, y=calculations, group=1)) +
    geom_line() +
    geom_point()

我得到了这个错误：

错误：美学长度必须为1或与数据16:x，y，组相同

我在r中的数据帧中有数据。我的X轴是年，Y轴是一些计算，我每年构造16个

编辑以添加

structure(list(`2001` = c(349.315750645518, 217.47436370343, 
5.17963850977499, 126.661748432313, 57, 39), `2002` = c(703.26693877551, 
429.92, 9.32897959183673, 264.017959183673, 161, 108), `2003` = c(314.897774687065, 
193.792420027816, 4.08936022253129, 117.015994436718, 54, 37), 
    `2004` = c(305.988451086957, 190.680027173913, 3.87839673913043, 
    111.430027173913, 55, 38), `2005` = c(118.528015659408, 74.3175923660387, 
    1.50942011255199, 42.7010031808172, 10, 8), `2006` = c(120.531992244304, 
    73.8279205041202, 1.54362578768783, 45.1604459524964, 10, 
    8), `2007` = c(113.973899988451, 69.7619817530893, 1.44693382607691, 
    42.7649844092851, 10, 8), `2008` = c(110.676242590059, 67.3693570451436, 
    1.36285909712722, 41.9440264477884, 9, 7), `2009` = c(101.965558714192, 
    63.1446534003936, 1.22982724688388, 37.5910780669145, 9, 
    7), `2010` = c(93.9744360902256, 59.8894736842105, 1.14199785177229, 
    32.9429645542427, 9, 7), `2011` = c(91.8911316298046, 58.5660296328108, 
    1.15675327464033, 32.1683487223534, 9, 7), `2012` = c(91.2302181013592, 
    58.598356337583, 1.16773785691708, 31.4641239068591, 8, 6
    ), `2013` = c(87.1390443392165, 55.0509040034438, 1.10277658200603, 
    30.9853637537667, 8, 6), `2014` = c(85.7812132234942, 56.0456831068792, 
    1.09725045469134, 28.6382796619236, 8, 6), `2015` = c(88.331452900479, 
    58.526237360298, 1.22362959020756, 28.5815859499734, 8, 6
    )), .Names = c("2001", "2002", "2003", "2004", "2005", "2006", 
"2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", 
"2015"), row.names = c("AllDataMeanByYear", "AllDataMeanAggAssault", 
"AllDataMeanMurderManSlaughter", "AllDataMeanRobbery", "AllDataMedianByYear", 
"AllDataMedianAggAssault"), class = "data.frame")


All Code:

