02-databasics.Rmd

# (PART) Part II: Basics {-}

# Entering and cleaning data #1

The video lectures for this chapter are embedded at relevant places in the text, 
with links to download a pdf of the associated slides for each video. 
You can also access [a full playlist for the videos for this chapter](https://www.youtube.com/playlist?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl).

## Objectives

After this chapter, you should (know / understand / be able to ):

- Understand what a flat file is and how it differs from data stored in a binary file format
- Be able to distinguish between delimited and fixed width formats for flat files
- Be able to identify the delimiter in a delimited file
- Be able to describe a working directory
- Be able to read in different types of flat files
- Be able to read in a few types of binary files (SAS, Excel) 
- Understand the difference between relative and absolute file pathnames
- Describe the basics of your computer's directory structure
- Reference files in different locations in your directory structure using relative and absolute pathnames
- Use the basic `dplyr` functions `rename`, `select`, `mutate`, `slice`, `filter`, and `arrange` to work with data in a dataframe object
- Convert a column to a date format using `lubridate` functions
- Extract information from a date object (e.g., month, year, day of week) using `lubridate` functions
- Define a logical operator and know the R syntax for common logical operators
- Use logical operators in conjunction with `dplyr`'s `filter` function to create subsets of a dataframe based on logical conditions 
- Use piping to apply multiple `dplyr` functions in sequence to a dataframe

## Overview

<iframe width="768" height="480" src="https://www.youtube.com/embed/HIWxtOK0-DI?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_1.pdf) 
a pdf of the lecture slides for this video.

There are four basic steps you will often repeat as you prepare to analyze data
in R:

1. Identify where the data is (If it's on your computer, which directory? If
it's online, what's the url?)
2. Read data into R (e.g., `read_delim`, `read_csv` from the `readr` package)
using the file path you figured out in step 1
3. Check to make sure the data came in correctly (`dim`, `head`, `tail`, `str`)
4. Clean the data up 

In this chapter, I'll go basics for each of these steps, as well as dive a bit
deeper into some related topics you should learn now to make your life easier as
you get started using R for research.

## Reading data into R

Data comes in files of all shapes and sizes. R has the capability to read data
in from many of these, even proprietary files for other software (e.g., Excel
and SAS files). As a small sample, here are some of the types of data files that
R can read and work with:

- Flat files (much more about these in just a minute)
- Files from other statistical packages (SAS, Excel, Stata, SPSS)
- Tables on webpages (e.g., the table on ebola outbreaks near the end of [this
Wikipedia
page](http://en.wikipedia.org/wiki/Ebola_virus_epidemic_in_West_Africa))
- Data in a database (e.g., MySQL, Oracle)
- Data in JSON and XML formats
- Really crazy data formats used in other disciplines (e.g., netCDF files from
climate research, MRI data stored in Analyze, NIfTI, and DICOM formats)
- Geographic shapefiles
- Data through APIs 

Often, it is possible to read in and clean up even incredibly messy data, by
using functions like `scan` and `readLines` to read the data in a line at a
time, and then using regular expressions (which I'll cover in the "Intermediate"
section of the course) to clean up each line as it comes in. In over a decade of
coding in R, I think the only time I've come across a data file I couldn't get
into R was for proprietary precision agriculture data collected at harvest by a
combine.

### Reading local flat files

Much of the data that you will want to read in will be in flat files. Basically,
these are files that you can open using a text editor; the most common type
you'll work with are probably comma-separated files (often with a `.csv` or
`.txt` file extension). Most flat files come in two general categories:

1. Fixed width files; and

2. Delimited files: 

- ".csv": Comma-separated values
- ".tab", ".tsv": Tab-separated values
- Other possible delimiters: colon, semicolon, pipe ("|")

*Fixed width files* are files where a column always has the same width, for all
the rows in the column. These tend to look very neat and easy-to-read when you
open them in a text editor. For example, the first few rows of a fixed-width
file might look like this:

```
Course            Number          Day          Time
Intro to Epi      501             M/W/F        9:00-9:50
Advanced Epi      521             T/Th         1:00-2:15
```

Fixed width files used to be very popular, and they make it easier to look at data
when you open the file in a text editor. However, now it's pretty rare to just use 
a text editor to open a file and check out the data, and these files can be a bit 
of a pain to read into R and other programs because you sometimes have to specify 
exactly how wide each of the columns is. You may come across a fixed width file 
every now and then, though, particularly when working with older data sets, so it's 
useful to be able to recognize one and to know how to read it in.

*Delimited files* use some *delimiter* (for example, a column or a tab) to
separate each column value within a row. The first few rows of a delimited file
might look like this:

```
Course, Number, Day, Time
"Intro to Epi", 501, "M/W/F", "9:00-9:50"
"Advanced Epi", 521, "T/Th", "1:00-2:15"
```

Delimited files are very easy to read into R. You just need to be able to figure
out what character is used as a delimiter (commas in the example above) and
specify that to R in the function call to read in the data.

These flat files can have a number of different file extensions. The most
generic is `.txt`, but they will also have ones more specific to their format,
like `.csv` for a comma-delimited file or `.fwf` for a fixed with file.

<iframe width="768" height="480" src="https://www.youtube.com/embed/A5VRVjSQ_Ws?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_2.pdf) 
a pdf of the lecture slides for this video.

R can read in data from both fixed with and delimited flat files. The only catch
is that you need to tell R a bit more about the format of the flat file,
including whether it is fixed width or delimited. If the file is fixed width,
you will usually have to tell R the width of each column. If the file is
delimited, you'll need to tell R which delimiter is being used.

If the file is delimited, you can use the `read_delim` family of functions from
the `readr` package to read it in. This family of functions includes several
specialized functions. All members of the `read_delim` family are doing the same
basic thing. The only difference is what defaults each function has for the
delimiter (`delim`). Members of the `read_delim` family include:

Function     | Delimiter
------------ | ---------- 
`read_csv`    |  comma     
`read_csv2`   | semi-colon 
`read_table2` | whitespace
`read_tsv`    | tab

You can use `read_delim` to read in any delimited file, regardless of the delimiter.
However, you will need to specify the delimiter using the `delim` parameters. If you
remember the more specialized function call (e.g., `read_csv` for a comma delimited
file), therefore, you can save yourself some typing. 

