Apply two sample proportion test across rows

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I'm trying to conduct a series of two-sample proportion tests across all rows of a data frame. Here is an example of first 3 rows, where x is the coiunt of yes responses and n is the total.

df <- data.frame("x1" = c(370,450,490), "x2" = c(150, 970, 120), "n1" = c(1500, 2700, 4500), "n2" = c(3000, 4900, 3200))

I'm using the function "prop.test" which compares two proportions as below:

  test <- prop.test(x = c(370, 150), n = c(1500, 3000), correct = "FALSE")

I've tried:

Map(prop.test, x = c(df$x1, df$x2), n = c(df$n1, df$n2), correct = "FALSE")

but it is returning output for 6 rows of 1-sample binomial tests instead of output for 3 rows of 2-sample binomial tests. I must be using Map incorrectly. Any ideas?

purrr categorical-data r
1 Answers

pmap allows to iterate over each row of the input data.frame.

Try:

library(purrr)
purrr::pmap(df,~{prop.test(x = c(..1, ..2), n = c(..3, ..4), correct = "FALSE")})

or

purrr::pmap(df,~with(list(...),prop.test(x = c(x1, x2), n = c(n1, n2), correct = "FALSE"))


[[1]]

    2-sample test for equality of proportions without continuity correction

data:  c(..1, ..2) out of c(..3, ..4)
X-squared = 378.44, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.1734997 0.2198336
sample estimates:
   prop 1    prop 2 
0.2466667 0.0500000 


[[2]]

    2-sample test for equality of proportions without continuity correction

data:  c(..1, ..2) out of c(..3, ..4)
X-squared = 11.22, df = 1, p-value = 0.0008094
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.04923905 -0.01334598
sample estimates:
   prop 1    prop 2 
0.1666667 0.1979592 


[[3]]

    2-sample test for equality of proportions without continuity correction

data:  c(..1, ..2) out of c(..3, ..4)
X-squared = 130.66, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.06015674 0.08262104
sample estimates:
   prop 1    prop 2 
0.1088889 0.0375000
user_135133280
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