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First, we need to install packages that we need during the workshop.

```{r, echo = TRUE, eval=FALSE}
install.packages(c("mice", "lme4", "dplyr", "plyr", "mlmRev"))
```

In practical I, we are using only the **plyr** package.

```{r setup, echo = TRUE}
library(plyr)
```

# Combining Datasets & Missing Data

Lets assume we have two datsets, which we want to combine. This can be done in two ways: **join** and **add**. When we want to **join** two datasets they need to have some similar subjects (the variables may differ). When we want to **add** two datasets they need to contain similar variables, but may contain different subjects.

## Join two datasets
First, we will generate two datasets A and B, which have some similar subjects and different variables:
```{r}
#the randomly generated numbers will be same each time we use this set.seed
set.seed(40917) 
df <- data.frame(subject = seq(1, 15, 1), 
                 mean = seq(10, 24, 1), 
                 sd = seq(2, 2.14, 0.01))

datasetA <- cbind(seq(1, 15,1), 
                  data.frame(matrix(rnorm(15*3), 15, 3) * df$sd + df$mean))
datasetA[, 2:4] <- round(datasetA[, 2:4], 2)
names(datasetA) <- c("subjectID", "X1", "X2", "X3")

df <- data.frame(subject = seq(1, 15, 1), 
                 mean = seq(110, 124, 1), 
                 sd = seq(2, 2.14, 0.01))
datasetB <- cbind(seq(8, 22,1), 
                  data.frame(matrix(rnorm(15*3), 15, 3) * df$sd + df$mean))
datasetB[, 2:4] <- round(datasetB[, 2:4], 2)
names(datasetB) <- c("subjectID", "X4", "X5", "X6")
```

This leads to the following summary statistics, where datasetA contains subjects 1 to 15, and datasetB contains subjects 8 to 22:
```{r}
datasetA #subjectIDs from 1 to 15
datasetB #subjectIDs from 8 tot 22.
```

###Inner Join
With **Inner join** only keep the *subjects that exists in both datasets*:
```{r}
AB.innerjoin <- join(datasetA, datasetB, by = "subjectID", type = "inner")
AB.innerjoin #keep subjects 8 to 15
``` 
Note, we have 8 observations (for subjectID 8 untill 15) and that for each subject we have an observation for each variabele.

###Full Outer Join
With **Full outer join** keep *all subjects*:
```{r}
AB.fullouterjoin <- join(datasetA, datasetB, by = "subjectID", type = "full")
AB.fullouterjoin #keep all subjects 
``` 
Note, we have 22 observations and there are some non-availables (NA's) for each variabele. We have NA's for X1 till X3 for subjectID 16 till 22 and NA's for X4 till X6 for subjectID 1 till 7.

###Master Join
With **Master join (left outer join)** keep *all subjects of one dataset and only the matching rows of the other*:
```{r}
AB.leftjoin <- join(datasetA, datasetB, by = "subjectID", type = "left")
AB.leftjoin #keep all subjects from datasets A and match rows from B
``` 
Note, that we have 15 observations and NA's for X4 till X6 for subjectIDs 1 till 7. 

###Detail Join
With **Detail join (right outer join)** keep *all subjects of one dataset and only the matching rows of the other*:
```{r}
AB.rightjoin <- join(datasetA, datasetB, by = "subjectID", type = "right")
AB.rightjoin #keep all subjects from datasets A and match rows from B
``` 
Note, that we have 15 observations and NA's for X1 till X3 for subjectIDs 16 till 22.

##Add two datasets
Besides joining datasets, we can also **add** datasets. In this case we measured the same variables (not all have to be the same) on different subjects. First we will simulate two datasets C and D, with some variables similar and different subjects.
```{r}
df <- data.frame(subject = seq(1, 15, 1), 
                 mean = seq(10, 24, 1), 
                 sd = seq(2, 2.14, 0.01))
datasetC <- cbind(seq(1, 15,1), 
                  data.frame(matrix(rnorm(15*3), 15, 3) * df$sd + df$mean))
datasetC[, 2:4] <- round(datasetC[, 2:4], 2)
names(datasetC) <- c("subjectID", "X1", "X2", "X3")

df <- data.frame(subject = seq(1, 15, 1), 
                 mean = seq(10, 24, 1), 
                 sd = seq(2, 2.14, 0.01))
datasetD <- cbind(seq(16, 30,1), 
                  data.frame(matrix(rnorm(15*3), 15, 3) * df$sd + df$mean))
datasetD[, 2:4] <- round(datasetD[, 2:4], 2)
names(datasetD) <- c("subjectID", "X4", "X2", "X3")
```

This leads to the following summary statistics, where datasetA contains subjectsIDs from 1 to 15 with variables X1, X2, and X3 and datasetB contains subjectIDs from 16 tot 30 with variables X4, X2, and X3:
```{r}
datasetC
datasetD
```

When adding two dataframes that do not have all the same variables there are two options:
1. Drop the variables that are not similar
2. Keep the variables that are not similar and put them equal to NA for the other dataset.

###Drop variables
This look as follows when we drop the variables that are not similar (in this case X1 in dataset C and X4 in dataset D):
```{r}
datasetC.dropX1 <- subset(datasetC, select = c("subjectID", "X2", "X3"))
datasetD.dropX4 <- subset(datasetD, select = c("subjectID", "X2", "X3"))
```

```{r}
datasetC.dropX1
datasetD.dropX4
```
Now that we dropped variables X1 and X4 we are left with two datasets that contain the same variables. Hence, we can add them.
```{r}
add.CD.drop <- rbind(datasetC.dropX1, datasetD.dropX4)
add.CD.drop #subjectID are from 1 to 30.
```

###Keep Variables
However, normally we want to avoid dropping variables since they contain information. Hence, another way to add two datasets is to keep the variables that are not similar and make them NA for the other dataset:
```{r}
datasetC.addX4 <- cbind(datasetC, rep("NA", 15))
names(datasetC.addX4) <- c("subjectID", "X1", "X2", "X3", "X4")

datasetD.addX1 <- as.data.frame(cbind(datasetD$subjectID, rep("NA", 15), 
                                      datasetD$X2, datasetD$X3, datasetD$X4))
names(datasetD.addX1) <- c("subjectID", "X1", "X2", "X3", "X4")
```

Now the datasets look as follows:
```{r}
datasetC.addX4
datasetD.addX1
```

Now, we can add the two datasets:
```{r}
add.CD.keep <- rbind(datasetC.addX4, datasetD.addX1)
add.CD.keep
```

So to conclude, we can **join** and **add** datasets. If we have observations from similar subjects on different variabiles we can join the datasets in four ways:

* Inner join
* Outer join
* Master join
* Detail join

When two datasets measures some similar variables on different subjects we can add theses datasets. To do this we have to decide on how to handle variables that were not included in both datasets:

* Drop these variables
* Keep these variables

