R-ggpattern (绘图花式大赏-2)

生信技能树

发布于 2022-12-16 14:28:39

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上一小节中，我们介绍了ggpattern并鉴赏了ggpattern中两大pattern之一的array_based_pattern。详见：R-ggpattern（绘图花式大赏-1）

接下来，我们继续介绍一下另一大pattern-geometry_based pattern,鉴赏一下geometry_based pattern绘制的图。

Geometry-based pattern

geometry-based pattern的特点就是比较的丑，不能加这么多特别的图片啥的。

geometry-based pattern 有三个重要的参数：

density控制一个图形和其他相邻图形之间的距离，范围【0，1】

image.png

spacing控制两个图形之间的距离

image.png

控制角度

image.png

1 黑白条纹

df <- data.frame(level = c("a", "b", "c", 'd'), outcome = c(2.3, 1.9, 3.2, 1))

p <- ggplot(df, aes(level, outcome)) +#设置x轴y轴
  geom_col_pattern(
    aes(pattern = level, pattern_angle = level, pattern_spacing = level), #控制的三个参数
    fill            = 'white',#  柱子内里是白色          
    colour          = 'black', #柱子轮廓是黑色
    pattern_density = 0.35, #设置密度
    pattern_fill    = 'black',#图形颜色
    pattern_colour  = 'black'#图形的轮廓
  ) +
  theme_bw() +
  labs(
    title    = "ggpattern::geom_col_pattern()",
    subtitle = 'geometry-based patterns'
  ) +
  scale_pattern_spacing_discrete(range = c(0.01, 0.05)) + #这个设定了两个图形之间的间隔
  theme(legend.position = 'none ') + 
  coord_fixed(ratio = 1)#横纵轴比

p

image.png

2 彩色条纹

p <- ggplot(df, aes(level, outcome)) +
  geom_col_pattern(
    aes(pattern = level, fill = level, pattern_fill = level), 
    colour                   = 'black', #柱子的轮廓是黑色
    pattern_density          = 0.35, 
    pattern_key_scale_factor = 1.3) +#这个是控制图例中图案大小的，这里没有设置就没用
#一般来说这个值设置为1是比较合适的
  theme_bw() +
  labs(
    title    = "ggpattern::geom_col_pattern()",
    subtitle = 'geometry-based patterns'
  ) +
  scale_pattern_fill_manual(values = c(a='blue', b='red', c='yellow', d='darkgreen')) +#设置图形的颜色 
  theme(legend.position = 'none') + 
  coord_fixed(ratio = 1)

p

image.png

3 所有的图

接下来的操作就和array-based pattern的套路一样了，会教学你不同的图的画法。这里把代码抄写并且注释一下，尤其是比较新的操作：

##geom_bar_pattern()柱状图
p <- ggplot(mpg, aes(class)) +
  geom_bar_pattern(
    aes(
      pattern = class, 
      pattern_angle = class#这两个参数使得不同的class之间有不同的角度和形状
    ), 
    fill            = 'white', 
    colour          = 'black',#决定了主体是黑白色
    pattern_spacing = 0.025
  ) +
  theme_bw(18) +
  labs(title = "ggpattern::geom_bar_pattern()") + 
  theme(legend.position = 'none') +
  coord_fixed(ratio = 1/15) + 
  scale_pattern_discrete(guide = guide_legend(nrow = 1))#熟悉的操作，让所有的图例变成一行

p

##使用geom_bar_pattern绘制饼图
df <- data.frame(
  group = factor(c("Cool", "But", "Use", "Less"), levels = c("Cool", "But", "Use", "Less")),
  value = c(10, 20, 30, 40)#这里要设置factor
)

p <- ggplot(df, aes(x="", y = value, pattern = group, pattern_angle = group))+#这里x为空的
  geom_bar_pattern(
    width                = 1,#这里设置的是柱体的宽度
    stat                 = "identity", #这样设置的柱状图就是叠叠乐，stat表示一种统计方式
    #https://www.cnblogs.com/muchen/p/5279727.html
    fill                 = 'white', 
    colour               = 'black',#黑白色
    pattern_aspect_ratio = 1, 
    pattern_density      = 0.3
  ) +
  coord_polar("y", start=0) + #设置极坐标是重点
  theme_void(20) + #空白的背景
  theme(
    legend.key.size = unit(2, 'cm')#设置图例的大小
  ) + 
  labs(title = "ggpattern::geom_bar_pattern() + coord_polar()")

