Question

数据框命名为＆＃39; temp＆＃39; （下方）有三列（1）Canopy Index; （2）Under_tree; （3）Open_Canopy。列 Under_tree 和 Open_Canopy 是每个5级的因素。

         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

数据框＆＃39; temp＆＃39; 被重新格式化为名为 df.melt （下图）的长格式，以生成的条形图y轴表示为 Canopy_index ， x轴表示因子地形（第3列），其中五个级别按两个条件分组（第二列 - Under_Open）:( 1）Under_tree; （2）Open_Canopy。

问题

我想制作一个看起来像示例1（下图）的条形图，其中包含以下键：（1）在Canopy下; （2）开放冠层。

为了制作条形图，我尝试使用ggplot;但是，结果似乎是错误的，因为每个柱子的高度完全相同（示例2）。总之，我不确定如何修复错误。

如果有人可以提供帮助，那么请提前多多谢意。

将数据框格转换为长格式的代码：

   ##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

条形码代码

 ##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")

示例（1）：

示例（2）

DATAFRAME（临时）

       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")

Answer 1

假设您想为每个Canopy_Index，Under_Open单元格绘制Topography的方法，您可以先形成一种方法：

df.means <- aggregate(Canopy_Index ~ Under_Open + Topography, df.melt, mean)

然后，使用您问题中的代码绘制df.means：

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")

结果：

条形图目前几乎全部具有相同高度的原因是您为每个单元格覆盖多个值（as pointed out in the comments by Marijn Stevering），有效地绘制了最大值：

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

如何为具有多个级别的因子制作分组条形图

1 个答案: