0Hands-On-Exercise0.Rmd

---
title: "Quantitative Research Skills 2022, Hands-On Exercise 0"
subtitle: "Getting started with RStudio and R Markdown, visualisation, data wrangling"

author: "Rong Guang"
date: "14092022"

output: 
  html_document:
    theme: flatly
    highlight: haddock
    toc: true
    toc_depth: 2
    number_section: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Hands-On Exercise 0: Getting started with RStudio and R Markdown, visualisation, data wrangling

This R Markdown sheet includes a quite large set of R code chunks taken from the RHDS book
**R for Health Data Science** by Ewen Harrison and Riinu Pius (CRC Press, 2021), available online at https://argoshare.is.ed.ac.uk/healthyr_book/. 
The structure here follows the structure of the book (chapters, sections and subsections).

*******************************************************************************

# RHDS Chapter 1: Why we love R

You should begin by reading (or browsing through) this chapter from the RHDS book:

https://argoshare.is.ed.ac.uk/healthyr_book/why-we-love-r.html

**Note1:** On this QRS course, we focus on writing the steps of the analysis in R Markdown. R scripts that are also used in the RHDS book are a bit simpler, but these R Markdown sheets are often more useful.

**Note2:** We have already copy-pasted many of the R codes from the RHDS book for you in this R Markdown sheet. Your task is to run those codes and analyse their outputs - or copy more code from the book. We have also included the example datasets of RHDS book using links to our GitHub repository, so all the datasets of the book are easily accessible on the fly, without a need to download them separately.

The following is the first example of an **R code chunk**, where you can activate R functions. Its contents have been copy-pasted from Section 1.7. Try now to activate the R "command" ("1001:1004") by putting the cursor on that line below, and pressing Ctrl+Enter (on Windows) or Cmd+Enter (on Mac)!

```{r}
library(knitr)
# Now we're printing bigger numbers:
1001:1004

```

As you see above, the results (in this case, the numbers 1001, 1002, 1003, and 1004) appear immediately below the R code chunk (preceded by the usual [1], which you will also get used to, very soon.)

OK, as soon as you have read Chapter 1, you are ready to move on!

*******************************************************************************

We will begin the actual studying of the RHDS book from **Chapter 4**, in order to create some nice visualisations!

# RHDS Chapter 4: Different types of plots

In this assignment, you should explore (most of) Chapter 4 of the RHDS book, reading the contents and creating the graphs. In the end you should have tried a good set of different R graphs. This is where you start!

You can easily copy the R code from the book (use the one-click method "Copy to clipboard" in the upper-right corners of the R code boxes!)

https://argoshare.is.ed.ac.uk/healthyr_book/chap04-h1.html

Please follow the given structure below (the book sections) and continue similarly. Have fun! :)

## 4.1 Get the data

In order to draw Figure 4.1 step-by-step, we of course need the data! Here, we will use the famous Gapminder data that is available as an R package.

So, you may have to install the tidyverse and gapminder packages first. Then you must call the 'library' functions that will open those packages for use.

**Remember:** activate one line by Ctrl+Enter (on Windows) or Cmd+Enter (on Mac). The results will be seen below the R code chunk (the gray area).

```{r}
# copied from https://argoshare.is.ed.ac.uk/healthyr_book/chap04-data.html

library(tidyverse)
library(gapminder)
library(patchwork)   

glimpse(gapminder)

# continue with the code given on the same page and follow what has been done there!
gapminder$year %>% unique()
gapminder$country %>% n_distinct()
gapminder$continent %>% unique()

gapdata2007 <- gapminder %>% 
  filter(year == 2007)
gapdata2007

gapdata <- gapminder
```

## 4.2 Anatomy of ggplot explained

Here, we begin plotting the data step-by-step!

https://argoshare.is.ed.ac.uk/healthyr_book/chap04-gganatomy.html

In addition to activating R functions in this editor, try also **knitting** this document to an HTML report (webpage)!

```{r}

# LOT of R code in that section (4.2)!
# Copy all code here, piece by piece, and try each one to see what happens!

# (you may also create additional R code chunks below - no need to put all code in one chunk!)

gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp))

