I have a large dataset with multiple categorical values that have different integer values (counts) in two different groups.
As an example
Element <- c("zinc", "calcium", "magnesium", "sodium", "carbon", "nitrogen")
no_A <- c(45, 143, 10, 35, 70, 40)
no_B <- c(10, 11, 1, 4, 40, 30)
elements_df <- data.frame(Element, no_A, no_B)
Element | no_A | no_B |
---|---|---|
Zinc | 45 | 10 |
Calcium | 143 | 11 |
Magnesium | 10 | 1 |
Sodium | 35 | 4 |
Carbon | 70 | 40 |
Nitrogen | 40 | 30 |
Previously I’ve just been using the code below and changing x manually to get the output values:
x = "calcium"
n1 = (elements_df %>% filter(Element== x))$no_A
n2 = sum(elements_df$no_A) - n1
n3 = (elements_df %>% filter(Element== x))$no_B
n4 = sum(elements_df$no_B) - n3
fisher.test(matrix(c(n1, n2, n3, n4), nrow = 2, ncol = 2, byrow = TRUE))
But I have a very large dataset with 4000 rows and I’d like the most efficient way to iterate through all of them and see which have significant p values.
I imagined I’d need a for loop and function, although I’ve looked through a few previous similar questions (none that I felt I could use) and it seems using apply might be the way to go.
So, in short, can anyone help me with writing code that iterates over x in each row and prints out the corresponding p values and odds ratio for each element?
CodePudding user response:
You could get them all in a nice data frame like this:
`row.names<-`(do.call(rbind, lapply(seq(nrow(elements_df)), function(i) {
f <- fisher.test(matrix(c(elements_df$no_A[i], sum(elements_df$no_A[-i]),
elements_df$no_B[i], sum(elements_df$no_B[-i])), nrow = 2));
data.frame(Element = elements_df$Element[i],
"odds ratio" = f$estimate, "p value" = scales::pvalue(f$p.value),
"Lower CI" = f$conf.int[1], "Upper CI" = f$conf.int[2],
check.names = FALSE)
})), NULL)
#> Element odds ratio p value Lower CI Upper CI
#> 1 zinc 1.2978966 0.601 0.6122734 3.0112485
#> 2 calcium 5.5065701 <0.001 2.7976646 11.8679909
#> 3 magnesium 2.8479528 0.469 0.3961312 125.0342574
#> 4 sodium 2.6090482 0.070 0.8983185 10.3719176
#> 5 carbon 0.3599468 <0.001 0.2158107 0.6016808
#> 6 nitrogen 0.2914476 <0.001 0.1634988 0.5218564