Note: new exercises coming soon.
data.frameMake a data frame, called mydata, that has 3 variables: var1 which has the numbers 1 to 10, var2 which has the letters A-J, and var3 which has any 10 numbers you want to include.
After you’ve made the data frame, then add a fourth column, month, with the first 10 months:
mydata<-data.frame(var1=1:10, var2=LETTERS[1:10],
var3=c(9,4.5,33,45.6,-14,3,7,0.2,0,7))
mydata$month <- month.name[1:10]
mydata## var1 var2 var3 month
## 1 1 A 9.0 January
## 2 2 B 4.5 February
## 3 3 C 33.0 March
## 4 4 D 45.6 April
## 5 5 E -14.0 May
## 6 6 F 3.0 June
## 7 7 G 7.0 July
## 8 8 H 0.2 August
## 9 9 I 0.0 September
## 10 10 J 7.0 OctoberRun each command below to figure out what type of data you get back.
Hint: Use the function typeof() to examine what is returned in each case.
mydata[1]
mydata[[1]]
mydata$var2
mydata["var2"]
mydata[1, 1]
mydata[, 1]
mydata[1,]
mydata[-1,]Why is mydata$var2 of type integer? Factors are stored as integers. Why is var2 a factor in the first place? Take a look at the help page for data.frame to see if you can find the option that turned letters into factors.
data.frameR has some built-n data sets. One of them is called iris. You can just use the iris object (it’s a data.frame) without creating it first.
Hint: if you want iris to show up in the Environment tab, load it into your environment with data(iris). Otherwise, you can still use it, but it may not show in that tab.
Get the dimensions of iris. Then get a list of the names.
Output the first 10 rows of iris.
View the iris data frame in the RStudio data viewer.
Select from the iris data frame the observations where the Sepal.Width is less than 2.5. Do the same, but add the condition that the Sepal.Length must also be less than 5.
Using which.max, select a row with maximum Sepal.Length. Is there only one row with the maximum value?
Rename the column Petal.Width to petalwidth. Challenge: do this without hard coding in the column number.
dim(iris)## [1] 150 5names(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
## [5] "Species"head(iris, 10)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosaView(iris) # or click on the name iris in the Environment tab to the right if you've loaded it there.iris[iris$Sepal.Width < 2.5,]## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 42 4.5 2.3 1.3 0.3 setosa
## 54 5.5 2.3 4.0 1.3 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 120 6.0 2.2 5.0 1.5 virginicairis[iris$Sepal.Width < 2.5 & iris$Sepal.Length < 5,]## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 42 4.5 2.3 1.3 0.3 setosa
## 58 4.9 2.4 3.3 1.0 versicoloriris[which.max(iris$Sepal.Length),] # which.max only returns first index of a maximum value## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2 virginicairis[iris$Sepal.Length == max(iris$Sepal.Length),] # check for other max Sepal.Length rows## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2 virginicanames(iris)[names(iris)=='Petal.Width'] <- 'pedalwidth'
names(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "pedalwidth"
## [5] "Species"data.frame: mtcarsAnother built-in data set is called mtcars.
Explore mtcars. Look at the help page for mtcars to see the variable definitions.
mtcars[mtcars$cyl==6,]## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6mtcars$wt # or mtcars[,"wt"]## [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440
## [12] 4.070 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520
## [23] 3.435 3.840 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780fuel_efficient <- mtcars[mtcars$mpg > 25,]
mean(fuel_efficient$hp) < mean(mtcars$hp)## [1] TRUEtmp <- mtcars[mtcars$gear==3,]
tmp[which.max(tmp$hp),]## mpg cyl disp hp drat wt qsec vs am gear carb
## Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4subsetLook up the help for the subset function. Can you use it to repeat some of the operations in the two exercises above?
Note: we’ll cover this function later in the workshop.