```
```{r}
ggplot(gapdata2007, aes(x = gdpPercap, y = lifeExp))
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = continent, y = lifeExp)) +
  geom_point()

```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point()
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(shape = 1)
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(shape = 1) +
  facet_wrap(~continent)
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(shape = 1) +
  facet_wrap(~pop > 50000000)
```
```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap/1000, y = lifeExp, colour = continent)) +
  geom_point(shape = 23) +
  facet_wrap(~continent) +
  theme_bw()
```


## 4.3 Set your theme - grey vs white


```{r}

# continue similarly, copying the R code and working with it step-by-step!
# (remember to save this document from time to time!)

theme_set(theme_bw())
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp, colour = continent)) +
  geom_point(shape = 1) +
  facet_wrap(~pop > 10000000)
```

## 4.4 Scatter plots/bubble plots


```{r}
a1 <- gapdata2007 %>% 
  ggplot(aes(x = gdpPercap/1000, y = lifeExp, size = pop)) +
  geom_point(shape = 1, alpha = 0.5) +
  theme(legend.position = "none")

a2 <- gapdata %>% 
  filter (country == "United Kingdom") %>% 
  ggplot (aes(x = year, y = lifeExp)) +
  geom_line()

a3 <- gapdata %>% 
  ggplot(aes(x = year, y = lifeExp, group = country, color = continent)) +
  geom_line() +
  facet_wrap (~continent) +
   scale_colour_brewer(palette = "Paired")+
  theme(legend.position = "none")
 
a1/a2|a3
```


```{r}
# continue the work...
# perhaps try to knit this, too, to see the web page that gathers everything you have done so far!
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap/1000, y = lifeExp, size = pop)) +
  geom_point()
```

```{r}
gapdata2007 %>% 
  ggplot(aes(x = gdpPercap/1000, y = lifeExp, size = pop)) +
  geom_point(shape = 1, alpha = 0.5)
```
## 4.5 Line plots/time series plots
```{r}
gapdata %>% 
  filter(country == "United Kingdom") %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line()
```

```{r}
gapdata %>% 
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line()
```

```{r}
gapdata %>% 
  ggplot(aes(x = year, y = lifeExp, group=country)) +
  geom_line()
```

```{r}
gapdata %>% 
  ggplot(aes(x = year, y = lifeExp, group=country, colour = continent)) +
  geom_line() +
  facet_wrap(~continent) +
  scale_colour_brewer(palette = "Paired")
```






## 4.6 Bar plots


```{r}
# continue the work...
a4 <- gapdata2007 %>% 
  filter (country %in% c("United Kingdom", "France", "Germany")) %>% 
  ggplot (aes(x = country, y = lifeExp)) +
  geom_col ()
a5 <- gapdata2007 %>% 
  filter (country %in% c("United Kingdom", "France", "Germany")) %>% 
  ggplot (aes(x= country, y = lifeExp)) + 
  geom_col() +
  coord_cartesian (ylim = c(79, 81))
a4|a5  

```

```{r}
gapdata2007 %>% 
  count(continent)
```

```{r}
a6 <- gapdata2007 %>% 
  ggplot(aes(x = continent)) +
  geom_bar(fill = "lavender", alpha = 0.99) 

a7 <- gapdata2007 %>% 
  ggplot (aes(x = continent, colour = country)) +
  geom_bar(fill = NA) +
  theme(legend.position = "none")
  #scale_color_brewer (palette = "Paired") #why doesn't it work here?
a6|a7
```

```{r}
gapdata2007 %>% 
  ggplot(aes(x = "Global", fill = continent)) +
  geom_bar(width = 0.2, colour = "white", size = 0.2) 

```
```{r}
gapdata2007 %>% 
  filter (continent == "Europe") %>% 
  ggplot (aes(x = fct_reorder(country, lifeExp), y = lifeExp)) + 
  geom_col(fill = NA, colour = "deepskyblue", size = 0.2)+
  coord_flip()
```
## 4.7 Histograms


