In this practical, a number of R packages are used. The packages used (with versions that were used to generate the solutions) are:
survival (version: 3.2.7)For this practical, we will use the heart and retinopathy data sets from the survival package. More details about the data sets can be found in:
https://stat.ethz.ch/R-manual/R-devel/library/survival/html/heart.html
https://stat.ethz.ch/R-manual/R-devel/library/survival/html/retinopathy.html
Sometimes we want to obtain a subset of the data sets before investigating the descriptive statistics and performing the statistical analysis.
Using the heart data set:
surgery.heart[1, ]heart[, 1]## [1] 0.0 0.0 0.0 1.0 0.0 36.0 0.0 0.0 0.0 51.0 0.0 0.0 0.0 12.0 0.0
## [16] 26.0 0.0 0.0 17.0 0.0 37.0 0.0 0.0 28.0 0.0 0.0 20.0 0.0 0.0 18.0
## [31] 0.0 8.0 0.0 12.0 0.0 3.0 0.0 83.0 0.0 25.0 0.0 0.0 0.0 71.0 0.0
## [46] 0.0 16.0 0.0 0.0 17.0 0.0 51.0 0.0 23.0 0.0 0.0 46.0 0.0 19.0 0.0
## [61] 4.5 0.0 2.0 0.0 41.0 0.0 58.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 0.0
## [76] 21.0 0.0 0.0 36.0 0.0 83.0 0.0 32.0 0.0 0.0 41.0 0.0 0.0 10.0 0.0
## [91] 67.0 0.0 0.0 21.0 0.0 78.0 0.0 3.0 0.0 0.0 0.0 27.0 0.0 33.0 0.0
## [106] 12.0 0.0 0.0 57.0 0.0 3.0 0.0 10.0 0.0 5.0 0.0 31.0 0.0 4.0 0.0
## [121] 27.0 0.0 5.0 0.0 0.0 46.0 0.0 0.0 210.0 0.0 67.0 0.0 26.0 0.0 6.0
## [136] 0.0 0.0 32.0 0.0 37.0 0.0 0.0 8.0 0.0 60.0 0.0 31.0 0.0 139.0 0.0
## [151] 160.0 0.0 0.0 310.0 0.0 28.0 0.0 4.0 0.0 2.0 0.0 13.0 0.0 21.0 0.0
## [166] 96.0 0.0 0.0 38.0 0.0 0.0 0.0heart["surgery"]heart[["surgery"]]## [1] no no no no no no no no no no no no no no no no no no no no no yes no
## [24] no no no no no no no no no no no no no no no no no no no no no no no
## [47] no no no no no no no no no no no no no no no no no yes yes yes yes no yes
## [70] yes no no yes yes no no no yes yes yes yes no no no no no no no no no no no
## [93] yes yes yes yes no no no no no no yes yes no no no no no no no no no no no
## [116] no no no no no no no no no yes yes no no no no no yes yes no no no no no
## [139] no no no no no no no no no no no yes yes no no no no no yes yes no no no
## [162] no no no no no no yes yes no no no
## Levels: no yesheart[, "surgery"]## [1] no no no no no no no no no no no no no no no no no no no no no yes no
## [24] no no no no no no no no no no no no no no no no no no no no no no no
## [47] no no no no no no no no no no no no no no no no no yes yes yes yes no yes
## [70] yes no no yes yes no no no yes yes yes yes no no no no no no no no no no no
## [93] yes yes yes yes no no no no no no yes yes no no no no no no no no no no no
## [116] no no no no no no no no no yes yes no no no no no yes yes no no no no no
## [139] no no no no no no no no no no no yes yes no no no no no yes yes no no no
## [162] no no no no no no yes yes no no no
## Levels: no yesCreate a matrix that takes the values 1:4 and has 2 rows and 2 columns. You can name this object mat. Select the second row of all columns.
mat <- matrix(1:4, 2, 2)
mat[2, ]## [1] 2 4Create an array that consists of 2 matrices. Matrix 1 will consist of the values 1:4 and matrix 2 will consist of the values 5:8. Both matrices will have 2 columns and 2 rows. Give the name ar1 to the this array. Select the 2nd row of all columns from each matrix.
