Publications by Kitada Smalley
AQI: Numeric Summaries and Graphics
Learning Objectives In this lesson students will learn to work with numeric data to create graphics and summaries. Example : Oregon Air Quality Index (AQI) These data were reported on AirNow on October 19, 2022 for the states of Oregon, Washington, and Colorado. https://www.airnow.gov/state/?name=oregon Step 0: Library Tidyverse library(tidyver...
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DataViz: HCL
Hue, Chroma, Luminance Color Space The hcl() function generates HEX codes and can be used to create color palettes. library(tidyverse) # CAN USE BASE #install.packages("hcl") #library(hcl) Default colors: df<-data.frame(x=1:5) ggplot(df, aes(x, y=1, fill=x))+ geom_tile()+ coord_equal()+ theme_void()+ guides(fill="none") Hue The hue of ...
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DATA151: Comparing Distributions
Learning Objectives In this lesson students will compare distributions from multiple populations of interest using: dplyr: group_by and summarise side-by-side boxplots side-by-side violin plots dot plots beeswarmplots faceting Step 0: Library Tidyverse library(tidyverse) Step 1: Load the Data aqi<-read.csv("https://raw.githubusercontent.com/ki...
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DataViz: Coffee
Learning Objectives In this lesson students will learn how to Create a map of the United States Create a map of the World Try different map projections Color a map based on variables Example 1: Dutch Bros Dutch Bros Coffee is a drive-through coffee chain headquartered in Grants Pass, Oregon, with company-owned and franchise locations throughout...
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DATA151: Time and Space
Learning Objectives In this lesson students will learn how to create Time series plots Choropleths (colored map plots) Time Series Plots Time series plots show how a variable (on the y-axis) changes over time (on the x-axis). Example 1: Salem, Oregon AQI Step 0: Library Tidyverse library(tidyverse) Step 1: Load the Data salem<- read.csv("htt...
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DataViz: Projections
Using map_data in ggplot2 0: Library Tidyverse library(tidyverse) A. Load the data # Load map data world_map = map_data("world") B. Basic Map world_map%>% ggplot(aes(map_id = region)) + geom_map(map = world_map)+ expand_limits(x = world_map$long, y = world_map$lat) C. Projections Since the globe is a 3D object, we must project it onto ...
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DATA151: Candy Tables
Halloween Candy Ranking Read the fivethirtyeight article and watch the short movie https://fivethirtyeight.com/videos/the-ultimate-halloween-candy-power-ranking/ Here’s some documentation on these data: https://github.com/fivethirtyeight/data/tree/master/candy-power-ranking Step 0: Load the Data candy<-read.csv("https://raw.githubusercontent.c...
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TDF Solutions
Learning Objectives: Students will use this exercise to assess their ability to translate questions into code using tidyverse packages and verbs. dplyr: filter(), mutate(), group_by(), summarise(), count(), ect ggplot2: ggplot(), aes(), geom_bar, geom_col, geom_histogram, geom_boxplot, geom_density Step 0: Library tidyverse library(tidyverse) ...
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DATA151: Regression (Take a Hike)
Learning Objectives: Students will learn to create scatterplots and describe the relationships between two numeric variables. Example: Take A Hike! STEP 1: Load the data: body_wgt<-c(120, 187, 109, 103, 131, 165, 158, 116) backpack_wgt<-c(26, 30, 26, 24, 29, 35, 31, 28) backpack_df<-data.frame(body_wgt, backpack_wgt) Which variable should be t...
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DATA151: Introduction to Regression Models
Learning Objectives Students will learn how to use linear regression to model relationships between variables and also explore subgroups. Example 1: Climate Change and Fish Habitats As the climate grows warmer, we expect many animal species to move toward the poles in an attempt to maintain their preferred temperature range. Do data on fish in t...
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