exercise4.Rmd

## Exercise 4. Matrix manipulation

Create the script "exercise4.R" and save it to the "Rcourse/Module1" directory: you will save all the commands of exercise 4 in that script.
<br>Remember you can comment the code using #.


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```{r, eval=F}
getwd()
setwd("Rcourse/Module1")
setwd("~/Rcourse/Module1")
```

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**1- Create three numeric vectors x, y, z, each of 4 elements of your choice.**

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```{r}
x <- 2:5
y <- 6:9
z <- 7:4
```
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Use rbind() to create a matrix **mat** (3 rows and 4 columns) out of x, y and z.

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```{r}
mat <- rbind(x, y, z)
```

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**2- Create the same matrix now using the matrix function.**

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```{r}
mat <- matrix(data=c(x, y, z), nrow=3, ncol=4)
# Try with the "byrow=TRUE" parameter: what is different ?
mat <- matrix(data=c(x, y, z), nrow=3, ncol=4, byrow=TRUE)
```

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**3- Add names to mat's columns: "a", "b", "c", "d", respectively.**

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```{r}
colnames(mat) <- c("a", "b", "c", "d")
```

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**4- Calculate the sum of each row, and the sum of each column**

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```{r}
rowSums(mat); colSums(mat)
```

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**5- Create the matrix mat2 as:**

```{r}
mat2 <- matrix(c(seq(from=1, to=10, by=2), 5:1, rep(x=2017, times=5)), ncol=3)
```

What does function seq() do?

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**seq** generate sequences of numbers. Here, it creates a sequences from 1 to 10 with a step of 2 numbers.

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**6- What are the dimensions of mat2 (number of rows and number of columns)?**

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```{r}
# number of rows
nrow(mat2)
# number of columns
ncol(mat2)
# dimensions: number of rows, number of columns
dim(mat2)
```

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**7- Add column names to mat2: "day", "month" and "year", respectively.**

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```{r}
colnames(mat2) <- c("day", "month", "year")
```

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**8- Add row names to mat2: letters "A" to "E"**

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```{r}
rownames(mat2) <- c("A", "B", "C", "D", "E")
rownames(mat2) <- LETTERS[1:5]
```

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**9- Shows row(s) of mat2 where the month column is greater than or equal to 3.**

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```{r}
# select column month
mat2[, "month"]
# element(s) of column month that is (are) greater than or equal to 3
mat2[,"month"] >= 3
# finally select row(s) where the month columns is greater than or equal to 3
mat2[mat2[,"month"] >= 3,]
```

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**10- Replace all elements of mat2 that are equal to 2017 with 2018.**

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```{r}
# which elements of mat2 that are exactly equal to 2017
mat2==2017
# retrieve actual elements
mat2[mat2==2017]
# replace all 2017 with 2018
mat2[mat2==2017] <- 2018
```

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**11- Multiply all elements of the 2nd column of mat2 by 7. Reassign mat2!**

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```{r}
# multiply all elements of the 2nd column of mat2 by 7
mat2[,2] * 7
# reassign mat2 with the new values of column 2
mat2[,2] <- mat2[,2] * 7
```

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**12- Add the column named "time" to mat2, that contains values 8, 12, 11, 10, 8. Save in the new object mat3.**

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```{r}
mat3 <- cbind(mat2, time=c(8, 12, 11, 10, 8))
```

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**13- Replace all elements of mat3 that are less than 3 with NA.**

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```{r}
# which elements of mat3 that are less than 3
mat3 < 3
# actually elements of mat3 that are less than 3
mat3[mat3 < 3]
# reassign elements of mat3 that are less than 3 with NA
mat3[mat3 < 3] <- NA
```

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**14- Remove rows from mat3 if a NA is present. Save in the new object mat4.**

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```{r}
mat4 <- na.omit(mat3)
```

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**15- Retrieve the smaller value of each column of mat4.**

Try different approaches:

* Retrieve the minimum for each column one by one.

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```{r}
min(mat4[,"day"])
min(mat4[,"month"])
min(mat4[,"year"])
min(mat4[,"time"])
```

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* Retrieve the minimum of all columns simultaneously using the apply() function.

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```{r}
# mat4: object
# 2: by column
# min: function to apply
apply(mat4, 2, min)
```

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