Introduction to R. Dataset Basics. March 2018

Transcription

1 Introduction to R March Preliminaries.... a) Suggested packages for importing/exporting data.... b) FAQ: How to find the path of your dataset (or whatever). 2. Import/Export Data a) R (.Rdata) b) Text file tab delimited (.txt).. c) Excel file (.csv)... d) Excel file (.xlsx).. e) Stata file (.dta) versions 13 and beyond..... f) Stata file (.dta) versions g) SAS (.sas7bdat).. h) SPSS file (.sav)... i) Minitab (.mtp) Create an R Data Frame from Scratch Describe Your Data Structure. 5. List Your Data \R handout 2018.docx Page 1 of 16

2 1. Preliminaries a. Suggested packages for importing/exporting data b. FAQ: How to find the path of your dataset (or whatever) a. Suggested Packages for Importing/Exporting Data To install a package (for example foreign), type in the console window (tip! don t forget the quotes) install.packages( foreign ) To attach a package to your R session (for example foreign), type in the console window (tip! NO quotes) library(foreign) Data Format Type Text Excel (.csv ) Excel (.xlsx ) Stata versions (.dta ) Stata versions ( dta ) SAS (.sas7bdat ) SPSS (.sav ) Minitab (.mtp ) Package Name (notes) No package installation required openxlsx openxlsx foreign haven haven haven foreign \R handout 2018.docx Page 2 of 16

3 b. FAQ: How to Find the Path of Your Dataset (or whatever) PC Users (Beware - R is fussy about the direction of slashes) 1. Navigate to the dataset or file for which you want to copy and paste the full path 2. Highlight to select the dataset or file 3. SHIFT + RIGHT click 4. Click COPY AS PATH 5. CONTROL v to paste (Important The full path will be displayed with LEFT slashes) 6. Important last step: In R session: a) change to RIGHT slashes; b) enclose in quotes MAC Users (Beware - R is fussy about the direction of slashes) 1. From APPLICATIONS > UTILITIES, launch TERMINAL 2. Navigate to the dataset or file for which you want to copy and paste the full path 3. HIGHLIGHT to select the dataset or file and drag this into terminal You should now see the full path. 4. HIGHLIGHT again 5. COMMAND c to copy 6. In R Session: COMMAND v to paste 7. Important last step: In R session: enclose in quotes Illustration: Command-c followed by Command-v will now produce: /Users/cbigelow/Desktop/framingham_1000.dta \R handout 2018.docx Page 3 of 16

4 2. Import/Export Data a. R (.Rdata) Package Required = none Load R Data Frame which comes with your R installation; no package to install here! Command is s are load ( ) load (file= filename.rdata ) Save R Data Frame Command is save( ) which comes with your R installation; no package to install here! save (object, file= filename.rdata ) \R handout 2018.docx Page 4 of 16

5 b. Text File tab delimited (.txt) Package Required = none Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import Text File Commands are read.table( ) and write.table( ) which come with your R installation; no package to install here! Your.txt file does NOT have headers object <- read.table( filename.txt, header=false) Option 2: Your.txt file DOES have headers object <- read.table( filename.txt, header=true) Export Text File Command is write.table( ) which comes with your R installation; no package to install here! Output a tab delimited text file, NO Headers write.table(object, file= filename.txt, sep= \ ) Option 2: Output a tab delimited text file, Headers write.table(object, file= filename.txt, col.names=true, sep= \ ) \R handout 2018.docx Page 5 of 16

6 c. Excel File (.csv) Package Required = none Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Preliminary Determine what separator is used in your.csv file (comma or semi-colon) Import Excel (.csv ) File The separator is a comma (,) and NO headers object <- read.table( filename.csv, header=false, sep=, ) Option 2: The separator is a semi-colon (;) and NO headers object <- read.table( filename.csv, header=false, sep= ; ) Export Excel (.csv ) File The separator is a comma (,) and NO headers write.table(object, file= filename.csv, header=false, sep=, ) Option 2: The separator is a semi-colon (;) and NO headers write.table(object, file= filename.csv, header=false, sep= ; ) Tip - For additional help (e.g working with strings, factors, headings, etc, visit \R handout 2018.docx Page 6 of 16

7 d. Excel File (.xlsx) Package Required = openxlsx install.packages( openxlsx ) library(openxlsx) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import Excel (.xlsx ) File Sheet=1, NO column headings object <- read.xlsx( filename.xlsx, sheet=1, colnames=false) Option 2: Sheet=1, Column headings object <- read.xlsx( filename.xlsx, sheet=1, colnames=true) Export Excel (.xlsx ) File NO column headings write.xlsx(object, file= filename.xlsx, colnames=false) Option 2: Column headings write.xlsx(object, file= filename.xlsx, colnames=true) \R handout 2018.docx Page 7 of 16

