4.summarise-and-combine-exercises.Rmd

---
title: "Summarising, Grouping and Joining Exercise"
date: '`r format(Sys.time(), "Last modified: %d %b %Y")`'
output: html_document
---

### Summarising

The first part of the exercise uses the patients dataset we've been using in previous sections of the course. After reading this into R, answer the following questions using the `summarise`, `summarise_at` and `mutate_all` functions.

```{r message = FALSE}
library(dplyr)
patients <- read.delim("patient-data-cleaned.txt", stringsAsFactors = FALSE)
patients %>% head(5)
```

Compute the mean age, height and weight of patients in the patients dataset

- First compute the means using `summarise` and then try to do the same using `summarise_at`

```{r}
## YOUR CODE HERE
```

- Modify the output by adding a step to round to 1 decimal place

```{r}
## YOUR CODE HERE
```

Compute the means of all numeric columns

```{r}
## YOUR CODE HERE
```

See what happens if you try to compute the mean of a logical (boolean) variable

- What proportion of our patient cohort has died?

```{r}
## YOUR CODE HERE
```


### Grouping

The following questions require grouping of patients based on one or more attributes using the `group_by` function.

Compare the average height of males and females in this patient cohort.

Are smokers heavier or lighter on average than non-smokers in this dataset?

```{r}
## YOUR CODE HERE
```


### Joining

The patients are all part of a diabetes study and have had their blood glucose concentration and diastolic blood pressure measured on several dates.

This part of the exercise combines grouping, summarisation and joining operations to connect the diabetes study data to the patients table we've already been working with.

```{r}
diabetes <- read.delim("diabetes.txt", stringsAsFactors = FALSE)
diabetes %>% head(5)
```

The goal is to compare the blood pressure of smokers and non-smokers, similar to the comparison of the average weight we made in the previous part of the exercise.

First, calculate the average blood pressure for each individual in the `diabetes` data frame.

```{r}
## YOUR CODE HERE
```

Now use one of the join functions to combine these average blood pressure measurements with the `patients` data frame containing information on whether the patient is a smoker.

```{r}
## YOUR CODE HERE
```

Finally, calculate the average blood pressure for smokers and non-smokers on the resulting, combined data frame.

```{r}
## YOUR CODE HERE
```

Can you write this as a single dplyr chain?

```{r}
## YOUR CODE HERE
```