This lesson reviews best practices associated with clean coding.

Clean code means that your code is organized in a way that is easy for you and for someone else to follow and read. Certain conventions are suggested to make code easier to read. For example, many guides suggest the use of a space after a comment. Like so:

#poorly formatted  comments are missing the space after the pound sign.
# good comments have a space after the pound sign

While these types of guidelines may seem unimportant when you first begin to code, after a while you’ll realize that consistently formatted code is much easier for your eye to scan and quickly understand.

Consistent, Clean Code

Take some time to review Hadley Wickham’s style guide. From here on in, you will follow this guide for all of the assignments in this class.

Object Naming Best Practices

1. Keep object names short: This makes them easier to read when scanning through code
2. Use meaningful names: For example, precip is a more useful name that tells us something about the object compared to x
3. Don’t start names with numbers! Objects that start with a number are NOT VALID in R
4. Avoid names that are existing functions in R: e.g.if, else, for, see here

A few other notes about object names in R:

• R is case sensitive (e.g. weight_kg is different from Weight_kg).
• Avoid other function names (e.g. c, T, mean, data, df, weights).
• Use nouns for variable names and verbs for function names.
• Avoid using dots in object names - e.g. my.dataset - dots have a special meaning in R (for methods) and other programming languages. Instead use underscores my_dataset.

#my code

#load stuff
library(ggplot2)

#turn off factors
options(stringsAsFactors = FALSE)

1variable <- 3 * 6
meanVariable <- 1variable

#calculate something important
mean-variable <- meanvariable * 5

thefinalthingthatineedtocalculate <- mean-variable + 5

#get things that are important
download.file(url = "https://ndownloader.figshare.com/files/7010681",
              destfile = "data/boulder-precip.csv")

my.data <- read.csv(file="data/boulder-precip.csv")
head(my_data)

str(my.data)

qplot(x=my.data$DATE,
      y=my.data$PRECIP)