class: center, middle, inverse, title-slide # Review and recap ### Dr. Çetinkaya-Rundel ### 2018-02-07 --- ## Announcements - Slack -- Have you joined the channel? downloaded the app? set up email notifications? - My OH this week: Thursday and Friday 10-11:30am - Reading due Monday (posted) - HW due Monday (details to be sent later tonight) - Extra credit due at the end of Unit 1 (details to be sent later tonight) - Labs: - Read all instructions, don't just jump to exercises - Make a decision as a team whether to read the document ahead of time or not, but don't get started until you hear the intro from your TA - Schedule weekly meeting times **NOW**, don't wait till the need arises --- class: center, middle # R/RStudio --- # R/RStudio - R: statistical programming language - RStudio: integrated development environment for the R language - RStudio Cloud allows us to use RStudio without local installation (which can be hairy) - Interested in learning about other ways of using RStudio -- come by office hours - Already have another way of using RStudio -- make sure to maintain Cloud account for quick classroom execises --- # R packages - R is an open source language - Packages developed by open source developers  --- class: center, middle # Git/GitHub --- ## Merge conflicts .pull-left[ Confused about merge conflicts? Everyone is... ] .pull-right[  ] -- - Why does a merge conflict happen? -- - TELL ME YOUR WOES... --- ## Caching your password Needs to be done on a per-project basis (for now): - Go to the Terminal within a project (the most recent lab?) - Type the following to cache your password for `\(60 * 60 * 24 * 7 = 604800\)` seconds ```bash git config --global credential.helper 'cache --timeout 604800' ``` --- ## Tips for collaborating via Git/GitHub - Always pull first before you start working. - Commit, and push, often to minimize merge conflicts and/or to make merge conflicts easier to resolve. - If you find yourself in a situation that is difficult to resolve, ask questions asap, don't let it linger and get bigger. --- class: center, middle # Visualizing Star Wars (better) Follow along on ☁️: Review and recap - 02-07 -- `viz-star-wars.Rmd` --- ## Starting point ```r library(tidyverse) starwars <- starwars %>% filter(mass < 500) ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() ``` <!-- --> --- ## Add title ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters") ``` <!-- --> --- ## Add x and y labels ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)") ``` <!-- --> --- ## Change legend label ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") ``` <!-- --> --- ## Change zooming ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") + xlim(c(100, 200)) + ylim(c(40, 120)) ``` ``` ## Warning: Removed 14 rows containing missing values (geom_point). ``` <!-- --> --- ## Change transparency ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point(alpha = 0.3) + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") ``` <!-- --> --- ## Change theme - `theme_dark()` ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") + theme_dark() ``` <!-- --> --- ## Change theme - `theme_minimal()` ```r ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") + theme_minimal() ``` <!-- --> --- ## 538 theme ```r # install.packages("ggthemes") library(ggthemes) ggplot(data = starwars, aes(x = height, y = mass, color = gender)) + geom_point() + labs(title = "Mass vs. height of Star Wars characters", x = "Height (in cm)", y = "Mass (in kg)", color = "Gender") + theme_fivethirtyeight() ``` <!-- --> --- ## Further customization - It is possible to do a lot more customization to your plots - See http://ggplot2.tidyverse.org/ for help on using the ggplot2 package + You can find a list of all ggplot2 functions in the Reference tab of the package homepage - You'll find that sometimes customization is valuable, and sometimes the defaults work well and while customization might be doable (technically) it might not be necessary --- class: center, middle # Activity Follow along on ☁️: Review and recap - 02-07 -- `examine-world-trend.Rmd` --- ## Examining world trends Examine the code given to produce the existing plots in the "Examining Gapminder data" document. Then, make the appropriate changes listed below. - Task 1: Subset for 1952 (instead of 2007) - Task 2: Plot life expectancy (`lifeExp`) vs. population (`pop`) - Task 3: In addition to coloring the points by continent, size the points by GPD (`gpdPercap`) - Task 4: A brief (1-2 sentences only) description of what you see in the plot. Work on it individually, but consult with your teammates alogn the way. --- class: center, middle # Wrangling diamonds 💍 Follow along on ☁️: Review and recap - 02-07 -- `wrangling-diamonds.Rmd` --- ## The data Diamond prices are driven by 4Cs: carat, cut, color, and clarity. We'll explore a dataset containing the prices and other attributes of almost 54,000 diamondsn from the tidyverse package. Take a peek at the codebook with ```r ?diamonds ``` or at http://ggplot2.tidyverse.org/reference/diamonds.html. --- ## 4Cs - Carat: unit of mass equal to 200 mg used for measuring gemstones and pearls - Cut: objective measure of a diamond's light performance / sparkle - Color:  - Clarity:  --- ## Measurements  --- ## Price What type of variable is price? Would you expect its distribution to be symmetric, right-skewed, or left-skewed? Why? Make a histogram of the distribution of diamond prices. Does the shape of the distribution match your expectation? --- ## Cut How many diamonds of each type of cut are there? --- ## Clarity Calculate the relative frequency of each clarity of diamonds. --- ## Clarity by cut Calculate the relative frequency of each clarity of diamonds by cut. --- ## Depth and price Plot the relationship between depth and price of only fair cut diamonds. --- ## Cut and price For each type of cut, calculate minimum (`min`), maximum (`max`), mean (`mean`), and median (`median`) price of diamonds of that type. --- ## Recap - Start with data frame - Pipe into a verb - Only print output if counting, summarizing, etc. - Save output as the original data frame (or something else) if more will be done with the data frame in another step/pipe