R and R Studio Lab #1. Introductions & Basics February 2017

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1 R and R Studio Lab #1 Introductions & Basics February Preliminaries.... a) Suggested packages for use in BIOSTATS b) FAQ: How to find the path of your dataset (or whatever). c) TIP: Use R Markdown to Archive Your Work.. 2. Import Data into R a) Text file tab delimited (.txt).. b) Excel file (.csv)... c) Excel file (.xlsx).. d) Stata file (.dta)... e) SAS (.sas7bdat).. f) SPSS file (.sav)... g) Minitab (.mtp).. 3. Describe Your Data Set Structure List Your Data Describe Your Data Numerical Descriptions 6. Describe Your Data Graphical Descriptions. 7. One and Two Sample Inference KEY GREEN = comments. In R, commands begin with the pound or hash symbol (#). BLACK = commands BLUE = output \R session 1 Spring 2017.docx Page 1 of 23

2 1. Preliminaries a. Suggested packages for use in BIOSTATS 640 b. FAQ: How to find the path of your dataset (or whatever) c. TIP: Use R Markdown to Archive Your Work 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) a. Suggested Packages for Use in BIOSTATS 640 Package Name openxlsx foreign mosaic dplyr psych gmodels car ggplot2 Uses/Description Import excel data Import STATA, SAS, SPSS, Minitab Elementary statistics (very user friendly!) Companion to applied regression Produce nice graphs \R session 1 Spring 2017.docx Page 2 of 23

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 session 1 Spring 2017.docx Page 3 of 23

4 c. FAQ: Use R Markdown to Archive Your Work Introduction A R Markdown file (.Rmd ) is a record of your R commands. You can do 2 things with it; 1. Save your.rmd file for reloading and re-executing later; and/or 2. Perform an action called knitting. When you knit, the commands are executed, the output is produced, and two are put into a single record of your work (as html, or pdf or word ) A more detailed step-by-step introduction can be found elsewhere Visit the BIOSTATS 640 Course website page, COMPUTER ILLUSTRATIONS. Under (2) R, scroll down to find Getting Started: How to Use R and R Markdown (pdf, 7 pp) How to Use R Markdown to Archive Your Work Brief Version Step 1. Launch R Studio Step 2. From the top menu bar: FILE > NEW FILE > R Markdown Supply/Edit: Title: Author: Default Output Format: (HTML is pre-selected. This is fine) Step 3. Delete everything that is provided below the title and author. Step 4. Now you will cycle: 1 st : Insert a blank chunk (see below, How to Insert a Blank Chunk ) 2 nd : Edit this blank chunk with the commands you want to run 3 rd : Run/execute your chunk (see below, How to Run/Execute a Chunk ) 4 rd : Edit your chunk 4 th : Rerun your chunk to confirm that it executes without error and in the way you want. \R session 1 Spring 2017.docx Page 4 of 23

How to Insert a Blank Chunk Click on the little green insert a chunk icon at top (on the right) How to Run/Execute Use your cursor to select the lines or chunk that you want to run/execute.

5 How to Insert a Blank Chunk Click on the little green insert a chunk icon at top (on the right) How to Run/Execute Use your cursor to select the lines or chunk that you want to run/execute. From the Run drop down at top right, make your selection Step 5. All done? Knit your work and create your Word R Markdown file! From the menu at top (left) click on the drop down menu for KNIT WORD and make your selection. Tip Take care to choose the destination folder (I always choose DESKTOP and then move it later) \R session 1 Spring 2017.docx Page 5 of 23

6 2. Import Data into R a) Text file tab delimited (.txt) b) Excel file (.csv) c) Excel file (.xlsx) d) Stata file (.dta) e) SAS (.sas7bdat) f) SPSS file (.sav) g) Minitab (.mtp) Preliminary (Recommended!) Step 1 Save the data to be imported into R onto your desktop. Step 2 - Then set your working directory to be your desktop. To do this, issue the following command (appropriately edited, of course) in the console window: setwd( full pathname of your desktop ) a. Text File tab delimited (.txt) Command is read.table( ) which comes with your R installation; no package to install here! Option 1: Your.txt file does NOT have headers data_text <- read.table( filename.txt, header=false) Option 2: Your.txt file DOES have headers data_text <- read.table( filename.txt, header=true) \R session 1 Spring 2017.docx Page 6 of 23

7 b. Excel file (.csv) Preliminary Determine what separator is used in your.csv file: comma or semi-colon. Command is read.table( ) which comes with your R installation; no package to install here! Option 1: The separator is a comma (,) data_csv <- read.table( filename.csv, header=false, sep=, ) Option 2: The separator is a semi-colon (;) data_csv <- read.table( filename.csv, header=false, sep= ; ) For additional help (e.g working with strings, factors, headings, etc, visit c. Excel File (.xlsx) Preliminaries: Must first install and load the package openxlsx install.packages( openxlsx ) library(openxlsx) Option 1: data_xlsx <- read.xlsx( filename.xlsx ) \R session 1 Spring 2017.docx Page 7 of 23

