We do a Fisher test to test whether males cross their legs with the right leg above significantly more frequently than females.

male_left_leg_above <- 97 #85 #75 #71 #51 #48 #40 #22  #8
male_right_leg_above <- 163 #149 #125 #121 #101 #86 #69 #41  #17
female_left_leg_above <- 336 #308 #271 #264 #226 #216 #170 #113  #45
female_right_leg_above <- 383 #366 #320 #311 #279 #261 #198 #142  #47
legPosition <-t(matrix(c(male_left_leg_above, male_right_leg_above,female_left_leg_above,female_right_leg_above), nrow=2, dimnames=list(LegPosition=c("LeftLegOver", "RightLegOver"), sex=c("Male", "Female"))))
legPosition
        LegPosition
sex      LeftLegOver RightLegOver
  Male            97          163
  Female         336          383
fisher.test(legPosition, alternative="less")

    Fisher's Exact Test for Count Data

data:  legPosition
p-value = 0.005247
alternative hypothesis: true odds ratio is less than 1
95 percent confidence interval:
 0.0000000 0.8747096
sample estimates:
odds ratio 
 0.6786071 
fisher.test(legPosition, alternative="two.sided")

    Fisher's Exact Test for Count Data

data:  legPosition
p-value = 0.008806
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.5010386 0.9160688
sample estimates:
odds ratio 
 0.6786071

With Data from Janelia farm

male_left_leg_aboveJF <- 4
male_right_leg_aboveJF <- 13
female_left_leg_aboveJF <- 4
female_right_leg_aboveJF <- 4
legPositionJF <- t(matrix(c(male_left_leg_aboveJF, male_right_leg_aboveJF,female_left_leg_aboveJF,female_right_leg_aboveJF), nrow=2, dimnames=list(LegPosition=c("LeftLegOver", "RightLegOver"), sex=c("Male", "Female"))))
total <- legPositionJF + legPosition
print(total)
        LegPosition
sex      LeftLegOver RightLegOver
  Male           101          176
  Female         340          387
fisher.test(total, alternative="less")

    Fisher's Exact Test for Count Data

data:  total
p-value = 0.001971
alternative hypothesis: true odds ratio is less than 1
95 percent confidence interval:
 0.0000000 0.8376084
sample estimates:
odds ratio 
 0.6534736 
fisher.test(total, alternative="two.sided")

    Fisher's Exact Test for Count Data

data:  total
p-value = 0.003522
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.4856752 0.8763214
sample estimates:
odds ratio 
 0.6534736
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