```{r}
# continue the work...
histo1 <- gapdata2007 %>% 
  ggplot (aes(x = lifeExp)) +
  geom_histogram (binwidth = 10)

histo2 <- gapdata %>% 
  ggplot (aes(x = lifeExp)) +
  geom_histogram (binwidth = 10, fill = "deepskyblue", colour = "deepskyblue", size = 0.2, alpha = 0.5)

histo3 <- gapdata2007 %>% 
  ggplot (aes(x = lifeExp)) +
  geom_histogram ()

histo1/histo3 | histo2
```

## 4.8 Box plots


```{r}
# continue the work...
gapdata2007 %>% 
  ggplot (aes(x = continent, y = lifeExp)) +
  geom_boxplot()+
  facet_wrap (~pop > 10000000)
  
glimpse(gapdata2007)

```

## 4.9 Multiple geoms, multiple aes()


```{r}
# continue the work... this is the last section required!
multi1 <- gapdata2007 %>% 
  ggplot (aes(x = continent, y = lifeExp)) +
  geom_boxplot () +
  geom_point() +
  xlab("")


multi2 <- gapdata2007 %>% 
  ggplot (aes(x = continent, y = lifeExp)) +
  geom_boxplot () +
  geom_jitter () 
  
  
multi3 <- gapdata2007 %>% 
  ggplot (aes(x = continent, y = lifeExp, colour = continent)) +
  geom_boxplot() +
  geom_jitter() +
  theme (legend.position = "none") +
  xlab ("")+
  ylab("")
  
 
multi4 <- gapdata2007 %>% 
  ggplot (aes(x = continent, y = lifeExp)) +
  geom_boxplot() +
  geom_jitter(aes(colour = continent)) +
  theme(legend.position = "none") +
  ylab("")

multi1/multi2|multi3/multi4

```
```{r}
label_data <- gapdata2007 %>% 
  group_by (continent) %>% 
  filter (lifeExp == max(lifeExp)) %>% 
  select (country, continent, lifeExp)
label_data

multi5 <- gapdata2007 %>% 
  ggplot(aes(x = continent, y = lifeExp)) +
  geom_boxplot() +
  geom_jitter(aes(colour = continent)) +
  geom_label (data = label_data, aes (label = country)) +
  theme (legend.position = "none")
 
multi6 <- gapdata2007 %>% 
  ggplot(aes(x = continent, y = lifeExp)) +
  geom_boxplot() +
  geom_jitter(aes(colour = continent)) +
  geom_text (data = label_data, aes (label = lifeExp)) +
  theme (legend.position = "none")
multi5|multi6

```

**Good job!** 

You will now continue with the RHDS Chapter 2. This time we have already copy-pasted the required R code from the book. Your task is to actively go through all the sections below, activate the R functions and study their results. It may be a good idea to start also writing your own notes about things you may wonder - in this very same R Markdown sheet.

Brief instructions for the next steps towards **Assignment 0** are waiting for you in the end. Have fun!


*******************************************************************************


# RHDS Chapter 2: R basics

Again, the idea is that you read (or at least browse) the contents of the book while following the work flow (the R code chunks that have been created here for you). 

So, begin reading at https://argoshare.is.ed.ac.uk/healthyr_book/r-basics.html.

As soon as you see R code in the book, look at your RStudio, in this R Markdown sheet. Activate the R functions one at a time and see what happens. Write your own notes freely (for example, if you do not understand some part of the code, write some notes about it).


## 2.1 Reading data into R

```{r}
library(tidyverse)
example_data  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/example_data.csv")
example_data # look at the data (and then come back here - you may also close the data view)
```

### 2.1.2 Reading in the Global Burden of Disease example dataset

