http://pad.software-carpentry.org/2018-09-05-dundee

• Fundamentals of RStudio (refresher)
• RStudio project creation and version control (refresher)
• Flow control in R (refresher)
• Functions in R (refresher)
• Literate programming with RMarkdown and knitr
• Good practice for programming and project management

• The elements of an RStudio session
  • interactive code
  • writing scripts/documents
  • live view of graphical output
  • getting help
  • interaction with the filesystem
  • project and environment management

• RStudio is an integrated development environment (IDE) - all platforms

• Function (log(), sin() etc.) ≈ “canned script”
  • automate complicated tasks
  • make code more readable and reusable
• Some functions are built-in (in base packages, e.g. sqrt(), lm(), plot())
• Some functions are imported from libraries

?fname                 # help page for fname
help(fname)            # help page for fname
??fname                # any mention of fname
args(fname)            # arguments for fname
vignette(fname)        # worked examples for fname
vignette()             # show all available vignettes
help.search("text")    # any mention of "text"

• Computers can have limits to numeric precision
  • (they do what you tell them, not necessarily what you want)

> pi - 1e-8 == pi
[1] FALSE
> all.equal(pi - 1e-8, pi)
[1] TRUE

> log(0.01 ^ 200)
[1] -Inf
> 200 * log(0.01)
[1] -921.034

• At the console (you’ve done this)
• In a script
• As an interactive notebook or markdown file
• As a Shiny app

We’re going to create a new dataset and R script.

• Putting code in a script makes it easier to modify, share and run

• Good practice for RStudio project structure
• Load data into an RStudio project
• Produce summary statistics of data
• Extract subsets of data
• Plotting data in R

• Use a single working directory per project/analysis
  • easier to move, share, and find files
  • use relative paths to locate files
• Treat raw data as read-only
  • keep in separate subfolder (data?)
• Clean data ready for work/analysis
  • keep cleaned/modified data in separate folder (clean_data?)
• Consider output generated by analysis to be disposable
  • can be regenerated by running analysis/code
  • don’t place under version control

• RStudio tries to help you manage your projects
  • R Project concept - files and subdirectory structure
  • integration with version control
  • switching between multiple projects within RStudio
  • stores project history

• You created data manually earlier, but this is rare
• Data are most commonly read in from plain text files

gapminder <- read.table("data/gapminder-FiveYearData.csv", sep=",", header=TRUE)

str(gapminder)              # structure of the data.frame
typeof(gapminder$year)      # data type of a column
length(gapminder)           # length of the data.frame
nrow(gapminder)             # number of rows in data.frame
ncol(gapminder)             # number of columns in data.frame
dim(gapminder)              # number of rows and columns in data.frame
colnames(gapminder)         # column names from data.frame
head(gapminder)             # first few rows of dataframe
summary(gapminder)          # summary of data in data.frame columns

• How to make data-dependent choices in R
• Use if() and else()
• Repeat operations in R
• Use for() loops
• vectorisation to avoid repeating operations

• We often want to perform operations (or not) conditional on a piece of data
  • The if() and else construct is useful for this

# if
if (condition is true) {
  PERFORM ACTION
}

# if ... else
if (condition is true) {
  PERFORM ACTION
} else {  # i.e. if the condition is false,
  PERFORM ALTERNATIVE ACTION
}

In the console, can you use an if() statement to report whether there are any records from 2002 in the gapminder dataset?

Can you do the same for 2012?

• for() loops are a very common construction in programming
  • for each <item> in a group, <do something (with the item)>
• Not as useful in R as in some other languages

for(iterator in set of values){
  do a thing
}

• while() loops are useful when you need to do something while some condition is true

while(this condition is true){
  do a thing
}

In your script, can you use a for() loop and an if() statement to loop over each country in the gapminder data, and report TRUE when it starts with the letter M, and FALSE when it does not?

• for() and while() loops are useful for program control (making decisions), but are not efficient for data manipulation in R
• Many operations in R are vectorised
  • Applying functions to a vector, applies to all elements in that vector
  • No need to loop

x < 1:4
x * 2
y <- 6:9
x + y

In your script, can you use vectorisation to identify all countries in the gapminder data that start with the letter M?

