Use the package panel in RStudio to install the following packages: - “psych” - “lsr” - “car” packages
Run the code chunk to remove the variables in your workspace.
rm(list = ls())Below each comment in the code chunk, write code that does what the comment describes.
# the code below creates a "seeker" variable that contains the value 3.1415
seeker <- 3.1415
# create a "lover" variable that contains the value 2.7183
lover <- 2.7183
# multiply "seeker" and "lover" to create a third variable called "keeper"
keeper <- seeker * lover
# print out the value of "keeper"
print(keeper)## [1] 8.539539# use the objects function to print the names of the variables in your workspace
objects()## [1] "keeper" "lover" "seeker"who()Run the code chunk.
library(lsr)
who()## -- Name -- -- Class -- -- Size --
## keeper numeric 1
## lover numeric 1
## seeker numeric 1Create a variable called myName that stores your first
name. For example, I would assign the value “Brandi” to the
myName variable. Print out the value of
myName. Then use the who() function to show
info about current workspace variables.
# create a "myName" variable, with your first name as it assigned value
myName <- "Eli"
# print the value of "myName"
myName## [1] "Eli"# use the who function below to show the name, class, and size for each variable in the workspace
who()## -- Name -- -- Class -- -- Size --
## keeper numeric 1
## lover numeric 1
## myName character 1
## seeker numeric 1Remove the variables created in #3 above (seeker,
lover, and keeper) and then use the
who() function to check whether those variables are still
in your workspace.
# remove the seeker, lover, and keeper variables
rm(seeker, lover, keeper)
# use the who function below to check whether the variables are removed
who()## -- Name -- -- Class -- -- Size --
## myName character 1Edit the code chunk so that it loads booksales.Rdata
from wherever you saved the “data” folder you downloaded for HW1. Then
use the who() to see info about the variables you added to
your workspace.
# edit code below to load "booksales.Rdata" from your "data" folder
load("~/Downloads/.RData")
# list workspace variables & info about them
who() ## -- Name -- -- Class -- -- Size --
## apples numeric 1
## february.sales numeric 1
## months character 12
## months.sold character 4
## myName character 1
## price numeric 1
## sales.by.month numeric 12
## summer character 3
## totalCost numeric 1
## winter character 3Run the code chunk to see all the files within your working directory.
Print() functionUse the print() function to see the values for the
months and sales.by.month variables you loaded
from booksales.Rdata. Both should contain 12 values,
corresponding to the the months of the year.
# print months
print(months)## [1] "January" "February" "March" "April" "May" "June"
## [7] "July" "August" "September" "October" "November" "December"# print sales.by.month
print(sales.by.month)## [1] 0 100 200 50 25 0 0 0 0 0 0 0Run the code chunk, which uses the months vector to
assign the names of the months to sales.by.month.
#assign the contents of "months" as names for the elements of "sales.by.month"
names(sales.by.month) <- months
#print out the result, which should now include the assigned names
print(sales.by.month)## January February March April May June July August
## 0 100 200 50 25 0 0 0
## September October November December
## 0 0 0 0Edit the code chunk so that it prints only the February
sales from the sales.by.month vector:
# print February sales (hint: the 2nd element of sales.by.month)
print(sales.by.month)[february.sales]## January February March April May June July August
## 0 100 200 50 25 0 0 0
## September October November December
## 0 0 0 0## <NA>
## NACorrect the code so that it multiplies 5 times 4.
# edit the code below to assign numeric rather than text values
x <- 5
y <- 4
# multiply x times y
x*y## [1] 20# check the class of the variables
class(x)## [1] "numeric"class(y)## [1] "numeric"# check the class of any.sales.this.month (from booksales.Rdata)
class(sales.by.month)["September"]## [1] NACheck the class of the any.sales.this.month that you
loaded into your workspace from the booksales.Rdata
file.
# check the class of the variable
class(any.sales.this.month)## Error: object 'any.sales.this.month' not foundCreate a variable called group that is assigned the
following values: 1,1,1,2,2,2,3,3,3,4,4,4.
Then use class() to check the class of the
group variable.
# assign the values above to a variable called "group"
group <- c(1,1,1,2,2,2,3,3,3,4,4,4)
# check the class of the "group" variable
class(group)## [1] "numeric"# the code below prints group
print(group)## [1] 1 1 1 2 2 2 3 3 3 4 4 4Now use the as.factor() function to tell R to treat the
variable group as a factor. Then use the
class() function to check the class of the
group variable again.
# make the "group" variable a factor
group<- as.factor(group)
# check the class of the "group" variable
class(group)## [1] "factor"Run the code chunk, which creates a gender variable that
includes values for each of 12 participants. Each value corresponds to a
particular gender - in this example there are 4 gender options:
1=“female”, 2=“male”, 3=“nonbinary”, 4=“other”. (Feel free to edit the
code below to include other options, as long as there are at least 2
different levels. Or just run the code as it is.)
