The dataframe named 'temp' (below) has three columns (1) Canopy Index; (2) Under_tree; and (3) Open_Canopy. The columns Under_tree and Open_Canopy are factors with 5 levels each.
data(temp)
Canopy_index Under_tree Open_Canopy
1 75 Undergrowth Grass
2 85 Litter Grass
3 75 Litter Grass
4 35 Litter Grass
5 85 Undergrowth Grass
The dataframe 'temp' was reformatted to be in long format named df.melt (below) to produce a barplot where the y-axis is denoted as Canopy_index and the x-axis represents the factor Topography (3rd column) which has five levels grouped by two conditions (2nd column - Under_Open):(1) Under_tree; and (2) Open_Canopy.
Problem
I would like to produce a barplot that looks like example 1 (below) with a key for: (1) Under the Canopy; and (2) Open Canopy.
In order to produce the barplot, I attempted to used ggplot; however, the results appear to be erroneous because each bar is exactly the same height (example 2). In summary, I am unsure how to fix the error.
If anyone can help, then many thanks in advance.
Code to convert the dataframe into long format:
##response variable = y = Canopy Index
##explanatory variables = under and open
##Reshape the data to produce one column with under and open
library(reshape2)
library(ggplot2)
under<-factor(temp$Under_tree)
open<-factor(temp$Open_Canopy)
data(df.melt)
df.melt <- melt(temp, id="Canopy_index")
colnames(df.melt)<-c("Canopy_Index", "Under_Open", "Topography")
Canopy_Index Under_Open Topography
1 75 Under_tree Undergrowth
2 85 Under_tree Litter
3 75 Under_tree Litter
4 35 Under_tree Litter
5 85 Under_tree Undergrowth
Code for barplot
##Grouped barplot showing the topography grouped by under_tree and Open_Canopy
ggplot(df.melt, aes(x=Topography, y=Canopy_Index, fill=factor(Under_Open)))+
geom_bar(stat="identity",position="dodge")+
scale_fill_discrete(name="Topographical Feature",
breaks=c(1, 2),
labels=c("Open_Canopy", "Under_tree"))+
xlab("Topographical Feature")+ylab("Canopy Index")
EXAMPLE (1):
EXAMPLE (2)
DATAFRAME (temp)
structure(list(Canopy_index = c(75, 85, 75, 35, 85, 95, 85, 65,
75, 95, 75, 95, 85, 75, 85, 95, 75, 85, 85, 85, 75, 75, 85, 85,
65, 85, 75, 85, 95, 95, 85, 55, 75, 95, 75, 95, 95, 65, 65, 55,
95, 85, 85, 45, 85, 85, 35, 95, 85, 85, 35, 85, 45, 85, 85, 85,
95, 85, 85, 75, 85, 35, 85, 85, 65, 65, 85, 45, 55, 95, 75, 95,
45, 75, 75, 95, 95, 85, 75, 95, 75, 65, 85, 75, 75, 55, 75, 85,
85, 85, 15, 75, 85, 85, 85, 95, 85, 85, 75, 85, 85, 95, 65, 75,
95, 55, 75, 85, 85, 85, 95, 55, 85, 75, 75, 85, 85, 85, 85, 55,
75, 55, 75, 85, 75, 85, 85, 75, 85, 75, 95, 25, 95, 95, 25, 75,
75, 85, 35, 55, 85, 65, 85, 75, 85, 85, 85, 75, 65, 85, 85, 95,
65, 55, 95, 95, 85, 95, 85, 65, 55, 65, 55, 95, 75, 85, 85, 35,
75, 75, 85, 65, 85, 65, 65, 95, 85, 95, 75, 75, 55, 95, 65, 85,
65, 15, 35, 55, 95, 15, 15, 75, 65, 85, 5, 5, 35, 35, 85, 65,
45, 35, 65, 65, 75, 65, 15, 75, 65, 45, 25, 65, 85, 45, 85, 75,
15, 65, 45, 55, 45, 15, 45, 75, 65, 75, 65, 35, 95, 65, 35, 35,
65, 45, 75, 35, 75, 85, 35, 55, 65, 85, 65, 65, 85, 55, 15, 75,
65, 45, 45, 85, 55, 15, 85, 15, 95, 75, 5, 55, 15, 35, 45, 85,
65, 65, 65, 65, 25, 85, 35, 55, 65, 75, 5, 45, 65, 15, 75, 55,
65, 55, 35, 75, 65, 65, 85, 35, 65, 55, 75, 15, 55, 65, 75, 55,
85, 35, 55, 55, 25, 75, 15, 55, 75, 75, 65, 55, 45, 75, 25, 45,
95, 55, 75, 45, 25, 35, 55, 15, 15, 75, 35, 55, 55, 65, 45, 65,
25, 55, 45, 65, 65, 25, 25, 65, 45, 95, 55, 25, 55, 85, 45, 85,
15, 75, 65, 35, 75, 15, 55, 85, 35, 55, 45, 85, 45, 65, 55, 75,
65, 85), Under_tree = structure(c(6L, 5L, 5L, 5L, 6L, 4L, 6L,
5L, 5L, 5L, 4L, 6L, 3L, 6L, 4L, 6L, 4L, 5L, 6L, 5L, 5L, 3L, 5L,
6L, 5L, 5L, 6L, 4L, 6L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 6L, 6L, 4L,
4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 6L, 5L, 5L, 6L, 4L, 6L, 4L,
4L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 5L, 5L, 4L, 4L, 4L, 4L, 5L, 3L,
4L, 5L, 5L, 5L, 4L, 4L, 5L, 6L, 4L, 4L, 5L, 4L, 5L, 6L, 6L, 4L,
4L, 4L, 5L, 4L, 6L, 4L, 4L, 5L, 4L, 6L, 5L, 5L, 4L, 6L, 5L, 6L,
4L, 3L, 6L, 6L, 6L, 3L, 5L, 6L, 6L, 6L, 5L, 5L, 3L, 4L, 4L, 6L,
4L, 3L, 5L, 6L, 4L, 2L, 5L, 5L, 5L, 5L, 6L, 5L, 4L, 4L, 4L, 4L,
6L, 5L, 6L, 6L, 4L, 6L, 6L, 4L, 5L, 4L, 6L, 5L, 6L, 6L, 5L, 6L,
6L, 4L, 5L, 4L, 5L, 4L, 6L, 5L, 4L, 6L, 3L, 3L, 4L, 4L, 4L, 4L,
3L, 4L, 5L, 4L, 5L, 4L, 5L, 6L, 4L, 5L, 4L, 4L, 6L, 4L, 4L, 6L,
6L, 5L, 5L, 5L, 4L, 4L, 6L, 5L, 5L, 5L, 4L, 6L, 3L, 4L, 5L, 4L,
4L, 5L, 6L, 5L, 5L, 3L, 5L, 6L, 6L, 5L, 6L, 6L, 4L, 4L, 5L, 5L,
4L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 4L, 3L, 4L, 5L, 5L, 3L, 5L, 5L,
5L, 6L, 4L, 6L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 2L, 4L, 4L, 5L, 4L,
4L, 6L, 4L, 3L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 6L, 4L, 4L, 5L,
4L, 5L, 4L, 4L, 5L, 5L, 6L, 5L, 3L, 6L, 5L, 5L, 6L, 5L, 6L, 6L,
