Recap of week 2

• Visualizing data with ggplot2
  • Different types of plots
  • Key elements: data, geometries, aesthetics
• Transforming data with dplyr
  • select
  • mutate
  • arrange
  • filter
  • summarize
  • group_by

Agenda for today

• Review of syntax in R
  
• Creating projects in R
  
• Importing data with readr

“Official” cheat sheet for readr available here.

Function syntax

The most important syntax in R is the function call. All R syntax has function calls underlying it.

function_name(<inputs to the function>,
              <arguments which change 
              how the function operates>)

x <- c(-5, -3, -1, 1, 3, NA)
mean(x, na.rm = TRUE)

## [1] -1

Function calls read “inside out”

abs(x): If x is positive, return x. If x is negative, return x without the negative sign.

mean(abs(x), na.rm = TRUE)

## [1] 2.6

Function calls read “inside out”

abs(x): If x is positive, return x. If x is negative, return x without the negative sign.

mean(abs(x), na.rm = TRUE)

## [1] 2.6

%>% syntax with dplyr

Take the mtcars dataset, select just the wt and mpg columns, then select rows with mpg < 15

mtcars %>% 
    select(wt, mpg) %>% 
    filter(mpg < 15)

+ syntax with ggplot2

library(ggplot2)
ggplot(data = mtcars, mapping = aes(x = wt, y = hp)) +
    geom_point() +
    labs(title = "Horsepower vs. Weight", x = "Weight", 
         y = "Horsepower") +
    theme_classic()

Scripts in R

• So far, we have not saved anything
• If you don’t save workspace, upon reopening R:
  • All variables you created will be gone
  • If you want to use packages (e.g. dplyr), you have to reload them
• This is a good thing!
  • Forces you to save all important code in scripts
  • Allows you to reproduce your results further down the road

Working directories in R

• Directory where R looks for files that you ask it to load
• Also where R will put any files that you ask it to save
• You can see your current working directory at the top of the console or by typing getwd()

Projects in R

• With more projects, it’s a good idea to organize your projects by directory
• You will know where to find your project files
• You won’t mix files from different projects together

Today’s dataset: Drought in California

Data source: United States Drought Monitor (USDM)

(Source: droughtmonitor.unl.edu)

USDM: data download

USDM: data selection

The data in Excel

• None to D4: drought levels of increasing severity
• Numbers: % by land area at that drought severity level

Optional material

Different packages for working with different data formats

• .txt, .csv files (readr)
• .xls, .xlsx files (readxl)
• SPSS, Stata & SAS files (haven)
• Databases (DBI)
• JSON files (jsonlite)
• XML files (xml2)
• Web APIs (httr)
• HTML web scraping (rvest)

USDM: data selection details

• Spatial Scale: County (by State), then just select California
  
• Statistics Category: Percent Area
  
• Statistics Type: Categorical
  
• Output Format: CSV

tidyr functions: gather and spread

gather: Used when some column names are not variables, but values of a variable

(Source: R for Data Science)

spread: Opposite of gather

(Source: R for Data Science)

tidyr functions: separate and unite

separate: Used to separate values in one column into multiple columns

(Source: R for Data Science)

unite: Opposite of separate

(Source: R for Data Science)