MonkmanMH

Categorical data analysis in R - a resource list

R packages

vcd & vcdExtra http://cran.r-project.org/web/packages/vcd/ http://cran.r-project.org/web/packages/vcdExtra/

MonkmanMH

# modified from / inspired by @GShotwell
# gist: https://gist.github.com/GShotwell/b19ef520b6d56f61a830fabb3454965b

df <- tibble(
  value = rnorm(100, 50, 10), 
  date = seq.Date(from = ymd("2022-01-01"), 
                  ymd("2022-04-10"), 
                  by = "day"))

MonkmanMH

# OBJECTIVE: 
# * latitude and longitude stored with degrees with degree symbol and minutes with decimal
# * separate into two columns for each, with degrees and minutes separated

# step 1 - create a test tibble called "geo_loc"
geo_loc <- tribble( ~ latitude, ~ longitude,
                    "48º 4.206", "124º 45.553",
                    "46º 59.4942", "124º 12.6362")
# ---
# step 2 - split into degrees and minutes columns

MonkmanMH

# {dplyr}'s `ungroup()` function

```{r}
# packages
library(gapminder)
library(dplyr)
```

In this example, we calculate the difference in a country's life expectancy from the continent's mean life expectancy
 - for example, the difference between life expectancy in Canada and the mean life expectancy of countries in the Americas

MonkmanMH

penguins <- palmerpenguins::penguins

penguins %>%
  filter(!is.na(sex)) %>% 
  mutate(bm_quart = gtools::quantcut(body_mass_g, q=4))
  
  penguins_2 <- penguins %>%
  filter(!is.na(sex)) %>% 
  select(species, sex, body_mass_g) %>% 
#  mutate(bm_quart = gtools::quantcut(body_mass_g, q=4))

MonkmanMH

rename from "master" to "main"

git branch -m master main

push to github

git push -u origin main

and delete the original

git push origin --delete master

But...you can do this as an Rmd with the git commands in a bash chunk

MonkmanMH

---
title: "rename github repo"
author: "Martin Monkman"
date: "2020/10/05"
output: html_document
---

From
https://www.r-bloggers.com/2020/07/5-steps-to-change-github-default-branch-from-master-to-main/

MonkmanMH

# short script to install the {annotater} package
# reference: https://github.com/luisDVA/annotater

# Step 1: install the {remotes} package
install.packages("remotes")

# Step 2: install {annotater} from the GitHub source
remotes::install_github("luisDVA/annotater")

MonkmanMH

Random numbers in R

The creation of random numbers, or the random selection of elements in a set (or population), is an important part of statistics and data science. From simulating coin tosses to selecting potential respondents for a survey, we have a heavy reliance on random number generation.

R offers us a variety of solutions for random number generation; here's a quick overview of some of the options.

runif, rbinom, rnorm

One simple solution is to use the runif function, which generates a stated number of values between two end points (but not the end points themselves!) The function uses the continuous uniform distribution, meaning that every value between the two end points has an equal probability of being sampled.

MonkmanMH

# dplyr::case_when to find and clean FSA
#
# Notes:
# * FSA = "Forward Sortation Area" in Canadian postal parlance
# * the regex finds British Columbia FSAs (starting with "V")

FSA_list <- df %>% 
  mutate(FSA_clean = case_when(
    str_detect(FSA, "V\\d.$") == TRUE ~ FSA,
    TRUE ~ NA_character_