2d_manip.Rmd

## Two-dimensional structures manipulation

### Dimensions

* Get the number of rows and the number of columns:

```{r}
# Create a data frame
d <- data.frame(c("Maria", "Juan", "Alba"),
	c(23, 25, 31),
	c(TRUE, TRUE, FALSE),
	stringsAsFactors = FALSE)
# number of rows
nrow(d)
# number of columns
ncol(d)
```

* Check the dimensions of the object: both number of rows and number of columns:

```{r}
# first element: number of rows
# second element: number of columns
dim(d)
```

* Dimension names

Column and/or row names can be added to matrices and data frames

```{r}
colnames(d) <- c("Name", "Age", "Vegetarian")
rownames(d) <- c("Patient1", "Patient2", "Patient3")
```

Column and/or row names can be used to retrieve elements or sets of elements from a 2-dimensional object:

<img src="images/df_names.png" width="450"/>


```{r}
d[,"Name"]
# same as:
d[,1]

d["Patient3", "Age"]
# same as:
d[3,2]

# for data frames only, the $ sign can be used to retrieve columns:
# d$Name is d[,1] is d[, "Name"]

```

* Include names as you create objects:
	+ Matrix:
```{r}
m <- matrix(1:4, ncol=2, 
	dimnames=list(c("row1", "row2"), c("col1", "col2")))
```
	+ Data frame:
```{r}
df <- data.frame(col1=1:2, col2=1:2, 
	row.names=c("row1", "row2"))
```

### Manipulation

Same principle as vectors... but in 2 dimensions!
<br>

<h4>Examples</h4>

 + select the columns of b if **at least one element in the 3rd row is less than or equal to 4**:

```{r}
# create b
b <- matrix(c(1, 0, 34, 44, 12, 4), 
	nrow=3, 
	ncol=2)
# third row of b:
b[3, ]
# element(s) in the third row of b that is (are) less than or equal to 4
b[3, ] <= 4
# retrieve the corresponding sub-matrix
b[ ,b[3, ] <= 4]
```
 
 + Select rows of b if **at least one element in column 2 is greater than 24**:

```{r}
# build data frame d
d <- data.frame(Name=c("Maria", "Juan", "Alba"), 
        Age=c(23, 25, 31),
        Vegetarian=c(TRUE, TRUE, FALSE),
        stringsAsFactors = FALSE)
rownames(d) <- c("Patient1", "Patient2", "Patient3")
# The following commands all output the same result:
d[d[,2] > 24, ]
d[d[,"Age"] > 24, ]
d[d$Age > 24, ]
```

<img src="images/df_col2.png" width="450"/>

 + Select patients (rows) based on 2 criteria: age of the patient (column 2) should be great than or equal to 25, and the patient should be vegetarian (column 3):

```{r}
d[ d$Age >= 25 & d$Vegetarian == TRUE, ]
```

<img src="images/df_col2_col3.png" width="650"/>

<h4>More useful commands</h4>

* Add a row or a column with **rbind** and **cbind**, respectively

```{r}
# add a column
cbind(d, 1:3)
# add a row
rbind(d, 4:6)
```

Add a patient to our data frame **d**:

```{r}
d <- rbind(d, c("Jordi", 33, FALSE))
```

* Process the sum of all rows or all columns with **rowSums** and **colSums**, respectively.

```{r}
# create a matrix
b <- matrix(1:20, ncol=4)
# process sum of rows and sum of cols
rowSums(b)
colSums(b)
```

* The **apply** function

Powerful tool to apply a command to all rows or all columns of a data frame or a matrix.<br>
For example, instead of calculating the sum of each row, you might be interested in calculating the median ? But rowMedians doesn't exist !
<br><br>
**apply** takes 3 arguments:
- first argument **X**: 2-dimensional object
- second argument **MARGIN**: apply by row or by column?
	+ 1: by row
	+ 2: by column
- third argument **FUN**: function to apply to either rows or columns

```{r}
# median value of each row of b
apply(X=b, MARGIN=1, FUN=median)
# median value of each column of b
apply(X=b, MARGIN=2, FUN=median)
```