For example, to read in the Ebola data, which is comma-delimited, you could
either use `read_table` with a `delim` argument specified or use `read_csv`, in
which case you don't have to specify `delim`:

```{r, message = FALSE}
library(package = "readr")

# The following two calls do the same thing
ebola <- read_delim(file = "data/country_timeseries.csv", delim = ",")
```

```{r}
ebola <- read_csv(file = "data/country_timeseries.csv")
```

```{block, type = 'rmdtip'}
The message that R prints after this call ("Parsed with column specification:..
") lets you know what classes were used for each column (this function tries to
guess the appropriate class and typically gets it right). You can suppress the
message using the `cols_types = cols()` argument.

If `readr` doesn't correctly guess some of the columns classes you can use the
`type_convert()` function to take another go at guessing them after you've
tweaked the formats of the rogue columns.
```

This family of functions has a few other helpful options you can specify. For example,
if you want to skip the first few lines of a file before you start reading in the data,
you can use `skip` to set the number of lines to skip. If you only want to read in 
a few lines of the data, you can use the `n_max` option. For example, if you have a 
really long file, and you want to save time by only reading in the first ten lines
as you figure out what other options to use in `read_delim` for that file, you could
include the option `n_max = 10` in the `read_delim` call. Here is a table of some of
the most useful options common to the `read_delim` family of functions: 

Option  | Description
------- | -----------
`skip`    | How many lines of the start of the file should you skip?
`col_names`  | What would you like to use as the column names?
`col_types`   | What would you like to use as the column types?
`n_max`   | How many rows do you want to read in?
`na` | How are missing values coded?


```{block, type = 'rmdnote'}
Remember that you can always find out more about a function by looking at its
help file. For example, check out `?read_delim` and `?read_fwf`. You can also
use the help files to determine the default values of arguments for each
function.
```

So far, we've only looked at functions from the `readr` package for reading in data
files. There is a similar family of functions available in base R, the `read.table`
family of functions. The `readr` family of functions is very similar to the base R 
`read.table` functions, but have some more sensible defaults. Compared to the
`read.table` family of functions, the `readr` functions: 

- Work better with large datasets: faster, includes progress bar
- Have more sensible defaults (e.g., characters default to characters, not factors)

I recommend that you always use the `readr` functions rather than their base R 
alternatives, given these advantages. However, you are likely to come across code
that someone else has written that uses one of these base R functions, so it's 
helpful to know what they are. Functions in the `read.table` family include:

- `read.csv`
- `read.delim`
- `read.table` 
- `read.fwf`

```{block, type = 'rmdnote'}
The `readr` package is a member of the tidyverse of packages. The *tidyverse*
describes an evolving collection of R packages with a common philosophy, and
they are unquestionably changing the way people code in R. Many were developed
in part or full by Hadley Wickham and others at RStudio. Many of these packages
are less than ten years old, but have been rapidly adapted by the R community.
As a result, newer examples of R code will often look very different from the
code in older R scripts, including examples in books that are more than a few
years old. In this course, I'll focus on "tidyverse" functions when possible,
but I do put in details about base R equivalent functions or processes at some
points---this will help you interpret older code. You can download all the
tidyverse packages using `install.packages("tidyverse")`, `library("tidyverse")`
makes all the tidyverse functions available for use. 
```

### Reading in other file types

Later in the course, we'll talk about how to open a variety of other file types
in R. However, you might find it immediately useful to be able to read in files
from other statistical programs.

There are two "tidyverse" packages---`readxl` and `haven`---that help with this.
They allow you to read in files from the following formats:

```{r echo = FALSE}
read_funcs <- data.frame(file_type = c("Excel",
                                       "SAS",
                                       "SPSS",
                                       "Stata"),
                         func = c("`read_excel`",
                                  "`read_sas`",
                                  "`read_spss`",
                                  "`read_stata`"),
                         pkg = c("`readxl`",
                                 "`haven`",
                                 "`haven`",
                                 "`haven`"))
knitr::kable(read_funcs, col.names = c("File type", "Function", "Package"))
```

## Directories and pathnames

### Directory structure

<iframe width="768" height="480" src="https://www.youtube.com/embed/Ll5seRpzekY?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_3.pdf) 
a pdf of the lecture slides for this video.

So far, we've only looked at reading in files that are located in your current
working directory. For example, if you're working in an R Project, by default
the project will open with that directory as the working directory, so you can
read files that are saved in that project's main directory using only the file
name as a reference.

However, you'll often want to read in files that are located somewhere else on
your computer, or even files that are saved on another computer (for example,
data files that are posted online). Doing this is very similar to reading in a
file that is in your current working directory; the only difference is that you
need to give R some directions so it can find the file.

The most common case will be reading in files in a subdirectory of your current
working directory. For example, you may have created a "data" subdirectory in
one of your R Projects directories to keep all the project's data files in the
same place while keeping the structure of the main directory fairly clean. In
this case, you'll need to direct R into that subdirectory when you want to read
one of those files.

To understand how to give R these directions, you need to have some
understanding of the directory structure of your computer. It seems a bit of a
pain and a bit complex to have to think about computer directory structure in
the "basics" part of this class, but this structure is not terribly complex once
you get the idea of it. There are a couple of very good reasons why it's worth
learning now.

First, many of the most frustrating errors you get when you start using R trace
back to understanding directories and filepaths. For example, when you try to
read a file into R using only the filename, and that file is not in your current
working directory, you will get an error like:

```
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") : cannot open file 'Ex.csv': No such file or directory
```

This error is especially frustrating when you're new to R because it happens at
the very beginning of your analysis---you can't even get your data in. Also, if
you don't understand a bit about working directories and how R looks for the
file you're asking it to find, you'd have no idea where to start to fix this
error. Second, once you understand how to use pathnames, especially relative
pathnames, to tell R how to find a file that is in a directory other than your
working directory, you will be able to organize all of your files for a project
in a much cleaner way. For example, you can create a directory for your project,
then create one subdirectory to store all of your R scripts, and another to
store all of your data, and so on. This can help you keep very complex projects
more structured and easier to navigate. We'll talk about these ideas more in the
course sections on Reproducible Research, but it's good to start learning how
directory structures and filepaths work early.

Your computer organizes files through a collection of directories. Chances are,
you are fairly used to working with these in your daily life already (although
you may call them "folders" rather than "directories"). For example, you've
probably created new directories to store data files and Word documents for a
specific project.

