4.10 Exercises
For these exercises, we will be using the data from the survey collected by the United States National Center for Health Statistics (NCHS). This center has conducted a series of health and nutrition surveys since the 1960’s. Starting in 1999, about 5,000 individuals of all ages have been interviewed every year and they complete the health examination component of the survey. Part of the data is made available via the NHANES package. Once you install the NHANES package, you can load the data like this:

library(NHANES) data(NHANES)
The NHANES data has many missing values. The mean and sd functions in R will return NA if any of the entries of the input vector is an NA. Here is an example:

library(dslabs) data(na_example) mean(na_example) #> [1] NA sd(na_example) #> [1] NA
To ignore the NAs we can use the na.rm argument:

mean(na_example, na.rm = TRUE) #> [1] 2.3 sd(na_example, na.rm = TRUE) #> [1] 1.22
Let’s now explore the NHANES data.

1. We will provide some basic facts about blood pressure. First let’s select a group to set the standard. We will use 20-to-29-year-old females. AgeDecade is a categorical variable with these ages. Note that the category is coded like ” 20-29”, with a space in front! What is the average and standard deviation of systolic blood pressure as saved in the BPSysAve variable? Save it to a variable called ref.

Use filter and summarize and use the na.rm = TRUE argument when computing the average and standard deviation. You can also filter the NA values using filter.

2. Using a pipe, assign the average to a numeric variable ref_avg. Hint: Use the code similar to above and then pull.

3. Now report the min and max values for the same group.

4. Compute the average and standard deviation for females, but for each age group separately rather than a selected decade as in question 1. Note that the age groups are defined by AgeDecade. Hint: rather than filtering by age and gender, filter by Gender and then use group_by.

5. Repeat exercise 4 for males.

6. We can actually combine both summaries for exercises 4 and 5 into one line of code. This is because group_by permits us to group by more than one variable. Obtain one big summary table using group_by(AgeDecade, Gender).

7. For males between the ages of 40-49, compare systolic blood pressure across race as reported in the Race1 variable. Order the resulting table from lowest to highest average systolic blood pressure.