In brouwern/mammalsmilkRA: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warnings = F,
  message = F
)

For your independent project you need to carry out relevant model diagnostics. If your major analysis only requires t-tests, you should re-fit at least one of your t-tests a linear model using lm() and check for normality and homogeneity of variance using relevant techniques.

The following script is just a place holder. For full information on model diangostics see the the (diagnostics tutorial](https://brouwern.github.io/mammalsmilk/articles/g-linear_reg_diagnostics.html) in the mammalsmilk package.

In this the mammalsmilkRA repository I have split up the analysis into several subscripts:

• analysis
• diagnostics
• figure

These can be combined if you want.

Preliminaries

library(dplyr)  # for exploratory analyses
library(ggpubr) # plotting using ggplto2
library(cowplot)
library(lme4)
library(arm)
library(stringr) 
library(bbmle)
library(plotrix) ##std.error function for SE
library(psych)
library(here)

Load data in R

file. <- "Appendix-2-Analysis-Data_mammalsmilkRA.csv"
path. <- here("/inst/extdata/",file.)

milk <- read.csv(path., skip = 3)


genus_spp_subspp_mat <- milk$spp %>% str_split_fixed(" ", n = 3)

milk$genus <- genus_spp_subspp_mat[,1]

Check input

head(milk)
tail(milk)
summary(milk)

brouwern/mammalsmilkRA documentation built on May 3, 2019, 7:39 p.m.