R 自动计算数据帧的摘要统计信息并创建新表

我有以下数据帧：

col1 <- c("avi","chi","chi","bov","fox","bov","fox","avi","bov",
          "chi","avi","chi","chi","bov","bov","fox","avi","bov","chi")
col2 <- c("low","med","high","high","low","low","med","med","med","high",
          "low","low","high","high","med","med","low","low","med")
col3 <- c(0,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0)

test_data <- cbind(col1, col2, col3)
test_data <- as.data.frame(test_data)

阻力百分比列基于上述col3，其中1=阻力，0=非阻力。我尝试了以下方法：

library(dplyr)
test_data<-test_data %>%
  count(col1,col2,col3) %>%
  group_by(col1, col2) %>%
  mutate(perc_res = prop.table(n)*100)

binom.test(resistant samples,total samples)$conf.int*100

然而，我不知道如何与其他人一起实施它。 有没有一种简单快捷的方法可以做到这一点？

应该这样做

library(tidyverse)
library(broom)

test_data %>%
  mutate(col3 = ifelse(col3 == 0, "NonResistant", "Resistant")) %>%
  count(col1, col2, col3) %>%
  spread(col3, n, fill = 0) %>%
  mutate(PercentResistant = Resistant / (NonResistant + Resistant)) %>%
  mutate(test = map2(Resistant, NonResistant, ~ binom.test(.x, .x + .y) %>% tidy())) %>%
  unnest() %>%
  transmute(Species = col1, Pop.density = col2, PercentResistant, CI_low = conf.low * 100, CI_high = conf.high * 100, TotalSamples = Resistant + NonResistant)

改变0/1电阻列，使其具有可读值

计算每个存储桶中的值

将col3/n分为两列，电阻/非电阻，并将计数（n）放入这些列中。现在，每一行都有了进行测试所需的一切

计算阻力的百分比

对每个bucket执行测试，并将结果放入名为

test

的嵌套框架中

取消测试数据帧，以便处理测试结果

清理干净，给所有东西起个好名字

结果

我想

library(data.table)
setDT(DT)

DT[, { 
  bt <- binom.test(sum(resists), .N)$conf.int*100
  .(res_rate = mean(resists)*100, res_lo = bt[1], res_hi = bt[2], n = .N)
}, keyby=.(species, popdens)]

    species popdens  res_rate    res_lo    res_hi n
 1:     avi     low   0.00000  0.000000  70.75982 3
 2:     avi     med   0.00000  0.000000  97.50000 1
 3:     bov     low 100.00000 15.811388 100.00000 2
 4:     bov     med  50.00000  1.257912  98.74209 2
 5:     bov    high 100.00000 15.811388 100.00000 2
 6:     chi     low   0.00000  0.000000  97.50000 1
 7:     chi     med  50.00000  1.257912  98.74209 2
 8:     chi    high  66.66667  9.429932  99.15962 3
 9:     fox     low   0.00000  0.000000  97.50000 1
10:     fox     med  50.00000  1.257912  98.74209 2

---

我建议先使用group_by，然后使用summarise函数。使用

data.frame（col1，col2，col3）

，而不是

cbind

，这会强制每个列在此处字符串。示例数据没有（“avi”，“high”）对。您是否希望该行以任何方式显示（使用NAs和零样本计数）？如果它们不存在，我不需要它们显示。伟大的解决方案！我能问一下

conf.low

是从哪里来的吗

unest（）

我只看到

estimate

和

statistic

？@PoGibas:conf.low来自

tidy（）

，然后是

unest

ed。如果你看到estimate，它应该在那里。胡乱猜测，您的窗口没有那么宽，结果下面有“…多X个变量”？aaa，它是

tbl_df

，

%>%data.frame（）

显示它。无法习惯tibble，这太棒了！哪一部分来自“扫帚”包装？它是最新的/嵌套和转换吗？@Haakonkas:broom使用

tidy（）

方法将模型转换为数据帧。

library(data.table)
setDT(DT)

DT[, { 
  bt <- binom.test(sum(resists), .N)$conf.int*100
  .(res_rate = mean(resists)*100, res_lo = bt[1], res_hi = bt[2], n = .N)
}, keyby=.(species, popdens)]

    species popdens  res_rate    res_lo    res_hi n
 1:     avi     low   0.00000  0.000000  70.75982 3
 2:     avi     med   0.00000  0.000000  97.50000 1
 3:     bov     low 100.00000 15.811388 100.00000 2
 4:     bov     med  50.00000  1.257912  98.74209 2
 5:     bov    high 100.00000 15.811388 100.00000 2
 6:     chi     low   0.00000  0.000000  97.50000 1
 7:     chi     med  50.00000  1.257912  98.74209 2
 8:     chi    high  66.66667  9.429932  99.15962 3
 9:     fox     low   0.00000  0.000000  97.50000 1
10:     fox     med  50.00000  1.257912  98.74209 2

DT[CJ(species = species, popdens = popdens, unique = TRUE), on=.(species, popdens), {
  bt <- 
    if (.N > 0L) binom.test(sum(resists), .N)$conf.int*100 
    else NA_real_
  .(res_rate = mean(resists)*100, res_lo = bt[1], res_hi = bt[2], n = .N)    
}, by=.EACHI]

    species popdens  res_rate    res_lo    res_hi n
 1:     avi     low   0.00000  0.000000  70.75982 3
 2:     avi     med   0.00000  0.000000  97.50000 1
 3:     avi    high        NA        NA        NA 0
 4:     bov     low 100.00000 15.811388 100.00000 2
 5:     bov     med  50.00000  1.257912  98.74209 2
 6:     bov    high 100.00000 15.811388 100.00000 2
 7:     chi     low   0.00000  0.000000  97.50000 1
 8:     chi     med  50.00000  1.257912  98.74209 2
 9:     chi    high  66.66667  9.429932  99.15962 3
10:     fox     low   0.00000  0.000000  97.50000 1
11:     fox     med  50.00000  1.257912  98.74209 2
12:     fox    high        NA        NA        NA 0

DT = data.frame(
  species = col1, 
  popdens = factor(col2, levels=c("low", "med", "high")), 
  resists = col3
)