Why are my ggplot2 aesthetics the wrong length? - r

Question

I am trying to average reps of data, subset one treatment, then make a bar graph of the response and another factor. My plot ends up not working. Any help would be much appreciated.
My data:
data <- structure(list(Sample = c(1011L, 1012L, 1014L, 1024L, 1025L,
1026L), Collection = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1",
"2"), class = "factor"), Irrigation = structure(c(3L, 3L, 3L,
5L, 5L, 5L), .Label = c("Rate1", "Rate2", "Rate3", "Rate4", "Rate5"
), class = "factor"), Variety = structure(c(2L, 1L, 3L, 3L, 2L,
1L), .Label = c("Hodag", "Lamoka", "Snowden"), class = "factor"),
Suc = c(0.7333, 0.4717, 0.5883, 0.6783, 0.8283, 0.6833),
Gluc = c(0.03, 0.04, 0.043, 0.075, 0.057, 0.087), L = c(59.48,
57.59, 59.25, 66.45, 68.29, 65.65), a = c(4.36, 6.85, 3.43,
1.7, 0.78, 2.84), b = c(26.82, 27.6, 26.2, 26.14, 25.37,
27.19), NoDefect = c(100L, 100L, 100L, 92L, 100L, 100L),
Defect = c(0L, 0L, 0L, 8L, 0L, 0L)), row.names = c(NA, 6L
), class = "data.frame")
Averaging between reps:
dataAvgSuc <- data %>%
dplyr::group_by(Collection, Irrigation, Variety) %>%
dplyr::summarise(meanSuc=mean(Suc))
Made 'Collection' a factor:
dataAvgSuc$Collection <- as.factor(dataAvgSuc$Collection)
Subset by variety:
subLamoka <- subset(dataAvgSuc, Variety=="Lamoka")
subHodag <- subset(dataAvgSuc, Variety=="Hodag")
subSnowden <- subset(dataAvgSuc, Variety=="Snowden")
Attempted ggplot:
sucPlot <-ggplot(data=subLamoka, aes(x=dataAvgSuc$Collection,
y=meanSuc)) + geom_bar(stat="identity")
Error code:
Error: Aesthetics must be either length 1 or the same as the data (10):
x, y
However, both the x and y have 30 entries when I look at them.

Answer 1

Trev,
Had some trouble re-generating the issue as the sample data provided are for just 6 observations, not 30. So not sure if the below solution would work for you or not.
I used the code you supplied to create the dataframe:
data <- structure(list(Sample = c(1011L, 1012L, 1014L, 1024L, 1025L, 1026L),
Collection = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1",
"2"), class = "factor"),
Irrigation = structure(c(3L, 3L, 3L,5L, 5L, 5L), .Label = c("Rate1", "Rate2",
"Rate3", "Rate4", "Rate5"
), class = "factor"), Variety = structure(c(2L, 1L, 3L, 3L, 2L,
1L), .Label = c("Hodag", "Lamoka", "Snowden"), class = "factor"),
Suc = c(0.7333, 0.4717, 0.5883, 0.6783, 0.8283, 0.6833),
Gluc = c(0.03, 0.04, 0.043, 0.075, 0.057, 0.087),
L = c(59.48, 57.59, 59.25, 66.45, 68.29, 65.65),
a = c(4.36, 6.85, 3.43, 1.7, 0.78, 2.84),
b = c(26.82, 27.6, 26.2, 26.14, 25.37,27.19),
NoDefect = c(100L, 100L, 100L, 92L, 100L, 100L),
Defect = c(0L, 0L, 0L, 8L, 0L, 0L)),
row.names = c(NA, 6L), class = "data.frame")
data$Collection
However, your Collection factor is defined with two levels but only one is shown in the example. Perhaps this could be why the averages were coming out greater than 1?I modified the code below to have 2 levels of collection represented in the data.
data2 <- structure(list(Sample = c(1011L, 1012L, 1014L, 1024L, 1025L, 1026L),
Collection = structure(c(1L, 1L, 1L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"),
Irrigation = structure(c(3L, 3L, 3L,5L, 5L, 5L), .Label = c("Rate1", "Rate2",
"Rate3", "Rate4", "Rate5"
), class = "factor"), Variety = structure(c(2L, 1L, 3L, 3L, 2L,
1L), .Label = c("Hodag", "Lamoka", "Snowden"), class = "factor"),
Suc = c(0.7333, 0.4717, 0.5883, 0.6783, 0.8283, 0.6833),
Gluc = c(0.03, 0.04, 0.043, 0.075, 0.057, 0.087),
L = c(59.48, 57.59, 59.25, 66.45, 68.29, 65.65),
a = c(4.36, 6.85, 3.43, 1.7, 0.78, 2.84),
b = c(26.82, 27.6, 26.2, 26.14, 25.37,27.19),
NoDefect = c(100L, 100L, 100L, 92L, 100L, 100L),
Defect = c(0L, 0L, 0L, 8L, 0L, 0L)),
row.names = c(NA, 6L), class = "data.frame")
data2$Collection
Since you're using dplyr just keep piping that object into ggplot-- I don't think you would need to create subsets of new dataframes, but can instead graph them all separately with a facet_wrap command. I also am using geom_col instead of geom_bar, which the latter is generally trying to graph count data. Since you want to plot an average, geom_col may be better. Also since the example below is piping to the next line, the "data=" definition typically used in ggplot commands is not needed.
First with data:
data %>%
dplyr::group_by(Collection,Irrigation, Variety) %>%
dplyr::summarise(meanSuc=mean(Suc)) %>%
ggplot(aes(x = Collection, y = meanSuc)) +
geom_col() +
facet_wrap(.~Variety)
Incorporate Irrigation:
data %>%
dplyr::group_by(Collection,Irrigation, Variety) %>%
dplyr::summarise(meanSuc=mean(Suc)) %>%
ggplot(aes(x = Collection, y = meanSuc, fill = Irrigation)) +
geom_col() +
facet_wrap(.~Variety)
And using data2 instead, as defined above, will produce the Collection levels 1 and 2 side by side on the graph. With this method I was able to generate a result and all averages were less than 1, between .4~.8