8 d. Stata File (.dta) Preliminaries: Must first install and load the package foreign. install.packages( foreign ) library(foreign) Option 1: data_dta <- read.dta( filename.dta ) Option 2: Asks R NOT to automatically convert categorical variables into factor variables: data_dta <- read.dta( filename.dta, convert.factors=false) e. SAS File (.sas7bdat) Preliminaries: Must first install and load the package sas7bdat. install.packages( sas7bdat ) library(sas7bdat) Option 1: data_sas7bdat <- read.sas7bdat( filename.sas7bdat ) f. SPSS File (.sav) Preliminaries: Make sure that you have saved your SPSS data in transport format. Then must first install and load the package foreign. install.packages( foreign ) library(foreign) Option 1: data_sav <- read.spss( filename.sav, to.data.frame=true, use.value.labels=false) g. Minitab File (.mtp) Preliminaries: Must first install and load the package foreign. install.packages( foreign ) library(foreign) Option 1: data_mtp <- read.mtp( filename.mtp ) \R session 1 Spring 2017.docx Page 8 of 23

9 1. Describe Your Dataset Structure Preliminaries: To follow along, be sure to have downloaded larvae.dta install.packages( foreign ) library(foreign) setwd( full pathname of your desktop ) larvae_dta <- read.dta( larvae.dta ) # Observations, # Variables Display variable names R Command # Example colnames(larvae_data) Display number of rows (observations) nrow( ) # Example nrow(larvae_data) [1] 15 Display number of columns (variables) ncol( ) Display dimensions of data set frame (rows x columns) (# observations x # variables) # Example ncol(larvae_data) [1] 4 dim( ) # Example dim(larvae_data) [1] 15 4 \R session 1 Spring 2017.docx Page 9 of 23

10 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 session 1 Spring 2017.docx Page 10 of 23

11 4. 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 # Example head(larvae_data) # Example tail(larvae_data) # Example larvae_data # Example View(larvae_data) # Example larvae_data[8:10,] id y x1 x # Example larvae_data[,2] [1] # Example larvae_data[8:10,2] [1] \R session 1 Spring 2017.docx Page 11 of 23

12 5. Describe Your Data - Numerical Descriptions Preliminaries: To follow along, be sure to have downloaded ivf.dta install.packages( foreign ) library(foreign) setwd( full pathname of your desktop ) ivfdata <- read.dta( ivf.dta ) # We will use some packages install.packages("gmodels") install.packages("psych") install.packages("mosaic") install.packages("readr") library(gmodels) library(psych) library(mosaic) library(readr) # Quick check of dataset structure using command str() str(ivfdata) 'data.frame': 641 obs. of 6 variables: $ id : num $ matage : int $ hyp : int $ gestwks: num $ sex : Factor w/ 2 levels "male","female": $ bweight: int attr(*, "datalabel")= chr "In Vitro Fertilization data" - attr(*, "time.stamp")= chr "27 Aug :11" - attr(*, "formats")= chr "%9.0g" "%8.0g" "%8.0g" "%9.0g"... - attr(*, "types")= int attr(*, "val.labels")= chr "" "" "" ""... - attr(*, "var.labels")= chr "identity number" "maternal age (years)" "hypertension (1=yes, 0=no)" "gestational age (weeks)"... - attr(*, "version")= int 7 - attr(*, "label.table")=list of 1..$ sex: Named int attr(*, "names")= chr "male" "female" \R session 1 Spring 2017.docx Page 12 of 23

13 # ONE discrete variable: sex options(digits=2) # Obtain frequencies, FREQ FREQ <- table(ivfdata$sex) # Obtain cumulative frequencies, CUMFREQ CUMFREQ <- cumsum(freq) # Obtain relative frequencies, RELFREQ RELFREQ <- FREQ/length(ivfdata$sex) # Obtain cumulative relative frequencies CUMRELFREQ CUMRELFREQ <- cumsum(relfreq) # Create table for descriptives, TABLE_sex TABLE_sex <- cbind(freq,relfreq,cumfreq,cumrelfreq) colnames(table_sex) <- c("freq", "Rel Freq","Cum Freq", "Cum Rel Freq") # Display desriptives TABLE_sex Freq Rel Freq Cum Freq Cum Rel Freq male female # TWO discrete variables (sex and hyp) # Obtain frequencies table(ivfdata$sex) table(ivfdata$hyp) sexhyp <-table(ivfdata$sex,ivfdata$hyp) # Obtain row percents prop.table(sexhyp,1) 0 1 male female # Obtain column percents prop.table(sexhyp,2) 0 1 male female \R session 1 Spring 2017.docx Page 13 of 23

