Planning Notes

[snystrom]

2019 Plan:

8 days, 1.5 hr each.

Need students to use ? function to read documentation for how things work.

before class students will create github account to access lessons & login to rstudio cloud.

• note: tell students they will need their login for class so be sure to have passwords & usernames on hand

Part1: Data analysis

• Intro (Day 1)

  • Rstudio.cloud
  • install.packages("tidyverse")
  • Rstudio interface
    • configure project settings (no save environment, move console?)
  • data.frame & ggplot intro
    • View(df), str(df), df$variable

• Plotting (Day 2)

  • assigning plots to variables with <-
  • adding to plots with p + ggtitle('test')
  • non-transforming plots
  • aesthetic mappings (fill vs color, alpha, linetype, shape)
  • facets
  • xlab/ylab/ggtitle/labs(tag = "A")
  • https://moderndive.netlify.com/images/ggplot_cheatsheet-1.png

• Data Manipulation (Day 3)

  • dplyr + pipes
  • filter, mutate, select
    • use these to subset data, etc. to make plots
  • plots are good, but sometimes we want numbers out too
    • group_by, summarize
    • use these to make new tables to add lines to plots or something? (ie draw vertical line & mean of each group)
  • https://moderndive.netlify.com/images/dplyr_cheatsheet-1.png

Can we combine data import & tidying?

• Data import & Export (Day 4)

  • readr::read_csv, readr::write_csv

• Alternative: Tidy data (Day 4)

  • melting/reshaping so data works better with dplyr/ggplot
  • emphasize to students that most of their real data can be reformatted into a "tidy" structure
  • we can use dplyr/tidyr/reshape2 functions to clean data so you can use R instead of excel

• Lists/vectors & looping

  • list, list[['name']]
  • problem: multiple csv files in a directory, want to read them in
    • for loop w/ readr::read_csv
  • want to make a plot of each subset of data (not facet)
    • use df %>% split %>% for name in names()

Part 2: Programing?

Now that students feel comfortable in R environment we'll dig into programming a bit more

• functions + if-else (Day 5)
  • write functions that subset data, etc.
  • this way we use data as the focus for why things matter instead of making new esoteric examples
• loops & vectors (Day 6)
• (Day 7)
• Extra day, can cover Rmd if time or old material (Day 8)

2018 Schedule:

Day	Subject	Homework
1	intro	Homework 1
2 & 3	plotting (& factors)	Homework 2
4 & 5	dplyr & pipes	Homework 3
6	reshaping data	Exercise 1
7	reshaping data 2	Exercise 2
8	data import & cleaning	Exercise
9	functions I	Exercise 1
10	functions II	Exercise 2
11 - 12	Final Project	Final Project

2018 Schedule:

Day	Subject	Homework
1	intro	Homework 1
2 & 3	plotting (& factors)	Homework 2
4 & 5	dplyr & pipes	Homework 3
6	reshaping data	Exercise 1
7	reshaping data 2	Exercise 2
8	data import & cleaning	Exercise
9	functions I	Exercise 1
10	functions II	Exercise 2
11 - 12	Final Project	Final Project