如何使用ggplot創建圓形條形圖

我正在嘗試使用我的資料創建一個圓形條形圖，但我什至無法組織資料框來完成它。我有來自 3 個不同年份（名為 campana 的列）和來自一個省的 4 個地區（名為 zona 的列）的 121 個種子分析。我想制作一個類似于影像中的圖表，使用不同的區域（zonas）而不是字母 ABCD，并使用堆疊條顯示每年真菌的發病率（頻率）。每個真菌頻率都在一個帶有真菌名稱的列中，例如 A.padwickii、Microdochium、Bipolaris 等。我不指望你做所有的作業，只是幫助我以正確的方式組織資料的第一步.

tibble::tribble(
     ~muestra_n, ~campana, ~zona, ~inc_pan, ~percent_gran_m_pan, ~peso_pan, ~percent_perdida_peso, ~severidad, ~phoma, ~microdochium, ~a_padwickii, ~bipolaris, ~curvularia, ~fusarium, ~exserohilum,
             16,        2,     2,       98,                14.6,      2.85,      9.90007401924501,        0.1,      0,            11,            0,          0,           0,         0,            0,
             18,        2,     2,     97.1,                 7.8,      1.34,      29.9991559044484,       0.44,     13,             0,            0,          0,           0,         0,            0,
             19,        2,     2,      100,                14.3,      1.35,    0.0288073746879383,       0.66,      0,            16,            0,          0,          18,         0,            0,
             20,        2,     1,      100,                 9.6,      1.49,      1.64930877284015,       0.09,      0,            12,            1,          2,          44,         0,            0,
             21,        2,     1,      100,                17.9,      3.04,      1.46085792877853,       0.56,      0,             9,            0,          0,           1,         0,            0,
             22,        2,     1,      100,                37.4,       2.1,      5.60829881602581,        0.6,     41,             8,            0,          0,           1,         0,            1
     )

uj5u.com熱心網友回復：

沒有足夠的資訊可以確定，但希望這可以通過一些提示使您朝著正確的方向前進。根據我的經驗，極坐標圖往往需要根據其適合的位置進行大量定制，而不是任何特定的標準。當你制作它們時，這會使它們變得更加復雜。

library(tidyverse)
YOUR_DATA %>%

  # this step reshapes your data to a format more suited for ggplot2; each
  # fungi appearance is given its own row
  pivot_longer(phoma:last_col()) %>%

  # this sets up the main plot
  ggplot(aes(name, value))  
  geom_col()  

  # these add the lines and text for zonas. If you have many of them, it 
  # might make sense to figure these out as a separate data frame and feed
  # these into lines like `geom_segment(data = SUMMARY)   geom_text(data = 
  # SUMMARY)`
  annotate("segment", x = c(0.5,2.5,4.5), xend = c(2.4,4.4,7), y = -10, yend = -10)  
  annotate("text", x = c(1.5,3.5,5.5), y = -20, label = LETTERS[1:3])  

  # This adds space in the middle, changing it from a pie chart to a donut
  scale_y_continuous(limits = c(-100, NA))  
  coord_polar()     # converts into polar coordinates
  theme_void()      # gets rid of grid lines and axis labels

uj5u.com熱心網友回復：

正如喬恩斯普林所說，極地情節可能很難以您描述的方式定制。它們通常也難以閱讀和解釋，但那是另一回事。由于這些原因，大多數繪圖系統都無法輕松創建高度定制的極坐標圖。

為了實作您想要的，我們需要做很多手動作業，包括：

1. 將資料重塑為“長”格式以使用 ggplot。
2. 自定義計算以正確旋轉每個真菌文本標簽。
3. “區域”輔助 x 軸的自定義計算。
4. 使用 ggplot 的摘要函式來確保文本標簽正確放置在每個條形堆疊的頂部。

以下代碼實作了所有這些。在這里，我在“campana”的第二個值下復制了資料，以驗證當條形堆疊時是否正確繪制。旁邊的評論：

# reshape the data to "long" format
df.long <- df %>% # YOUR DATA
  select(zona, campana, phoma:exserohilum) %>% 
  pivot_longer(-c(zona, campana), names_to = 'fungus', values_to = 'freq') %>% 
  filter(freq > 0)

# duplicate the data and change campana so that we have some stacks to work with
df.long <- df.long %>% 
  bind_rows(mutate(df.long, campana = 3))

df.long.summary <- df.long %>% 
  group_by(combined_label = paste(zona, fungus, sep = '_'), fungus, campana, zona) %>% # a label that combines zona and fungus for the x-axis
  summarize(freq = sum(freq)) %>% # totals
  ungroup %>% 
  mutate(
    x = as.numeric(factor(combined_label)), # the numerical position of each x-axis label
    campana = factor(campana), # optional, but ggplot produces better colors this way
    angle = (max(x)   0.45 - x) / max(x) * 2*pi * 180/pi   90, # calculate an angle for each text label
    angle = angle %% 360, # constrain to [0-360]
    flip = angle > 90 & angle < 270, # angles that fall in this range will need to be flipped for easier reading
    angle = ifelse(flip, angle   180, angle), # flip the angle
    hjust = ifelse(flip, 1.1, -0.1) # flip the hjust parameter to match
  )

# create secondary x-axis parameters for "zona"
df.zona <- df.long.summary %>% 
  group_by(zona) %>% 
  summarize(
    zona_x = min(x) - 0.4,
    zona_xend = max(x)   0.4,
    label_x = mean(x),
    angle = 90 - (max(x) - zona_x) / max(x) * 2*pi * 180/pi   90
  )

plot.fungi <- df.long.summary %>% 
  ggplot(data = ., aes(x = x, y = freq))  
  geom_col(position = 'stack', aes(fill = campana))  
  geom_text(aes(label = fungus, x = x, y = freq, angle = angle, hjust = hjust), stat = 'summary', fun = sum)  
  geom_segment(data = df.zona, aes(x = zona_x, xend = zona_xend), y = -10, yend = -10)  
  geom_text(data = df.zona, aes(label = zona, x = label_x, y = -12, angle = 0, vjust = sign(angle) * 2))  
  coord_polar(clip = 'off')  
  scale_y_continuous(limits = c(-100, NA))  
  theme_void()
print(plot.fungi)

創建相同資料的笛卡爾（非極坐標）圖會更簡單（在我看來，這也更容易閱讀）：

# reshape the data to "long" format
df.long <- df %>% 
  select(zona, campana, phoma:exserohilum) %>% 
  pivot_longer(-c(zona, campana), names_to = 'fungus', values_to = 'freq') %>% 
  filter(freq > 0)

df.bar <- df.long %>% 
  mutate(
    campana = factor(campana),
    zona = paste0('Zona ', zona)
  ) %>% 
  ggplot(data = ., aes(x = fungus, y = freq, fill = campana))  
  geom_col(position = 'stack')  
  facet_grid(facets = ~zona, scales = 'free_x', space = 'free_x')  
  theme_minimal()  
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
    strip.background.x = element_rect(fill = '#cccccc', color = NA)
  )
print(df.bar)

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