Figure \@ref(fig:filedirstructure) gives an example file directory structure for
a hypothetical computer. Directories are shown in blue, and files in green.

```{r filedirstructure, echo = FALSE, fig.cap= "An example of file directory structure.", out.width = "600pt", fig.align = "center"}
knitr::include_graphics("figures/FileDirectoryStructure.png")
```

You can notice a few interesting things from Figure \@ref(fig:filedirstructure).
First, you might notice the structure includes a few of the directories that you
use a lot on your own computer, like `Desktop`, `Documents`, and `Downloads`.
Next, the directory at the very top is the computer's root directory, `/`. For a
PC, the root directory might something like `C:`; for Unix and Macs, it's
usually `/`. Finally, if you look closely, you'll notice that it's possible to
have different files in different locations of the directory structure with the
same file name. For example, in the figure, there are files names
`heat_mort.csv` in both the `CourseText` directory and in the `example_data`
directory. These are two different files with different contents, but they can
have the same name as long as they're in different directories. This fact---that
you can have files with the same name in different places---should help you
appreciate how useful it is that R requires you to give very clear directions to
describe exactly which file you want R to read in, if you aren't reading in
something in your current working directory.

You will have a home directory somewhere near the top of your structure,
although it's likely not your root directory. In the hypothetic computer in
Figure \@ref(fig:filedirstructure), the home directory is
`/Users/brookeanderson`. I'll describe just a bit later how you can figure out
what your own home directory is on your own computer.

### Working directory

When you run R, it's always running from within some working directory, which
will be one of the directories somewhere in your computer's directory structure.
At any time, you can figure out which directory R is working in by running the
command `getwd()` (short for "get working directory"). For example, my R session
is currently running in the following directory:

```{r}
getwd()
```

This means that, for my current R session, R is working in the
`RProgrammingForResearch` subdirectory of my `brookeanderson` directory (which
is my home directory).

There are a few general rules for which working directory R will start in when
you open an R session. These are not absolute rules, but they're generally true.
If you have R closed, and you open it by double-clicking on an R script, then R
will generally open with, as its working directory, the directory in which that
script is stored. This is often a very convenient convention, because often any
of the data you'll be reading in for that script is somewhere near where the
script file is saved in the directory structure. If you open R by
double-clicking on the R icon in "Applications" (or something similar on a PC),
R will start in its default working directory. You can find out what this is, or
change it, in RStudio's "Preferences". Finally, if you open
an R Project, R will start in that project's working directory (the directory in
which the `.Rproj` file for the project is stored).

### File and directory pathnames

<iframe width="768" height="480" src="https://www.youtube.com/embed/vutYRvQj36c?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_4.pdf) 
a pdf of the lecture slides for this video.

Once you get a picture of how your directories and files are organized, you can
use pathnames, either absolute or relative, to read in files from different
directories than your current working directory. Pathnames are the directions
for getting to a directory or file stored on your computer.

When you want to reference a directory or file, you can use one of two types of
pathnames:

- *Relative pathname*: How to get to the file or directory from your current
working directory
- *Absolute pathname*: How to get to the file or directory from anywhere on the
computer

Absolute pathnames are a bit more straightforward conceptually, because they
don't depend on your current working directory. However, they're also a lot
longer to write, and they're much less convenient if you'll be sharing some of
your code with other people who might run it on their own computers. I'll
explain this second point a bit more later in this section.

*Absolute pathnames* give the full directions to a directory or file, starting
all the way at the root directory. For example, the `heat_mort.csv` file in the
`CourseText` directory has the absolute pathname:

```
"/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv"
```

You can use this absolute pathname to read this file in using any of the `readr`
functions to read in data. This absolute pathname will *always* work, regardless
of your current working directory, because it gives directions from the
root---it will always be clear to R exactly what file you're talking about.
Here's the code to use to read that file in using the `read.csv` function with
the file's absolute pathname:

```{r eval = FALSE}
heat_mort <- read_csv(file = "/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv")
```

The *relative pathname*, on the other hand, gives R the directions for how to
get to a directory or file from the current working directory. If the file or
directory you're looking for is pretty close to your current working directory
in your directory structure, then a relative pathname can be a much shorter way
to tell R how to get to the file than an absolute pathname. However, the
relative pathname depends on your current working directory---the relative
pathname that works perfectly when you're working in one directory will not work
at all once you move into a different working directory.

As an example of a relative pathname, say you're working in the directory
`RCourseFall2015` within the file structure shown in Figure
\@ref(fig:filedirstructure), and you want to read in the `heat_mort.csv` file in
the `CourseText` directory. To get from `RCourseFall2015` to that file, you'd
need to look in the subdirectory `CourseText`, where you could find
`heat_mort.csv`. Therefore, the relative pathname from your working directory
would be:

```
"CourseText/heat_mort.csv"
```

You can use this relative pathname to tell R where to find and read in the file:

```{r eval = FALSE}
heat_mort <- read_csv("CourseText/heat_mort.csv")
```

While this pathname is much shorter than the absolute pathname, it is important
to remember that if you are working in a different working directory, this
relative pathname would no longer work.

There are a few abbreviations that can be really useful for pathnames:

```{r echo = FALSE}
dirpath_shortcuts <- data.frame(abbr = c("`~`", "`.`", "`..`", "`../..`"),
                                meaning = c("Home directory",
                                            "Current working directory",
                                            "One directory up from current working directory",
                                            "Two directories up from current working directory"))
knitr::kable(dirpath_shortcuts, col.names = c("Shorthand", "Meaning"))
```

These can help you keep pathnames shorter and also help you move "up-and-over"
to get to a file or directory that's not on the direct path below your current
working directory.

For example, my home directory is `/Users/brookeanderson`. You can use the
`list.files()` function to list all the files in a directory. If I wanted to
list all the files in my `Downloads` directory, which is a direct sub-directory
of my home directory, I could use:

```
list.files("~/Downloads")
```

As a second example, say I was working in the working directory `CourseText`,
but I wanted to read in the `heat_mort.csv` file that's in the `example_data`
directory, rather than the one in the `CourseText` directory. I can use the `..`
abbreviation to tell R to look up one directory from the current working
directory, and then down within a subdirectory of that. The relative pathname in
this case is:

```
"../Week2_Aug31/example_data/heat_mort.csv"
```

This tells R to look one directory up from the working directory (`..`) (this is
also known as the **parent directory** of the current directory), which in this
case is to `RCourseFall2015`, and then down within that directory to
`Week2_Aug31`, then to `example_data`, and then to look there for the file
`heat_mort.csv`.

