test 1

Question 1

== means

Answer 1

“are the 2 things equal to each other?” or “is equal to”

Question 2

!= means

Answer 2

is not equal to

Question 3

> = means

Answer 3

is greater than or equal to

Question 4

<= means

Answer 4

is less than or equal to

Question 5

> means

Answer 5

is greater than

Question 6

< means

Answer 6

is less than

Question 7

logical operators in R- what means true and what means false?

Answer 7

true = 1
false = 0

(what the computer tells you when you ask it)

Question 8

what are the ways I can ask this question and what type of responses would I get?

are any of the x greater than or equal to 40 AND less than 60?

Answer 8

x >= 40 & x <60 - you would get a string of statements that say “true” or “false” for each element in the vector

sum ( x >= 40 & x < 60) - adding the sum command would count how many of them are true

which ( x >= 40 & x < 60) - tells you which element is true to the statement don’t use much but still useful

Question 9

what does $ do?

Answer 9

• grabs out a column from what you defined
• can use it to add a new column too

Question 10

how to add another column to a data frame?

Answer 10

df.name$column <- c(…)

Question 11

if you have already defined “seedlings,” how can you extract the number of times the count “0” was observed?

Answer 11

sum(seedlings == 0)

sum gives the count, otherwise would just give the elements

Question 12

if you defined seedlings, and now you want to see how many times each count occurred, what would you do?

Answer 12

df.seedlings <- data.frame (seedlings = c(0,1,2,3,4,5), freq = c(sum(seedlings==0), sum(seedlings ==1), sum(seedlings==2), sum(seedlings==3), sum(seedlings==4), sum(seedlings==5)))

first create data frame and then create 2 columns. one for the seedlings and one for the frequency. and then use the sum command to count how many seedlings were equal to 1, 2, 3, 4, and 5

Question 13

you’ve defined x, now you want to get the 3rd and 4th elements of x, how?

all but the 3rd and 4th elements?

Answer 13

x[c(3,4)]

have to use both the square brackets and the vector ones

x[-c(3,4)]

put the negative outside of the c

Question 14

defined x, how to get only even numbers out?

to get the elements in reverse?

Answer 14

x[seq(2, length(x), by=2)]

x[10:1]

Question 15

if x is defined, how to get the sum of the first and last element if there are 10 elements? the product of second and ninth?

Answer 15

x[1] + x[10]

x[2] * x[9]

Question 16

how to get square of each element in x?

Answer 16

x ^ 2

Question 17

what does the sample command do and how to use it?

Answer 17

generates a random sample of numbers, used like this:

sample(1:100, 25, replace=FALSE)

1:100 is the range of which you want the numbers to be from

25 is the amount of numbers you want

and replace is always FALSE

Question 18

what does set.seed() do?

Answer 18

makes the sample the same for sample1 and sample2

Question 19

with the cdc file we used, how would you find how many of the subjects are male?

Answer 19

sum(gender==”m”)

Question 20

list the 2 ways to find the median of the ages of nonsmokers vs smokers

Answer 20

many ways to find the median using the median command, here are 2:

• median(cdc[smoke100==1,]$age)
• smokers <- subset(cdc, smoke100=1)
  & smokers_median <- median (smokers$age)

Question 21

what does the ~ line mean in box plots?

Answer 21

used to specify relationships between variables in various functions

like:
boxplot(age ~ smoke100, data = cdc)

tells R to show the distribution of ages for smokers and nonsmokers separately, allowing you to compare the age distributions between the two groups visually

Question 22

what is the comma used for in [smoke100==1,]?

Answer 22

used to separate row indices from column indices. In this case, we’re leaving the column part empty, meaning we’re selecting all columns

Question 23

sapply()

Answer 23

simplify apply, applies the same function to each vector

Question 24

how to solve this using sapply function:

Make a cumulative distribution of the ages of the males or females in the dataset by finding the number of subjects not older than each age in the sequence seq(from = 0,to = 100,by = 5). Use barplot() to make and attach a plot of your results.

Answer 24

df.males <- data.frame(Age = seq(0,100,by = 5), Cumulative_Frequency = sapply(seq(0,100,by = 5), function(X) sum(age[gender == “m”] <= X))) # males age cumulative distribution

Question 25

what does adding a type =1 inside of quantile function do?

Answer 25

changes to empirical instead (kinda like rounding to the closest number)

Question 26

how to solve this problem : What fraction of men have heights greater than the 90th percentile of height among women? What fraction of women have heights less than the 10th percentile of height among men?

Answer 26

quantile(height[gender == “f”], probs = 0.9, type = 1) # 90th percentile of height among females

sum(height[gender == “m”] > quantile(height[gender==”f”], probs = 0.9, type = 1)) / sum(gender == “m”) # fraction of males taller than the tallest females

and then you can go from there for the other one