WEEK 3

Question 1

INDEXING

Answer 1

With R we can relate one group of vector with another.

Question 2

INDEXING EXAMPLE PROGRAM

Answer 2

MURDER$RATE <- #MURDER$TOTAL/MURDERS$POPULATION * 100000

#MURDERS$RATE<=0.71
#MURDERS$STATE[MURDERS$RATE]

Question 3

THE SUM FUNCTION

Answer 3

The function sum returns the sum of the entries oF a vector and logical vectors get coerced to numeric with TRUE coded as 1 and FALSE as 0.
Thus we can count the states using:
SUM[MURDERS$RATE]

Question 4

LOGICAL OPERATOR PROGRAMMING EXAMPLE

Answer 4

WEST <- MURDER$REGION == “WEST”
SAFE <- MURDERS$RATE < 1
INDEX <- WEST & SAFE
MURDERS$STATE [INDEX]

Question 5

WHICH FUNCTION

Answer 5

This helps us to find the specific entry by converting vectors of logical into indexes

example
index <- murder$state == “California”
murder$rate[index]

Question 6

MATCH

Answer 6

This function tells us which
indexes of a second vector match each of the entries of a first vector
example
index<- match(c(“California”,”New York”, “Florida”), murder$state)
ind

Question 7

%in%

Answer 7

If rather than an index we want a logical that tells us whether or not each element of a
first vector is in a second, we can use the function %in%.
c(“Boston”, “Dakota”, “Washington”) %in% murders$state
#> [1] FALSE FALSE TRUE

Question 8

PLOT

Answer 8

PLOT FUNCTION CAN BE USED TO MAKE SCATTERPLOTS

EXAMPLE
X<- MURDERS$POPULATION / 10^6
Y<- MURDERS$TOTAL
PLOT(X,Y)

ALSO

X <-WITH(MURDERS(POPULATION/10^6,TOTAL)
PLOT(X)

Question 9

HISTOGRAM

Answer 9

HISTOGRAMS ARE A POWERFUL GRAPHICAL SUMMARY OF A LIST OF NUMBERS THAT GIVES YOU A GENERAL OVERVIEW OF NUMBERS YOU HAVE.

HIST()

Question 10

BOXPLOT

Answer 10

They provide a
more terse summary than histograms, but they are easier to stack with other boxplots.

murders$rate <- with(murders, total / population * 100000)
boxplot(rate~region, data = murders)

Question 11

DPLYR

Answer 11

Library(dplyr)

Question 12

MUTATE FUNCTION

Answer 12

This function is used to change the date table by adding more columns, or rows.

Question 13

FILTER FUNCTION

Answer 13

This is used to filter the data.

Question 14

How to select a specific column in a data table?

Answer 14

By using select function.

Question 15

EXAMPLE FOR MUTATE - ADD A NEW COLUMN CALLED RATE IN MURDERS DATA TABLE

Answer 15

murders <- mutate(murders, rate = total / population * 100000)

Question 16

Filter the states with murder rate less than 0.71

Answer 16

filter(murders, rate <= 0.71)

Question 17

Select only state, region and rate assign it to an object called new_table and show the states less 0.71 murder rate?

Answer 17

new_table <- select(murders, state, region, rate)
filter(new_table, rate<= 0.71)

Question 18

Select only state, region and rate assign it to an object called new_table and show the states less 0.71 murder rate in a single line of code?

Answer 18

murders %>% select(murders,state,region,rate) %>% filter(rate<=0.71)

Question 19

MUTATE FUNCTION

Answer 19

The mutate function is used to add a column to a dataset. A mutate takes the dataframe as first argument, and names and value as the second argument.

Question 20

ADD MURDER RATE USING MUTATE FUNCTION

Answer 20

library(dslabs)
data(“murders”)
murders <- mutate(murders, rate = total / population * 100000)

Question 21

Filter function to filter data

Answer 21

The filter function, which takes the data
table as the first argument and then the conditional statement as the second.

Question 22

Selecting columns with select

Answer 22

new_table <- select(murders, state, region, rate)
filter(new_table, rate <= 0.71)

This selects only rate, state, region column of murders dataset

Question 23

The pipe function

Answer 23

murders %>% select(state, region, rate) %>% filter(rate <= 0.71)
In general, the pipe sends the result of the left side of the pipe to be the first argument of
the function on the right side of the pipe

Question 24

summarize() function

Answer 24

1, The main purpose is to create new summary table.
example:
s <- heights %>%
filter(sex == “Female”) %>%
summarize(average = mean(height), standard_deviation = sd(height))
This takes our original data table as input, filters it to keep only females, and then produces
a new summarized table with just the average and the standard deviation of heights

Question 25

Pull() function

Answer 25

us_murder_rate <- murders %>%
summarize(rate = sum(total) / sum(population) * 100000) %>%
pull(rate)

The resulting value is numeric not a data frame.

Question 26

groupby()

Answer 26

heights %>%
group_by(sex) %>%
summarize(average = mean(height), standard_deviation = sd(height))

The summarize function applies the summarization to each group separately.

Question 27

Arrange()

Answer 27

murders %>%
arrange(rate) %>%

Note that the default behavior is to order in ascending order. In dplyr, the function desc
transforms a vector so that it is in descending order.

example:
murders %>%
arrange(desc(rate))

Question 28

Nested Sorting/ Arrange

Answer 28

murders %>%
arrange(region, rate) %>%
Here
we order by region, then within region we order by murder rate:

Question 29

What is tibbles

Answer 29

The functions group_by and
summarize always return this type of data frame. The group_by function returns a special
kind of tbl, the grouped_df.

Question 30

Tibbles display it better?

Answer 30

The print method for tibbles is more readable than that of a data frame. We
can do this using as_tibble(murders).

Question 31

Subset of tibbles are tibbles?

Answer 31

If you subset the columns of a data frame, you may get back an object that is not a data
frame, such as a vector or scalar.
With tibbles this does not happen.
class(as_tibble(murders)[,4])
if you want to access the vector that defines a column, and not get back a
data frame, you need to use the accessor $:
class(as_tibble(murders)$population)

Question 32

Create a tibble using tibble?

Answer 32

To create a data frame in the
tibble format, you can do this by using the tibble function.
grades <- tibble(names = c(“John”, “Juan”, “Jean”, “Yao”),
exam_1 = c(95, 80, 90, 85),
exam_2 = c(90, 85, 85, 90))

Question 33

How to convert rectangular dataframe into a tibble?

Answer 33

To convert a regular data frame to a tibble, you can use the as_tibble function.
ex: as_tibble(grades) %>% class()

Question 34

The Dot Operator?

Answer 34

rates <-filter(murders, region == “South”) %>%
mutate(rate = total / population * 10^5) %>%
.$rate
median(rates)

Question 35

the do operator?

Answer 35

heights %>%
group_by(sex) %>%
do(my_summary(.))