The relative pathname to read this file while R is working in the `CourseTest`
directory would be:

```
heat_mort <- read_csv("../Week2_Aug31/example_data/heat_mort.csv")
```

Relative pathnames "break" as soon as you tried them from a different working
directory---this fact might make it seem like you would never want to use
relative pathnames, and would always want to use absolute ones instead, even if
they're longer. If that were the only consideration (length of the pathname),
then perhaps that would be true. However, as you do more and more in R, there
will likely be many occasions when you want to use relative pathnames instead.
They are particularly useful if you ever want to share a whole directory, with
all subdirectories, with a collaborator. In that case, if you've used relative
pathnames, all the code should work fine for the person you share with, even
though they're running it on their own computer. Conversely, if you'd used
absolute pathnames, none of them would work on another computer, because the
"top" of the directory structure (i.e., for me, `/Users/brookeanderson/Desktop`)
will almost definitely be different for your collaborator's computer than it is
for yours.

If you're getting errors reading in files, and you think it's related to the
relative pathname you're using, it's often helpful to use `list.files()` to make
sure the file you're trying to load is in the directory that the relative
pathname you're using is directing R to.

### Diversion: `paste`

This is a good opportunity to explain how to use some functions that can be very
helpful when you're using relative or absolute pathnames: `paste()` and
`paste0()`.

As a bit of important background information, it's important that you understand
that you can save a pathname (absolute or relative) as an R object, and then use
that R object in calls to later functions like `list.files()` and `read_csv()`.
For example, to use the absolute pathname to read the `heat_mort.csv` file in
the `CourseText` directory, you could run:

```
my_file <- "/Users/brookeanderson/Desktop/RCourseFall2015/CourseText/heat_mort.csv"
heat_mort <- read_csv(file = my_file)
```

You'll notice from this code that the pathname to get to a directory or file can
sometimes become ungainly and long. To keep your code cleaner, you can address
this by using the `paste` or `paste0` functions. These functions come in handy
in a lot of other applications, too, but this is a good place to introduce them.

The `paste()` function is very straightforward. It takes, as inputs, a series of
different character strings you want to join together, and it pastes them
together in a single character string. (As a note, this means that your result
vector will only be one element long, for basic uses of `paste()`, while the
inputs will be several different character stings.) You separate all the
different things you want to paste together using with commas in the function
call. For example:

```{r}
paste("Sunday", "Monday", "Tuesday")

length(x = c("Sunday", "Monday", "Tuesday"))
length(x = paste("Sunday", "Monday", "Tuesday"))
```

The `paste()` function has an option called `sep = `. This tells R what you want
to use to separate the values you're pasting together in the output. The default
is for R to use a space, as shown in the example above. To change the separator,
you can change this option, and you can put in just about anything you want. For
example, if you wanted to paste all the values together without spaces, you
could use `sep = ""`:

```{r}
paste("Sunday", "Monday", "Tuesday", sep = "")
```

As a shortcut, instead of using the `sep = ""` option, you could achieve the
same thing using the `paste0` function. This function is almost exactly like
`paste`, but it defaults to `""` (i.e., no space) as the separator between
values by default:

```{r}
paste0("Sunday", "Monday", "Tuesday")
```

With pathnames, you will usually not want spaces. Therefore, you could think
about using `paste0()` to write an object with the pathname you want to
ultimately use in commands like `list.files()` and `setwd()`. This will allow
you to keep your code cleaner, since you can now divide long pathnames over
multiple lines:

```
my_file <- paste0("/Users/brookeanderson/Desktop/",
                  "RCourseFall2015/CourseText/heat_mort.csv")
heat_mort <- read_csv(file = my_file)
```

You will end up using `paste()` and `paste0()` for many other applications, but
this is a good example of how you can start using these functions to start to
get a feel for them.

### Reading online flat files

So far, I've only shown you how to read in data from files that are saved to
your computer. R can also read in data directly from the web. If a flat file is
posted online, you can read it into R in almost exactly the same way that you
would read in a local file. The only difference is that you will use the file's
url instead of a local file path for the `file` argument.

With the `read_*` family of functions, you can do this both for flat files from
a non-secure webpage (i.e., one that starts with `http`) and for files from a
secure webpage (i.e., one that starts with `https`), including GitHub and
Dropbox.

For example, to read in data from this [GitHub repository of Ebola
data](https://raw.githubusercontent.com/cmrivers/ebola/master/country_timeseries.csv),
you can run:

```{r message = FALSE}
library("dplyr")
url <- paste0("https://raw.githubusercontent.com/cmrivers/",
              "ebola/master/country_timeseries.csv")
ebola <- read_csv(file = url)
slice(.data = (select(.data = ebola, 1:3)), 1:3)
```


## Data cleaning

<iframe width="768" height="480" src="https://www.youtube.com/embed/uimHCVdYgwM?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_5.pdf) 
a pdf of the lecture slides for this video.

Once you have loaded data into R, you'll likely need to clean it up a little
before you're ready to analyze it. Here, I'll go over the first steps of how to
do that with functions from `dplyr`, another package in the tidyverse. Here are
some of the most common data-cleaning tasks, along with the corresponding
`dplyr` function for each:

```{r echo = FALSE}
library(package = "tibble")
dc_func <- tibble(task = c("Renaming columns",
                           "Filtering to certain rows",
                           "Selecting certain columns",
                            "Adding or changing columns"),
                  func = c("`rename`",
                           "`filter`",
                           "`select`",
                           "`mutate`"))
knitr::kable(dc_func, col.names = c("Task", "`dplyr` function"))
```

In this section, I'll describe how to do each of these tasks; in later
sections of the course, we'll go much deeper into how to clean messier data.

