3.workflows-solutions.Rmd

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


******

Take the steps used to clean the patients dataset and calculate BMI (see below for the code)
- Re-write in the piping framework


******

```{r message = FALSE}
library(dplyr)
library(stringr)

patients <- read.delim("patient-data.txt")
patients <- tbl_df(patients)
patients_clean <- mutate(patients, Sex = factor(str_trim(Sex)))
patients_clean <- mutate(patients_clean, Height= as.numeric(str_replace_all(patients_clean$Height, pattern = "cm", "")))
patients_clean <- mutate(patients_clean, Weight = as.numeric(str_replace_all(patients_clean$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")))
```


```{r}
## Re-write the above template using 'pipes'
patients_clean <- read.delim("patient-data.txt") %>%
  tbl_df %>%
  mutate(Sex = factor(str_trim(Sex))) %>%
  mutate(Height= as.numeric(str_replace_all(Height, pattern = "cm", ""))) %>%
  mutate(Weight = as.numeric(str_replace_all(Weight, "kg", ""))) %>%
  mutate(BMI = (Weight/(Height/100)^2), Overweight = BMI > 25) %>%
  mutate(Smokes = str_replace_all(Smokes, "Yes", "TRUE")) %>%
  mutate(Smokes = as.logical(str_replace_all(Smokes, "No", "FALSE")))

patients_clean

```


******
Use `filter` to print the following subsets of the dataset


- Choose the Female patients from New York or New Jersey

```{r}
filter(patients_clean, Sex == "Female", State == "New York" | State == "New Jersey")
filter(patients_clean, Sex == "Female", State %in% c("New York", "New Jersey"))
filter(patients_clean, Sex == "Female", grepl("New", State))
```

- Choose the overweight smokers that are still alive

```{r}
filter(patients_clean, Overweight, Smokes, !Died)
```


- Choose the patients who own a Pet that is not a dog

```{r}

filter(patients_clean, !Pet %in% c("None", "NONE", "NULL"), !is.na(Pet), !Pet %in% c("DOG", "Dog"))

```

- Patients born in June
 
```{r message = FALSE}
filter(patients_clean, substr(Birth, 6, 7) == "06")

library(lubridate)
filter(patients_clean, month(ymd(Birth)) == 6)
```

- Patients with a Number > 100

```{r}
filter(patients_clean, as.numeric(substr(patients$ID, 7, 9)) > 100)
```

- Patients that entered the study on 2016-05-31

```{r}
library(tidyr)
mutate(patients_clean, Date.Entered.Study = ifelse(Date.Entered.Study == "", NA, as.character(Date.Entered.Study))) %>%
  fill(Date.Entered.Study) %>%
  filter(Date.Entered.Study == "2016-05-31")
```