如何在列中有条件地计数(对于所有行)，并通过所有列循环以在R中创建一个应急表

Question

这是我的数据

disease <- c("high", "high", "high", "high", "low","low","low","low");
ToA <- c("P","A","P","P","A","A","A","P");
ToB <- c("P","A","A","P","A","P","A","P");
ToC <- c("P","P","A","P","A","A","A","P"); 
df <- data.frame(disease, ToA, ToB, ToC)

我正在寻找应急表作为图像，其中，第1行= ToA，第2行= ToB，第3行= ToC

    high_P high_A low_P low_A
1      3      1     1     3
2      2      2     2     2
3      2      1     1     3

对于数据中的每一列，我需要计算P& high (疾病)、P、above和above组合的频率(计数)，就像你在上面的图像中看到的那样。我可以对每一栏分别按nrow方式进行如下操作：

df中用于二次谐波的##count

high_P=nrow(df[df$disease=="high" & df$ToA=="P", ])
high_A=nrow(df[df$disease=="high" & df$ToA=="A", ])
low_P=nrow(df[df$disease=="low" & df$ToA=="P", ])
low_A=nrow(df[df$disease=="low" & df$ToA=="A", ])
ToA_df=data.frame(high_P,high_A,low_P,low_A)

#在df中计算col 3

high_P=nrow(df[df$disease=="high" & df$ToB=="P", ])
high_A=nrow(df[df$disease=="high" & df$ToB=="A", ])
low_P=nrow(df[df$disease=="low" & df$ToB=="P", ])
low_A=nrow(df[df$disease=="low" & df$toB=="A", ])
ToB_df=data.frame(high_P,high_A,low_P,low_A)

#计算df中的col 4

high_P=nrow(df[df$disease=="high" & df$ToC=="P", ])
high_A=nrow(df[df$disease=="high" & df$ToC=="A", ])
low_P=nrow(df[df$disease=="low" & df$ToC=="P", ])
low_A=nrow(df[df$disease=="low" & df$ToC=="A", ])
ToC_df=data.frame(high_P,high_A,low_P,low_A)


Data = rbind(ToA_df,ToB_df,ToC_df)

它可以做我想做的事情，但是我想用循环来计算每一列，对于一个大数据集来说，手工计算是很困难的。有人能建议/帮助我如何使用图像中的循环or....as来计算R中的应急表吗？

Answer 1

您的数据有一个p，而不是一个P，然后使用以下代码：

使用reshape::recast

reshape2::recast(df, variable~disease+value, id.var = 'disease')    
  variable high_A high_P low_A low_P
1      ToA      1      3     3     1
2      ToB      2      2     2     2
3      ToC      1      3     3     1

使用tidyverse

df %>%
   pivot_longer(-disease, values_transform = toupper)%>%
   pivot_wider(name, names_from = c(disease, value), 
               values_from = disease, values_fn = length)

# A tibble: 3 x 5
  name  high_P high_A low_A low_P
  <chr>  <int>  <int> <int> <int>
1 ToA        3      1     3     1
2 ToB        2      2     2     2
3 ToC        3      1     3     1

R基选项：

table(rev(stack(Map(\(x,y)paste(y,x,sep='_'), df[-1], df[1]))))
     values
ind   high_A high_P low_A low_P
  ToA      1      3     3     1
  ToB      2      2     2     2
  ToC      1      3     3     1

Answer 2

你可以这样做：

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(!disease, names_to = 'columns', values_to = 'vals') %>%
  count(disease, columns, vals) %>%
  pivot_wider(names_from = c(disease, vals), values_from = n, 
              names_sep = '_')

# A tibble: 3 × 5
  columns high_A high_P low_A low_P
  <chr>    <int>  <int> <int> <int>
1 ToA          1      3     3     1
2 ToB          2      2     2     2
3 ToC          1      3     3     1

Answer 3

将table by病循环到列上。

by(df[-1], df$disease, \(x) t(sapply(x, table))) |> do.call(what=cbind)
#     A P A P
# ToA 1 3 3 1
# ToB 2 2 2 2
# ToC 1 3 3 1

数据：

df <- structure(list(disease = c("high", "high", "high", "high", "low", 
"low", "low", "low"), ToA = c("P", "A", "P", "P", "A", "A", "A", 
"P"), ToB = c("P", "A", "A", "P", "A", "P", "A", "P"), ToC = c("P", 
"P", "A", "P", "A", "A", "A", "P")), row.names = c(NA, -8L), class = "data.frame")