For the examples in this section, I'll use example data listing guests to the
Daily Show. To follow along with these examples, you'll want to load that data,
as well as load the `dplyr` package (install it using `install.packages` if you
have not already):

```{r message = FALSE}
library("dplyr")
daily_show <- read_csv(file = "data/daily_show_guests.csv", skip = 4)
```

I've used this data in previous examples, but as a reminder, here's what it looks like: 

```{r}
head(x = daily_show)
```

### Renaming columns 

A first step is often re-naming the columns of the dataframe. It can be hard to
work with a column name that:

- is long
- includes spaces or other special characters
- includes upper case

You can check out the column names for a dataframe using the `colnames`
function, with the dataframe object as the argument. Several of the column names
in `daily_show` have some of these issues:

```{r}
colnames(x = daily_show)
```

To rename these columns, use `rename`. The basic syntax is:

```{r eval = FALSE}
## Generic code
rename(.data = dataframe, 
       new_column_name_1 = old_column_name_1,
       new_column_name_2 = old_column_name_2)
```

The first argument is the dataframe for which you'd like to rename columns. Then
you list each pair of new versus old column names (in that order) for each of
the columns you want to rename. To rename columns in the `daily_show` data using
`rename`, for example, you would run:

```{r}
daily_show <- rename(.data = daily_show,
                     year = YEAR,
                     job = GoogleKnowlege_Occupation, 
                     date = Show, 
                     category = Group,
                     guest_name = Raw_Guest_List)
head(x = daily_show, 3)
```

```{block, type = 'rmdwarning'}
Many of the functions in tidyverse packages, including those in `dplyr`, provide
exceptions to the general rule about when to use quotation marks versus when to
leave them off. Unfortunately, this may make it a bit hard to learn when to use
quotation marks versus when not to. One way to think about this, which is a bit
of an oversimplification but can help as you're learning, is to assume that
anytime you're using a `dplyr` function, every column in the dataframe you're
working with has been loaded to your R session as its own object.
```

### Selecting columns

<iframe width="659" height="412" src="https://www.youtube.com/embed/yAYm_V6Y1Cw?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_6.pdf) 
a pdf of the lecture slides for this video.

Next, you may want to select only some columns of the dataframe. You can use the
`select` function from `dplyr` to subset the dataframe to certain columns. The
basic structure of this command is:

```{r eval = FALSE}
## Generic code
select(.data = dataframe, column_name_1, column_name_2, ...)
```

In this call, you first specify the dataframe to use and then list all of the
column names to include in the output dataframe, with commas between each column
name. For example, to select all columns in `daily_show` except `year` (since
that information is already included in `date`), run:

```{r}
select(.data = daily_show, job, date, category, guest_name)
```

```{block, type = 'rmdwarning'}
Don't forget that, if you want to change column names in the saved object, you
must reassign the object to be the output of `rename`. If you run one of these
cleaning functions without reassigning the object, R will print out the result,
but the object itself won't change. You can take advantage of this, as I've done
in this example, to look at the result of applying a function to a dataframe
without changing the original dataframe. This can be helpful as you're figuring
out how to write your code.
```

The `select` function also provides some time-saving tools. For example, in the
last example, we wanted all the columns except one. Instead of writing out all
the columns we want, we can use `-` with the columns we don't want to save time:

```{r}
daily_show <- select(.data = daily_show, -year)
head(x = daily_show, n = 3)
```

### Extracting and arranging rows

<iframe width="659" height="412" src="https://www.youtube.com/embed/vIxlSmbFsQ0?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_7.pdf) 
a pdf of the lecture slides for this video.

There are a number of different actions you can take to extract or rearrange rows from a dataset to clean it up for your current analysis, including: 

- `slice`
- `sample_n`
- `arrange`
- `filter`

We'll go through what each of these does and how to use them.

### Slicing and sampling

The `slice` function from the `dplyr` package can extract certain rows based on
their position in the dataframe. 

We already looked at this a bit in Chapter 1. 
In the last chapter, you learned how to use the `slice` function 
to limit a dataframe to certain rows by row position.

For example, to print the first three rows of the `daily_show` data, you can
run:

```{r}
library("dplyr")
slice(.data = daily_show, 1:3)
```

There are some other functions you can use to extract rows from a tibble dataframe, 
all from the "dplyr" package. 

For example, if you'd like to extract a random subset of *n* rows, you can use the 
`sample_n` function, with the `size` argument set to *n*. 

To extract two random rows from the `daily_show` dataframe, run: 

```{r}
sample_n(tbl = daily_show, size = 2)
```

### Arranging rows

There is also a function, `arrange`, you can use to re-order the rows in a
dataframe based on the values in one of its columns. The syntax for this
function is:

```{r eval = FALSE}
# Generic code
arrange(.data = dataframe, column_to_order_by)
```

If you run this function to use a character vector to order, it will order the
rows alphabetically by the values in that column. If you specify a numeric
vector, it will order the rows by the numeric value.

For example, we could reorder the `daily_show` data alphabetically by the values
in the `category` column with the following call:

```{r}
daily_show <- arrange(.data = daily_show, category)
slice(.data = daily_show, 1:3)
```

If you want the ordering to be reversed (e.g., from "z" to "a" for character
vectors, from higher to lower for numeric, latest to earliest for a Date), you
can include the `desc` function.

For example, to reorder the `daily_show` data by job category in descending 
alphabetical order, you can run:

```{r}
daily_show <- arrange(.data = daily_show, 
                      desc(x = category))
slice(.data = daily_show, 1:2)
```

### Filtering to certain rows

Next, you might want to filter the dataset down so that it only includes certain
rows. For example, you might want to get a dataset with only the guests from
2015, or only guests who are scientists.

You can use the `filter` function from `dplyr` to filter a dataframe down to a
subset of rows. The syntax is:

```{r eval = FALSE}
## Generic code
filter(.data = dataframe, logical expression)
```

The `logical expression` in this call gives the condition that a row must meet to
be included in the output data frame. For example, if you want to create a data
frame that only includes guests who were scientists, you can run:

```{r}
scientists <- filter(.data = daily_show, 
                     category == "Science")
head(x = scientists)
```

To build a logical expression to use in `filter`, you'll need to know some of R's
logical operators. Some of the most commonly used ones are:

Operator  | Meaning | Example
--------- | ------- | ---------------------------------
`==`      | equals  | `category == "Acting"`
`!=`      | does not equal | `category != "Comedy"`
`%in%`    | is in | `category %in% c("Academic", "Science")`
`is.na()` | is missing | `is.na(job)`
`!is.na()`| is not missing | `!is.na(job)`
`&`       | and | `year == 2015 & category == "Academic"`
`|`       | or | `year == 2015 | category == "Academic"`

We'll use these logical operators and expressions a lot more as the course
continues, so they're worth learning by heart.

```{block, type = 'rmdwarning'}
Two common errors with logical operators are: (1) Using `=` instead of `==` to
check if two values are equal; and (2) Using `== NA` instead of `is.na` to check
for missing observations.
```



### Add or change columns

<iframe width="659" height="412" src="https://www.youtube.com/embed/_oC0WrrTf5Q?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_8.pdf) 
a pdf of the lecture slides for this video.