The repurrrsive package has example list objects in it. Load the package (install first if needed). Then:
sw_people list with the View function. What’s this a list of?install.packages("repurrrsive")library(repurrrsive)View(sw_people)sw_people[[1]]## $name
## [1] "Luke Skywalker"
##
## $height
## [1] "172"
##
## $mass
## [1] "77"
##
## $hair_color
## [1] "blond"
##
## $skin_color
## [1] "fair"
##
## $eye_color
## [1] "blue"
##
## $birth_year
## [1] "19BBY"
##
## $gender
## [1] "male"
##
## $homeworld
## [1] "http://swapi.co/api/planets/1/"
##
## $films
## [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
## [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
## [5] "http://swapi.co/api/films/7/"
##
## $species
## [1] "http://swapi.co/api/species/1/"
##
## $vehicles
## [1] "http://swapi.co/api/vehicles/14/" "http://swapi.co/api/vehicles/30/"
##
## $starships
## [1] "http://swapi.co/api/starships/12/" "http://swapi.co/api/starships/22/"
##
## $created
## [1] "2014-12-09T13:50:51.644000Z"
##
## $edited
## [1] "2014-12-20T21:17:56.891000Z"
##
## $url
## [1] "http://swapi.co/api/people/1/"sw_people[[1]]$name## [1] "Luke Skywalker"sw_people[[length(sw_people)]]$gender## [1] "female"A researcher is trying to select observations from the iris data frame for the setosa species, but gets an error:
iris[Species="setosa"]## Error in `[.data.frame`(iris, Species = "setosa"): unused argument (Species = "setosa")Correct this expression to select the observations the researcher wants. Hint: there may be more than 1 thing wrong.
Answer: Three mistakes, in the order that R will give you errors: 1) Use ==, not =, to test for equality; 2) Include the name of the data frame before the column: iris$Species; and 3) The researcher forgot the comma after the condition to indicate to select all columns from the data frame.
iris[iris$Species=="setosa",]## Sepal.Length Sepal.Width Petal.Length pedalwidth Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosaUsing the built-in data set mtcars:
mpg greater than 30?Hint: you can use the sum function to count the number of TRUE observations in a vector (as we did with sum(is.na(x))). This works because as.numeric(TRUE) == 1 and as.numeric(FALSE) == 0.
sum(mtcars$mpg > 30)## [1] 4sum(mtcars$hp < 100)## [1] 9Using information from Linear Algebra in R by Søren Højsgaard if needed, create a 10 x 5 matrix of random normal numbers and a 5 x 1 vector of values 1 to 5 and multiply them using matrix multiplication. Transpose your result.
Create a 6 x 6 matrix of random normal draws and take it’s inverse (solve it). Extract the diagonal from the result.
Hint: Generate random normal draws with rnorm: look it up to see the options.
Hint: Matrix multiplication operator is %*%.
X <- matrix(rnorm(50), nrow=10)
y <- 1:5
t(X %*% y) # t() is for transpose## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] -6.186134 -8.698666 -13.45169 9.365158 -11.67129 6.173199 -2.014841
## [,8] [,9] [,10]
## [1,] 7.671724 -0.8886068 -0.1088036X <- matrix(rnorm(36), nrow=6)
solve(X)## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.898008661 -4.371979 1.5687571 -1.6835242 0.68364196 1.1551904
## [2,] 0.307268033 -1.911628 0.8315138 -1.0306877 0.32797496 0.6538460
## [3,] 0.217433175 -1.684304 0.8171005 -0.5388321 0.33416961 0.7782129
## [4,] -0.005916087 -1.897348 0.7975908 -0.9158237 -0.03031322 0.4244748
## [5,] 0.487403907 -2.252167 1.1872060 -0.7517260 0.73619933 0.3407939
## [6,] -0.193256628 1.992749 -0.3031673 0.7206709 -0.52226175 -0.5259394diag(solve(X))## [1] 0.8980087 -1.9116280 0.8171005 -0.9158237 0.7361993 -0.5259394Note that the specific numbers may differ when you run this code due to randomness. To prevent numbers from changing, set the random seed with set.seed(123) where 123 is any integer.
Select the vector of letters from the following object.
Do it both using names (hint: printing the object might give you a hint) and using index numbers.
Extra hint: use the object viewer in RStudio to view the list after you create it. See if there’s any functionality to help you with this challenge.
nested_list <- list(level1 = list(level2 = list(letters = LETTERS)))nested_list$level1$level2$letters## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
## [18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"nested_list[[1]][[1]][[1]]## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
## [18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"applyUse the apply function to get the average value of each variable in mtcars. You’ll need to look at the help page to figure out how to use it.
apply(mtcars, 2, mean)## mpg cyl disp hp drat wt
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250
## qsec vs am gear carb
## 17.848750 0.437500 0.406250 3.687500 2.812500