• Any questions from last time?

• Any questions from the reading?

"Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread." - Hadley Wickham

• Style guide for this course is based on the Tidyverse style guide: http://style.tidyverse.org/

• There's more to it than what we'll cover today, but we'll mention more as we introduce more functionality, and do a recap later in the semester

• Do not use spaces in file names, use - or _ to separate words
• Use all lowercase letters

# Good
ucb-admit.csv

# Bad
UCB Admit.csv

• Use _ to separate words in object names
• Use informative but short object names
• Do not reuse object names within an analysis

# Good
acs_employed

# Bad
acs.employed
acs2
acs_subset
acs_subsetted_for_males

• Put a space before and after all infix operators (=, +, -, <-, etc.), and when naming arguments in function calls.
• Always put a space after a comma, and never before (just like in regular English).

# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)

# Bad
average<-mean(feet/12+inches,na.rm=TRUE)

• Always end a line with +
• Always indent the next line

# Good
ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram()

# Bad
ggplot(diamonds,mapping=aes(x=price))+geom_histogram()

• Limit your code to 80 characters per line. This fits comfortably on a printed page with a reasonably sized font.

• Take advantage of RStudio editor's auto formatting for indentation at line breaks.

• Use <- not =

# Good
x <- 2

# Bad
x = 2

Use ", not ', for quoting text. The only exception is when the text already contains double quotes and no single quotes.

ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram() +
  # Good
  labs(title = "`Shine bright like a diamond`",
  # Good
       x = "Diamond prices",
  # Bad
       y = 'Frequency')

Data are from 1997! The dataset is in the moasicData package, and we will also make use of dplyr and ggplotot2.

library(mosaicData)
library(ggplot2)
library(dplyr)

ggplot(SAT, mapping = aes(x = salary, y = sat)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

SAT <- SAT %>%
  mutate(frac_cat = case_when(
    frac <= 22             ~ "low",
    frac > 22 & frac <= 49 ~ "medium",
    frac > 50              ~ "high"
  ))

ggplot(SAT, mapping = aes(x = salary, y = sat, color = frac_cat)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

Let's get started on Mini HW 6:

• Brief presentation of findings so far at the end of class
• Finish up write up by next class