You can change a column or add a new column using the `mutate` function from the
`dplyr` package. That function has the syntax:

```{r eval = FALSE}
# Generic code
mutate(.data = dataframe,
       changed_column = function(changed_column),
       new_column = function(other arguments))
```

For example, the `job` column in `daily_show` sometimes uses upper case and
sometimes does not (this call uses the `unique` function to list only unique
values in this column):

```{r}
head(x = unique(x = daily_show$job), n = 10)
```

To make all the observations in the `job` column lowercase, use the `str_to_lower` function from the `stringr` package within a `mutate` function: 

```{r}
library(package = "stringr")
mutate(.data = daily_show, 
       job = str_to_lower(string = job))
```

## Piping

<iframe width="659" height="412" src="https://www.youtube.com/embed/fSJR0cM1qT0?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_9.pdf) a pdf of the lecture slides for this video.

So far, I've shown how to use these `dplyr` functions one at a time to clean up
the data, reassigning the dataframe object at each step. However, there's a
trick called "piping" that will let you clean up your code a bit when you're
writing a script to clean data.

If you look at the format of these `dplyr` functions, you'll notice that they
all take a dataframe as their first argument:

```{r eval = FALSE}
# Generic code
rename(.data = dataframe, 
       new_column_name_1 = old_column_name_1,
       new_column_name_2 = old_column_name_2)
select(.data = dataframe, 
       column_name_1, column_name_2)
filter(.data = dataframe, 
       logical expression)
mutate(.data = dataframe,
       changed_column = function(changed_column),
       new_column = function(other arguments))
```

Without piping, you have to reassign the dataframe object at each step of this
cleaning if you want the changes saved in the object:

```{r eval = FALSE, message = FALSE}
daily_show <-read_csv(file = "data/daily_show_guests.csv",
                      skip = 4)
daily_show <- rename(.data = daily_show, 
                     job = GoogleKnowlege_Occupation,
                     date = Show,
                     category = Group,
                     guest_name = Raw_Guest_List)
daily_show <- select(.data = daily_show, 
                     -YEAR) 
daily_show <- mutate(.data = daily_show, 
                     job = str_to_lower(job))
daily_show <- filter(.data = daily_show, 
                     category == "Science")
```

Piping lets you clean this code up a bit. It can be used with any function that
inputs a dataframe as its first argument. It *pipes* the dataframe created right
before the pipe (`%>%`) into the function right after the pipe. With piping,
therefore, the same data cleaning looks like:

```{r message = FALSE}
daily_show <-read_csv(file = "data/daily_show_guests.csv",
                      skip = 4) %>%
  rename(job = GoogleKnowlege_Occupation,
         date = Show,
         category = Group,
         guest_name = Raw_Guest_List) %>%
  select(-YEAR) %>%
  mutate(job = str_to_lower(job)) %>%
  filter(category == "Science")
```

Notice that, when piping, the first argument (the name of the dataframe) is
excluded from all function calls that follow a pipe. This is because piping
sends the dataframe from the last step into each of these functions as the
dataframe argument.

<iframe width="659" height="412" src="https://www.youtube.com/embed/sD94JyvOMn4?list=PLuGPtwgRXxqIXVqTKUrnMT9Mhpl7eqCxl" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[Download](https://github.com/geanders/RProgrammingForResearch/raw/master/slides/CourseNotes_Week2_part_10.pdf) a pdf of the lecture slides for this video.

### Base R equivalents to `dplyr` functions

Just so you know, all of these `dplyr` functions have alternatives, either
functions or processes, in base R:

```{r echo = FALSE}
dplyr_vs_base <- data.frame(dplyr = c("`rename`",
                                      "`select`",
                                      "`filter`",
                                      "`mutate`"),
                            base = c("Reassign `colnames`",
                                     "Square bracket indexing",
                                     "`subset`",
                                     "Use `$` to change / create columns"))
knitr::kable(dplyr_vs_base, col.names = c("`dplyr`", 
                                          "Base R equivalent"))
```

You will see these alternatives used in older code examples.


## In-course Exercise Chapter 2

Use the `sample` function to determine the order for your group to rotate in 
sharing screens. 

### Downloading and checking out the example data

First, you'll download a directory from GitHub that has the data for this
week's exercise and explore the files in that directory. 

Have the first person in your group share their screen. Then take the following 
steps (everyone in the group should do this, but have one person share their 
screen and talk through what they're doing as you do this):

- Create an R Project directory to use as you practice coding for this class.
You may have already done this as you watched this week's slides. If not, 
take these steps: (1) Open RStudio; (2) In RStudio, go to "File" -> "New Project" 
-> "New Directory" -> "New Project". This will prompt you to pick a name for your
R Project directory and also select where to save it. You can use a name like 
"learn_r" for your Project and save it somewhere easy to find, like you Desktop
or Documents folder.
- Download the whole directory for this week from Github
(https://github.com/geanders/week_2_data). To do that, go the the [GitHub page
with data for this week's exercise](https://github.com/geanders/week_2_data)
and, in the top right, click on the green "Code" button near the top right. When
you click on this green button, one of the choices that you see should be
"Download ZIP". Click on this. This will download a compressed file with the
full directory of data, probably to your computer's "Downloads" folder.
- Next, "unzip" the zipped downloaded file from GitHub. To do this, try
double-clicking the file, or right click on the file and see if there's a
"decompress" or "unzip" option. If you still don't have much luck, try googling
how to unzip a zipped file on your computer's operating system. You should end
up with six files and one directory ("measles_data") which itself has sixteen files
inside). 
- Next move all these files/directories **except "week_2_data.RProj"** into
the R Project directory you created in the first step. Don't do this in R, just
use whatever technique you usually use on your computer to move files between
directories (clicking and dragging the files from one folder to another, for 
example).
- Open RStudio to the Project you created in the first step, if you haven't already.
- Look through the structure of these files. What files are included?  Which
files are **flat files**? For any flat files, look inside the files---you can
open them with RStudio or another text editor. If you are in your Project in
RStudio, look in the "Files" pane, click on the flat file's name, and select
"View File" to get R to open the file with a text editor so you can look at the
structure of the file. Which flat files are **delimited** (one category of flat
files), and what are their delimiters?


