Here's the code to get a sample data set:

```
set.seed(0)
practice <- matrix(sample(1:100, 20), ncol = 2)
data <- as.data.frame(practice)
data <- cbind( lob = sprintf("objective%d", rep(1:2,each=5)), data)
data <- cbind( student = sprintf("student%d", rep(1:5,2)), data)
names(data) <- c("student", "learning objective","attempt", "score")
data[-8,]
```

The data looks like this:

```
student learning objective attempt score
1 student1 objective1 90 6
2 student2 objective1 27 19
3 student3 objective1 37 16
4 student4 objective1 56 60
5 student5 objective1 88 34
6 student1 objective2 20 66
7 student2 objective2 85 42
9 student4 objective2 61 82
10 student5 objective2 58 31
```

What I want is:

```
student objective1 objective2
attempt score attempt score
1 student1 90 6 20 66
2 student2 27 19 85 42
3 student3 ... 0 0
4 student4 ... ...
5 student5 ... ...
```

There are 70 learning objectives, so it's going to be tedious to just copy and paste the attempts and scores, so I wonder whether there's a better way to clean the data.

**R:** I was trying to use the `melt`

function in R to get the new data, but it doesn't work well. There are missing scores for some of the students and the student name is not listed, for example `student3`

in this case, so I cannot just `cbind`

the score.

**Excel:** There are 70 learning objectives, and because of missing names, I have to check all corresponding rows for all those 70 objectives for `VLOOKUP`

:

```
(=VLOOKUP($C7,'0learning.csv'!$B$372:$G$395,5,0)
(=VLOOKUP($C7,'0learning.csv'!$B$372:$G$395,6,0)
```

Is there a better way?

Answer:

We could use the devel version of `data.table`

i.e. `v1.9.5`

which can take take multiple `value.var`

columns and reshape the 'long' form to 'wide'. Instructions to install are `here`

.

```
library(data.table)#v1.9.5+
names(data)[2] <- 'objective'
dcast(setDT(data), student~objective, value.var=c('attempt', 'score'))
# student attempt_objective1 attempt_objective2 score_objective1
#1: student1 90 20 6
#2: student2 27 85 19
#3: student3 37 96 16
#4: student4 56 61 60
#5: student5 88 58 34
# score_objective2
#1: 66
#2: 42
#3: 87
#4: 82
#5: 31
```

Or using `reshape`

from `base R`

```
reshape(data, idvar='student', timevar='objective', direction='wide')
# student attempt.objective1 score.objective1 attempt.objective2
# 1 student1 90 6 20
# 2 student2 27 19 85
# 3 student3 37 16 96
# 4 student4 56 60 61
# 5 student5 88 34 58
# score.objective2
# 1 66
# 2 42
# 3 87
# 4 82
# 5 31
```

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