Beginning R

In our Python section we learned about predicates (or conditionals). These were tests that returned true or false and that we could use in while loops.

How many cars are there that can go faster than 10, but not more than 20?

length(cars$dist[cars$speed > 10 & cars$speed < 20])

[1] 29

Can you do that easily in Excel?

What is going on here?

1. cars is the name of the data frame.
2. We access a column of the data frame with the dollar sign notation cars$dist. You can see the names of the columns in a data frame with names(cars).
3. We use the square brackets to index the column of data we are interested in. Here we do not use specific numbers, but rather we use a boolean to compare the values in a column and we only include the TRUE values. Here we ask for all the indices where the speed is greater than 10, but less than 20, and we use those indices to get the values for the dist column.

1. Sort (or order) cars by the dist variable.
2. Find the mean and standard deviation of the speed of the cars.

3. Are there other datasets?

   library(help="datasets")
   

4. Open any of the datasets that catches your eye.
5. What are the column names?
6. How many rows?
7. What is the comment designator for R?
8. What is the ending extension of an R script?

ml = seq(1:10)

for  (m in ml) {
    print(m)
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10

Compare to the Python code. Look for the ":" and the "{}"'s in both examples. You can see how your knowledge of looping in one language helps you understand looping in the other, but the details may be different.

1. Edit the above so that it prints the individual number each time it goes through the loop, and not the whole list.
2. Repeat the Python Assessment on for loops, but using R this time. I give you a working example below, but try on your own for a while first, then look at my code and try it line by line in your interpreter to get the feel for how things work in R.
   1. Create a list of at least 8 individual characters.
   2. Make sure they are not in alphabetical order
   3. Print the letters one at a time.
   4. Print the letters sorted alphabetically one at a time, but do not overwrite your original list.
   5. Print the letters from both lists with a format command that says which position the letter is in.
   6. String formatting is less nice in R! To help with this look for help on paste and sprintf. To acccess the help try ?<commandname>.

myName = "brittAnderson"
myList = unlist(strsplit(myName,""))

for (l in myList){
  print(l)
}



for (l in myList[order(myList)]){
  print(l)
}

i = 1
for (n in order(myList)){
  t  <- sprintf("The %.0fth letter of myList is: %s, but is %s in the sorted list.",i,myList[i],myList[n])
  print(t)
  i = i+1  
  }

[1] "b"
[1] "r"
[1] "i"
[1] "t"
[1] "t"
[1] "A"
[1] "n"
[1] "d"
[1] "e"
[1] "r"
[1] "s"
[1] "o"
[1] "n"
[1] "A"
[1] "b"
[1] "d"
[1] "e"
[1] "i"
[1] "n"
[1] "n"
[1] "o"
[1] "r"
[1] "r"
[1] "s"
[1] "t"
[1] "t"
[1] "The 1th letter of myList is: b, but is A in the sorted list."
[1] "The 2th letter of myList is: r, but is b in the sorted list."
[1] "The 3th letter of myList is: i, but is d in the sorted list."
[1] "The 4th letter of myList is: t, but is e in the sorted list."
[1] "The 5th letter of myList is: t, but is i in the sorted list."
[1] "The 6th letter of myList is: A, but is n in the sorted list."
[1] "The 7th letter of myList is: n, but is n in the sorted list."
[1] "The 8th letter of myList is: d, but is o in the sorted list."
[1] "The 9th letter of myList is: e, but is r in the sorted list."
[1] "The 10th letter of myList is: r, but is r in the sorted list."
[1] "The 11th letter of myList is: s, but is s in the sorted list."
[1] "The 12th letter of myList is: o, but is t in the sorted list."
[1] "The 13th letter of myList is: n, but is t in the sorted list."

i = 0
while (i<=10) {
  print(unlist(strsplit("brittAnderson",""))[i])
  i = i+1
}

character(0)
[1] "b"
[1] "r"
[1] "i"
[1] "t"
[1] "t"
[1] "A"
[1] "n"
[1] "d"
[1] "e"
[1] "r"

Provide an org or Rmd file that demonstrates the following functions with the cars data set in R.

Demonstrate some of the basic data processing tasks in R.

• To find the names of columns in a data.frame try the names function (names(cars)).
• To get help you can type a ? at the command line of the R interpreter.
• You may need to find an online resource you like for R help, for example to find out what the name of the function is for standard deviation. It is not "standard deviation", but something much shorter.
• Extensions: For instance "docx" is the extension for a word file and "py" is the extension for a python script.

This was hard in Python, and won't be trivial in R, but having done it once, the translation should be easier. But R is not as nice when processing text as is Python to text process as Python. So, I will be expecting less full featured functions. Just demonstrate you understand the concepts of what we are trying to achieve.

To write the beginning pieces of a hangman game in R. This version will hard code the word to be guessed. It will not produce any sort of graphic, but merely ask for letters and report if the player spells out the word or not. Note that as R is harder to run as a program from the command line I will test your code by loading it into my R interpreter. The command for this is source. I will download your filename.R and then I will source(filename.R). Next, I will execute whatever function you tell me to. Remember, your script can contain comments so I should be able to tell what you want me to do just by reading your .R file.

1. Get input from me, the user.
2. Write a function that takes that input and and returns a list of indices of the positions where the input letter exists in your hard-coded word. An empty list means the letter is never in the word. Otherwise the elements of this list are the indices where that letter is found. For example, if the word were tree and the letter "e" you would get back [1] 3 4 from this function. Remember that R starts counting from 1, not zero. , but would get back integer(0) if the letter were 's'. This weirdness tells you why R might be nicer for statistics, but not for text processing. Different task require different tools.
3. Write a function that loops through the above process a certain number of times.
4. Make the function terminate when all the letters are guessed or max number of guesses is exceeded.

I will load (source) your file and try to test the functions. If they basically work then you will get the credits.

You might find the following R functions helpful:

1. which
2. strsplit
3. unlist
4. !=
5. Using a variable more than once, e.g. al[al != "e"]