To practice our dplyr skills we will be working with 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 then they complete the health examination component of the survey. Part of this dataset is made available via the NHANES package which can be loaded this way:
library(NHANES) data(NHANES)
The NHANES data has many missing values. Remember that the main summarization function 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) sd(na_example)
To ignore the NAs, we can use the na.rm argument:
mean(na_example, na.rm = TRUE) sd(na_example, na.rm = TRUE)
Let’s explore the NHANES data. We will be exploring blood pressure in this dataset.
First let’s select a group to set the standard. We will use 20-29 year old females. Note that the category is coded with 20-29, with a space in front of the 20! The AgeDecade is a categorical variable with these ages.
To know if someone is female, you can look at the Gender variable.
Filter the NHANES dataset so that only 20-29 year old females are included and assign this new data frame to the object tab.
Use the pipe to apply the function filter, with the appropriate logicals, to NHANES.
Remember that this age group is coded with 20-29, which includes a space. You can use head to explore the NHANES table to construct the correct call to filter.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(NHANES)
data(NHANES)
tab <- NHANES %>% filter(AgeDecade == " 20-29", Gender == "female")Now we will compute the average and standard deviation for the subgroup we defined in the previous exercise (20-29 year old females), which we will use reference for what is typical.
You will determine the average and standard deviation of systolic blood pressure, which are stored in the BPSysAve variable in the NHANES dataset.
Complete the line of code to save the average and standard deviation of systolic blood pressure as average and standard_deviation to a variable called ref.
Use the summarize function after filtering for 20-29 year old females and connect the results using the pipe %>%. When doing this remember there are NAs in the data!
library(dplyr)
library(NHANES)
data(NHANES)
## complete this line of code.
ref <- NHANES %>% filter(AgeDecade == " 20-29" & Gender == "female") %>% summarize(average = mean(BPSysAve, na.rm = TRUE), standard_deviation = sd(BPSysAve, na.rm = TRUE)) Now we will repeat the exercise and generate only the average blood pressure for 20-29 year old females. For this exercise, you should review how to use the place holder . in dplyr or the pull function.
Modify the line of sample code to assign the average to a numeric variable called ref_avg using the . or pull.
library(dplyr)
library(NHANES)
data(NHANES)
## modify the code we wrote for previous exercise.
ref_avg <- NHANES %>%
filter(AgeDecade == " 20-29" & Gender == "female") %>%
summarize(average = mean(BPSysAve, na.rm = TRUE),
standard_deviation = sd(BPSysAve, na.rm=TRUE)) %>% .$averageLet’s continue practicing by calculating two other data summaries: the minimum and the maximum.
Again we will do it for the BPSysAve variable and the group of 20-29 year old females.
Report the min and max values for the same group as in the previous exercises. Use filter and summarize connected by the pipe %>% again. The functions min and max can be used to get the values you want.
Within summarize, save the min and max of systolic blood pressure as minbp and maxbp.
library(dplyr)
library(NHANES)
data(NHANES)
## complete the line
NHANES %>%
filter(AgeDecade == " 20-29" & Gender == "female") %>%
summarize(minbp = min(BPSysAve, na.rm = TRUE), maxbp = max(BPSysAve, na.rm = TRUE))## # A tibble: 1 x 2
## minbp maxbp
## <int> <int>
## 1 84 179Now let’s practice using the group_by function.
What we are about to do is a very common operation in data science: you will split a data table into groups and then compute summary statistics for each group.
We will compute the average and standard deviation of systolic blood pressure for females for each age group separately. Remember that the age groups are contained in AgeDecade.
Use the functions filter, group_by, summarize, and the pipe %>% to compute the average and standard deviation of systolic blood pressure for females for each age group separately.
Within summarize, save the average and standard deviation of systolic blood pressure (BPSysAve) as average and standard_deviation.
Note: ignore warnings about implicit NAs. This warning will not prevent your code from running or being graded correctly.
library(dplyr)
library(NHANES)
data(NHANES)
##complete the line with group_by and summarize
NHANES %>%
filter(Gender == "female") %>%
group_by(AgeDecade) %>%
summarize(average = mean(BPSysAve, na.rm = TRUE),
standard_deviation = sd(BPSysAve, na.rm=TRUE))## Warning: Factor `AgeDecade` contains implicit NA, consider using
## `forcats::fct_explicit_na`## # A tibble: 9 x 3
## AgeDecade average standard_deviation
## <fct> <dbl> <dbl>
## 1 " 0-9" 100.0 9.07
## 2 " 10-19" 104. 9.46
## 3 " 20-29" 108. 10.1
## 4 " 30-39" 111. 12.3
## 5 " 40-49" 115. 14.5
## 6 " 50-59" 122. 16.2
## 7 " 60-69" 127. 17.1
## 8 " 70+" 134. 19.8
## 9 <NA> 142. 22.9Now let’s practice using group_by some more. We are going to repeat the previous exercise of calculating the average and standard deviation of systolic blood pressure, but for males instead of females.
