表格输出编号与维恩图编号不匹配

Question

我有以下 R 代码，借用了 here，它生成了一个可重现的

tibble

表：

# Install/load packages only if needed
# ************************************
if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr, expss, ggplot2, grid, purrr, rlang, tibble)

# Data Generation
# ***************

# Set the seed for reproducibility
set.seed(123)

# Generate random data
n <- 490
PTSD <- sample(c(1, 2, NA), n, replace = TRUE) #class(PTSD) = "numeric"
ANX <- sample(c(1, 2, NA), n, replace = TRUE) #class(ANX) = "numeric"
DEP <- sample(c(1, 2, NA), n, replace = TRUE) #class(DEP) = "numeric"

# Create the data frame
df <- data.frame(PTSD, ANX, DEP) #class(df) = "data.frame"

# Label the values: 1 = Low, 2 = High
expss::val_lab(df$PTSD) = expss::num_lab("1 Low\n2 High")
expss::val_lab(df$ANX) = expss::num_lab("1 Low\n2 High")
expss::val_lab(df$DEP) = expss::num_lab("1 Low\n2 High")

# Create a list of tables for each variable to count 1s, 2s, and NAs
count_results <- list(
  PTSD = table(df$PTSD, useNA = "ifany"),
  ANX = table(df$ANX, useNA = "ifany"),
  DEP = table(df$DEP, useNA = "ifany")
)

# Frequency count and data summary
# ********************************

# Combine the count tables into a single table
count_table <- do.call(rbind, count_results)

# Initialize empty vectors to store results
variable_names <- character()
sample_sizes <- numeric()

# Loop through the test results and extract relevant information
for (variable_name in names(count_results)) {
  sample_sizes <- c(sample_sizes, sum(count_results[[variable_name]]))
  variable_names <- c(variable_names, variable_name)
}

# Create summary data frame
summary_df <- data.frame(
  Variable = variable_names,
  N = sample_sizes
)

# Combine the count table and chi-squared summary table by columns
final_result <- cbind(count_table, summary_df)

# Remove Variable column in the middle of the table
final_result <- subset(final_result, select = -c(Variable))

# Combination of CMDs (CMD ≥ 1)
# *****************************

cmd <- c("PTSD","ANX","DEP")

combs <- map(seq_along(cmd),\(n)combn(cmd,n,simplify = FALSE)) |> purrr::flatten()

filts <- rlang::parse_exprs(map_chr(combs,\(x)paste0(x ,'== 2',collapse=' & ')))
filtsnames <- rlang::parse_exprs(map_chr(combs,\(x)paste0(x ,collapse=' + ')))
names(filts) <- filtsnames

output <- purrr::map_int(filts,\(x){
  df %>%
    mutate(id = row_number())%>%
    filter(!!(x))%>%
    summarise(
      n = n())
} |> pull(n)
)

tibble::enframe(output)

tibble

表的输出应该显示

N = 490

中有多少人患有以下常见精神障碍（CMD），即仅PTSD、仅ANX、仅DEP、PTSD和ANX、PTSD和DEP ，ANX 和 DEP，以及所有 3 个 CMD：

# A tibble: 7 × 2
  name             value
  <chr>            <int>
1 PTSD               167
2 ANX                156
3 DEP                156
4 PTSD + ANX          56
5 PTSD + DEP          52
6 ANX + DEP           51
7 PTSD + ANX + DEP    23

我想以图形方式可视化该表，因此我考虑生成维恩图。我期望在图中看到的内容如下。

期望清单：

• 所有 3 的交集 = 23
• ANX 和 DEP 的交集 = 51
• PTSD 和 DEP 的交集 = 52
• PTSD 和 ANX 的交集 = 56
• 仅限 DEP = 156
• 仅 ANX = 156
• 仅 PTSD = 167

但是，虽然所有代码（下面的示例）都没有生成任何技术错误（即 R 代码错误），但我尝试的所有软件包（

VennDiagram

和

ggVennDiagram

）都没有显示出预期的结果（参见

Expectation list

）。

下面是用于生成 4 个不同维恩图的 4 个代码，其中没有一个给出

Expectation list

中概述的结果：

使用包

VennDiagram

版本1

pacman::p_load(VennDiagram)

# Move to new plotting page
grid::grid.newpage()

# Calculate percentages
total_samples <- nrow(df)
percentages <- output / total_samples * 100

venn.plot <- VennDiagram::draw.triple.venn(
  area1 = output["PTSD"],
  area2 = output["ANX"],
  area3 = output["DEP"],
  n12 = output["PTSD + ANX"],
  n23 = output["ANX + DEP"],
  n13 = output["PTSD + DEP"],
  n123 = output["PTSD + ANX + DEP"],
  category = c("PTSD", "ANX", "DEP"),
  fill = c("red", "green", "blue"),
  lty = "blank",
  cex = rep(1.5,7),
  cat.cex = rep(1.5,3),
  cat.pos = c(-20,-40,-60),
  cat.dist = c(0.05,0.05,0.05),
  ind = TRUE,
  euler.d =TRUE,
)

grid.draw(venn.plot)

使用套件

VennDiagram

2

pacman::p_load(VennDiagram)

