具有多个条件和 4 个结果的 If else 语句

If else statement with multiple conditions and 4 outcomes

我正在尝试根据共性对物种进行分类。共有4个分类:

  1. 稀有 - 频率<平均值和相对丰度<平均值
  2. 偶尔 - 频率<平均值和相对丰度>平均值
  3. 常见 - 频率>平均值和相对丰度<平均值
  4. 显性 - 频率>平均值和相对丰度>平均值

我正在尝试创建一个 if else 语句,以将具有这些分类的列添加到我的数据框中,看起来像

species <- c("a", "b", "c", "d", "e", "f")
relabund <- c(.5, .11, .23, .06, .36, .19) #relative abundance
freq <- c(6, 3, 20, 2, 11, 4) #number of sites species occurs at
df = data.frame(species, relabund, freq)

我试过这样的事情:

if (df[,2]>mean(relabund) && df[,3]>mean(freq)) {
    df$Classification = "Dominant"
  } else if (df[,2]<mean(relabund) && df[,3]<mean(freq)) {
    df$Classification = "Rare"
  } else if (df[,2]<mean(relabund) && df[,3]>mean(freq)) {
    df$Classification = "Common"
  } else 
    df$Classification = "Occasional"

但这不起作用,因为它将所有物种都归类为“稀有”。我对 if else 语句还很陌生,如有任何帮助,我们将不胜感激。

谢谢!

我使用你的代码得到“偶尔”。

您的 if 语句查看逻辑向量,但为所有行返回一个值,例如:

df[,2] 是整列:0.50 0.11 0.23 0.06 0.36 0.19

df[,2]>mean(relabund) returns 的逻辑向量:

TRUE FALSE FALSE FALSE TRUE FALSE

通过使用 &&,您正在对两个逻辑向量执行逻辑比较。由于这些向量不相同,因此您总是得到错误的结果:

df[,2]>mean(relabund) && df[,3]>mean(freq)

==

c(TRUE, FALSE, FALSE, FALSE, TRUE, FALSE) && c(FALSE, FALSE, TRUE, FALSE, TRUE, FALSE)

==

FALSE

此外,df$Classification 将列设置为相同的值,即它在整个数据集上工作,而不是逐行处理。你需要做的是对每一行执行向量运算。

使用 dplyr 你可以获得更容易阅读的答案(对某些人来说!)

library(tidyverse)

species <- c("a", "b", "c", "d", "e", "f")
relabund <- c(.5, .11, .23, .06, .36, .19) #relative abundance
freq <- c(6, 3, 20, 2, 11, 4) #number of sites species occurs at
df = data.frame(species, relabund, freq)

df %>% 
  mutate(classify = 
           ifelse(freq < mean(freq) & relabund < mean(relabund),
                  "Rare",
           ifelse(freq < mean(freq) & relabund > mean(relabund),
                  "Occaisonal",
           ifelse(freq > mean(freq) & relabund < mean(relabund),
                 "Common",
           ifelse(freq > mean(freq) & relabund > mean(relabund),
                 "Dominant",
                 "ERROR")))))

我们可以使用 case_when,其中 if~ 的左侧,右侧是您要分配给该条件的值。

library(tidyverse)

df %>% 
  mutate(classify = case_when(freq < mean(freq) & relabund < mean(relabund) ~ "Rare",
                              freq < mean(freq) & relabund > mean(relabund) ~ "Occaisonal",
                              freq > mean(freq) & relabund < mean(relabund) ~ "Common",
                              freq > mean(freq) & relabund > mean(relabund) ~ "Dominant",
                              TRUE ~ "ERROR"))

输出

  species relabund freq   classify
1       a     0.50    6 Occaisonal
2       b     0.11    3       Rare
3       c     0.23   20     Common
4       d     0.06    2       Rare
5       e     0.36   11   Dominant
6       f     0.19    4       Rare

数据

df <- structure(list(species = c("a", "b", "c", "d", "e", "f"), relabund = c(0.5, 
0.11, 0.23, 0.06, 0.36, 0.19), freq = c(6, 3, 20, 2, 11, 4)), class = "data.frame", row.names = c(NA, 
-6L))