Prob $3\mathrm{.}02$

Here are some useful operators for taking a quick look at data

frames:

names Lists the names of the components.

ncol

Tells how many components there are.

nrow

head

Tells how many lines of data there are.

Prints the rst several lines of the data frame.

Here are some examples of these commands applied to the

CO$2$ data frame:

CO$2=$ fetchData$("$CO$2")$

Called from: fetchData$("$CO$2")$

names$($CO$2)$

$[1]$ "Plant"

ncol$($CO$2)$

$[1]5$

"Type"

"Treatment" "conc"

"uptake"

nrow$($CO$2)$

Prob $3\mathrm{.}03$

$[1]84$

head$($CO$2)$

Plant Type Treatment conc uptake

$1$ Qn$1$ Quebec nonchilled $9516\mathrm{.}0$

$2$ Qn$1$ Quebec nonchilled $17530\mathrm{.}4$

$3$ Qn$1$ Quebec nonchilled $25034\mathrm{.}8$

$4$ Qn$1$ Quebec nonchilled $35037\mathrm{.}2$

$5$ Qn$1$ Quebec nonchilled $50035\mathrm{.}3$

$6$ Qn$1$ Quebec nonchilled $67539\mathrm{.}2$

$$ The data frame iris records measurements on owers.

You can read in with

iris $=$ fetchData$("$iris$")$

Called from: fetchData$("$iris$")$

creating an object named iris.

Use the above operators to answer the following ques

tions.

$1.$ Which of the following is the name of a column in

iris?

ower Color Species Length

$2.$ How many rows are there in iris?

$150100150200$

$3.$ How many columns are there in iris?

$234567810$

$4.$ What is the Sepal.Length in the third row?

$1\mathrm{.}23\mathrm{.}64\mathrm{.}24\mathrm{.}75\mathrm{.}9$

$$ Thedata frame mtcars has data on cars from the $1970$s$.$

You can read it in with

cars $=$ fetchData$("$mtcars$")$

Called from: fetchData$("$mtcars$")$

creating an object named cars.

Use the above operators to answer the following ques

tions.

$1.$ Which of the following is the name of a column in

cars?

carb color size weight wheels

$2.$ How many rows are there in cars?

$303132333435$

$3.$ How many columns are there in cars?

$7891011$

$4.$ What is the wt in the second row?

$2\mathrm{.}1252\mathrm{.}2252\mathrm{.}6202\mathrm{.}8753\mathrm{.}215$