R 中多样性的 Blau 指数 [英] Blau index of Diversity in R

问题描述

我正在尝试在我的数据框中计算 R 中的 Blau 多样性指数 (gini-simpson).我为一组中的每个人设置了 6 列，值范围从学生"、教师"、校友"不适用".如果组小于 6，则列中也有 NA.

我想计算跨行的 Blau 指数(整个组的多样性)，而不是在每列中，na.rm= TRUE.

有谁知道如何在 R 中做到这一点?

非常感谢！

其中 R 表示类型总数， 是 ith 类型的比例丰度.

我们定义了一个函数，它接受一个字符串向量并返回 GS 索引:

#定义计算基尼-辛普森指数的函数# 我们确保 x 的相同级别(存在或不存在)# 按因子(x，级别 = 类型)# 请注意，默认情况下不会考虑 NAget.GS.index <- function(x, type) {x <-因子(x，水平=类型)；return(1 - sum(prop.table(table(x))^2));}

我们现在可以将 get.GS.index 应用到数据帧的所有行

apply(df, 1, get.GS.index, type)#[1] 0.6250000 0.4444444 0.6250000 0.0000000 0.6400000 0.6666667 0.5000000#[8] 0.6250000 0.6400000 0.5000000 0.4444444 0.6400000 0.3750000 0.3750000#[15] 0.0000000 0.0000000 0.6111111 0.4444444 0.6666667 0.6400000

<小时>

更新

如果一组中只有一种类型，我们可以修改函数get.GS.index以返回NA.

get.GS.index <- function(x, type) {x <-因子(x，水平=类型)；t <-表(x);if (length(t[t>0]) == 1) return(NA) else return(1 - sum(prop.table(t)^2));}应用(df，1，get.GS.index，类型)；# [1] 0.6250000 0.4444444 0.6250000 不适用 0.6400000 0.6666667 0.5000000# [8] 0.6250000 0.6400000 0.5000000 0.4444444 0.6400000 0.3750000 0.3750000#[15] 不适用 不适用 0.6111111 0.4444444 0.6666667 0.6400000

I am trying to calculate the Blau index of diversity (gini-simpson) in R on my data frame. I have 6 columns for each person in a group, with values ranging from "Student", "Faculty", "Alumni" "Not Applicable". There are also NA's within the columns if a group is smaller than 6.

I would like to calculate the Blau index across the rows (the diversity across the entire group) not within each column, with na.rm= TRUE.

Does anyone know how to do this in R?

Thanks so much!

See here for a picture of data frame

解决方案

We can calculate the Gini-Simpson index quite easily by hand.

First off, I'll generate some sample data:

# Generate sample data
set.seed(2017);
type <- c("Student", "Faculty", "Alumni");
data <- sample(type, 6 * 20, replace = TRUE);

# Replace 40 entries with NAs
set.seed(2017);
data[sample(6 * 20, 40)] <- NA;

# Reformat as 6 column dataframe
df <- as.data.frame(matrix(data, ncol = 6), stringsAsFactors = FALSE);
names(df) <- paste0("e", seq(1:6), "_affiliation");
head(df);
#e1_affiliation e2_affiliation e3_affiliation e4_affiliation e5_affiliation
#1           <NA>        Faculty           <NA>        Student        Student
#2           <NA>           <NA>           <NA>        Faculty         Alumni
#3           <NA>         Alumni        Student        Faculty        Faculty
#4        Student           <NA>           <NA>           <NA>           <NA>
#5           <NA>        Student         Alumni         Alumni        Student
#6         Alumni         Alumni        Faculty        Faculty        Student
# e6_affiliation
#1         Alumni
#2         Alumni
#3           <NA>
#4        Student
#5        Faculty
#6        Student

The Gini-Simpson (= Gibbs-Martin = Blau) index of diversity is given by

where R denotes the total number of types, and is the proportional abundance of the ith type.

We define a function that takes a vector of strings and returns the GS index:

# Define function to calculate the Gini-Simpson index
# We ensure the same levels (present or absent) of x
# by factor(x, levels = type)
# Note that NAs will not be considered by default
get.GS.index <- function(x, type) {
    x <- factor(x, levels = type);
    return(1 - sum(prop.table(table(x))^2));
}

We can now apply get.GS.index to all rows of the dataframe

apply(df, 1, get.GS.index, type)
#[1] 0.6250000 0.4444444 0.6250000 0.0000000 0.6400000 0.6666667 0.5000000
#[8] 0.6250000 0.6400000 0.5000000 0.4444444 0.6400000 0.3750000 0.3750000
#[15] 0.0000000 0.0000000 0.6111111 0.4444444 0.6666667 0.6400000

---

Update

We can modify the function get.GS.index to return NA if there is only one type present in a group.

get.GS.index <- function(x, type) {
    x <- factor(x, levels = type);
    t <- table(x);
    if (length(t[t>0]) == 1) return(NA) else return(1 - sum(prop.table(t)^2));
}

apply(df, 1, get.GS.index, type);
# [1] 0.6250000 0.4444444 0.6250000        NA 0.6400000 0.6666667 0.5000000
# [8] 0.6250000 0.6400000 0.5000000 0.4444444 0.6400000 0.3750000 0.3750000
#[15]        NA        NA 0.6111111 0.4444444 0.6666667 0.6400000

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