In carlos-r-git/scriptsearch: A package for searching for text within existing scripts

Effect of fertilisation on the length of time female

parasitic wasps take to lay eggs.

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Introduction

Wasps and other Hymenopterans (Ants and Bees) are haplo-diplod:

unfertilised eggs are haploid and develop into males, whereas

fertilised eggs are diploid and develop into females.

Researchers wanted to know if mating status affected the time taken

for a female wasp to to lay its eggs on a beetle larva..

The data are in ../data/wasp.txt Each row represents an individual

wasp. The first column gives the time taken (in hours) and the

second column indicates whether they are mated (1) or unmated (2)

Set up

working directory (in my case, yours will differ)

setwd("M:/web/17C - 2018/scripts")

packages

library(tidyverse)

Hadley Wickham (2017). tidyverse: Easily Install and Load the 'Tidyverse'. R package version 1.2.1.

https://CRAN.R-project.org/package=tidyverse

Import data

data are in ../data

wasp <- read.table("../data/wasp.txt", header = T)

check structure

str(wasp)

'data.frame': 60 obs. of 2 variables:

# $ time : int 22 33 14 24 26 18 17 12 35 27 ...

$ status: Factor w/ 2 levels "mated","unmated": 1 1 1 1 1 1 1 1 1 1 ...

Explore

quick plot

ggplot(data = wasp, aes(x = status, y = time)) + geom_violin()

the figure suggested unmated females may take longer to lay their

eggs

create summary statistics

waspsummary <- wasp %>% group_by(status) %>% summarise(mean = mean(time), std = sd(time), n = length(time), se = std/sqrt(n))

the scenario suggusts a two sample t test as the females are

either mated or unmated, not both. There is no link between the

first mated value and the first unmated value

Test

carry out a two-sample t-test

t.test(data = wasp, time ~ status, var.equal = T)

Conclusion

Unmated females take significantly longer (31.1 +/- 1.9 hr) than

mated females (23.4 +/- 1.6 hr) to complete egg laying (t-test:

t = 3.14; d.f. = 58; p = 0.003)

Check assumptions

the assumptions are that the residuals are normally and homogenously

distributed across x.

calculate the residuals - the difference between predicted

(i.e., the group mean) and observed values.

add column that holds the mean for the group each value

belongs to by 'merging' the summary data into the raw data:

wasp <- merge(wasp, waspsummary[,1:2], by = "status")

Add a column for each 'residual'

wasp <- wasp %>% mutate(residual = time - mean)

examine the distribution of the residuals.

shapiro.test(wasp$residual)

Not significant - our data are consistent with meeting the assumption.

ggplot(data = wasp, aes(x = mean, y = residual)) + geom_point()

There's a bit of an outlier in one group but this looks 'ok' -

homogenously distributed across x.

Figure

ggplot() + geom_point(data = wasp, aes(x = status, y = time), position = position_jitter(width = 0.1, height = 0), colour = "gray50") + geom_errorbar(data = waspsummary, aes(x = status, ymin = mean - se, ymax = mean + se), width = 0.3) + geom_errorbar(data = waspsummary, aes(x = status, ymin = mean, ymax = mean), width = 0.2) + ylab("Time (hr)") + xlab(NULL) + ylim(0, 70) + scale_x_discrete(labels = c("Mated", "Unmated")) + theme_classic()

carlos-r-git/scriptsearch documentation built on Sept. 1, 2020, 6:38 p.m.