1、Introduction to R: Lesson 2 - Manipulating Data,Andrew Jaffe 9/13/10,Reminder,Here is the course website: http:/www.biostat.jhsph.edu/ajaffe/rseminar.html There is a running collection of functions that we have covered in class,Dataset,For the remaining sessions, were going to learn R by using data
2、from the Baltimore Dog Study Data collection is ongoing, and dataset will be updated weekly,http:/ Data Examining Data Recoding Variables Exporting Data,Importing Data,Here is a link to the data: http:/www.biostat.jhsph.edu/ajaffe/files/lecture_2_data.csv So how do we get it into R? Two options! Bot
3、h involve read.table(),Importing Data,read.table(filename, header = F, sep = “, as.is = !stringsAsFactors, ) In functions, “ means additional parameters can be passed/used These are some of the options associated with this functions all can be seen typing ?read.table in the console,Importing Data,fi
4、lename: the path to your file, in quotes If no path is specified (ie “C:Docsdata.txt“ or “UsersAndrewdata.txt“), then R will look in your working directory for the file (ie “data.txt“) For PCs, you need double backslashes to designate paths (ie “C:Docsdata.txt“) Basically, a single backslash is the
5、escape character,Importing Data,filename - you can: Write out the full file path using quotes and the correct syntax Manually set your working directory to where your script and files are located setwd() Or, if your script and files are in the same place, use Notepad+. It sets the scripts location t
6、o be the working directory,Importing Data,header default is false Does the first row of your file contain column names? If so, include header = T in your read.table() call,Importing Data,sep = “ what character separates columns? The escape character followed by the delimiter is used here: Tab: “t“ N
7、ewline/Enter/Return: “n“ Ampersand: “&“, etc,Importing Data,CSV is an exception A special case of read.table() exists: read.csv(), which takes all of the same parameters, except defaults sep = “,“ Analogously, read.delim() defaults sep = “t“,Importing data,as.is = F (as stringsAsFactors=T) : should
8、character strings be treated as factors? I prefer character strings as characters (ie as.is = T) and not factors Easier to manipulate, search, and match You can always change to factors later,Importing Data,Lets open up a new script: Notepad+ : File New Mac: File New Document Save it somewhere you c
9、an find later Write a header (using #) If Mac, use setwd() and include the folder you put the script,Importing Data,Lets get our data R Option 1: remember scan from last session? file = “http:/www.biostat.jhsph.edu/ajaffe/files/lecture_2_data.csv“,Importing Data,Option 2: Right click on the link to
10、the data on the webpage, and save it as a csv file in the same folder as your script file = “lecture_2_data.csv“,Importing Data,Either way: dat - read.csv(file, header = T, as.is=T),Overview,Importing Data Examining Data Recoding Variables Exporting Data,Examining Data,What are the dimensions of the
11、 dataset?,Examining Data,What are the dimensions of the dataset? dim(dat) 1 1000 7,Rows,Columns,Examining Data,What variables are included? What are their names?,Examining Data,What variables are included? What are their names?, head(dat)id age sex height weight dog dog_type 1 1 40 F 63.5 134.5 no 2
12、 2 36 M 65.6 191.6 no 3 3 69 M 68.2 170.0 no 4 4 56 F 62.9 134.5 no 5 5 66 F 63.7 133.4 no 6 6 84 M 70.8 200.6 no ,Examining Data,What variables are included? What are their names?, names(dat) 1 “id“ “age“ “sex“ “height“ 5 “weight“ “dog“ “dog_type“,Examining Data,What class of data is id? dog_type?,
