r_dplyr

Question 1

What is dplyr and what functions does it include?

Answer 1

• a grammar of data manipulation, providing a consistent set of verbs
• Includes:
  
  • mutate()
  • select()
  • filter()
  • summarise()
  • arrange()

Question 2

Store within the variable ‘s’:

• mean(height) for “Male”
• sd(height) for “Male”

Answer 2

s <- heights %>%

filter(sex == “Male”) %>%

summarize( average = mean(height), standard_deviation = (height) )

s

Question 3

Given the following, print out the “average” and “standard_deviation”:

s <- heights %>%

filter(sex == “Male”) %>%

summarize( average = mean(height), standard_deviation = (height) )

Answer 3

• s$average
• s$standard_deviation

Question 4

For the murders dataset, calculate the us_murder_rate ( total divided by population)

Answer 4

us_murder_rate <- murders %>% +

summarize(rate = sum(total) / sum(population) * 100000 )

Question 5

Calculate the mean and sd of “height” within the heights dataset, grouping by “sex”

Answer 5

heights %>% +

group_by(sex) %>% +

summarize(average = mean(height), standard_deviation = sd(height) )

Question 6

Calculate the median_rate for the “murder_rate” variable within the murders dataset, grouped by “region”

Answer 6

murders %>% +

group_by(region) %>% +

summarize(median_rate = median(murder_rate) )

Question 7

Order the “murders” dataset by “population, and then print the first five rows

Answer 7

murders %>% arrange(population) %>% head()

Question 8

Order the “murders” dataset by “murder_rate” in descending order, and then print the first five rows

Answer 8

murders %>% arrange(desc( murder_rate) ) %>% head()

Question 9

Order the “murders” dataset by “region” first and then by “murder_rate” second, and then print the first five rows

Answer 9

murders %>% arrange(region, murder_rate) %>% head()

Question 10

Show the top 10 murder rates within the “murders” dataset (not ordered)

Answer 10

murders %>% top_n(10, murder_rate)

Question 11

Show the top 10 murder rates from the “murders” dataset in descending order

Answer 11

murders %>% arrange(desc (murder_rate) ) %>% top_n(10)

Question 12

Filter the NHANES dataset so that:

• assign this new data frame to the object “tab”
• only “ 20-29” year old “females” are included
• save the average and standard deviation of systolic blood pressure (BPSysAve) as average and standard_deviation
• return the average as a numeric value (not an object)

Answer 12

tab <- NHANES %>%

filter(AgeDecade == “ 20-29”, Gender == “female”) %>%

summarize(average = mean(BPSysAve, na.rm = TRUE),

standard_deviation = sd(BPSysAve, na.rm = TRUE)) %>%

.$average

Question 13

Filter the NHANES dataset so that:

• assign this new data frame to the object “tab”
• only “ 20-29” year old “females” are included
• save the min and max of systolic blood pressure (BPSysAve) as “min” and “max”

Answer 13

NHANES %>%
filter(AgeDecade == “ 20-29” & Gender == “female”) %>%
summarize(min = min(BPSysAve, na.rm=TRUE), max = max(BPSysAve, na.rm=TRUE))

Question 14

Filter the NHANES dataset so that:

• 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.

Answer 14

NHANES %>%
filter(Gender == “female”) %>%
group_by(AgeDecade) %>%
summarize(average = mean(BPSysAve, na.rm = TRUE), standard_deviation = sd(BPSysAve, na.rm = TRUE))

Question 15

Filter the NHANES dataset so that:

• 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.

Answer 15

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)