# Move to new plotting page
grid::grid.newpage()

# Use pre-calculated values from 'output'
VennDiagram::draw.triple.venn(
  area1 = output["PTSD"],
  area2 = output["ANX"],
  area3 = output["DEP"],
  n12 = output["PTSD + ANX"] + output["PTSD + ANX + DEP"], # Adjust for overlaps
  n23 = output["ANX + DEP"] + output["PTSD + ANX + DEP"], # Adjust for overlaps
  n13 = output["PTSD + DEP"] + output["PTSD + ANX + DEP"], # Adjust for overlaps
  n123 = output["PTSD + ANX + DEP"],
  category = c("PTSD", "ANX", "DEP"),
  col = "Red", fill = c("Green", "Yellow", "Blue"),
  cex = 1.5, cat.cex = 1.5, cat.pos = c(-20, 20, 180)
)

使用套件

VennDiagram

3

pacman::p_load(VennDiagram)

# Calculate exclusive counts for Venn diagram
ptsd_only <- output["PTSD"] - output["PTSD + ANX"] - output["PTSD + DEP"] + output["PTSD + ANX + DEP"]
anx_only <- output["ANX"] - output["PTSD + ANX"] - output["ANX + DEP"] + output["PTSD + ANX + DEP"]
dep_only <- output["DEP"] - output["PTSD + DEP"] - output["ANX + DEP"] + output["PTSD + ANX + DEP"]

ptsd_anx <- output["PTSD + ANX"] - output["PTSD + ANX + DEP"]
ptsd_dep <- output["PTSD + DEP"] - output["PTSD + ANX + DEP"]
anx_dep <- output["ANX + DEP"] - output["PTSD + ANX + DEP"]

ptsd_anx_dep <- output["PTSD + ANX + DEP"]

# Move to new plotting page
grid::grid.newpage()

# Create Venn diagram with 3 sets using adjusted values
VennDiagram::draw.triple.venn(
  area1 = ptsd_only,
  area2 = anx_only,
  area3 = dep_only,
  n12 = ptsd_anx,
  n23 = anx_dep,
  n13 = ptsd_dep,
  n123 = ptsd_anx_dep,
  category = c("PTSD", "ANX", "DEP"),
  col = "Red", fill = c("Green", "Yellow", "Blue"),
  cex = 1.5, cat.cex = 1.5, cat.pos = c(-20, 20, 180)
)

使用套件

ggVennDiagram

pacman::p_load(ggVennDiagram)

# Prepare data for Venn diagram
venn_data <- list(
  PTSD = which(df$PTSD == 2),
  ANX = which(df$ANX == 2),
  DEP = which(df$DEP == 2)
)

# Create Venn diagram with ggVennDiagram
ggVennDiagram(venn_data) +
  ggplot2::scale_fill_gradient(low = "white", high = "darkgrey") +
  theme_void()

---

我的问题：除了手工绘制图表之外，有没有一种方法可以用 R 生成维恩图，它反映的结果与

tibble

表中的结果相同？

（或者我错过了关于维恩图生成的要点？）

Answer 1

这是绘制维恩图的简单方法，表示这些疾病何时出现

High

。

library(dplyr)
library(ggplot2)
library(ComplexUpset)

set.seed(123)

# Generate random data
n <- 490
PTSD <- sample(c(1, 2, NA), n, replace = TRUE) #class(PTSD) = "numeric"
ANX <- sample(c(1, 2, NA), n, replace = TRUE) #class(ANX) = "numeric"
DEP <- sample(c(1, 2, NA), n, replace = TRUE) #class(DEP) = "numeric"

# Create the data frame

disorders <- c('PTSD', 'ANX', 'DEP')
df <- data.frame(PTSD, ANX, DEP)

# Create boolean indicators where "High" == TRUE
df[disorders] = df[disorders] == 2

# Either drop rows with NA
# df = na.omit(df)

# or impute missing values
df[is.na(df)] <- FALSE

glimpse(df)
#> Rows: 490
#> Columns: 3
#> $ PTSD <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE,…
#> $ ANX  <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
#> $ DEP  <lgl> FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE,…

ggplot() +
  theme_void() +
  coord_fixed() +
  geom_venn_circle(df, sets = disorders, size = 1) +
  geom_venn_label_set(df, sets = disorders, aes(label = region), outwards_adjust = 2) +
  geom_venn_label_region(df, sets = disorders, aes(label = size))

此处的交叉点大小与您的期望列表不符，因为您错误定义了它们

Intersection of all 3 = 23 

正确

Intersection of ANX and DEP = 51
Intersection of PTSD and DEP = 52
Intersection of PTSD and ANX = 56

仅当您包括所有疾病均高的 23 种情况时

DEP only = 156
ANX only = 156
PTSD only = 167

这些都不匹配仅无序度较高的行数。将维恩图中的结果与计数表进行比较。

df |>
  count(PTSD, ANX, DEP)
#>    PTSD   ANX   DEP   n
#> 1 FALSE FALSE FALSE 147
#> 2 FALSE FALSE  TRUE  76
#> 3 FALSE  TRUE FALSE  72
#> 4 FALSE  TRUE  TRUE  28
#> 5  TRUE FALSE FALSE  82
#> 6  TRUE FALSE  TRUE  29
#> 7  TRUE  TRUE FALSE  33
#> 8  TRUE  TRUE  TRUE  23