Statistics

source: rstudio/statistics.md

:link: As always links first

Some commonly used statistics calculations for use in RStudio.

Load data

We'll load some data on Tasmanian devils and presence of DFTD (cancer) and Trypanosoma presence. The data is coded so that 0 is negative and 1 is positive.

library(readr)
# Load data
tasdevil <- read_csv("data/tasdevil-parasite.csv")

Basics

Sructure of data set

str(tasdevil)

How many had DFTD

sum(tasdevil$DFTDStatus)

Proportion and CIs

Load libraries

# Install first if you need
#
# install.packages("DescTools")
# install.packages("PropCIs")
# install.packages("binom")
# install.package("rcompanion")
# install.package("tidyverse")

library(DescTools)
library(PropCIs)
library(binom)
library(rcompanion)
library(tidyverse)

Calculate different in Trypanosoma prevalence between males and females with 95% CIs

groupwiseMean(TrypStatus ~ Sex,
              data = tasdevil,
              conf = 0.95,
              digits = 3)

We could plot the values above using this…

#save values to a data.frame
CI <- groupwiseMean(TrypStatus ~ Sex,
              data = tasdevil,
              conf = 0.95,
              digits = 3)
#plot
qplot(x= Sex,
      y = Mean,
      data = CI,
      shape= Sex) +

  geom_point(size=2.5) +

  geom_errorbar(aes(
    ymin = Trad.lower,
    ymax = Trad.upper,
    width = 0.15)) + theme_bw() + ylim(0,1)

Calculate different in Trypanosoma prevalence between males and females and 4 different sites with 95% CIs

groupwiseMean(TrypStatus ~ Sex + Site_code,
              data   = tasdevil,
              conf   = 0.95,
              digits = 3)

Simple stuff

If you need here are some simple bits of code where you have basic numbers such as…7 positive out of sample size of 21.

binom.test(7, 21,
           0.5,
           alternative="two.sided",
           conf.level=0.95)

Now we'll calculate the 95% CIs using the Jeffreys method.

BinomCI(7, 21,
        conf.level=0.95,
        method="jeffreys")

Odds ratio & Relative risk

Using epitools - manual here

Reminder: If you need more information on the tests use the help command in the console (e.g. ?riskratio, ?oddsratio).

Library

library(epitools)
# if you don't have this package, first install using `install.packages("epitools")`

Create a simple dataframe. In this case we'll test effect of gender on parasite presence with a simple positive/negative summary. Of course if you have a your raw data in a spreadsheet you could make your own by summarising the releavnt information into a dataframe. (Need help tidying and summarising your data…check out this tutorial to check you hooked on the dplyr and tidyr packages

factor1 <- c("Female", "Male")
factor2 <- c("Positive", "Negative")

dat <- matrix(c(16, 30, 15, 34), nrow = 2, ncol = 2, byrow = TRUE)
dimnames(dat) <- list("Sex" = factor1, "Parasite present" = factor2)

Your dataframe should look like this

dat

Now lets calculate our odds ratio

oddsratio(dat)

and relative risk

riskratio(dat)

Modelling

Manual here and webpage