1.How many items are there in the built-in package MASS? List the names of the item indices from 94 to 103?

library(MASS)
str(ls("package:MASS")) # 165
##  chr [1:165] "abbey" "accdeaths" "addterm" "Aids2" "Animals" ...
ls("package:MASS")[94:103]
##  [1] "minn38"            "motors"            "muscle"           
##  [4] "mvrnorm"           "nclass.freq"       "neg.bin"          
##  [7] "negative.binomial" "negexp.SSival"     "newcomb"          
## [10] "nlschools"

2.Compute the mean height for the women{datasets} example and save it as a variable height_centered. Extract regression coefficients as follows:

library(datasets)
dta <- women
dta$height_centered <- dta$height - mean(dta$height)

coef(lm(weight ~ height, data = dta))
## (Intercept)      height 
##   -87.51667     3.45000
coef(lm(weight ~ height_centered, data = dta))
##     (Intercept) height_centered 
##        136.7333          3.4500
plot(weight ~ height, data = dta)
abline(lm(weight ~ height, data = dta))

plot(weight ~ height_centered, data = dta)
abline(lm(weight ~ height_centered, data = dta))

The scale of x(height) has changed.

3.Investigate the side effects of the operator ‘c’ on data frames and on matrices. In particular, explain the difference between c(women) and c(as.matrix(women))

c(women)
## $height
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 
## $weight
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
c(as.matrix(women))
##  [1]  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72 115 117
## [18] 120 123 126 129 132 135 139 142 146 150 154 159 164

It changes dataframe into lists. It changes matrices to vectors.

4. Use help to examine the race variable in the birthwt{MASS} dataset. Does it make sense?What happens if you run the above R command?

c("White", "Black", "Other")[birthwt$race]
##   [1] "Black" "Other" "White" "White" "White" "Other" "White" "Other"
##   [9] "White" "White" "Other" "Other" "Other" "Other" "White" "White"
##  [17] "Black" "White" "Other" "White" "Other" "White" "White" "Other"
##  [25] "Other" "White" "White" "White" "Black" "Black" "Black" "White"
##  [33] "Black" "White" "Black" "White" "White" "White" "White" "White"
##  [41] "Black" "White" "Black" "White" "White" "White" "White" "Other"
##  [49] "White" "Other" "White" "Other" "White" "White" "Other" "Other"
##  [57] "Other" "Other" "Other" "Other" "Other" "Other" "Other" "White"
##  [65] "Other" "Other" "Other" "Other" "White" "Black" "White" "Other"
##  [73] "Other" "Black" "White" "Black" "White" "White" "Black" "White"
##  [81] "White" "White" "Other" "Other" "Other" "Other" "Other" "White"
##  [89] "White" "White" "White" "Other" "White" "White" "White" "White"
##  [97] "White" "White" "White" "White" "White" "White" "Other" "White"
## [105] "Other" "Black" "White" "White" "White" "Black" "White" "Other"
## [113] "White" "White" "White" "Other" "White" "Other" "White" "Other"
## [121] "White" "Other" "White" "White" "White" "White" "White" "White"
## [129] "White" "White" "Other" "White" "Black" "Other" "Other" "Other"
## [137] "Other" "Black" "Other" "White" "White" "White" "Other" "Other"
## [145] "White" "White" "Black" "White" "Other" "Other" "Other" "White"
## [153] "White" "White" "White" "Other" "Black" "White" "Black" "Other"
## [161] "White" "Other" "Other" "Other" "Black" "White" "Other" "Other"
## [169] "White" "White" "Black" "Black" "Black" "Other" "Other" "White"
## [177] "White" "White" "White" "Black" "Other" "Other" "White" "Other"
## [185] "White" "Other" "Other" "Black" "White"

It turns the levels of race from numeric to strings.