Entering and Outputting Data 2 nd best TA ever: Steele H. Valenzuela February 2-6, 2015

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1 Entering and Outputting Data 2 nd best TA ever: Steele H. Valenzuela February 2-6, 2015 Contents Things to Know Before You Begin Entering and Outputting Data Extra: Dates Things to Know Before You Begin Variable Types and Storage It actually matters 1. If a variable is supposed to be numerical, you don t want it to be inadvertently imported as a character variable. 2. A good practice is to use the storage you need only, and not waste space. 3. Formatting date variables are their own special challenge. In R, there are remedies to these potential problems, but it can be very meticulous and time consuming Review of Variable Types. In PubHlth 540, we distinguished a variety of types of measurement: discrete versus continuous. Within each of these broader headings of measurement type, we learned sub-types. These distinctions were important because, depending on the variable type, some analyses are possible while others are not. Depending on the variable type, some analyses are possible and others are not. Measurement Type Discrete Continuous Sub-Type Nominal & Ordinal Interval & Ratio R functions a little differently. It distinguishes several types of data: numeric, character, and logical. From my 1.5 years of experience with R, data storage has never been a problem and hopefully it doesn t present itself as such during this semester. Tips Tip #1 Here are the numeric, character, and logical vectors. The difference should be apparent. a <- c(1, 2, 5.3, 6, -2, 4) # numeric vector b <- c("one", "two", "three") # character vector - must always be enclosed in quotes. ALWAYS. d <- c(true, TRUE, TRUE, FALSE, TRUE, FALSE) # logical vector - must all capital letters By the way, I skipped the letter c because that is how we create objects. We would hate to confuse the atomic functions of R. It s like dividing by zero. You never do it. Tip #2 Empty or missing variables should be labeled as NA s, as such: 1

2 e <- c(1, 2, 3, NA, 5) Missing or NA values will complicate a few procedures, but that s for another time. Tip #3 For this course, we will primarily use two types of data sets: matrices and data frames. Matrices are homogenous and data frames are heterogenous. Simply put, matrices either have all characters or all numbers while data frames can have a mixture of both. mat <- matrix(1:20, nrow = 4, ncol = 5) # 1:20 displays all numbers from 1 to 20 mat ## [,1] [,2] [,3] [,4] [,5] ## [1,] ## [2,] ## [3,] ## [4,] # nrow and ncol specify the number of rows and columns, respectively Data frames contain similar syntax, dat <- data.frame(first.row = c(1:4), secnd.row = c(5:8), third.row = c("nine", "ten", "eleven", "twelve")) dat ## first.row secnd.row third.row ## nine ## ten ## eleven ## twelve names(dat) ## [1] "first.row" "secnd.row" "third.row" Tip #4 Categorical variables and factors will be a big part of this course. Other than yes and no, male and female, there may be ordinal factors, such as very bad, bad, neutral, good, and very good. These will take a bit of time to understand, so please be patient. Let s try our hand at the code. gender <- c(rep("male", 10), rep("female", 10)) gender ## [1] "male" "male" "male" "male" "male" "male" "male" ## [8] "male" "male" "male" "female" "female" "female" "female" ## [15] "female" "female" "female" "female" "female" "female" 2

3 gender <- factor(gender) # the function factor stores the number of unique categories as factors It is very important that the variable gender is reassigned as shown above. Remember, to the left of the arrow is our new object and to the right is our old object, or the object we are tranforming. Now, let s determine what type of variable gender is now with new commands. is.numeric(gender) # obviously it is not identified as numeric ## [1] FALSE is.character(gender) # before implementing the command factor, it would have been a character vector ## [1] FALSE is.factor(gender) # and there we go ## [1] TRUE Tip #5 In order to change gender back to a character vector, we use the following command: gender <- as.numeric(gender) # as.numeric, as.factor, as.character are all useful commands is.factor(gender) ## [1] FALSE is.numeric(gender) # it should check out as TRUE ## [1] TRUE Tip #6 Lastly, numeric (0s and 1s) AND alpha-numeric (1F, 2F, 1G, 2G) observations may be transformed into factored data. As for ordinal factored data, we ll cover that when you stumble upon it. Entering and Outputting Data Import a Stata Dataset (or any dataset) from the internet (using R) We have already seen how to download a Stata data set from the internet using R. First, we need to have the package foreign installed followed by loading the library. install.packages("foreign") library(foreign) Next, we specify the URL in quotes and use the command read.dta(...). 3

4 url = " dat <- read.dta(file = url) In order to see more options or help, simply type?read.dta. The other option that was previous covered is clicking on the Import Dataset icon, as seen below. Click on From Web URL... and paste the URL into the empty box #EasyAsPie Import Excel, CSV, Text/Table and Stata files There are fancy packages that may help you open excel files and workbooks in R, but the easiest method, in my opinion, is to save the file as a CSV file. Unlike Stata, you cannot copy and paste into R. After altering and saving the file, click on the familiar Import Datatset icon, again, as seen above, and rather than click on From Web URL..., click on From Text File.... Another option is from the menu bar: Tools > Import Dataset > From Text File... /From Web URL... After you select the dataset file, you ll get a box with several options, as seen below. 4

5 As you can see, the options (and their explanations) consist of the following: Name - default name will be name of the file. You may change it if you like. Heading - does the dataset contain variable names? If so, then Yes is your option. Separator - Comma (for CSV) and Tab (.txt extension/tab-separated/tables) are your most common options Decimal - This option should be a period almost every time unless you are dealing with a dataset from the UK (I think???). Quote - This option should be a Double quote almost every time unless the original file contains single quotes. na.strings - When datasets have missing values, some will display a 99 or a 0. After entering the missing value, R will transform all observations into a NA. Strings as factors - option to check strings (characters or alpha-numeric) into factors 2 boxes: Input File and Data Frame - Input File is how your file appears and Data Frame is what R will transform and import your file. Essentially, the Data Frame box is what you want to work. Upon selecting an option, the Data Frame box will interact with what you have chosen. This is a great indicator as to what you would like to work with in your working environment. With a more complicated dataset, such as some fancy table from a website, then that s when I recommend to code in datasets. For now, pointing and clicking is recommended and will save you countless hours. 5

6 Stata Files that are offline If the Stata file does not have a URL but rather is in your folders, then there is a slight alteration that must be made in the code. First, check your working directory. getwd() To proceed with ease, make sure you working directory is set to the same location as your project and the Stata file is also in this same location. Everything will be much smoother. If it is not, use the command setwd(...) to set the working directory. From there, load the package foreign and read the Stata data file you wish to work with. library(foreign) # assuming you've already implemented the command 'install.packages("foreign")' read.dta(file = "filename.dta") Not easy as pie, but you ll get the hang of it. Save a Dataset In order to save dataframes to a CSV file, enter the following command: write.csv(dat, file = "larvae.csv") Remember, dat started out as a Stata data file. Well, we converted it into a data frame to work with in R and then with the command above, we transformed that data frame into a CSV file. I imagine one would only write the data frame into a CSV file if you are sharing or implementing it into another program. Most, if not all programs are friendly to CSV files. Upon quitting R, it will ask you to save the workspace image and any files you have not saved. Click Save and upon opening R again, it will be just as you left it. Everything from your datasets, functions, and objects will all be saved, so you don t have to save an altered CSV everytime you quit. Extra: Dates Dates are on whole nother level and what I would give here wouldn t be justified. Instead I have linked the following site. There s another package in R that eases the complexity of formatted dates, but once again, that s on a whole nother level. The package, lubridate, is linked here. 6