GGPlot2: Error: Aesthetics must be either length 1 or the same as the data (16): x, y, group

Question

Here is the code I have for what I thought was a simple line graph

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

And I got this error:

Error: Aesthetics must be either length 1 or the same as the data (16): x, y, group

I have data in a dataframe in r. My X-Axis was going to be years and the Y-axis was going to be some calculations (16 of them) I constructed per year.

Edited to add

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)

link to full >100K set of data is here

Answer 1

Your data frame (let's call it df) has a column for each year, and rownames for each of your calculated variables. This is "wide" data, in which the same data type is stored across multiple columns. ggplot is meant to work with "long" data, in which each column contains a unique aspect of the data (i.e., separate columns for the variable, year, and data value).

The tidyverse library of packages, by Hadley Wickham (who also wrote ggplot), makes it easy to transform data from wide to long and back again. As of tidyr 1.0, this is accomplished via the pivot_wider and pivot_longer functions (previously spread and gather, respectively). I show both methods below.

library(tidyverse)

# current pivot_longer() implementation:
df.new <- mutate(df, variable = rownames(df)) %>%
    pivot_longer(-variable, names_to = 'year', values_to = 'value')

# deprecated gather() function
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

This long data can then be sent to ggplot. Note that your original attempt used a variable called "years", which did not exist in the data frame. R (and ggplot) have no way of knowing that your column names (2001:2015) somehow magically represent years.

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

Answer 2

Based on your data, I would do this:

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

The point of this gather call is to restructure your data into this form:

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

Having done that, we can proceed to plotting:

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

Is this your desired result?