5L, 4L, 6L, 6L, 5L, 4L, 4L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
5L, 4L, 5L, 4L, 6L, 4L, 5L, 5L, 5L, 4L, 2L, 5L, 5L, 5L, 6L, 5L,
5L, 5L, 4L, 6L, 4L, 3L, 6L, 5L, 6L, 6L, 5L, 6L, 6L, 4L, 5L, 5L,
6L, 5L, 5L, 4L, 5L, 5L, 6L, 5L, 6L, 4L, 4L, 5L, 4L, 3L, 3L, 4L,
4L, 3L, 6L, 4L, 3L, 6L, 4L, 5L, 4L, 4L, 5L, 5L, 4L, 4L, 4L, 4L,
4L, 6L, 5L), .Label = c("", "Artificial_Surface", "Bare_soil",
"Grass", "Litter", "Undergrowth"), class = "factor"), Open_Canopy = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 5L, 6L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 4L, 3L,
4L, 4L, 4L, 6L, 6L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 3L, 6L,
2L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 6L, 4L, 5L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 4L, 4L,
5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 6L, 4L, 4L, 4L, 3L,
4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 6L, 3L, 4L, 4L, 4L, 4L, 6L, 6L,
4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 4L, 2L, 4L, 5L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 6L, 4L, 6L, 6L, 4L, 4L, 6L, 4L, 4L, 4L, 4L, 6L, 4L, 4L,
4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 6L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
4L, 3L, 4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 3L, 4L, 4L, 5L,
4L, 2L, 4L, 4L, 6L, 4L, 4L, 5L, 4L, 6L, 6L, 4L, 4L, 3L, 3L, 4L,
4L, 5L, 5L, 2L, 5L, 2L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 6L,
6L, 2L, 4L, 4L, 6L, 4L, 4L, 4L, 3L, 4L, 3L, 5L, 5L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L,
6L, 5L, 5L, 4L, 3L, 4L, 5L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 2L,
4L, 6L, 4L, 4L, 6L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 6L, 4L, 4L,
2L, 4L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 6L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 3L), .Label = c("", "Artificial_Surface",
"Bare_soil", "Grass", "Litter", "Undergrowth"), class = "factor")), .Names = c("Canopy_index",
"Under_tree", "Open_Canopy"), row.names = c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L,
71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L,
97L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L,
109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L,
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L,
131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L,
142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L,
153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L,
164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L,
175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L,
186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L,
197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L,
208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L,
219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L,
230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L,
241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L,
252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L,
263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L,
274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L,
296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L,
307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L,
318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L,
329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L,
340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L,
351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L,
362L, 363L), class = "data.frame")
Thales
Assuming you want to plot means of Canopy_Index for each Under_Open, Topography cell, you can form means first:
df.means <- aggregate(Canopy_Index ~ Under_Open + Topography, df.melt, mean)
Then, plot df.means using the code from your question:
ggplot(df.means, aes(x=Topography, y=Canopy_Index, fill=Under_Open)) +
geom_bar(stat="identity", position="dodge") +
scale_fill_discrete(name="Canopy Type",
labels=c("Under_tree"="Under Canopy", "Open_Canopy"="Open Canopy")) +
xlab("Topographical Feature") + ylab("Canopy Index")
Result:
The reason why the bars are currently almost all of the same height is that you overlay multiple values per cell (as pointed out in the comments by Marijn Stevering), effectively plotting the max:
df.max <- aggregate(Canopy_Index ~ Under_Open + Topography, df.melt, max)
# Under_Open Topography Canopy_Index
# 1 Under_tree Artificial_Surface 75
# 2 Open_Canopy Artificial_Surface 95
# 3 Under_tree Bare_soil 95
# 4 Open_Canopy Bare_soil 95
# 5 Under_tree Grass 95
# 6 Open_Canopy Grass 95
# 7 Under_tree Litter 95
# 8 Open_Canopy Litter 95
# 9 Under_tree Undergrowth 95
# 10 Open_Canopy Undergrowth 95
来源：https://stackoverflow.com/questions/41549596/how-to-make-a-grouped-barplot-for-a-factor-with-many-levels