```{r}
library(tidyverse)
gbd_short <-read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/global_burden_disease_cause-year.csv")
View(gbd_short)

label_death <- gbd_short %>% 
  mutate (deaths_millions = deaths_millions+3)
label_death

disease1 <- gbd_short %>% 
  ggplot(aes(x = year, y = deaths_millions, colour = cause))+
  geom_line() +
  facet_wrap (~cause) +
  geom_point() +
  theme(legend.position = "none") +
  ylab("DeathPerYear (Millions)") +
  xlab("") +
  geom_text (data = label_death, aes(label = deaths_millions), size =2)

disease2 <- gbd_short %>% 
  ggplot (aes(x = factor(year), y = deaths_millions, colour = cause, fill = cause)) +
  geom_col (width = 0.5) +
  theme (legend.position = "none") +
  coord_cartesian (ylim = c(20, 60)) 
disease1/disease2
```

## 2.2 Variable types and why we care

**Note:** Some outputs may differ a bit from the book, because R is constantly developed further. One example is the following. (We have modified the code chunk a bit to reveal the outputs shown in the book.)

```{r}
library(tidyverse)
typesdata  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/typesdata.csv")
spec(typesdata)
typesdata
```

```{r}
typesdata_faulty  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/typesdata_faulty.csv")
spec(typesdata_faulty)
typesdata_faulty
```

### 2.2.1 Numeric variables (continuous)

```{r}
typesdata$measurement  %>%  mean()
measurement_mean  <-  typesdata$measurement %>%  mean()

measurement_mean == 3.333333

(0.10 + 0.05) == 0.15

library(tidyverse)
near(0.10+0.05, 0.15)

near(measurement_mean, 3.333333, 0.000001)
near (measurement_mean, 3.33333333)
```

### 2.2.2 Character variables

```{r}
library(tidyverse)
typesdata
typesdata  %>% 
  count(group)

typesdata %>% 
  count(group, sort = TRUE)

# all ids are unique:
typesdata  %>%  
  count(id, sort = TRUE)

# we add in a duplicate row where id = ID3,
# then count again:
typesdata  %>%  
  add_row(id = "ID3")  %>%  
  count(id, sort = TRUE)
typesdata

typesdata1 <- add_row(typesdata, id="ID3", group="Treatment")
typesdata1
```

### 2.2.3 Factor variables (categorical)

(just read - more about this topic will follow later...)

### 2.2.4 Date/time variables

```{r}
#Dear professor, I came across the problem that as long as I kept these codes relating to “lubricate”， the file will always not successfully save. I have no choice but turned them into notes for now. But I am sure that I have been clear about how these codes work.

#install.packages("lubridate")
#library(lubridate) # lubridate makes working with dates easier
#current_datetime  <-  Sys.time()
#current_datetime

#my_datetime  <-  "2020-12-01 12:00"
#my_datetime

#my_datetime - current_datetime

#current_datetime  %>%  class()
#my_datetime  %>%  class()
```

2.2.4 continued...

```{r}
#Dear professor, I came across the problem that as long as I kept these codes relating to “lubricate”， the file will always not successfully save. I have no choice but turned them into notes for now. But I am sure that I have been clear about how these codes work.

#install.packages("lubridate")
#library("lubridate")
#my_datetime_converted  <-  ymd_hm(my_datetime)
#my_datetime_converted

#my_datetime_converted - current_datetime

#my_datesdiff  <-  my_datetime_converted - current_datetime
#my_datesdiff  %>%  class()

#ymd_hm("2021-01-02 12:00") + my_datesdiff

#560/my_datesdiff
#560/as.numeric(my_datesdiff)
```

2.2.4 continued...

```{r}
#Dear professor, I came across the problem that as long as I kept these codes relating to “lubricate”， the file will always not successfully save. I have no choice but turned them into notes for now. But I am sure that I have been clear about how these codes work.