# create a "gender" variable that stores gender for 12 participants
gender <- c(4,2,1,3,1,2,4,3,2,3,4,1)
# tell R to treat "gender" as a factor
gender <- as.factor(gender)
# assign meaningful labels that for each of the 4 levels of gender
# (1: "female", 2: "male", 3: "nonbinary", 4: "other")
levels(gender) <- c("f", "m", "nb", "other")
#print gender
gender## [1] other m f nb f m other nb m nb other f
## Levels: f m nb otherAssign the following labels to the 4 different group
levels:
1: “group 1”, 2: “group 2”, 3: “group 3”, 4: “group 4”.
# assign meaningful labels that for each of the 4 levels of group
levels(group)<-c("group 1", "group 2", "group 3", "group 4")
# the code below prints "group"
group## [1] group 1 group 1 group 1 group 2 group 2 group 2 group 3 group 3 group 3
## [10] group 4 group 4 group 4
## Levels: group 1 group 2 group 3 group 4Create two variables: age and score. Select
12 values to assign to each variable, with age values
ranging between 18 and 40, and score values ranging from 0
to 20.
# Create an "age" variable and assign it 12 values (1 for each of 12 participants).
age<-c(19,21,37,18,19,38,18,19,23,20,30,40)
# Create a "score" variable and assign it 12 values, ranging between 0 and 20.
score<-c(0,3,5,8,10,7,20,18,13,4,11,12)
# the code below shows info about workspace variables, including age & score
who()## -- Name -- -- Class -- -- Size --
## age numeric 12
## apples numeric 1
## february.sales numeric 1
## gender factor 12
## group factor 12
## months character 12
## months.sold character 4
## myName character 1
## price numeric 1
## sales.by.month numeric 12
## score numeric 12
## summer character 3
## totalCost numeric 1
## winter character 3
## x numeric 1
## y numeric 1Create a dataframe called df that includes the following
variables: age, gender, group,
and score.
# create the dataframe below
df<-data.frame(age=age, gender=gender, group=group, score=score)
# the code below prints the dataframe
df## age gender group score
## 1 19 other group 1 0
## 2 21 m group 1 3
## 3 37 f group 1 5
## 4 18 nb group 2 8
## 5 19 f group 2 10
## 6 38 m group 2 7
## 7 18 other group 3 20
## 8 19 nb group 3 18
## 9 23 m group 3 13
## 10 20 nb group 4 4
## 11 30 other group 4 11
## 12 40 f group 4 12Pull out scores from the df dataframe you
created.
# pull out the "scores" variable from df
df$score## [1] 0 3 5 8 10 7 20 18 13 4 11 12Use the names() function to get the names of all the
variables stored in df.
# get the names of the variables stored in df
names(df)## [1] "age" "gender" "group" "score"Use the list() function to create a list with your first
name as the name of the list. The list should the following 3 variables
and assign each the values described below: - age: your
current age - hasPets: “TRUE”/“FALSE”, depending on whether
you have pets - hobbies: 2 hobbies you enjoy doing
# edit the code below to create your list
Eli <- list (age = 22, hasPets = TRUE, hobbies = c("video games", "hiking"))
# edit code below to print your list
Eli## $age
## [1] 22
##
## $hasPets
## [1] TRUE
##
## $hobbies
## [1] "video games" "hiking"Pick another variable to add to the list you just created, maybe your
favorite show/book/band. Or if you can’t pick just one favorite, you can
include multiple (like we did for hobbies). For example, I might add a
variable called favorite.shows and set the values to be
“Buffy the Vampire Slayer”, “Patriot”, “Broad City”, and “Fleabag”. Or I
might add a favorite.bands variable, with values such as
“Ron Gallo”, “La Luz”, “Kikagaku Moyo”, “Pink Floyd”, “Mazzy Star”, and
“Jackie Shane”.
# add the new variable to your dataframe
Eli$favorite.shows <- c("Dexter", "Blacklist", "Suits", "Ted Lasso")
# print the result of your dataframe
Eli## $age
## [1] 22
##
## $hasPets
## [1] TRUE
##
## $hobbies
## [1] "video games" "hiking"
##
## $favorite.shows
## [1] "Dexter" "Blacklist" "Suits" "Ted Lasso"Run the code chunk to see the help file for levels().
When you run it, the help panel should show the documentation for the
function.
# get help documentation for levels function
?levelsIn #25 you looked at help documentation for the levels()
function. What are the 2 arguments for this function? Type your answer
below: x
an object, for example a factor.
value
a valid value for levels(x). For the default method, NULL or a character
vector. For the factor method, a vector of character strings with length
at least the number of levels of x, or a named list specifying how to
rename the levels.