ar1 <- array(data = 1:8, dim = c(2, 2, 2))
ar1[2, , ]## [,1] [,2]
## [1,] 2 6
## [2,] 4 8Using the retinopathy data set:
futime for all adult patients.retinopathy$futime[retinopathy$type == "adult"]## [1] 46.23 46.23 58.07 13.83 46.43 48.53 44.40 7.90 39.57 39.57 30.83 38.57 66.27 14.10 58.43
## [16] 41.40 57.43 57.43 61.40 0.60 60.27 26.37 5.77 1.33 25.63 21.90 46.90 22.00 25.80 13.87
## [31] 5.73 48.30 9.90 9.90 46.73 2.67 18.73 13.83 32.03 4.27 69.87 13.90 56.57 56.57 8.30
## [46] 8.30 21.57 18.43 31.63 31.63 39.77 39.77 52.33 5.83 4.10 12.20 38.07 12.73 54.10 54.10
## [61] 50.47 50.47 38.83 38.83 26.23 40.03 38.07 38.07 65.23 65.23 7.07 66.77 9.63 9.63 33.63
## [76] 33.63 63.33 27.60 38.47 1.63 55.23 55.23 52.77 25.30 9.87 1.70 38.77 19.40 13.83 1.57
## [91] 46.50 13.37 42.47 22.20 38.73 38.73 51.13 51.13 55.33 55.33 12.93 4.97 54.20 26.47 24.43
## [106] 9.87 50.23 50.23 42.23 42.23 66.93 66.93 67.47 38.57 3.67 3.67 20.07 8.83 55.13 55.13
## [121] 42.20 42.20 38.27 38.27 63.63 26.17 54.37 54.37 54.60 10.97 63.87 21.10 62.37 43.70 62.80
## [136] 62.80 63.33 14.37 58.53 58.53 58.07 58.07 58.50 58.50 1.50 14.37 51.10 51.10 49.93 6.57
## [151] 46.27 46.27 10.60 10.60 42.77 42.77 74.97 61.83 62.00 62.00 51.60 42.33 49.97 2.90 41.93
## [166] 41.93retinopathy[retinopathy$trt == 1, ]Using the retinopathy data set:
age for patients that have futime more than 20.age for patients that have futime more than 20 and are adults.age.retinopathy$age[retinopathy$futime > 20]## [1] 28 28 12 12 9 9 9 9 13 12 12 8 12 12 21 23 23 44 47 47 48 48 26 10 23 23 5 5 46 46 5
## [32] 5 13 13 45 1 1 12 12 36 36 10 25 25 14 38 38 14 14 10 10 17 17 44 21 19 19 13 9 9 48 24
## [63] 55 17 17 5 5 19 12 12 45 45 43 4 4 45 45 32 32 13 13 15 10 10 17 17 37 18 13 14 14 12 12
## [94] 9 9 10 10 5 5 7 2 5 5 4 4 27 53 53 10 13 12 12 24 24 17 17 8 8 58 58 17 17 12 12
## [125] 25 25 15 21 21 20 20 23 5 5 8 30 30 7 7 39 39 26 50 50 34 34 10 10 13 13 11 11 9 5 5
## [156] 10 10 23 2 2 12 12 7 7 13 20 30 30 32 32 39 39 4 10 6 6 33 33 15 15 48 48 4 4 46 25
## [187] 25 12 12 12 26 26 11 11 36 36 12 12 50 50 8 8 14 14 56 9 9 5 5 1 1 57 57 33 33 46 46
## [218] 35 35 8 8 30 30 51 42 42 20 20 23 23 22 25 25 45 45 20 20 19 19 4 36 36 20 24 24 51 51 16
## [249] 16 16 16 10 10 20 20 10 16 16 10 10 11 11 17 17 7 7 29 29 5 22 22 33 3 32 32retinopathy$age[retinopathy$futime > 20 & retinopathy$type == "adult"]## [1] 28 28 21 23 23 44 47 47 48 48 26 23 23 46 46 45 36 36 25 25 38 38 44 21 48 24 55 45 45 43 45
## [32] 45 32 32 37 27 53 53 24 24 58 58 25 25 21 21 20 20 23 30 30 39 39 26 50 50 34 34 23 20 30 30
## [63] 32 32 39 39 33 33 48 48 46 25 25 26 26 36 36 50 50 56 57 57 33 33 46 46 35 35 30 30 51 42 42
## [94] 20 20 23 23 22 25 25 45 45 20 20 36 36 20 24 24 51 51 20 20 29 29 22 22 33 32 32retinopathy[!is.na(retinopathy$age), ]© Eleni-Rosalina Andrinopoulou