p

##geom_bin2d_pattern
p <- ggplot(diamonds, aes(x, y)) + 
  xlim(4, 10) + ylim(4, 10) +#设置坐标轴范围
  geom_bin2d_pattern(aes(pattern_spacing = ..density..), fill = 'white', bins = 6, colour = 'black', size = 1) +
  theme_bw(18) +
  theme(legend.position = 'none') + 
  labs(title = "ggpattern::geom_bin2d_pattern()")

p
#这个过程真的比array简单很多可见默认就是geom-based的形式

## geom_boxplot_pattern
p <- ggplot(mpg, aes(class, hwy)) +
  geom_boxplot_pattern(
    aes(
      pattern      = class,#不一样的pattern不一样的颜色 
      pattern_fill = class
    ), 
    pattern_spacing = 0.03#图形间距
  ) +
  theme_bw(18) +
  labs(title = "ggpattern::geom_boxplot_pattern()") + 
  theme(legend.position = 'none') + 
  coord_fixed(1/8)

p
## geom_col_pattern
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))

p <- ggplot(df, aes(trt, outcome)) +
  geom_col_pattern(
    aes(
      pattern = trt, 
      fill    = trt
    ), 
    colour                   = 'black', 
    pattern_density          = 0.5, 
    pattern_key_scale_factor = 1.11
  ) +
  theme_bw(18) +
  labs(title = "ggpattern::geom_col_pattern()") + 
  theme(legend.position = 'none') +  
  coord_fixed(ratio = 1/2)

p
## geom_crossbar_pattern
df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),#处理未处理有两个
  resp = c(1, 5, 3, 4),#因变量值
  group = factor(c(1, 2, 1, 2)),#分组
  upper = c(1.1, 5.3, 3.3, 4.2),#上限
  lower = c(0.8, 4.6, 2.4, 3.6)#下限
)

p <- ggplot(df, aes(trt, resp, colour = group)) +
    geom_crossbar_pattern(
      aes(
        ymin          = lower, 
        ymax          = upper, 
        pattern_angle = trt, #不同的角度
        pattern       = group#不同的图形
      ), width        = 0.2, 
      pattern_spacing = 0.02
    ) +
    theme_bw(18) +
    labs(title = "ggpattern::geom_crossbar_pattern()") + 
    theme(legend.position = 'none') + 
    coord_fixed(ratio = 1/3)

p
## geom_density_pattern
p <- ggplot(mtcars) +
   geom_density_pattern(
     aes(
       x            = mpg, 
       pattern_fill = as.factor(cyl), 
       pattern      = as.factor(cyl)
     ), 
     fill                     = 'white', 
     pattern_key_scale_factor = 1.2,
     pattern_density          = 0.4
   ) +
   theme_bw(18) +
   labs(title = "ggpattern::geom_density_pattern()") + 
   theme(legend.key.size = unit(2, 'cm')) +
   coord_fixed(ratio = 100)

p
##geom_map_pattern
if (require(maps)) {

crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)

states_map <- map_data("state")

p <- ggplot(crimes, aes(map_id = state)) +
    geom_map_pattern(
      aes(
        # fill            = Murder,
        pattern_fill    = Murder,
        pattern_spacing = state,
        pattern_density = state,
        pattern_angle   = state,#必须拥有的老三样
        pattern         = state
      ),
      fill   = 'white',
      colour = 'black',
      pattern_aspect_ratio = 1.8,
      map    = states_map
    ) +
    expand_limits(x = states_map$long, y = states_map$lat) +
    coord_map() +
    theme_bw(18) +
    labs(title = "ggpattern::geom_map_pattern()") + 
    scale_pattern_density_discrete(range = c(0.01, 0.3)) + 
    scale_pattern_spacing_discrete(range = c(0.01, 0.03)) + 
    theme(legend.position = 'none')