8 e. Stata File (.dta) versions 13 and beyond Package Required = haven install.packages( haven ) library(haven) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import Stata versions 13 and beyond (.dta ) File object <- read_dta( filename.dta ) Export Stata versions 13 and beyond (.dta ) File write_dta(object, filename.dta ) \R handout 2018.docx Page 8 of 16

9 f. Stata File (.dta) versions 5-12 Package Required = foreign install.packages( foreign ) library(foreign) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import Stata versions 5-12 (.dta ) File object <- read.dta( filename.dta ) Option 2: Asks R NOT to automatically convert categorical variables into factor variables: object <- read.dta( filename.dta, convert.factors=false) Export Stata to version 10 (.dta ) File write.dta(object, filename.dta,version=10) \R handout 2018.docx Page 9 of 16

10 g. SAS File (.sas7bdat) Package Required = haven install.packages( haven ) library(haven) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import SAS (.sas7bdat ) File object <- read_sas( filename.sas7bdat ) Export SAS (.sas7bdat ) File write_sas(object, filename.sas7bdat ) \R handout 2018.docx Page 10 of 16

11 h. SPSS File (.sav) Package Required = haven install.packages( haven ) library(haven) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import SPSS (.sav ) File object <- read_sav( filename.sav ) Export SPSS (.sav ) File write_sav(object, filename.sav ) \R handout 2018.docx Page 11 of 16

12 i. Minitab File (.mtp) Package Required = foreign install.packages( foreign ) library(foreign) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop (appropriately edited, of course) in the console window: Import Minitab (.mtp ) File object <- read.mtp( filename.mtp ) Export Minitab (.mtp ) File Currently unavailable \R handout 2018.docx Page 12 of 16

13 3. Create an R Data Frame From Scratch Overview Step 1: Create each variable as a vector object in R. Step 2: Then bind together into a dataset object using the command data.frame( ) Example # Numeric variable = id id <- c(1,2,3,4) # Character variable = name name <- c('bob', 'Jean', 'Larry', 'Carol') # Date variable = dob dob <- as.date( c(' ',' ',' ',' ') ) # Numeric variable = weight weight <- c(175, 115, 150, 95) # Create data frame = mydata mydata <- data.frame(id,name,dob,weight) # Check str(mydata) 'data.frame': 4 obs. of 4 variables: $ id : num $ name : Factor w/ 4 levels "Bob","Carol",..: $ dob : Date, format: " " " "... $ weight: num list(mydata) [[1]] id name dob weight 1 1 Bob Jean Larry Carol \R handout 2018.docx Page 13 of 16

14 4. Describe Your Dataset Structure Preliminaries: To follow along, be sure to have downloaded larvae.dta install.packages( foreign ) library(foreign) larvae_dta <- read.dta( larvae.dta ) # Observations, # Variables Display variable names R Command colnames(larvae_data) Display number of rows (observations) nrow( ) nrow(larvae_data) [1] 15 Display number of columns (variables) ncol( ) Display dimensions of data set frame (rows x columns) (# observations x # variables) ncol(larvae_data) [1] 4 dim( ) dim(larvae_data) [1] 15 4 \R handout 2018.docx Page 14 of 16

15 Detailed Structure of Data command str(dataframe) # str(dataframe) str(larvae_data) 'data.frame': 15 obs. of 4 variables: $ id: num $ y : num $ x1: num $ x2: num attr(*, "datalabel")= chr "PubHlth 640 Unit 2 Regression - Larvae data" - attr(*, "time.stamp")= chr "10 Feb :36" - attr(*, "formats")= chr "%9.0g" "%9.0g" "%9.0g" "%9.0g" - attr(*, "types")= int attr(*, "val.labels")= chr "" "" "" "" - attr(*, "var.labels")= chr "larva id" "log10(survival)" "log10(dose)" "log10(weight)" - attr(*, "expansion.fields")=list of 2..$ : chr "_dta" "note1" "\"Week 3 homework assignment exercises 2 and 3\""..$ : chr "_dta" "note0" "1" - attr(*, "version")= int 12 \R handout 2018.docx Page 15 of 16

16 5. List Your Data Use Subscripts to Select a Subset of Your Data Just like spreadsheets in Excel or Stata data sets, rows are observations and variables are columns To select a subset of your data requires using subscripts, which can be thought of as addresses. Subscripts appear in square brackets and are of the form: dataframe[1 st subscript is for rows, 2 nd subscript is for columns] = [observation selection, variable selection] List First 6 observations using head( ) List Last 6 observations using tail( ) List Entire Data Set (be careful!): Two ways: 1) Just type its name; or 2) View(dataframe). List Selected Observations: For observations 8, 9, 10, list the data for all variables List Selected Variables: For all observations, List the observations for only the 2 nd variable List Selected Variables on Selected Observations: For observations 8, 9, 10 only, List the observations for only the 2 nd variable R Command head(larvae_data) tail(larvae_data) larvae_data View(larvae_data) larvae_data[8:10,] id y x1 x larvae_data[,2] [1] larvae_data[8:10,2] [1] \R handout 2018.docx Page 16 of 16