RStudio - Excercises

KEY VERSION

Below are a set of excercises to get you practicing with Rstudio.

A. Getting data into R

You were out in the field and observed four insect species, You recorded their species code, weight and color. Your field notebook reads:

“The first butterfly species I saw weighed 3 grams and was purple. The first beetle species weighed 5 grams and was gold. The second beetle species weighed 10 grams and was aquamarine. Oh and the first moth species I saw weighed 1 gram and was pink”

Note that this is probably not the best way to take data in your field notebook… but oh well.

1. Go ahead and make an excel spreadsheet that has a column for species code name, a column for weight, and a column for color. Save your excel spreadsheet as a .csv file.

2. Load your dataframe into R studio, assigning it to an object named FieldData. Make sure color is loaded as a character vector and not as a factor. [you might get a warning that says “incomplete final line” (it’s a warning, so ignore it as your data should still be loaded into R).]

3. Print your new data object to the screen.

Answer: Your output should be something like this:

FieldData <- read.csv("FieldData.csv")

## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on 'FieldData.csv'

print(FieldData)

##     species weight      color
## 1 butterfly      3     purple
## 2   beetle1      5       gold
## 3   beetle2     10 aquamarine
## 4      moth      1       pink

Write some code to figure out what class of data object FieldData is.

class(FieldData)

## [1] "data.frame"

C. Types of data

Using the dataframe (FieldData) that you have loaded into R, write code that completes the following tasks

1. Using the str() function, take a look at your dataframe. How is each column of data stored (is it an integer, a character, a factor?)? How many observations (i.e. rows) are there in the dataframe?

str(data.frame.test) *ANSWER: The first row contains integers. However, the class() function will tell you it is a dataframe.

Now we want to explore the data a bit! Ideally we would subset or select specific columns from a dataframe, but for now we can just make each column an individual vector or string.

The code below assigns each column (using the $ operator) to a vector/string of the same name. Don’t worry, we will learn more about this later, but right now just copy the code so you can answer the following questions.

2. Make a logical vector describing whether any of the values of weight are less than the mean of all weights.

#ANSWER:
weight<mean(weight)

## [1]  TRUE FALSE FALSE  TRUE

3. Coerce the vector you just made (in #2 above) from a logical vector to a character vector.

#ANSWER:
as.character(weight<mean(weight))

## [1] "TRUE"  "FALSE" "FALSE" "TRUE"

4. Make a logical vector describing whether each data points is gold (TRUE) or not gold (FALSE)

#ANSWER:
color=="gold"

## [1] FALSE  TRUE FALSE FALSE

5. Sometimes for plotting we want to have data as factors, not characters. Coerce the vector colour into categorial vector (using factor()).

#ANSWER:
factor(color)

## [1] purple     gold       aquamarine pink      
## Levels: aquamarine gold pink purple

We can also make matrices and dataframes in R, without loading data in from spreadsheets!

6. Make a matrix with 4 rows and 3 columns that looks like this:

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9
## [4,]   10   11   12

#ANSWER:
matrix.test<-matrix(data=c(1,2,3,4,5,6,7,8,9,10,11,12),nrow=4, ncol=3, byrow=TRUE)

7. Coerce the matrix you you just made into a dataframe.

#ANSWER:
data.frame.test<-data.frame(matrix.test)