2.dplyr-intro-solutions.Rmd

---
title: "Solutions to dplyr introduction"
author: "Mark Dunning"
date: '`r format(Sys.time(), "Last modified: %d %b %Y")`'
output: html_document
---

## Tidy data

Read the simulated clinical dataset and put into the tidy form. Also load the `tidyr` package that we are going to use.
- Can you make a boxplot to visualise the effect of the different treatments?

```{r}
library(tidyr)
messyData <- read.delim("clinicalData.txt")
messyData
```

```{r}
## Your answer here
tidyData <- gather(messyData, key = Treatment, value = Value, -Subject)
tidyData
boxplot(tidyData$Value~tidyData$Treatment)
```

`spread` is useful if we want to see our data in the wide format

```{r}
spread(tidyData, key = Treatment,value=Value)
```

`separate` can be used to split columns that encode more than one piece of information

```{r}
separate(tidyData, "Treatment",into=c("Treatment","Replicate"))
```


## The patients dataset
```{r}
library(dplyr)
library(stringr)

patients <- read.delim("patient-data.txt")
patients <- tbl_df(patients)
```


- Print all the columns between `Height` and `Grade_Level`

```{r}
select(patients, Height:Grade_Level)
```

- Print all the columns between `Height` and `Grade_Level`, but NOT `Pet`

```{r}
select(patients, Height:Grade_Level, -Pet)
```

- Print the columns `Height` and `Weight`
    + try to do this without specifying the full names of the columns
    
```{r}
select(patients, contains("eight"))
select(patients, ends_with("eight"))
```

- (OPTIONAL)
- Print the columns in alphabetical order
- Print all the columns whose name is less than 4 characters in length

```{r}
select(patients, order(colnames(patients)))
select(patients, which(nchar(colnames(patients)) < 4))
```


- We want to calculate the Body Mass Index (BMI) for each of our patients
- $BMI = (Weight) / (Height^2)$ 
    + where Weight is measured in Kilograms, and Height in Metres
- Create a new BMI variable in the dataset
- A BMI of 25 is considered overweight, calculate a new variable to indicate which individuals are overweight
- For a follow-on study, we are interested in overweight smokers
    + clean the `Smokes` column to contain just `TRUE` or `FALSE` values
- How many candidates (Overweight and Smoker) do you have?
- (EXTRA) What other problems can you find in the data?


```{r}
patients_clean <- mutate(patients, Sex = factor(str_trim(Sex)))
patients_clean <- mutate(patients_clean, Height = as.numeric(str_replace_all(Height,pattern = "cm", "")))
patients_clean <- mutate(patients_clean, Weight = as.numeric(str_replace_all(patients_clean$Weight,"kg", "")))
patients_clean <- mutate(patients_clean, BMI = (Weight/(Height/100)^2), Overweight = BMI > 25)
patients_clean <- mutate(patients_clean, Smokes = str_replace_all(Smokes, "Yes", "TRUE"))
patients_clean <- mutate(patients_clean, Smokes = as.logical(str_replace_all(Smokes, "No", "FALSE")))
patients_clean
```