• Why functions are important
• How to write a new function
• Defining a function that takes arguments
• Returning a value from a function
• Set default values for function arguments

• Functions let us run a complex series of commands in one go
  • under a memorable/descriptive name
  • invoked with that name
  • with a defined set of inputs and outputs
  • to perform a logically coherent task

• You will often need to write your own functions
• They take a standard form

<function_name> <- function(<arg1>, <arg2>) {
  <do something>
  return(<result>)
}

my_sum <- function(a, b) {
  the_sum <- a + b
  return(the_sum)
}

• So far, you’ve been able to use R’s built-in help to see function documentation
  • This isn’t available for your own functions unless you write it

• State what the code does (and why)
• Define inputs and outputs
• Give an example

• We can define functions that take multiple arguments
• We can also define default values for arguments

# Report countries in gapminder data
list_countries <- function(data, letter=NULL) {
  countries <- levels(data$country)
  if (!is.null(letter)) {
    matches <- startsWith(countries, letter)
    countries <- countries[matches]
  }
  return(countries)
}

• Create dynamic, reproducible reports
• RMarkdown syntax
• Inline R code in documents
• Producing documents in .pdf, .html, etc.

• A programming paradigm introduced by Donald Knuth
• The program (or analysis) is explained in natural language
  • The source code is interspersed through the document
• The whole document is executable

• R Markdown files embody Literate Programming in R
• File \(\rightarrow\) New File \(\rightarrow\) R Markdown
• Enter a title
• Save the file (gets the extension .Rmd)

• Header information is fenced by ---

---
title: "Literate Programming"
author: "Leighton Pritchard"
---

• Natural language is written as plain text

This is an R Markdown document. Markdown is a simple formatting syntax

• R code (which is executable) is fenced by backticks

• We’re going to create an R Markdown report on the gapminder data
  • using dplyr
  • using ggplot2

• How to manipulate data.frames with the six verbs of dplyr
  • a ‘grammar of data manipulation’

• dplyr is a package in the Tidyverse
• Facilitates analysis by groups
  • Helps avoid repetition

> mean(gapminder[gapminder$continent == "Africa", "gdpPercap"])
[1] 2193.755
> mean(gapminder[gapminder$continent == "Americas", "gdpPercap"])
[1] 7136.11
> mean(gapminder[gapminder$continent == "Asia", "gdpPercap"])
[1] 7902.15

select(gapminder, year, country, gdpPercap)
gapminder %>% select(year, country, gdpPercap)

• filter() selects rows on the basis of a specified condition

filter(gapminder, continent=="Europe")

# Select gdpPercap by country and year, only for Europe
eurodata <- gapminder %>%
              filter(continent == "Europe") %>%
              select(year, country, gdpPercap)

Can you write a single line (which may span multiple lines in your RMarkdown by including pipes) to produce a dataframe containing:

• year, country, life expectancy data
• only for African nations

How many rows does the dataframe have?

group_by(gapminder, continent)
gapminder %>% group_by(continent)

# Produce table of mean GDP by continent
gapminder %>%
    group_by(continent) %>%
    summarize(meangdpPercap=mean(gdpPercap))

• Can you calculate the average life expectancy per country in the gapminder data?
• Which nation has longest life expectancy, and which the shortest?

• Two useful functions related to summarize()
  • count()/tally(): a function that reports a table of counts by group
  • n(): a function used within summarize(), filter() or mutate() to represent count by group

gapminder %>%
  filter(year == 2002) %>%
  count(continent, sort = TRUE)

gapminder %>%
  group_by(continent) %>%
  summarize(se_lifeExp = sd(lifeExp)/sqrt(n()))

• mutate() is a function allowing creation of new variables in a chain of dplyr verbs.

# Calculate GDP in $billion
gdp_bill <- gapminder %>%
  mutate(gdp_billion = gdpPercap * pop / 10^9)

# Calculate total/sd of GDP by continent and year
gdp_bycontinents_byyear <- gapminder %>%
    mutate(gdp_billion=gdpPercap*pop/10^9) %>%
    group_by(continent,year) %>%
    summarize(mean_gdpPercap=mean(gdpPercap),
              sd_gdpPercap=sd(gdpPercap),
              mean_gdp_billion=mean(gdp_billion),
              sd_gdp_billion=sd(gdp_billion))