sort
order
rank

exmaple(rank)
			
hsb2 <- read.table('http://www.ats.ucla.edu/stat/R/faq/hsb2.csv', header=T, sep=",")

attach(hsb2)

hsb2[1:10, ]

     id  female  race  ses  schtyp  prog  read  write  math  science  socst
 1   70       0     4    1       1     1    57     52    41       47     57
 2  121       1     4    2       1     3    68     59    53       63     61
 3   86       0     4    3       1     1    44     33    54       58     31
 4  141       0     4    3       1     3    63     44    47       53     56
 5  172       0     4    2       1     2    47     52    57       53     61
 6  113       0     4    2       1     2    44     52    51       63     61
 7   50       0     3    2       1     1    50     59    42       53     61
 8   11       0     1    2       1     2    34     46    45       39     36
 9   84       0     4    2       1     1    63     57    54       58     51
 10  48       0     3    2       1     2    57     55    52       50     51

    Let's start by just using the sort function with the variable read, since we can see from the output above that the data set is not sorted on read.

sort(read)

  [1] 28 31 34 34 34 34 34 34 35 36 36 36 37 37 39 39 39 39 39 39 39 39 41 41 42
 [26] 42 42 42 42 42 42 42 42 42 42 42 42 43 43 44 44 44 44 44 44 44 44 44 44 44
 [51] 44 44 45 45 46 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
 [76] 47 47 47 47 47 47 47 48 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
[101] 50 52 52 52 52 52 52 52 52 52 52 52 52 52 52 53 54 55 55 55 55 55 55 55 55
[126] 55 55 55 55 55 57 57 57 57 57 57 57 57 57 57 57 57 57 57 60 60 60 60 60 60
[151] 60 60 60 61 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 65 65 65 65 65
[176] 65 65 65 65 66 68 68 68 68 68 68 68 68 68 68 68 71 71 73 73 73 73 73 76 76

hsb2[1:10, ]

    id female race ses schtyp prog read write math science socst
1   70      0    4   1      1    1   57    52   41      47    57
2  121      1    4   2      1    3   68    59   53      63    61
3   86      0    4   3      1    1   44    33   54      58    31
4  141      0    4   3      1    3   63    44   47      53    56
5  172      0    4   2      1    2   47    52   57      53    61
6  113      0    4   2      1    2   44    52   51      63    61
7   50      0    3   2      1    1   50    59   42      53    61
8   11      0    1   2      1    2   34    46   45      39    36
9   84      0    4   2      1    1   63    57   54      58    51
10  48      0    3   2      1    2   57    55   52      50    51

    Clearly, this is not what we wanted. The data in the data frame are not sorted based on the value of read.  Rather, only the variable read was sorted, independently of the data frame.  Let's try again.  This time, we will use the order function.  For most applications, you will want to use the order function and not the sort function to sort data in a data frame.  For the purposes of example, we will save the newly sorted data into a new data frame, so that we can do multiple sorting examples without rearranging the original data frame.  Also, note how ties are handled:  if there is only one variable to be sorted on, the cases with tied values are left in their original order.  As we will see in the next example, if the data frame is sorted on multiple variables, the values of later variables will be used to "break the tie".

sort1.hsb2 <- hsb2[order(read) , ]
sort1.hsb2[1:15, ]

     id female race ses schtyp prog read write math science socst
141  19      1    1   1      1    1   28    46   43      44    51
79  164      0    4   2      1    3   31    36   46      39    46
8    11      0    1   2      1    2   34    46   45      39    36
26   53      0    3   2      1    3   34    37   46      39    31
64  108      0    4   2      1    1   34    33   41      36    36
88  117      0    4   3      1    3   34    49   39      42    56
99    1      1    1   1      1    3   34    44   40      39    41
130  45      1    3   1      1    3   34    35   41      29    26
108  89      1    4   1      1    3   35    35   40      51    33
82  165      0    4   1      1    3   36    49   54      61    36
116 106      1    4   2      1    3   36    44   37      42    41
192 175      1    4   3      2    1   36    57   42      50    41
25   12      0    1   2      1    3   37    44   45      39    46
57   67      0    4   1      1    3   37    37   42      33    32
40  153      0    4   2      1    3   39    31   40      39    51

    We can also sort the data frame by more than one variable.  Next, we will sort by read and then by prog.

sort2.hsb2 <- hsb2[order(read, prog), ]
sort2.hsb2[1:15, ]

     id female race ses schtyp prog read write math science socst
141  19      1    1   1      1    1   28    46   43      44    51
79  164      0    4   2      1    3   31    36   46      39    46
64  108      0    4   2      1    1   34    33   41      36    36
8    11      0    1   2      1    2   34    46   45      39    36
26   53      0    3   2      1    3   34    37   46      39    31
88  117      0    4   3      1    3   34    49   39      42    56
99    1      1    1   1      1    3   34    44   40      39    41
130  45      1    3   1      1    3   34    35   41      29    26
108  89      1    4   1      1    3   35    35   40      51    33
192 175      1    4   3      2    1   36    57   42      50    41
82  165      0    4   1      1    3   36    49   54      61    36
116 106      1    4   2      1    3   36    44   37      42    41
25   12      0    1   2      1    3   37    44   45      39    46
57   67      0    4   1      1    3   37    37   42      33    32
128  28      1    2   2      1    1   39    53   54      50    41