### Reading in different types of files

Now you'll try reading in data from a variety of types of file formats. You will
be working with the files you downloaded in the last part of the exercise. 

Switch the person in your group who is sharing their screen. Then try the
following tasks (there is example R code below if you get stuck):

- Create a new R script where you can put all the code you write for this exercise, 
as you develop that code over the next few parts of the exercise. Create a
subdirectory in your course directory called "R" and save this script there
using a `.R` extension (e.g., "week_2.R").
- What type of flat file do you think the "ld\_genetics.txt" file is? See if you
can read it in using a `readr` function (e.g., `read_delim`, `read_fwf`,
`read_tsv`, `read_csv`---pick depending on the type of file you think this is)
and save it as the R object `ld_genetics`. Use the `summary` function to check
out basic statistics on the data.
- Check out the file "measles\_data/02-09-2015.txt". What type of flat file do
you think it is? Since it's in a subdirectory, to read it into R, you'll need to
tell R how to get to it from the project directory, using something called a
**relative pathname**. Read this file into R as an object named `ca_measles`,
using the relative pathname ("measles\_data/02-09-2015.txt") in place of the
file name in the `read_tsv` function call. Use the `col_names` option to name
the columns "city" and "count". What would the default column names be if you
didn't use this option (try this out by running `read_csv` without the
`col_names` option)?
- Read in the Excel file "icd-10.xls" and assign it to the object name `idc10`.
Use the [`readxl` package to do that](https://github.com/hadley/readxl)
(examples are at the bottom of the linked page).
- Read in the SAS file `icu.sas7bdat`. To do this, use the [`haven`
package](https://github.com/hadley/haven). Read the file into the R object
`icu`.

Example R code:

```{r message = FALSE}
# Load the `readr` package
library(package = "readr")
```

```{r eval = FALSE}
# Use `read_tsv` to read this file. 
ld_genetics <- read_tsv(file = "ld_genetics.txt")
```
```{r echo = FALSE}
# Use `read_tsv` to read this file. 
ld_genetics <- read_tsv(file = "data/ld_genetics.txt")
```

```{r}
summary(object = ld_genetics)
```

```{r eval = FALSE}
# Use `read_tsv` to read this file. Because the first line
# of the file is *not* the column names, you need to specify what the column
# names should be with the `col_names` parameter. 
ca_measles <- read_tsv(file = "measles_data/02-09-2015.txt",
                       col_names = c("city", "count"))
```
```{r echo = FALSE}
ca_measles <- read_tsv(file = "data/measles_data/02-09-2015.txt",
                       col_names = c("city", "count"))
```

```{r}
head(x = ca_measles)
```

```{r}
# You'll need the `readxl` package to read in the Excel file. Load that. 
library(package = "readxl")
```

```{r eval = FALSE}
# Use the `read_excel` function to read in the file. 
icd10 <- read_excel(path = "icd-10.xls")
```
```{r echo = FALSE}
# Use the `read_excel` function to read in the file. 
icd10 <- read_excel(path = "data/icd-10.xls")
```

```{r}
head(x = icd10)
```

```{r}
# You'll need the `haven` function to read in the SAS file. Load that.
library(package = "haven")
```

```{r eval = FALSE}
# Use the `read_sas` function to read in this file.
icu <- read_sas(data_file = "icu.sas7bdat")
```
```{r echo = FALSE}
# Use the `read_sas` function to read in this file.
icu <- read_sas(data_file = "data/icu.sas7bdat")
```

```{r}
library(package = "dplyr")
slice(.data = select(.data = icu, 1:5), 1:5)
```

### Directory structure 

Next, you'll explore your current working directory and the files in that working
directory. 

Switch the person in your group who is sharing their screen. Then try the
following tasks (there is example R code below if you get stuck):

- Make sure you have opened R and have moved into the R Project you created 
at the beginning of this exercise. 
- In this Project directory, create a new subdirectory called "data" (if you 
don't already have that subdirectory---you may have created it if you followed
along with the code in the video lectures).
- In the last two steps, you downloaded and explored some data files. These
should currently be in the main level of your R Project directory. Move these
into the "data" subdirectory you just created. For this, use whatever tools you
would normally use on your computer to move files from one directory to
another---you don't have to do this part in R. Keep the "measles" data in its
own subdirectory (so, the "data" subdirectory of your project will have its own
"measles" subdirectory, which will have those files).
- Check that you are, in fact, in the working directory you think you're in. Run:

```{r eval = FALSE}
getwd()
```

Does it agree with the R project name in the top right hand corner of your R
Studio window?

- Now, use the `list.files` function to print out which files or subdirectories
you have in your current working directory (the R Project directory):

```{r eval = FALSE}
list.files()
```

What results do you get when you run this call? Is this what you were expecting?

- While staying in the same working directory, use `list.files()` to print the
names of the available files in the "data" subdirectory using the `path`
argument. 
- Now see if you can use `list.files()` to list all the files in
the "measles_data" subdirectory.
- Read into R the ebola data in `country_timeseries.csv` using the appropriate
`readr` function. This will require you to use a relative filename to direct R
to how to find that file in the "data" subdirectory of your current working
directory. Assign the data you read as an R object named `ebola`.
- How many rows and
columns does the `ebola` dataframe you just created have? What are the names of the columns?

Example R code:

Start by running `getwd()`. What you get
will depend on your computer set-up. Make sure that you understand the output
you get from this call on your computer.

