solutions.Rmd

---
title: "Exercises"
output:
  html_document:
    toc: true
    toc_depth: 4
---

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## Lesson 01: Analyzing Patient Data

### Slicing (Subsetting) Data

A subsection of a data frame is called a slice.
We can take slices of character vectors as well:

```{r}
animal <- c("m", "o", "n", "k", "e", "y")
# first three characters
animal[1:3]
# last three characters
animal[4:6]
```

1.  If the first four characters are selected using the slice `animal[1:4]`, how can we obtain the first four characters in reverse order?

2.  What is `animal[-1]`?
    What is `animal[-4]`?
    Given those answers,
    explain what `animal[-1:-4]` does.

3.  Use a slice of `animal` to create a new character vector that spells the word "eon", i.e. `c("e", "o", "n")`.

#### Solution

1.

```{r}
animal[4:1]
```

2. 
```{r}
animal[-1]
```

```{r}
animal[-4]
```

```{r}
animal[-1:-4]
```

-1:-4 creates a slice from -1 to -4: -1, -2, -3, -4.
Those numbers are then indexed after animal: animal[c(-1, -2, -3, -4)], which results in the first four entries of animal being removed. 

### Plotting Data

Create a plot showing the standard deviation of the inflammation data for each day across all patients.

#### Solution

```{r, eval=FALSE}
sd_day_inflammation <- apply(dat, 2, sd)
plot(sd_day_inflammation)
```

---

## Lesson 02: Creating Functions

### Functions to Create Graphs

Write a function called `analyze` that takes a filename as a argument
and displays the three graphs produced in the previous lesson (average, min and max inflammation over time).
`analyze("data/inflammation-01.csv")` should produce the graphs already shown,
while `analyze("data/inflammation-02.csv")` should produce corresponding graphs for the second data set.
Be sure to document your function with comments.

#### Solution

```{r}
analyze <- function(filename) {
  # Plots the average, min, and max inflammation over time.
  # Input is character string of a csv file.
  dat <- read.csv(file = filename, header = FALSE)
  avg_day_inflammation <- apply(dat, 2, mean)
  plot(avg_day_inflammation)
  max_day_inflammation <- apply(dat, 2, max)
  plot(max_day_inflammation)
  min_day_inflammation <- apply(dat, 2, min)
  plot(min_day_inflammation)
}
```

---

## Lesson 03: Analyzing Multiple Data Sets

### Printing Numbers

R has a built-in function called `seq` that creates a list of numbers:

```{r}
seq(3)
```

Using `seq`, write a function that prints the first **N** natural numbers, one per line:

```{r, echo=-1}
print_N <- function(N) {
  nseq <- seq(N)
  for (num in nseq) {
    print(num)
  }
}
print_N(3)
```

#### Solution

```{r}
print_N <- function(N) {
  nseq <- seq(N)
  for (num in nseq) {
    print(num)
  }
}
```


### Summing Values

Write a function called `total` that calculates the sum of the values in a vector.
(R has a built-in function called `sum` that does this for you.
Please don't use it for this exercise.)

```{r, echo=-1}
total <- function(vec) {
  #calculates the sum of the values in a vector
  vec_sum <- 0
  for (num in vec) {
    vec_sum <- vec_sum + num
  }
  return(vec_sum)
}
ex_vec <- c(4, 8, 15, 16, 23, 42)
total(ex_vec)
```

#### Solution

```{r}
total <- function(vec) {
  # calculates the sum of the values in a vector
  vec_sum <- 0
  for (num in vec) {
    vec_sum <- vec_sum + num
  }
  return(vec_sum)
}
```

### Exponentiation

Exponentiation is built into R:

```{r}
2^4
```

Write a function called `expo` that uses a loop to calculate the same result.

```{r, echo=-1}
expo <- function(base, power) {
  result <- 1
  for (i in seq(power)) {
    result <- result * base
  }
  return(result)
}
expo(2, 4)
```

#### Solution

```{r}
expo <- function(base, power) {
  result <- 1
  for (i in seq(power)) {
    result <- result * base
  }
  return(result)
}
```

### Using Loops to Analyze Multiple Files

Write a function called `analyze_all` that takes a filename pattern as its sole argument and runs `analyze` for each file whose name matches the pattern.

#### Solution

```{r}
analyze_all <- function(pattern) {
  # Runs the function analyze for each file in the "data" directory
  # that contains the given pattern.
  filenames <- list.files(path = "data", pattern = pattern, full.names = TRUE)
  for (f in filenames) {
    analyze(f)
  }
}
```

---

## Lesson 04: Making Choices

### Choosing Plots Based on Data

Write a function `plot_dist` that plots
a boxplot if the length of the vector is greater than a specified threshold
and a stripchart otherwise.
To do this you'll use the R functions `boxplot` and `stripchart`.

```{r using-conditions-01, eval=FALSE}
dat <- read.csv("data/inflammation-01.csv", header = FALSE)
plot_dist(dat[, 10], threshold = 10)     # day (column) 10
plot_dist(dat[1:5, 10], threshold = 10)  # samples (rows) 1-5 on day (column) 10
```

#### Solution

```{r}
plot_dist <- function(x, threshold) {
  if (length(x) > threshold) {
    boxplot(x)
  } else {
    stripchart(x)
  }
}
```

### Changing the Behavior of the Plot Command

One of your collaborators asks if you can recreate the figures with lines instead of points.
Find the relevant argument to `plot` by reading the documentation (`?plot`),
update `analyze`, and then recreate all the figures with `analyze_all`.

#### Solution

```{r}
analyze <- function(filename, output = NULL) {
  # Plots the average, min, and max inflammation over time.
  # Input:
  #    filename: character string of a csv file
  #    output: character string of pdf file for saving
  if (!is.null(output)) {
    pdf(output)
  }
  dat <- read.csv(file = filename, header = FALSE)
  avg_day_inflammation <- apply(dat, 2, mean)
  plot(avg_day_inflammation, type = "l")
  max_day_inflammation <- apply(dat, 2, max)
  plot(max_day_inflammation, type = "l")
  min_day_inflammation <- apply(dat, 2, min)
  plot(min_day_inflammation, type = "l")
  if (!is.null(output)) {
    dev.off()
  }
}
```

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