#parse_date_time("12:34 07/Jan'20", "%H:%M %d/%b'%y")

#Sys.time()
#Sys.time()  %>%  format("%H:%M on %B-%d (%Y)")

#Sys.time()  %>%  format("Happy days, the current time is %H:%M %B-%d (%Y)!")
```

## 2.3 Objects and functions

```{r}
library(tidyverse)
mydata  <-  tibble(
  id   = 1:4,
  sex  = c("Male", "Female", "Female", "Male"),
  var1 = c(4, 1, 2, 3),
  var2 = c(NA, 4, 5, NA),
  var3 = c(2, 1, NA, NA)
)
```

### 2.3.1 data frame/tibble

### 2.3.2 Naming objects

```{r}
mydata
```

### 2.3.3 Function and its arguments

```{r}
mydata$var1

mean(mydata$var1)

mean(mydata$var2)

mean(mydata$sex)

mean(mydata$var2, na.rm = TRUE)

Sys.time()
```

### 2.3.4 Working with objects

```{r}
a <-  103

a

seq(15, 30)

example_sequence <-  seq(15, 30)

example_sequence  <- example_sequence/2

example_sequence
```

### 2.3.5 <- and =

```{r}
mean_result  <-  mean(mydata$var2, na.rm = TRUE)
mean_result
```

### 2.3.6 Recap: object, function, input, argument

## 2.4 Pipe - %&gt;%

```{r}
library(tidyverse)
mydata$var1 %>%   mean()

mean_result  <-  mydata$var1  %>%  mean()
mean_result
```

### 2.4.1 Using . to direct the pipe

```{r}
mydata %>%  
  lm(var1~var2, data = .)
```

## 2.5 Operators for filtering data

```{r}
gbd_short %>%  
  filter(year < 1995)

gbd_short  %>%  
  filter(year <= 1995)

gbd_short  %>%  
  filter(year == 1995)
```

2.5 continued...

```{r}
gbd_short  %>%  
 filter(year <= 1995)

gbd_short  %>%  
  filter(year == 1995 | year == 2017)

gbd_short  %>%  
  filter(year == max(year) | year == min(year))
```

### 2.5.1 Worked examples

```{r}
mydata_year2000  <-  gbd_short  %>%  
  filter(year == 2000)

mydata_year2000
```

2.5.1 continued...

```{r}
new_data_selection  <-  gbd_short  %>%  
  filter((year == 1990 | year == 2013) & cause == "Communicable diseases")

new_data_selection
```

2.5.1 continued...

```{r}
# Or we can get rid of the extra brackets around the years
# by moving cause into a new filter on a new line:

new_data_selection <- gbd_short  %>%  
  filter(year == 1990 | year == 2013)  %>%  
  filter(cause == "Communicable diseases")


new_data_selection
```

2.5.1 continued...

```{r}
# Or even better, we can include both in one filter() call, as all
# separate conditions are by default joined with "&amp;":

new_data_selection  <-  gbd_short  %>%  
  filter(year == 1990 | year == 2013,
         cause == "Communicable diseases")

new_data_selection
```

## 2.6 The combine function: c()

```{r}
gbd_short$cause  %>%  unique()
class(gbd_short$cause)
gbd_short  %>%  count(cause)

gbd_short  %>%  
  # also filtering for a single year to keep the result concise
  filter(year == 1990)  %>%  
  filter(cause == "Communicable diseases" | cause == "Non-communicable diseases")

gbd_short  %>%  
  filter(year == 1990)  %>%  
  filter(cause %in% c("Communicable diseases", "Non-communicable diseases"))
```

## 2.7 Missing values (NAs) and filters

```{r}
mydata

mydata  %>%  
  filter(is.na(var2))

mydata  %>%  
  filter(!is.na(var2))

mydata  %>%  
  filter(var2 != 5) %>% 
  knitr::kable()

mydata %>% 
  filter(var2 != 5 | is.na(var2)) %>% 
  knitr::kable()
```

2.7 continued...