 ## Total
lwdata$total <- lwdata$murdermanslaughter + lwdata$Robbery +    lwdata$Aggravated_assault
## Data Calculations Top 15
top15 <- lwdata[lwdata$total >= lwdata$total[order(lwdata$Year, lwdata$total, decreasing=TRUE)][15] , ]
## Top 15 Means
Top15MeanByYear <- tapply(top15$total,top15$Year,mean)
Top15MeanAggAssault <- tapply(top15$Aggravated_assault,top15$Year,mean)
Top15MeanMurderManSlaughter <- tapply(top15$murdermanslaughter,top15$Year,mean)
Top15MeanRob <- tapply(top15$Robbery,top15$Year,mean)
## All Data Means
AllDataMeanByYear <- tapply(lwdata$total,lwdata$Year,mean)
AllDataMeanAggAssault <- tapply(lwdata$Aggravated_assault,lwdata$Year,mean)
AllDataMeanMurderManSlaughter <- tapply(lwdata$murdermanslaughter,lwdata$Year,mean)
AllDataMeanRobbery <- tapply(lwdata$Robbery,lwdata$Year,mean)
## Top 15 Medians
Top15MedianByYear <- tapply(top15$total,top15$Year,median)
Top15MedianAggAssault <- tapply(top15$Aggravated_assault,top15$Year,median)
Top15MedianMurderManSlaughter <- tapply(top15$murdermanslaughter,top15$Year,median)
Top15MedianRob <- tapply(top15$Robbery,top15$Year,median)
## All Data Medians
AllDataMedianByYear <- tapply(lwdata$total,lwdata$Year,median)
AllDataMedianAggAssault <- tapply(lwdata$Aggravated_assault,lwdata$Year,median)
AllDataMedianMurderManSlaughter <-  tapply(lwdata$murdermanslaughter,lwdata$Year,median)
AllDataMedianRobbery <- tapply(lwdata$Robbery,lwdata$Year,median)
## Rounding Data To Two Decimal Points
Top15MeanByYear <- round(Top15MeanByYear,digits=2)
Top15MeanAggAssault <- round(Top15MeanAggAssault,digits=2)
Top15MeanMurderManSlaughter <- round(Top15MeanMurderManSlaughter,digits=2)
Top15MeanRob <- round(Top15MeanRob,digits=2)
AllDataMeanByYear <- round(AllDataMeanByYear,digits=2)
AllDataMeanAggAssault <- round(AllDataMeanAggAssault,digits=2)
AllDataMeanAggAssault <- round(AllDataMeanAggAssault,digits=2)
AllDataMeanRobbery <- round(AllDataMeanRobbery,digits=2)
Top15MedianByYear <- round(Top15MedianByYear,digits=2)
Top15MedianAggAssault <- round(Top15MedianAggAssault,digits=2)
Top15MedianMurderManSlaughter <- round(Top15MedianMurderManSlaughter,digits=2)
Top15MedianRob <- round(Top15MedianRob,digits=2)
AllDataMedianByYear <- round(AllDataMedianByYear,digits=2)
AllDataMedianAggAssault <- round(AllDataMedianAggAssault,digits=2)
AllDataMedianMurderManSlaughter <-     round(AllDataMedianMurderManSlaughter,digits=2)
AllDataMedianRobbery <- round(AllDataMedianRobbery,digits=2)
## Summaries
AllDataSummary <- rbind(AllDataMeanByYear, AllDataMeanAggAssault, AllDataMeanMurderManSlaughter, AllDataMeanRobbery, AllDataMedianByYear, AllDataMedianAggAssault, AllDataMedianMurderManSlaughter, AllDataMedianRobbery)
Top15Summary <- rbind(Top15MeanByYear, Top15MeanAggAssault, Top15MeanMurderManSlaughter, Top15MeanRob,Top15MedianByYear,Top15MedianAggAssault,Top15MedianMurderManSlaughter,Top15MedianRob)
Top15andAllDatasummary <- rbind(AllDataSummary,Top15Summary)
## Class of New Items
class(AllDataSummary)
class(Top15Summary)
class(top15andAllDatasummary)
## Converting Matrices to Data Frames
AllDataSummary.df <- as.data.frame(AllDataSummary)
Top15Summary.df <- as.data.frame(Top15Summary)
Top15andAllDatasummary.df <- as.data.frame(Top15andAllDatasummary)
## Checking of New Classes
class(AllDataSummary.df)
class(Top15Summary.df)
class(Top15andAllDatasummary.df)
## Verifications for Names of New Components
colnames(Top15andAllDatasummary.df)
rownames(Top15andAllDatasummary.df)
## New Components
years <- colnames(Top15andAllDatasummary.df)
calculations <- colnames(Top15andAllDatasummary.df)
## Chicago
Chicago <- top15[which(top15$City=="Chicago"), ] 
## Basic Plots
plot(y=Chicago$total, x=Chicago$Year, type="l", xlab = "Year", ylab = "Total       Violent Crime (minus rape)", main="Chicago-Specific Data", col="blue")
## Data Types for Chicago
str(Chicago)

---

链接到完整的>100K数据集是

根据您的数据，我会这样做：

library(tidyr)
top15andAllDatasummary.df$variable = rownames(top15andAllDatasummary.df)
df.long = gather(data = top15andAllDatasummary.df, 
                 key = years, 
                 value = calculations, 
                 -variable)

此gather调用的目的是将数据重新构造为以下形式：

head(df.long)
#                        variable years calculations
# 1             AllDataMeanByYear  2001   349.315751
# 2         AllDataMeanAggAssault  2001   217.474364
# 3 AllDataMeanMurderManSlaughter  2001     5.179639
# 4            AllDataMeanRobbery  2001   126.661748
# 5           AllDataMedianByYear  2001    57.000000
# 6       AllDataMedianAggAssault  2001    39.000000