13、Examining Data,What class of data is id? dog_type?, class(dat$id) 1 “integer“ class(dat$dog_type) 1 “character“,Examining Data,What class of data is id? dog_type?, str(dat) data.frame: 1000 obs. of 7 variables:$ id : int 1 2 3 4 5 6 7 8 9 10 .$ age : int 40 36 69 56 66 84 40 73 76 38 .$ sex : chr “F
14、“ “M“ “M“ “F“ .$ height : num 63.5 65.6 68.2 62.9 63.7 70.8 67 67 62.6 62.2 .$ weight : num 134 192 170 134 133 .$ dog : chr “no“ “no“ “no“ “no“ .$ dog_type: chr NA NA NA NA .,Examining Data,How many total participants are there? How many men and how many women?,Examining Data,How many total partici
15、pants are there? How many men and how many women?, length(unique(dat$id) 1 1000 unique(c(1,1,2,2,3) 1 1 2 3 length(unique(c(1,1,2,2,3) 1 3 length(c(1,1,2,2,3) 1 5,Examining Data,How many total participants are there? How many men and how many women?, table(dat$sex)F M 493 507,Examining Data,How many
16、 people have dogs?,Examining Data,How many people have dogs?, table(dat$dog)no yes 518 482,Examining Data,How many different types of dogs are there? How many of each?,Examining Data,How many different types of dogs are there? How many of each?, table(dat$dog_type)husky lab poodle retriever 113 125
17、111 133,Overview,Importing Data Examining Data Recoding Variables Exporting Data,Recoding Data,Missingness: represented by NA default read.table(,na.strings = “NA“,) you can change based on your data NA is NOT a character string:, x = rep(NA,3) x 1 NA NA NA class(x) 1 “logical“,Recoding Data,NA valu
18、es are essentially ignored, except when you use certain functions, x = c(NA, 1, NA, 3, 4) x*2 1 NA 2 NA 6 8 mean(x) 1 NA mean(x, na.rm = TRUE) 1 2.666667,Recoding Data,is.na() tests for missing entries Returns TRUE or FALSE at each entry, x = c(NA, 1, NA, 3, 4) x 1 NA 1 NA 3 4 class(x) 1 “numeric“ i
19、s.na(x) 1 TRUE FALSE TRUE FALSE FALSE,Recoding Data,which() returns the indices for entries that are TRUE, which(is.na(x) 1 1 3,Recoding Data,! means not:, which(!is.na(x) 1 2 4 5 x 1 NA 1 NA 3 4 Index = which(!is.na(x) xIndex 1 1 3 4,Recoding Data,which is implicit when you subset using is.na (or !
20、is.na),# in one step x!is.na(x) 1 1 3 4,Recoding Data,Renaming binary variables ex: change sex from M/F to 0/1, head(dat$sex) 1 “F“ “M“ “M“ “F“ “F“ “M“ bin.sex = ifelse(dat$sex=“F“,1,0) head(bin.sex) 1 1 0 0 1 1 0,Recoding Data,?ifelse: ifelse(test, yes, no) test - an object which can be coerced to
21、logical mode (ie TRUE or FALSE) yes - return values for true elements of test no - return values for false elements of test,Recoding Data,Logical characters: =, !=, , = Also: is.type ie: is.na, is.character, is.data.frame, is.numeric, etc, x = c(1,3,7,9) x 3 1 FALSE FALSE TRUE TRUE x = 3 1 FALSE TRU
22、E FALSE FALSE,Recoding Data, bin.sex = ifelse(dat$sex=“F“,1,0) head(dat$sex = “F“) 1 TRUE FALSE FALSE TRUE TRUE FALSE head(bin.sex) 1 1 0 0 1 1 0,Recoding Data,Analogously, creating a cut-point in continuous data:, head(dat$age) 1 40 36 69 56 66 84 bin.age = ifelse(dat$age head(bin.age) 1 0 0 1 1 1
23、1,Overview,Importing Data Examining Data Recoding Variables Exporting Data,Exporting Data,write.table(data, filename, quote = T, row.names = T, col.names = T, sep = “ “) data is an R object ie dat in our case filename is similar to read.table you should include a .txt in the filename quote puts char
24、acter strings in quotes (I like setting that to be FALSE or F),Exporting Data,row.names: includes the row.names in the output, which is usually just a sequence from 1 to nrow(dat) I prefer FALSE, as excel automatically has row indices col.names: include the header names in the output file? Depending on the data, I usually use TRUE,Practice,Make a 2 x 2 table of sex and dog Create a BMI variable using height and weight Hint: BMI = weightlbs*703/(heightin)2 Create an overweight variable, which gives the value 1 for people with BMI 30 and 0 otherwise,