p

}
## geom_polygon_pattern
angle <- seq(0, 2*pi, length.out = 7) + pi/6
polygon_df <- data.frame(
  angle = angle,
  x     = cos(angle),
  y     = sin(angle)
)

p <- ggplot(polygon_df) +
  geom_polygon_pattern(
    aes(x = x, y = y), 
    fill            = 'white', 
    colour          = 'black', 
    pattern_spacing = 0.15, 
    pattern_density = 0.4, 
    pattern_fill    = 'lightblue', 
    pattern_colour  = '#002366',
    pattern_angle   = 45
  ) + 
  labs(title = "ggpattern") + 
  coord_equal() + 
  theme_bw(25) + 
  theme(axis.title = element_blank())

p
## geom_rect_pattern
plot_df <- data.frame(
  xmin    = c(0, 10),
  xmax    = c(8, 18),
  ymin    = c(0, 10),
  ymax    = c(5, 19),
  type    = c('a', 'b'),
  angle   = c(45, 0),
  pname   = c('circle', 'circle'),#设置里面的形状，其他的形状键R-ggpattern（1）
  pcolour = c('red', 'blue'),#设置颜色
  pspace  = c(0.03, 0.05),
  size    = c(0.5, 1),
  stringsAsFactors = FALSE
)

p <- ggplot(plot_df) +
  geom_rect_pattern(
    aes(
      xmin=xmin, ymin=ymin, xmax=xmax, ymax=ymax,
      pattern_angle   = I(angle),
      pattern_colour  = I(pcolour),
      pattern_spacing = I(pspace),
      pattern_size    = I(size)
    ),
    pattern         = 'circle',
    fill            = 'white',
    colour          = 'black',
    pattern_density = 0.3
  ) +
  theme_bw(18) +
  labs(title = "ggpattern::geom_rect_pattern()") + 
  theme(legend.key.size = unit(1.5, 'cm'))

p
## geom_violin_pattern
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))

p <- ggplot(huron, aes(year)) +
  geom_ribbon_pattern(
    aes(
      ymin = level - 1, 
      ymax = level + 1
    ), 
    fill            = NA, 
    colour          = 'black',
    pattern         = 'circle',
    pattern_spacing = 0.03, 
    pattern_density = 0.5,
    pattern_angle   = 30,
    outline.type    = 'legacy'
  ) +
  theme_bw(18) +
  labs(title = "ggpattern::geom_ribbon_pattern()")

p

4 动画化图案

library(ggpattern)
if (require("gganimate")) {

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 创建不同时间状态的数据框
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
df1 <- data.frame(time = 1, offset = 0    , trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2), stringsAsFactors = FALSE)
df2 <- data.frame(time = 2, offset = 0.045, trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2), stringsAsFactors = FALSE)
df  <- rbind(df1, df2)
#offset代表左右移动
#所以上表代表左右不移动，上下移动
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#将不同的状态进行转换
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
p <- ggplot(df, aes(trt, outcome)) +
  geom_col_pattern(
    aes(
      pattern_fill    = trt, 
      pattern_xoffset = I(offset), #移动情况
      pattern_yoffset = I(-offset)
    ), 
    colour          = 'black', 
    fill            = 'white',
    pattern_density = 0.5,
    pattern_angle   = 45
  ) +
  theme_bw() +
  labs(title = "ggpattern + gganimate") + 
  theme(legend.position = 'none') + 
  coord_fixed(ratio = 1/2) 

p <- p + transition_states(time, transition_length = 2,#转换的相对长度，和状态数量一致
                    state_length = 0, #暂停
                           wrap = FALSE)#是否需要重复播放（回到第一张图）

animate(p, nframes = 60, fps = 20)#nframe渲染帧数，fps动画的帧速率，单位为帧/秒(默认为10)

}

感兴趣的小伙伴可以来试试画各式各样的图额！

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原始发表：2022-11-10，如有侵权请联系 cloudcommunity@tencent.com 删除

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