完成后，我们可以继续绘制：

ggplot(data = df.long, aes(x = years, 
                           y = calculations, 
                           group=variable, 
                           color=variable)) +
   geom_line() +
   geom_point()

---

这是您想要的结果吗？

您的数据框（我们称之为df）每年都有一列，每个计算变量都有行名。这是宽数据，其中每行有多个数据值。ggplot用于处理长数据，其中每行有一个包含数据值的单列，以及其他列，这些列告诉我们有关该数据点的情况，即数据点表示什么变量以及它来自什么年份

Hadley Wickham的tidyverse软件包库也编写了ggplot，它使数据从宽到长再到宽的转换变得很容易

library(tidyverse)

df.new <- mutate(df, variable = rownames(df)) %>% 
    gather(year, value, -variable)

                        variable year      value
1              AllDataMeanByYear 2001 349.315751
2          AllDataMeanAggAssault 2001 217.474364
3  AllDataMeanMurderManSlaughter 2001   5.179639
4             AllDataMeanRobbery 2001 126.661748
5            AllDataMedianByYear 2001  57.000000
6        AllDataMedianAggAssault 2001  39.000000
7              AllDataMeanByYear 2002 703.266939
8          AllDataMeanAggAssault 2002 429.920000
9  AllDataMeanMurderManSlaughter 2002   9.328980
10            AllDataMeanRobbery 2002 264.017959
11           AllDataMedianByYear 2002 161.000000
12       AllDataMedianAggAssault 2002 108.000000
13             AllDataMeanByYear 2003 314.897775
14         AllDataMeanAggAssault 2003 193.792420
15 AllDataMeanMurderManSlaughter 2003   4.089360
16            AllDataMeanRobbery 2003 117.015994
17           AllDataMedianByYear 2003  54.000000
18       AllDataMedianAggAssault 2003  37.000000
19             AllDataMeanByYear 2004 305.988451
20         AllDataMeanAggAssault 2004 190.680027
... and 70 more rows

然后可以将这些长数据发送到ggplot。请注意，您最初的尝试使用了一个名为years的变量，该变量在数据框中不存在。R和ggplot无法知道您的列名2001:2015以某种方式神奇地代表年份

plot.years <- ggplot(data = df.new, aes(x = year, y = value, color = variable, group = variable)) +
    geom_line()
print(plot.years)

---

有数据会有帮助。请阅读我对R真的很陌生，我不得不说，即使在浏览了那个网站之后，我也在努力为你获取这些数据。我不想因为这个被踢出去，请再试一次。你不会被解雇，但是如果没有这些数据，你很难帮助你。请阅读@snoram提供的链接中的函数dput。如果您仍然觉得这很难，您可以将数据上传到CSV文件中，我们可以从中下载。同时，至少打电话给headyourdata并向我们展示输出，这样我们至少有一些想法。另外，用一个+而不是两个+连接ggplot层。因此，您的data.frame是top15和alldatasummary.df。您可以执行dputtop15和alldatasummary.df或dputhoheadtop15和alldatasummary.df并将结果作为代码粘贴到您的问题中吗？我运行了您的代码，它在evalexpr、envir、enclose中给我错误：找不到对象“年”。这是合乎逻辑的，因为在您的数据中没有称为年的变量。请用dput重复这个过程，但这次请提供实际调用绘图的数据。哦，天哪，jakub，非常感谢！去寻找给你信用或帮助得分或分数的方法。。。我可能想编辑的东西，但我希望我有它从这里@如果你通过点击复选标记来接受答案，这将对其他人有用。如果对这段特定代码有进一步的问题，请在注释中提问。您将如何按年份对所有数据进行子集？这不起作用：substallDataSummary.df.new，subset=variable=“AllDataMeanByYear”->AllDataMeanByYea。图形错误：substallDataSummary.df.new中出现意外的“=”，subset=variable=尝试读取子集的帮助页面-您可以通过调用R中的？subset来调用帮助页面。查看示例，他们应该给你一个提示。在我发布问题之前，我试过了，但仍然得到了错误：>subsetdf.long，variable==alldatameansbyyear evalexpr，envir，enclose中的错误：dims[product 15]与对象的长度不匹配[240]