This time we will not provide much sample code. You are on your own!
Calculate the average and standard deviation of systolic blood pressure for males for each age group separately using the same methods as in the previous exercise.
Note: ignore warnings about implicit NAs. This warning will not prevent your code from running or being graded correctly.
library(dplyr)
library(NHANES)
data(NHANES)
##complete the line with group_by and summarize
NHANES %>%
filter(Gender == "male") %>%
group_by(AgeDecade) %>%
summarize(average = mean(BPSysAve, na.rm = TRUE),
standard_deviation = sd(BPSysAve, na.rm=TRUE))## Warning: Factor `AgeDecade` contains implicit NA, consider using
## `forcats::fct_explicit_na`## # A tibble: 9 x 3
## AgeDecade average standard_deviation
## <fct> <dbl> <dbl>
## 1 " 0-9" 97.4 8.32
## 2 " 10-19" 110. 11.2
## 3 " 20-29" 118. 11.3
## 4 " 30-39" 119. 12.3
## 5 " 40-49" 121. 14.0
## 6 " 50-59" 126. 17.8
## 7 " 60-69" 127. 17.5
## 8 " 70+" 130. 18.7
## 9 <NA> 136. 23.5We can actually combine both of these summaries into a single line of code. This is because group_by permits us to group by more than one variable.
We can use group_by(AgeDecade, Gender) to group by both age decades and gender.
Create a single summary table for the average and standard deviation of systolic blood pressure using group_by(AgeDecade, Gender).
Note that we no longer have to filter! Your code within summarize should remain the same as in the previous exercises.
Note: ignore warnings about implicit NAs. This warning will not prevent your code from running or being graded correctly.
library(NHANES)
data(NHANES)
NHANES %>% group_by(AgeDecade, Gender) %>%
summarize(average = mean(BPSysAve, na.rm = TRUE),
standard_deviation = sd(BPSysAve, na.rm=TRUE))## Warning: Factor `AgeDecade` contains implicit NA, consider using
## `forcats::fct_explicit_na`
## Warning: Factor `AgeDecade` contains implicit NA, consider using
## `forcats::fct_explicit_na`## # A tibble: 18 x 4
## # Groups: AgeDecade [9]
## AgeDecade Gender average standard_deviation
## <fct> <fct> <dbl> <dbl>
## 1 " 0-9" female 100.0 9.07
## 2 " 0-9" male 97.4 8.32
## 3 " 10-19" female 104. 9.46
## 4 " 10-19" male 110. 11.2
## 5 " 20-29" female 108. 10.1
## 6 " 20-29" male 118. 11.3
## 7 " 30-39" female 111. 12.3
## 8 " 30-39" male 119. 12.3
## 9 " 40-49" female 115. 14.5
## 10 " 40-49" male 121. 14.0
## 11 " 50-59" female 122. 16.2
## 12 " 50-59" male 126. 17.8
## 13 " 60-69" female 127. 17.1
## 14 " 60-69" male 127. 17.5
## 15 " 70+" female 134. 19.8
## 16 " 70+" male 130. 18.7
## 17 <NA> female 142. 22.9
## 18 <NA> male 136. 23.5Now we are going to explore differences in systolic blood pressure across races, as reported in the Race1 variable.
We will learn to use the arrange function to order the outcome acording to one variable.
Note that this function can be used to order any table by a given outcome. Here is an example that arranges by systolic blood pressure.
NHANES %>% arrange(BPSysAve) If we want it in descending order we can use the desc function like this:
NHANES %>% arrange(desc(BPSysAve)) In this example, we will compare systolic blood pressure across values of the Race1 variable for males between the ages of 40-49.
Compute the average and standard deviation for each value of Race1 for males in the age decade 40-49.
Order the resulting table from lowest to highest average systolic blood pressure. Use the functions filter, group_by, summarize, arrange, and the pipe %>% to do this in one line of code.
Within summarize, save the average and standard deviation of systolic blood pressure as average and standard_deviation.
library(dplyr)
library(NHANES)
data(NHANES)
NHANES %>% filter(Gender == "male", AgeDecade == " 40-49") %>%
group_by(Race1) %>%
summarize(average = mean(BPSysAve, na.rm = TRUE),
standard_deviation = sd(BPSysAve, na.rm=TRUE)) %>%
arrange(average)## # A tibble: 5 x 3
## Race1 average standard_deviation
## <fct> <dbl> <dbl>
## 1 White 120. 13.4
## 2 Other 120. 16.2
## 3 Hispanic 122. 11.1
## 4 Mexican 122. 13.9
## 5 Black 126. 17.1