Next, while staying in the same working directory, use `list.files()` to print the
names of the available files in the "data" subdirectory by using the `path`
argument. How about in the "R" subdirectory (if you created one in your project)?

```{r eval = FALSE}
list.files(path = "data")
list.files(path = "R")
```

Note that you can use `list.files` to list the files in your "data" subdirectory using either:
    + A relative pathname
    + An absolute pathname
    
```{r eval = FALSE}
list.files(path = "data") # This is using a relative pathname
list.files(path = "/Users/brookeanderson/Documents/r_course_2018/data") # Absolute pathname
     # (Yours will be different and will depend on how your computer file
     # structure is set up.)
```

Now use a relative pathname along with `list.files()` to list all the files in
the "measles_data" subdirectory.

```{r eval = FALSE} 
list.files(path = "data/measles_data")
```

Read in the Ebola data in `country_timeseries.csv` from your current working
directory using the appropriate `readr` function and a relative pathname. This
will require you to use a relative filename to direct R to how to find that file
in the "data" subdirectory of your current working directory.

```{r eval = FALSE}
library("readr")
ebola <- read_csv(file = "data/country_timeseries.csv")
```

How many rows and columns does it have? What are the names of the columns?

```{r, eval = FALSE}
dim(x = ebola)    # Get the dimensions of the data (`nrow` and `ncol` would also work)
colnames(x = ebola) # Get the column names (you can also just print the object: `ebola`)
```


If you have extra time: 

- Find out some more about this Ebola dataset by checking out [Caitlin Rivers'
Ebola data GitHub repository](https://github.com/cmrivers/ebola). Who is Caitlin
Rivers? How did she put this dataset together?
- Search for R code related to Ebola research on GitHub. Go to the [GitHub home
page](https://github.com) and use the search bar to search for "ebola". On the
results page, scroll down and use the "Language" sidebar on the left to choose
repositories with R code. Did you find any interesting projects?
- When you `list.files()` for the "data" subdirectory, almost everything listed
has a file extension, like `.csv`, `.xls`, `.sas7bdat`. One thing does not.
Which one? Why does this listing not have a file extension?

### Cleaning up data #1

Switch the person in your group who is sharing their screen. Then try out the
following tasks:

- Copy the following code into an R script. Figure out what each line does, and
add comments to each line of code describing what the code is doing. Use the 
helpfiles for functions as needed to figure out functions we haven't covered yet.

```{r eval = FALSE}
# Copy this code to an R script and add comments describing what each line is doing
# Install any packages that the code loads but that you don't have.
library(package = "haven")
library(package = "forcats")
library(package = "stringr")

icu <- read_sas(data_file = "data/icu.sas7bdat")

icu <- select(.data = icu, ID, AGE, GENDER)

icu <- rename(.data = icu, 
              id = ID,
              age = AGE,
              gender = GENDER)

icu <- mutate(.data = icu,
              gender = as_factor(x = gender),
              gender = fct_recode(.f = gender,
                                  Male = "0",
                                  Female = "1"),
              id = str_c(id)) 

icu
```

- Following previous parts of the in-course exercise, you have an R object
called `ebola` (if you need to, use some code from earlier in this in-course
exercise to read in the data and create that object). Create an object called
`ebola_liberia` that only has the columns with the date and the number of cases
and deaths in Liberia. How many columns does this new dataframe have? How many
observations?
- Change the column names to `date`, `cases`, and `deaths`.
- Add a column called `ratio` that has the ratio of deaths to cases for each
observation (i.e., death counts divided by case counts).

Example R code:

```{r}
# Load the dplyr package
library(package = "dplyr")

## Create a subset with just the Liberia columns and Date
ebola_liberia <- select(.data = ebola, 
                        Date, Cases_Liberia, Deaths_Liberia)
head(x = ebola_liberia)

## How many colums and rows does the whole dataset have (could also use `dim`)?
ncol(x = ebola_liberia)
nrow(x = ebola_liberia)

## Rename the columns
ebola_liberia <- rename(.data = ebola_liberia,
                        date = Date,
                        cases = Cases_Liberia,
                        deaths = Deaths_Liberia)
head(ebola_liberia)

## Add a `ratio` column
ebola_liberia <- mutate(.data = ebola_liberia, 
                        ratio = deaths / cases)
head(x = ebola_liberia)
```

### Cleaning up data #2

Switch the person in your group who is sharing their screen. Then try out the
following tasks:

- Filter out all rows from the `ebola_liberia` dataframe that are missing death
counts for Liberia. How many rows are in the dataframe now?
- Create a new object called `first_five` that has only the five observations
with the highest death counts in Liberia. What date in this dataset had the most
deaths?

Example R code:

```{r}
## Filter out the rows that are missing death counts for Liberia
ebola_liberia <- filter(.data = ebola_liberia, 
                        !is.na(deaths))
head(x = ebola_liberia)
nrow(x = ebola_liberia)

## Create an object with just the top five observations in terms of death counts
first_five <- arrange(.data = ebola_liberia, 
                      desc(deaths)) # First, rearrange the rows by deaths
first_five <- slice(.data = first_five, 
                    1:5) # Limit the dataframe to the first five rows
first_five # Two days tied for the highest deaths counts (Jan. 2 and 3, 2015).
```

### Piping

Switch the person in your group who is sharing their screen. Then try out the
following tasks:

- Copy the following "piped" code into an R script. Figure out what each line
does, and add comments to each line of code describing what the code is doing.

```{r eval = FALSE}
# Copy this code to an R script and add comments describing what each line is doing
library(package = "haven")
icu <- read_sas(data_file = "data/icu.sas7bdat") %>% 
  select(ID, AGE, GENDER) %>% 
  rename(id = ID,
         age = AGE, 
         gender = GENDER) %>% 
  mutate(gender = as_factor(x = gender),
         gender = fct_recode(.f = gender, 
                             Male = "0",
                             Female = "1"),
         id = str_c(id)) %>% 
  arrange(age) %>% 
  slice(1:10)

icu
```
- In previous sections of the in-course exercise, you have created code to read
in and clean the Ebola dataset to create `ebola_liberia`. This included the
following cleaning steps: (1) selecting certain columns, (2) renaming those
columns, (3) adding a `ratio` column, and (4)
removing observations for which the count of deaths in Liberia is missing.
Re-write this code to create and clean `ebola_liberia` as "piped" code. Start
from reading in the raw data.

Example R code:

```{r message = FALSE}
ebola_liberia <- read_csv(file = "data/country_timeseries.csv") %>% 
  select(Date, Cases_Liberia, Deaths_Liberia) %>%  
  rename(date = Date, 
         cases = Cases_Liberia, 
         deaths = Deaths_Liberia) %>% 
  mutate(ratio = deaths / cases) %>% 
  filter(!is.na(x = cases))

head(x = ebola_liberia)
```