Lab 1 (fall, 2017) Introduction to R and R Studio

Size: px

Start display at page:

Download "Lab 1 (fall, 2017) Introduction to R and R Studio"

Myra Mills
5 years ago
Views:

Lab 1 (fall, 201) Introduction to R and R Studio Introduction: Today we will use R, as presented in the R Studio environment (or front end), in an introductory setting.

1 Lab 1 (fall, 201) Introduction to R and R Studio Introduction: Today we will use R, as presented in the R Studio environment (or front end), in an introductory setting. We will make some calculations, generate some tables, and produce a few graphs using categorical data. Categorical data is essentially a listing of frequencies of a variable by category. In your spare time this week, we want you to [1] download to your computer the latest version of R from the CRAN web site, and then [2] download R Studio from it's web site, along with automatically loaded packages. Until you are functional with R on your computer, feel free to use our machines in the computer rooms, MA 306 and MA 206 to complete your work, and either put your results on your flash drive, or them to your , for later submittal as an attachment to me at my . Every week you will submit a lab for this course in electronic attachment, written in the.rmd file format (this is the R Markdown format) from R Studio. We will talk about Markdown later in this lab, but, just think of it as a way to present your findings in HTML or WORD, after completing an R assignment. R Studio Environment: When you first open up R Studio, which has icon shown below, you will see a screen similar to the below. The screen will show 3 windows, the left window, top right, and bottom right. The left panel is called the R Console screen. This is the active computer screen of R, where input statements are written in blue print, and output from R commands is written in black writing.

2 Read the wording in the left window, and then erase it by clicking on EDIT CLEAR CONSOLE. This startup information will always appear in the Console window when you first start up the program. Notice that your cursor is sitting right next to the blue > symbol. This means that R is waiting for a command to execute. Type 2+2 and then ENTER, to arrive at the result of 4, written in black right next to the [1] writing. Note that all input lines are preceded by the > symbol, and all output occurs preceded by the [1]. Now, type 6/2 ENTER, followed by 6/0 ENTER. You see that 6/0 is infinity, as stated by R output as Inf. Note below. When we started R Console, in the background a whole bunch of data sets were loaded into our current memory. To find out which data sets are there, in the Console type the command data(). A 4th window in the upper left will appear, (called R data sets) listing all data sets currently loaded. We are interested in the data set HairEyeColor, so type?haireyecolor to find help on what this data set is all about. Notice that the help information on this data set shows up in the lower right panel of Studio. Also notice that R is case sensitive, so be sure to use capital letters when needed in the data title. See below.

Now type data1 <- HairEyeColor ENTER. This takes the 3 levels of information (Hair, Eye, Sex) in the data array and stores the information into a location label (or name), called data1.

3 Now type data1 <- HairEyeColor ENTER. This takes the 3 levels of information (Hair, Eye, Sex) in the data array and stores the information into a location label (or name), called data1. I usually name my labels ending with a number, so R doesn't confuse my labels with some stored library function it has with the same label no library labels I know about end in a number, so I can probably avoid confusion. Also, this label, called an R object, is listed in the upper right window. See below. We see that the 3 dimensional array has 4 rows (Hair), 4 columns (Eyes and depth dimension of 2 (Gender). Let us now see how the information is presented. Type the following. blackhair <- data1[1,,] This will list out table of eye color by gender of all black haired students. > blackhair Sex Eye Male Female Brown Blue 11 9 Hazel 10 5 Green 3 2

brownhair <- data1[2,,] > brownhair Sex Eye Male Female Brown 53 66 Blue 50 34 Hazel 25 29 Green 15 14 blueyes <- data1[,2,] > blueyes Sex Hair Male Female Black 11 9 Brown 50 34 Red 10 Blond 30 64

4 brownhair <- data1[2,,] > brownhair Sex Eye Male Female Brown Blue Hazel Green blueyes <- data1[,2,] > blueyes Sex Hair Male Female Black 11 9 Brown Red 10 Blond This will list eye color vs gender for all brown haired students. This is table of hair color by gender of all blue eyed students. maleblackhair <- data1[1,,1] > maleblackhair Brown Blue Hazel Green This is black haired males eye colors. femaleredhair <- data1[3,,2] > femaleredhair Brown Blue Hazel Green 16 This is eye color for red haired females. You see that the first number of the ordered triple of data1 lists hair color, next lists eye color, and last lists gender. If you leave one of the dimensions blank, it will list all values of that dimension. Now let us construct a bar graph of the black haired male eye colors (i.e., maleblackhair). Type barplot(maleblackhair) and you get the plot printed up in the lower right window. Now let us give the graph a title, so type barplot(maleblackhair,main="male Black Haired Eye Colors") to get

Shutting down Studio : In the EDIT menu click on CLEAR CONSOLE. Then in the upper left window, click the x in all file tabs which appear, ending up with a clear window when done with this.

5 Shutting down Studio : In the EDIT menu click on CLEAR CONSOLE. Then in the upper left window, click the x in all file tabs which appear, ending up with a clear window when done with this. Now go to the upper right window and click on the paint brush (see red arrow below). A window will open up asking if you want to delete everything, and click on YES, including checking the box which says INCLUDE HIDDEN OBJECTS?. See below. Finally, go to the bottom right window and click on the paint brush there, removing all plots made. Now you are ready to quit Studio (don't save anything), by clicking in the x on the top right corner. I never store things in the Studio memory, but rather save important stuff on my Desktop and not in Studio. As you become more familiar with the Studio architecture, you may want to save things in the Studio files. Extra Things, Interesting but not required to know: We have used square brackets to indicate columns (i.e., variables) in data sets we used above. Below are some other uses and explanations for various columns of variables.

6 We will talk more in depth about some of these (above) uses in later labs for now the explanations seem fairly self-explanatory. We talked briefly about packages to R. These are add-ons to the basic R suite of things. We can add other packages, those not automatically loaded at startup of the Studio

7 program by first installing them (using the install() command) and then loading them into current R memory (using the library() command), as shown among the list of related commands above. These packages have useful commands and nice data sets in them, which are sometimes handy for us to use. We will have you load some good packages during the course of the semester. There are thousands of packages available fora loading, and you don't want to load all of them (or else your computer will slow to a crawl). Also, some packages are better than others. My rule of thumb is to check out use of any extra packages with Dave Patterson or Jon Graham, 2 of our resident statistical experts, who have a good handle on which packages are good and which are not worth using or are not statistically correct in their execution.

Introduction to SPSS

Introduction to SPSS Purpose The purpose of this assignment is to introduce you to SPSS, the most commonly used statistical package in the social sciences. You will create a new data file and calculate