    We can also sort in reverse order by using a minus sign ( - ) in front of the variable that we want sorted in reverse order.  In this example, the data are sorted on prog, and within each category of prog, the variable read is sorted in reverse order.

sort3.hsb2 <- hsb2[order(prog, -read) , ]

sort3.hsb2[1:15, ]

     id female race ses schtyp prog read write math science socst
74  157      0    4   2      1    1   68    59   58      74    66
78  123      0    4   3      1    1   68    59   56      63    66
45   62      0    4   3      1    1   65    65   48      63    66
9    84      0    4   2      1    1   63    57   54      58    51
21  167      0    4   2      1    1   63    49   35      66    41
92  149      0    4   1      1    1   63    49   49      66    46
36  144      0    4   3      1    1   60    65   58      61    66
121  35      1    1   1      2    1   60    54   50      50    51
1    70      0    4   1      1    1   57    52   41      47    57
18  195      0    4   2      2    1   57    57   60      58    56
155  71      1    4   2      1    1   57    62   56      58    66
198 187      1    4   2      2    1   57    41   57      55    52
20   85      0    4   2      1    1   55    39   57      53    46
46  169      0    4   1      1    1   55    59   63      69    46
196  31      1    2   2      2    1   55    59   52      42    56

    Now let's consider the various ways that missing data can be handled. We do not have any missing data in our data file, so we will create some.  Remember that missing data are denoted as NA in R, regardless of the type of variable (even in numeric variables).  In our example, we will create missing values for the variable science for cases 2 through 5.  Also note that we need to indicate the data set in which the variable science is to be found, followed by the dollar sign ($), even though we have attached the hsb2 data set.

hsb2$science[2:5] <- NA

hsb2[1:10 ,]

    id female race ses schtyp prog read write math science socst
1   70      0    4   1      1    1   57    52   41      47    57
2  121      1    4   2      1    3   68    59   53      NA    61
3   86      0    4   3      1    1   44    33   54      NA    31
4  141      0    4   3      1    3   63    44   47      NA    56
5  172      0    4   2      1    2   47    52   57      NA    61
6  113      0    4   2      1    2   44    52   51      63    61
7   50      0    3   2      1    1   50    59   42      53    61
8   11      0    1   2      1    2   34    46   45      39    36
9   84      0    4   2      1    1   63    57   54      58    51
10  48      0    3   2      1    2   57    55   52      50    51

    Now that we have created some missing data, we can sort the data frame such that the missing data are at the top, the bottom, or deleted from the frame.  Note that once you create the missing values with the hsb2$science notation, you need to use exactly that in the order function.  Also, the argument after na.last = has to be in upper case letters (TRUE, FALSE and NA).  In the following examples, we will use the head and tail functions to display the first few and last few cases of the data frame, respectively, to show the effect of the argument of na.last = .

sort5.hsb2 <- hsb2[order(hsb2$science, na.last=FALSE) , ]

head(sort5.hsb2)

     id female race ses schtyp prog read write math science socst
2   121      1    4   2      1    3   68    59   53      NA    61
3    86      0    4   3      1    1   44    33   54      NA    31
4   141      0    4   3      1    3   63    44   47      NA    56
5   172      0    4   2      1    2   47    52   57      NA    61
56   15      0    1   3      1    3   39    39   44      26    42
130  45      1    3   1      1    3   34    35   41      29    26

sort6.hsb2 <- hsb2[order(hsb2$science, na.last=TRUE) , ]

tail(sort6.hsb2)

    id female race ses schtyp prog read write math science socst
34 150      0    4   2      1    3   42    41   57      72    31
74 157      0    4   2      1    1   68    59   58      74    66
2  121      1    4   2      1    3   68    59   53      NA    61
3   86      0    4   3      1    1   44    33   54      NA    31
4  141      0    4   3      1    3   63    44   47      NA    56
5  172      0    4   2      1    2   47    52   57      NA    61

    For this last example of removing the missing cases, we will create many missing values for the variable id, making is easier to see that the cases with missing values are not in the new data frame.

hsb2$id[2:195] <- NA

sort8.hsb2 <- hsb2[order(hsb2$id, na.last=NA) , ]

sort8.hsb2

     id female race ses schtyp prog read write math science socst
196  31      1    2   2      2    1   55    59   52      42    56
1    70      0    4   1      1    1   57    52   41      47    57
199 118      1    4   2      1    1   55    62   58      58    61
200 137      1    4   3      1    2   63    65   65      53    61
197 145      1    4   2      1    3   42    46   38      36    46
198 187      1    4   2      2    1   57    41   57      55    52

detach(hsb2)
