103.1.6 R Lists

In previous section, we studied about R Data Frames, now we will be studying about R Lists.

R list is a collection of Homogeneous / heterogeneous R components ,i.e., a dataset, a string, an image, an object can be put together. Imagine R objects which should have an image, a dataset, string, etc., then the list will be very useful. Let us see a demo.

 x<- c(1:20)    //Integer
 y<-FALSE      //Logical
 z<-"Mike"      //String
 k<-30
 l<-attitude
 Disc<-"This is a list of all my R elements"

Lets look at the datatype of each of these objects

    > str(x)
      int [1:20] 1 2 3 4 5 6 7 8 9 10 ...

    > str(y)
      logi FALSE

    > str(z)
      chr "Mike"

    > str(k)
      num 30

> str(l)
‘data.frame’: 30 obs. of 7 variables:
$ rating : num 43 63 71 61 81 43 58 71 72 67 …
$ complaints: num 51 64 70 63 78 55 67 75 82 61 …
$ privileges: num 30 51 68 45 56 49 42 50 72 45 …
$ learning : num 39 54 69 47 66 44 56 55 67 47 …
$ raises : num 61 63 76 54 71 54 66 70 71 62 …
$ critical : num 92 73 86 84 83 49 68 66 83 80 …
$ advance : num 45 47 48 35 47 34 35 41 31 41 …

We can create a list using list() funcion

> mylist<-list(Disc,x,y,z,k,l)
> mylist
[[1]]
[1] "This is a list of all my R elements"

[[2]]
 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

[[3]]
[1] FALSE

[[4]]
[1] "Mike"

[[5]]
[1] 30

[[6]]
 rating complaints privileges learning raises critical advance
1 43 51 30 39 61 92 45
2 63 64 51 54 63 73 47
3 71 70 68 69 76 86 48
4 61 63 45 47 54 84 35
5 81 78 56 66 71 83 47
6 43 55 49 44 54 49 34
7 58 67 42 56 66 68 35
8 71 75 50 55 70 66 41
9 72 82 72 67 71 83 31
10 67 61 45 47 62 80 41
11 64 53 53 58 58 67 34
12 67 60 47 39 59 74 41
13 69 62 57 42 55 63 25
14 68 83 83 45 59 77 35
15 77 77 54 72 79 77 46
16 81 90 50 72 60 54 36
17 74 85 64 69 79 79 63
18 65 60 65 75 55 80 60
19 65 70 46 57 75 85 46
20 50 58 68 54 64 78 52
21 50 40 33 34 43 64 33
22 64 61 52 62 66 80 41
23 53 66 52 50 63 80 37
24 40 37 42 58 50 57 49
25 63 54 42 48 66 75 33
26 66 77 66 63 88 76 72
27 78 75 58 74 80 78 49
28 48 57 44 45 51 83 38
29 85 85 71 71 77 74 55
30 82 82 39 59 64 78 39

Accessing a list

> mylist[1]
[[1]]
[1] "This is a list of all my R elements"

> mylist[2]
[[1]]
 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

> mylist[[2]][1]
[1] 1

> mylist[6]
[[1]]
 rating complaints privileges learning raises critical advance
1 43 51 30 39 61 92 45
2 63 64 51 54 63 73 47
3 71 70 68 69 76 86 48
4 61 63 45 47 54 84 35
5 81 78 56 66 71 83 47
6 43 55 49 44 54 49 34
7 58 67 42 56 66 68 35
8 71 75 50 55 70 66 41
9 72 82 72 67 71 83 31
10 67 61 45 47 62 80 41
11 64 53 53 58 58 67 34
12 67 60 47 39 59 74 41
13 69 62 57 42 55 63 25
14 68 83 83 45 59 77 35
15 77 77 54 72 79 77 46
16 81 90 50 72 60 54 36
17 74 85 64 69 79 79 63
18 65 60 65 75 55 80 60
19 65 70 46 57 75 85 46
20 50 58 68 54 64 78 52
21 50 40 33 34 43 64 33
22 64 61 52 62 66 80 41
23 53 66 52 50 63 80 37
24 40 37 42 58 50 57 49
25 63 54 42 48 66 75 33
26 66 77 66 63 88 76 72
27 78 75 58 74 80 78 49
28 48 57 44 45 51 83 38
29 85 85 71 71 77 74 55
30 82 82 39 59 64 78 39

The sixth element of our list is dataframe.

mylist[[6]][1]#Note the double braces [[]] in this command

 ##    rating
 ## 1      43
 ## 2      63
 ## 3      71
 ## 4      61
 ## 5      81
 ## 6      43
 ## 7      58
 ## 8      71
 ## 9      72
 ## 10     67
 ## 11     64
 ## 12     67
 ## 13     69
 ## 14     68
 ## 15     77
 ## 16     81
 ## 17     74
 ## 18     65
 ## 19     65
 ## 20     50
 ## 21     50
 ## 22     64
 ## 23     53
 ## 24     40
 ## 25     63
 ## 26     66
 ## 27     78
 ## 28     48
 ## 29     85
 ## 30     82

> mylist[[6]][[1]][1]
 ## [1] 43

There is a difference between List and Data frames. We can’t create a data frame with heterogeneous components, but a list can take all heterogeneous R objects and stores them as new List.

Other Datatypes

There are other data types like factors and Matrices. These data types work well with a specific set of problems.

  Factor

The factor is a categorical variable. There is already ‘string’ for a non-numeric variable, but Factor is a bit more like if we have categories “East, West, North, South”, rather than storing these as strings, we can use them as Factor and do categorical data analysis. Factor is a good feature in the statistics where categorical data should be handled very carefully.

Matrix

Matrix is a multi-dimensional array. It has its own advantages while computing specific class of problems.

Demo

> gender<-c("Male","Female")
> str(gender)
  chr [1:2] "Male" "Female"

> gender1<-factor(gender)
> str(gender1)
  Factor w/ 2 levels "Female","Male": 2 1

This is the end to the session datatypes in R.

In next section, we will be studying about R Functions.