• What is a vector?
  • How do you index a vector?
• How would you pass a vector to a function?
• What is a factor?
• What is a dataframe
  • How do you index a dataframe

Functions:

• have a name
• accept 0 or more arguments
• do something useful with those arguments
• produce a return value

There are 1000s of already named functions

Whenever you save variables (like we did in the tutorial), these get added to your workspace (technically they get added to the ‘global environment’).

R Studio helpfully shows you the variables currently defined in the “Environment” tab.

R Markdown documents are like scripts, but allow you to mix human readable text with snippets of R code.

At any time you can “knit” the R Markdown file, which causes the code snippets to be run.

Any results (figures or text output) get combined with the human readable parts, and combined into a single pdf, word doc, or html file.

You can create a new Rmarkdown using the File >> New File menu.

This sentence is just normal text, made for humans to read.

The following bit is a “code chunk”, which is set off by the ``` characters and consists of actual R code that gets run, and the results knitted back into the document.

You can also put R code inline like this. What is 2 + 2? The answer is `r 2 + 2`.

```{r examplechunk}
x <- rnorm(100)
y <- (x * 0.3) + rnorm(100, sd=0.2)
plot(x,y,pch=16)
```

This sentence is just normal text, made for humans to read.

The following bit is a “code chunk”, which is set off by the ``` characters and consists of actual R code that gets run, and the results knitted back into the document.

You can also put R code inline like this. What is 2 + 2? The answer is 4.

x <- rnorm(100)
y <- (x * 0.3) + rnorm(100, sd=0.2)
plot(x,y,pch=16)

One of the most important features of R is that it is has a vast ecosystem of user-contributed packages, which extend the base functionality of R.

Packages can be installed from the command line install.packages('ggplot2') or by using the graphical package manager in Rstudio in the Packages tab.

Each time you want to use functions from a package, you must make the package available with the library() function.

Getting data into R can be very frustrating for new users.

This is because we tend to be sloppy when collecting data in a spreadsheet.

R has rigid expectations.

• each column of a text file should contain a single type of data (text, numeric, factor)
• Each row should hold the observations for each column for a single individual
• That’s it. Period. No extra rows that are just for formatting and don’t contain data observations.

In the Environment tab, there is a tool called ‘Import Dataset’ That does exactly what it says it does. This also works with Excel files, but reading from Excel files is more complicated than reading plain text files like CSV or TXT files where the columns are separated by commas ‘,’ or tabs ’.

These plain delimited text files are mostly what you will use as data in R.

The primary function for reading data into R is the read.table() function. There are a few important arguments.

• file = The full path to the file as a text string. (Use the forward slash /, even on Windows.) You can also read from a remote URL (i.e. from the web).
• sep = the character that separates columns in your text file. The default is a single blank space " ", which kind of sucks because most of our files will be separated by either \t or ,.
• header = Whether or not there is a header row in your text file. Defaults to FALSE, but usually we need it to be TRUE.

An example

mydataset <- read.table(file="~/Desktop/cornbread-recipe-ratings.txt", sep=",", header=F)

This reads the file cornbread-recipe-ratings.txt which lives on my Desktop. It saves the result to a variable called `mydataset,

Notice that the file name is the complete path to the file on your computer (this could also be a complete URL for a file that lives on the inter-tubes). Notice also, I have used the ~ character as a shortcut to your home directory on Windows or Mac.

Always use forward slashes / in R, even on Windows.

Once you have typed this “~/” in the R console window, you can hit the TAB key, and R Studio will start auto-completing the file path for you. This is very helpul to not make mistakes.

Read in the file called “femur_lengths.txt” directly from the following URL

https://stats.are-awesome.com/datasets/femur_lengths.txt

Save to an object called femora.

Use the str() function to examine the data type of each column. You can also do the same thing in R Studio in the Environment tab.

Use the mean() function to calculate the mean of the femur lengths for Tragelaphus scriptus (don’t consider other species).

Usually, I would recommend not writing out modified data frames to text files.

It is far better to have a single input file, and to do all necessary manipulations in your saved R script file.

But, if you need to write data, do it with write.table()

• x = The name of the data frame to be saved.
• file = Path to the output file. You can’t use file.choose() though.
• quote = Do you want quotation marks around strings? Defaults to TRUE
• sep = Same as for read.table()
• row.names = Do you want row names? Defaults to TRUE, but usually you will set to FALSE

Add a new column of log transformed lengths to the femora dataframe and then write out the new file to your Desktop.