1. Jefferson Davis Research Analytics
R Bootcamp Day 2
Jefferson Davis
Research Analytics

2. Day 1 stuff From yesterday
R values have types/classes such as numeric, character, and logical
Much of R functionality is in libraries
For help on a function run ? sin() from the R console

3. R: Creating variables
We will spend the next few slides building up the ornithological data set below

4. R: Creating variables
Start by creating each column as a vector.. Wingcrd <- c(59, 55, 53.5, 55, 52.5, 57.5, 53, 55)] Tarsus <- c(22.3, 19.7, 20.8, 20.3, 20.8, 21.5, 20.6, 21.5) Head <- c(31.2, 30.4, 30.6, 30.3, 30.3, 30.8, 32.5, NA) Wt <- c(9.5, 13.8, 14.8, 15.2, 15.5, 15.6, 15.6, 15.7)

5. R: Creating variables
We can access values in the vector by indexing with brackets. Wingcrd[1] [1] 59 Wingcrd[3] [1] Wingcrd[1:3] [1] Wingcrd[-2] [1] sum(Wingcrd) [1] 440.5

6. R: Creating variables
We have some variables now. Find the following values
The 3rd entry in Wt
The sum of the entries in Tarsus
The sum of the entries in Head

7. R: Creating variables
Note the missing value in Head cascades through other calculations sum(Head) [1] NA
We can work around this if we want to sum(Head, na.rm = TRUE) [1] 216.1

8. R: Creating Dataframes
To assemble the data together we can bind our vectors coulmn-wise with cbind() BirdDF <- cbind(Wingcrd, Tarsus, Head, Wt) BirdDF Wingcrd Tarsus Head Wt [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] NA 15.7

9. R: Creating Dataframes
For typing reasons we us as.data.frame() to make sure we have a dataframe BirdDF <- as.data.frame(cbind(Wingcrd, Tarsus, Head, Wt)) Wingcrd Tarsus Head Wt NA

10. R: Creating Dataframes
We can pick out values by specifying rows and columns BirdDF[1, 2] Tarsus BirdDF[1, 2:4] Tarsus Head Wt BirdDF[, 3] [1] NA BirdDF[1, c(2, 3)] Tarsus Head

11. R: Creating Dataframes
Now the column names give you access to the data. BirdDF$Wingcrd [1]

12. R: Creating Dataframes
Keep in mind the dataframes look like matrices but they aren't. BirdDF %*% t(BirdDF) Error in BirdDF %*% t(BirdDF) : requires numeric/complex matrix/vector arguments
Casting from from datatype to the other is easy as.matrix(BirdDF) as.data.frame(BirdData)
The R code below works, although it's a bit pointless. as.matrix(BirdDF) %*% t(as.matrix(BirdDF))

13. R: Importing Data
Most data sets will be created outside of R and imported. We create a csv file to import by writing the data to the disk getwd() [1] "/Users/majdavis" write.table(BirdDF, "BirdDF.csv", sep=",") BirdDF2 <- read.csv("BirdData.csv", header = TRUE)

14. R: Altering Data
You will need to work with your data. This can be done at a low-level gBirdDF2 <- BirdDF BirdDF$Wt <- BirdDF$Wt * 28 #ounces to grams
The package plyr and relatives can be useful library(plyr) BirdDF2 <- mutate(BirdDF, Wt= -Wt)

15. R: Altering Data
This is handy with the forward pipe operator %>% from the magrittr library library(magrittr) BirdDF3 <- BirdDF %>% mutate(Wt = Wt*28) %>% tail(2) #Compare to BirdDF3 <- tail(mutate(BirdDF, Wt = Wt*28), 2)

16. R: Plotting Data The most basic plotting command in R is plot()
As a high-level function it will create axes, tick marks, etc.
Many user-written classes will have default plot() functions that act reasonably

17. R: Plotting Data
We'll explore this with a dataset of car speeds and stopping distances from the 1920s in R as cars (Ezekiel, M. (1930) Methods of Correlation Analysis. Wiley. ) head(cars) speed dist

18. R: Plotting Data By default plot() produces a scatterplot plot(cars)
Axis labels are from the names in the data frame
Axis scale is from the range of the data

19. R: Plotting Data
plot(cars$speed, cars$dist, main = "A Title", xlab = "The Speeds", ylab = "The Distances", col="steel blue")

20. R: Plotting Data Each plot() call creates its own axes, labels, etc
Combining plot() calls can be messy plot(cars) par(new=TRUE) plot((lowess(cars), type="l", col="red"))
The graphs labels are clearly bad. While not obvious, the scales don’t match either.

21. R: Plotting Data
To add details it’s better to use so-called low-level functions. plot(cars) line(lowess(cars), col="red")

22. R: Plotting Data
The default plot type for a dataframe is a scatterplot plot(BirdDF)

23. R: Plotting Data
For dataframes with lots of data scatterplots can be a bit much plot(mtcars)

24. R: Day 2 end
There are a handful of exercises on Datacamp. Have fun and see you tomorrow!