Heights and eye-color for 7 people are stored in two vectors, as shown below. Find the mean height for each eye-color.

height = c(5.8,5.7,5.7,5.9,6.2,4.9,5.2) eye.color = c("brown","blue","brown","brown","brown","green","brown")

> height = c(5.8,5.7,5.7,5.9,6.2,4.9,5.2) > eye.color = c("brown","blue","brown","brown","brown","green","brown") > tapply(height,factor(eye.color),mean) blue brown green 5.70 5.76 4.90

Notably, you can actually omit the call to`factor()`

above, and just pass`eye.color`

as the second argument to the`tapply()`

function. This is due to the fact that`tapply()`

automatically converts (as it is able) its second argument to a factor.Instructors $A$ and $B$ collect grades and genders for 10 students each, storing them in in the following vectors:

A_grades = c("A","B","B","D","A","A","C","A","B","B") A_genders = c(1,1,1,0,1,0,0,0,1,1) # here, 1 represents a male and 0 a female B_grades = c(97,93,92,57,75,90,72,88,82,60) B_genders = c("M","F","F","M","M","M","F","F","F","M")

Convert

`A_grades`

into a factor`a.grade.fac`

with ordered levels $A \gt B \gt C \gt D \gt F$.Convert

`B_grades`

into a factor`b.grade.fac`

with ordered levels $A \gt B \gt C \gt D \gt F$.Assume letter grades are associated with the following ranges:

$$\begin{array}{cc} A & 90-100\\ B & 80-89\\ C & 70-79\\ D & 60-69\\ F & 0-59 \end{array} $$Convert

`A_genders`

into a factor`a.gender.fac`

with levels M and F.Convert

`B_genders`

into a factor`b.gender.fac`

with levels M and F.Combine the two grade factors into a single factor

`grade.fac`

Combine the two gender factors into a single factor

`gender.fac`

Make a table showing how many earned each possible grade by gender, with marginal totals

> A_grades = c("A","B","B","D","A","A","C","A","B","B") > A_genders = c(1,1,1,0,1,0,0,0,1,1) > B_grades = c(97,93,92,57,75,90,72,88,82,60) > B_genders = c("M","F","F","M","M","M","F","F","F","M") # (a) > a.grades.fac = factor(A_grades,ordered=TRUE,levels=c("F","D","C","B","A")) > a.grades.fac [1] A B B D A A C A B B Levels: F < D < C < B < A # (b) > b.grades.fac = cut(B_grades,breaks=c(-0.5,59.5,69.5,79.5,89.5,100.5), labels=c("F","D","C","B","A"), ordered_result=TRUE) > b.grades.fac [1] A A A F C A C B B D Levels: F < D < C < B < A # (c) > a.genders.fac = factor(A_genders) > a.genders.fac [1] 1 1 1 0 1 0 0 0 1 1 Levels: 0 1 > levels(a.genders.fac) = c("F","M") > a.genders.fac [1] M M M F M F F F M M Levels: F M # (d) > b.genders.fac = factor(B_genders) > b.genders.fac [1] M F F M M M F F F M Levels: F M # (e) > grade.fac = factor(c(as.character(a.grades.fac), as.character(b.grades.fac)),ordered = TRUE) > grade.fac [1] A B B D A A C A B B A A A F C A C B B D Levels: A < B < C < D < F # (f) > gender.fac = factor(c(as.character(a.genders.fac), as.character(b.genders.fac))) > gender.fac [1] M M M F M F F F M M M F F M M M F F F M Levels: F M # (g) > t = table(gender.fac,grade.fac) > addmargins(t) grade.fac gender.fac A B C D F Sum F 4 2 2 1 0 9 M 4 4 1 1 1 11 Sum 8 6 3 2 1 20

The following vectors correspond to: 1) the alphabet; and 2) the letters in a famous quote by Ray Bradbury.

alphabet = c("a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p", "q","r","s","t","u","v","w","x","y","z") quote = c("l", "i", "f", "e", "i", "s", "t", "r", "y", "i", "n", "g", "t", "h", "i", "n", "g", "s", "t", "o", "s", "e", "e", "i", "f", "t", "h", "e", "y", "w", "o", "r", "k")

Construct a table in R that gives the freqency of occurrence (as a percentage) for each letter in the alphabet in Ray Bradbury's quote> quote.fac = factor(quote,levels=alphabet) > t = table(quote.fac) > round(t/sum(t),digits=2) quote.fac a b c d e f g h i j k l 0.00 0.00 0.00 0.00 0.12 0.06 0.06 0.06 0.15 0.00 0.03 0.03 m n o p q r s t u v w x 0.00 0.06 0.06 0.00 0.00 0.06 0.09 0.12 0.00 0.00 0.03 0.00 y z 0.06 0.00

- The responses for a survey question are broken down by gender, and the results are shown below. Build this table in R in such a way that the last column is computed by R (instead of you). $$\begin{array}{l|c|c|c|c|} & \textrm{agree} & \textrm{no opinion} & \textrm{disagree} & \textrm{Sum}\\\hline \textrm{males} & 75 & 10 & 85 & 170\\\hline \textrm{females} & 121 & 8 & 51 & 180\\\hline \end{array}$$
> t = as.table(matrix(c(75,10,85,121,8,51),ncol=3,byrow=TRUE)) > colnames(t) = c("agree","no opinion","disagree") > rownames(t) = c("males","females") > t.with.margins = addmargins(t) > t.with.margins.and.last.row.removed = t.with.margins[1:2,] > t.with.margins.and.last.row.removed