R 中的 5x5 气泡图

Question

我正在尝试在 R Shiny 中重现类似于加州学校责任仪表板上使用的图形。为了这个例子，考虑以下数据框：

student <- c("1234", "4321", "5678", "8765")
schools <- c("ABC", "ABC", "XYZ", "XYZ")
DFS_20 <- c(-34.2, -1.5, 2.8, 8.9)
DFS_21 <- c(-13.5, 27.8, 5.4, 3.9)
school_data <- data.frame("student_id" = student, "school_id" = schools, "DFS_2020" = DFS_20, "DFS_2021" = DFS_21, "Delta_DFS" = DFS_21 - DFS_20)

我想以某种方式将这些数据绘制在这样的网格上：

您将在 [x, y] = [4, 1] 处有一个数据点（左下角为 (0,0) 代表学生 1234，因为他们的 DFS_2021 分数很低（- 13.5)，但他们的同比增长显着增加 (20.7)；学生 4321 在 [x, y] = [4, 3] 中的一个点，因为他们的 DFS_2021 分数高 (27.8) 并且他们的年份-年增长率显着增加 (29.3) 等。我想要一个气泡图，以便相对于每个单元格内的数据点数量增加点的大小，但我不知道从哪里开始创建 canvas（用颜色）覆盖数据点。我知道我可以将他们的分数转化为格点以绘制在 5x5 网格上，但是用颜色制作网格超出了我的技能范围。

Answer 1

简而言之，您可以将值重新编码为因子并计算数据集中的每个组合。有了这个新的 table（包含当前 DFS 级别、DFS 差异级别和每个类别中的学生人数），您可以轻松创建点图。

要对您的点进行颜色编码，您可能需要在 table 中添加一个带有颜色的额外列。因此我创建了一个元颜色 table（所有 DFS 组合和相关颜色）并加入了 tables.

代码

# load packages
library(tidyverse)

# create color table
df_col <- crossing(DFS_current_status = factor(c("very low", "low", "medium",
                                                 "high", "very high"),
                                               levels = c("very high", "high", 
                                                              "medium", "low", "very low")), 
                   DFS_diff = factor(c("declined significantly", "declined",
                                "maintained" ,"increased", 
                                "increased significantly"), 
                                levels = c("declined significantly",
                                           "declined", "maintained",
                                           "increased", "increased significantly"))) %>%
  add_column(color = c("green", "green", "blue", "blue","blue",
                       "green", "green", "green", "green", "blue",
                       "yellow", "yellow", "yellow", "green", "green", 
                       "orange", "orange", "orange", "yellow", "yellow", 
                       "red", "red", "red", "orange", "orange"))


# transform data
df <- school_data %>%  
  mutate(DFS_current_status = case_when(DFS_2021 >=  45 ~ "very high",
                                        between(DFS_2021, 10, 44.9) ~ "high",
                                        between(DFS_2021, -5, 9.9) ~ "medium",
                                        between(DFS_2021, -70, -5.1) ~ "low",
                                        DFS_2021 < -70 ~ "very low",
                                        TRUE ~ NA_character_),
         DFS_diff = case_when(Delta_DFS < -15 ~ "declined significantly",
                              between(Delta_DFS, -15, -3) ~ "declined",
                              between(Delta_DFS, -2.9, 2.9) ~ "maintained",
                              between(Delta_DFS, 3, 14.9) ~ "increased",
                              Delta_DFS >= 15 ~ "increased significantly",
                              TRUE ~ NA_character_)) %>%
  count(DFS_current_status, DFS_diff) %>%
  left_join(df_col) %>%
  mutate(DFS_current_status = factor(DFS_current_status,
                                     levels = rev(c("very high", "high", 
                                                "medium", "low", "very low"))),
         DFS_diff = factor(DFS_diff, 
                           levels = c("declined significantly",
                                      "declined", "maintained",
                                      "increased", "increased significantly")))


# create plot
p <- ggplot(df) +
  geom_point(aes(x = DFS_diff,
                 y = DFS_current_status, 
                 size = n,
                 color = color)) +
  scale_y_discrete(drop = F) +
  scale_x_discrete(drop = F, position = "top") +
  scale_color_identity()

# display plot in plotly
ggplotly(p) %>% 
  layout(xaxis = list(side ="top"))

情节

编辑：评论 - 为网格而不是点着色

df_col %>% 
  ggplot() +
  geom_raster(aes(x = DFS_diff, 
                  y = rev(DFS_current_status),
                  fill= color)) +
  scale_fill_identity() +
  scale_x_discrete(position = "top") +
  geom_point(data = df, aes(x = DFS_diff,
                 y = DFS_current_status,
                 size = n))

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