I have a data frame where each row contains numbers of a contingency table on which I would like to run a chisq.test command (to each row in data frame) in R. The output from each row should be added into the data frame as new columns (X-squared-value,p-value).
DF1:
ID1 ID2 female_boxing female_cycling male_boxing male_cycling
A zit 43 170 159 710
B tag 37 134 165 744
C hfs 32 96 170 784
D prt 17 61 185 811
E its 31 112 169 762
F qrw 68 233 130 645
This is what I tried:
apply(DF1[,c('female_boxing','female_cycling','male_boxing','male_cycling')], 1, function(x) chisq.test(x) )
But this gives me only the summary table for each row.
You were close, just inspect one single test with str which helps you to decide which elements to select.
apply(dat[,c('female_boxing','female_cycling','male_boxing','male_cycling')],
1, function(x) chisq.test(x)[c('statistic', 'p.value')] )
The apply gives you a list, the results are a little nicer using sapply and looping over the rows.
chi <- t(sapply(seq(nrow(dat)), function(i)
chisq.test(dat[i, c('female_boxing','female_cycling','male_boxing','male_cycling')])[
c('statistic', 'p.value')]))
cbind(dat, chi)
# ID1 ID2 female_boxing female_cycling male_boxing male_cycling statistic p.value
# 1 A zit 43 170 159 710 988.7209 5.033879e-214
# 2 B tag 37 134 165 744 1142.541 2.146278e-247
# 3 C hfs 32 96 170 784 1334.991 3.762222e-289
# 4 D prt 17 61 185 811 1518.015 0
# 5 E its 31 112 169 762 1245.218 1.133143e-269
# 6 F qrw 68 233 130 645 752.3941 9.129485e-163
Data:
dat <- structure(list(ID1 = c("A", "B", "C", "D", "E", "F"), ID2 = c("zit",
"tag", "hfs", "prt", "its", "qrw"), female_boxing = c(43L, 37L,
32L, 17L, 31L, 68L), female_cycling = c(170L, 134L, 96L, 61L,
112L, 233L), male_boxing = c(159L, 165L, 170L, 185L, 169L, 130L
), male_cycling = c(710L, 744L, 784L, 811L, 762L, 645L)), class = "data.frame", row.names = c(NA,
-6L))