```{r}
subset1  <-  mydata  %>%  
  filter(var2 == 5)

subset2  <-  mydata  %>%  
  filter(! var2 == 5)

subset1; subset2

nrow(mydata)
nrow(subset1)
nrow(subset2)

nrow(subset1) + nrow(subset2) == nrow(mydata)

subset3  <-  mydata  %>%  
  filter(is.na(var2))
subset3

nrow(subset1) + nrow(subset2) + nrow(subset3) == nrow(mydata)
```

## 2.8 Creating new columns - mutate()

```{r}
typesdata

typesdata$measurement

typesdata$measurement/2

typesdata  %>%  
  mutate(measurement/2)

typesdata  %>%  
  mutate(measurement_half = measurement/2)
```

2.8 continued...

```{r}
#Dear professor, I came across the problem that as long as I kept these codes relating to “Nasty column name”， the file will always not successfully save. I have no choice but turned them into notes for now. But I am sure that I have been clear about how these codes work.

#mydata$`Nasty column name`
#mydata
# or

#mydata  %>%  
 #select(`Nasty column name`)
```

2.8 continued...

```{r}
typesdata_modified  <-  typesdata  %>%  
  mutate(measurement_half = measurement/2)

typesdata_modified
```

2.8 continued...

```{r}
library(lubridate)
typesdata
typesdata  %>%  
  mutate(reference_date = ymd_hm("2020-01-01 12:00"),
         dates_difference = reference_date - date)  %>%  
  select(date, reference_date, dates_difference)

typesdata  %>%  
  mutate(mean_measurement = mean(measurement))

typesdata  %>%  
  mutate(mean_measurement     = mean(measurement))  %>%  
  mutate(measurement_relative = measurement/mean_measurement)  %>%  
  select(matches("measurement"))

```

### 2.8.1 Worked example/exercise

```{r}
typesdata  %>%  
  mutate(reference_date   = ymd_hm("2020-01-01 12:00"),
         dates_difference = reference_date - date)  %>%  
  mutate(dates_difference = round(dates_difference))  %>%  
  select(matches("date"))

typesdata  %>% select(matches("date"))
```

## 2.9 Conditional calculations - if_else()


```{r}
typesdata  %>%  
  mutate(above_threshold = if_else(measurement > 3,
                                   "Above three",
                                   "Below three"))
```
```{r}
a <- tibble(number = c(1,2,3,2,5,10,25,12,23,7,9))
a
a %>% mutate(number_level = ifelse(
  number <= 10, "low", ifelse(
    number <= 20, "middle", "high"))) #nested ifelse
```

## 2.10 Create labels - paste()

```{r}
typesdata  %>%  
  mutate(plot_label = paste(id,
                            "was last measured at", date,
                            ", and the value was",    measurement))  %>%  
  select(plot_label)

pastedata  <-  tibble(year  = c(2007, 2008, 2009),
                   month = c("Jan", "Feb", "March"),
                   day   = c(1, 2, 3))
pastedata
```

2.10 continued...

```{r}
pastedata  %>%  
  mutate(date = paste(day, month, year, sep = "/"))

library(lubridate)

pastedata  %>%  
  mutate(date = paste(day, month, year, sep = "-"))  %>%  
  mutate(date = dmy(date))
```

## 2.11 Joining multiple datasets

```{r}
library(tidyverse)
patientdata  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/patient_data.csv")
patientdata

labsdata  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/labs_data.csv")
labsdata

 full_join(patientdata, labsdata)

inner_join(patientdata, labsdata)

 left_join(patientdata, labsdata)

right_join(patientdata, labsdata)
```

### 2.11.1 Further notes about joins

```{r}
patientdata_new  <-  read_csv("https://raw.githubusercontent.com/KimmoVehkalahti/RHDS/master/data/patient_data_updated.csv")
patientdata_new

bind_rows(patientdata, patientdata_new)

labsdata_updated  <-  labsdata  %>%  
  add_row(id = 5, measurement = 2.49)
labsdata_updated

left_join(patientdata, labsdata_updated)
```

**Well done! That was active reading of Chapter 2.**

You have now completed the Hands-On Exercise 0. **GOOD JOB!**

Your next task is to download and work through **Assignment 0**. You will need the information above, so it is a good idea to keep this file open too, while you are working with the Assignment.

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