When there are multiple measurements of the same subject, across time or using different tools, the data is often described as being in
“wide” format if there is one observation row per subject with each measurement
present as a different variable and “long” format if there is one observation
row per measurement (thus, multiple rows per subject). Different functions
require different formats, and so the need to reshape a dataset may arise.

Below, we start with a dataset in wide format. Students have been measured
using five metrics: read, write, math, science, and
socst.

hsb2 

    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

To reformat this dataset into long form, we will use the reshape
function. The arguments we provide include a list of variable names that define
the different times or metrics (varying), the name we wish to give the
variable containing these values in our long dataset (v.names), the name
we wish to give the variable describing the different times or metrics (timevar),
the values this variable will have (times), and the end format for the
data (direction). Additionally, we have provided new row names.

## Wide to long

l 
    id female race ses schtyp prog    subj score
99   1      1    1   1      1    3    read    34
299  1      1    1   1      1    3   write    44
499  1      1    1   1      1    3    math    40
699  1      1    1   1      1    3 science    39
899  1      1    1   1      1    3   socst    41
139  2      1    1   2      1    3    read    39
339  2      1    1   2      1    3   write    41
539  2      1    1   2      1    3    math    33
739  2      1    1   2      1    3 science    42
939  2      1    1   2      1    3   socst    41

After sorting by id, we can see that we have five rows per student and
their five scores appear in the score variable with the subj
variable indicating which test on which the score was measured. For each of the
five rows per id, the female, race, ses, schtyp,
and prog variables are unchanging.

We can similarly go from this long form back to our original wide form again
using reshape with different arguments (most importantly, direction = “wide”).
With timevar, we indicate the variable that will define the multiple
measurements per subject. With idvar, we list the variables that should
do not vary within subject.

## Long to wide

w 

    id female race ses schtyp prog score.read score.write score.math score.science score.socst
99   1      1    1   1      1    3         34          44         40            39          41
139  2      1    1   2      1    3         39          41         33            42          41
84   3      0    1   1      1    2         63          65         48            63          56
112  4      1    1   1      1    2         44          50         41            39          51
76   5      0    1   1      1    2         47          40         43            45          31
149  6      1    1   1      1    2         47          41         46            40          41
50   7      0    1   2      1    2         57          54         59            47          51
94   8      1    1   1      1    2         39          44         52            44          48
60   9      0    1   2      1    3         48          49         52            44          51
154 10      1    1   2      1    1         47          54         49            53          61

Once again, we have one observation per student. We can compare the
dimensions of our wide dataset w to our original dataset:

dim(w)
[1] 200  11

dim(hsb2)
[1] 200  11