Class Meeting 1.2 (cm012)

Worksheet

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Worksheet

Notepad

http://bit.ly/conj620-cm012

Set-up

• Load the tidyverse package (must be installed first! Do not install packages within your scripts)
• Using readr package, we import our csv with read_csv function
• The first argument in the read_csv() parentheses is the url to the dataset
• The second argument, na = ".", specifies that missing data in this dataset is denoted by a period
• Finally, we assign the data to an R object using <- and call that object heart

library(tidyverse)
heart <- read_csv("http://faculty.washington.edu/kenrice/heartgraphs/nhanesmedium.csv", 
                  na = ".") 

Data Dictionary

From the data dictionary:

• BPXSAR: systolic blood pressure (mmHg)
• BPXDAR: diastolic blood pressure (mmHg)
• BPXDI1, BPXDI2: two diastolic blood pressure readings
• race_ethc: race/ethnicity, coded as:
  • Hispanic,
  • White non-Hispanic,
  • Black non-Hispanic and
  • Other
• gender: sex, coded as Male/Female
• DR1TFOLA: folate intake (μg/day)
• RIAGENDR: sex, coded as 1/2
• BMXBMI: body mass index (kg/m2)
• RIDAGEY: age (years)

1. How many rows and columns are in the heart data?

# your code here

2. Use dplyr to answer these questions:

• What is the minimum age in years?
• What is the maximum age in years?

# your code here

If this was easy: there are actually (at least) 4 ways to do this with dplyr functions. Try to figure out four ways to do this!

3. Use dplyr to do the following:

• Make a new variable in heart called RIDAGEMOS that converts RIDAGEYR to months.
• For individuals 960 months or older, what is the range of BMI values?
• For individuals exactly 96 months of age, what is the range of BMI values?

# your code here

4. Use ggplot2 to make a scatterplot with age in years on the x-axis and systolic blood pressure on the y-axis.

# your code here

If this was easy: import the larger dataset (http://faculty.washington.edu/kenrice/heartgraphs/nhaneslarge.csv) and make a hexagonal heatmap of 2d bin counts. Apply a custom continuous color palette to the hexbins, and reverse the colors such that lighter colors are for lower counts and darker colors are for higher counts.

5. Use dplyr and ggplot2 to do the following:

• Type ?case_when to read the help documents for this dplyr function. Look carefully at the examples (hint: you might find the star_wars example most helpful- you can run that code in your console!). Use this function to make a new variable called age_cat with 3 values:
  • between(<name_of_age_var>, 0, 30) is age_cat == "Under 30"
  • between(<name_of_age_var>, 31, 55) is age_cat == "31-55"
  • all other ages are age_cat == "56+"
• Create a scatter plot with bmi on the x-axis and blood pressure on the y-axis, facetting by your new age_cat variable. Color the points by gender.
• Look up ?facet_grid, and recreate the same plot now using gender ~ age_cat.

# your case_when code here

# your plot code here

If this was easy, add linear regression lines to all six facetted grids (keep the standard error ribbons). Apply a custom color palette and remove the color legend guide. Play with alpha levels for the points. Use something other than the default theme_gray().

6. Knit to html

very important: delete the text below in order to be able to knit (you won’t have the image file in your directory)