14 # Obtain total percents prop.table(sexhyp) 0 1 male female # TWO discrete variables (sex and hyp) with xtab using # command CrossTable() from package gmodels # CrossTable(rowvariable,columnvariable, digits=2, expected=true,dnn=c("rowvariablename", "columnvariablename")) CrossTable(ivfdata$sex,ivfdata$hyp, digits=2, expected=true,dnn=c("sex", "Hypertension")) Cell Contents N Expected N Chi-square contribution N / Row Total N / Col Total N / Table Total Total Observations in Table: 639 Hypertension Sex 0 1 Row Total male female Column Total Statistics for All Table Factors Pearson's Chi-squared test Chi^2 = 2.4 d.f. = 1 p = 0.12 Pearson's Chi-squared test with Yates' continuity correction Chi^2 = 2 d.f. = 1 p = 0.15 \R session 1 Spring 2017.docx Page 14 of 23

15 # ONE continuous variable (bweight) summary(ivfdata$bweight) Min. 1st Qu. Median Mean 3rd Qu. Max favstats(ivfdata$bweight) min Q1 median Q3 max mean sd n missing # ONE continuous variable (bweight), over groups (sex) # using command describeby() from package psych describeby(ivfdata$bweight,ivfdata$sex) group: male vars n mean sd median trimmed mad min max range skew kurtosis se group: female vars n mean sd median trimmed mad min max range skew kurtosis se \R session 1 Spring 2017.docx Page 15 of 23

$table(ivfdata$hyp) # Bar Graph of frequencies with option main= title barplot(freqhyp,main="bar Chart of hyp: Hypertension") \R session 1 Spring 2017.$

16 6. Describe Your Data Graphical Descriptions # ONE discrete variable (hyp) - bar graph # Create object (freqhyp) containing the frequencies to be plotted freqhyp <- table(ivfdata$hyp) # Bar Graph of frequencies with option main= title barplot(freqhyp,main="bar Chart of hyp: Hypertension") \R session 1 Spring 2017.docx Page 16 of 23

17 # ONE Continuous Variable (matage) Box Plot boxplot(ivfdata$matage, main="box Plot of matage: Maternal Age") # ONE Continuous Variable (matage), over groups (sex) Box Plot # boxplot(continuousvariable~groupingvariable,options) boxplot(ivfdata$matage~ivfdata$sex,main="maternal Age, by Sex") \R session 1 Spring 2017.docx Page 17 of 23

18 # ONE Continuous Variable (bweight) - Histogram hist(ivfdata$matage, main="histogram of matage: Maternal Age") \R session 1 Spring 2017.docx Page 18 of 23

19 # XY Scatterplot Y=gestwks X=matage # plot(xvariable,yvariable) plot(ivfdata$matage,ivfdata$gestwks,main="scatterplot",xlab="maternal Age(yrs)", ylab="weeks Gestation") \R session 1 Spring 2017.docx Page 19 of 23

20 7. One and Two Sample Inference # ONE CONTINUOUS VARIABLE - One sample t-test of gestwks (NULL: mu=40) t.test(ivfdata$gestwks,alternative="two.sided",mu=40) One Sample t-test data: ivfdata$gestwks t = -14, df = 640, p-value < 2.2e-16 alternative hypothesis: true mean is not equal to percent confidence interval: sample estimates: mean of x 39 t.test(ivfdata$gestwks,alternative="greater",mu=40) One Sample t-test data: ivfdata$gestwks t = -14, df = 640, p-value = 1 alternative hypothesis: true mean is greater than percent confidence interval: 39 Inf sample estimates: mean of x 39 t.test(ivfdata$gestwks,alternative="less",mu=40) One Sample t-test data: ivfdata$gestwks t = -14, df = 640, p-value < 2.2e-16 alternative hypothesis: true mean is less than percent confidence interval: -Inf 39 sample estimates: mean of x 39 \R session 1 Spring 2017.docx Page 20 of 23

21 # ONE CONTINUOUS VARIABLE, 2 groups: Two (independent groups) Sample t-test t.test(ivfdata$gestwks~ivfdata$sex) Welch Two Sample t-test data: ivfdata$gestwks by ivfdata$sex t = 0.14, df = 627, p-value = alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: sample estimates: mean in group male mean in group female # ONE 0/1 DISCRETE VARIABLE: TEST of Proportion # (Null: proportion of sex=2 "female" babies=.5) options(digits=2) # Obtain frequencies, FREQ FREQ <- table(ivfdata$sex) # Obtain cumulative frequencies, CUMFREQ RELFREQ <- FREQ/length(ivfdata$sex) # Create table to display with test TABLE_test <- cbind(freq,relfreq) colnames(table_test) <- c("#", "%") TABLE_test # % male female # prop.test(#events,#trials,null p) prop.test(315,641,.5) 1-sample proportions test with continuity correction data: 315 out of 641 X-squared = 0.16, df = 1, p-value = alternative hypothesis: true p is not equal to percent confidence interval: sample estimates: p 0.49 \R session 1 Spring 2017.docx Page 21 of 23

22 # prop.test(#events,#trials,null p) WITHOUT continuity correction prop.test(315,641,.5, correct=false) 1-sample proportions test without continuity correction data: 315 out of 641 X-squared = 0.19, df = 1, p-value = alternative hypothesis: true p is not equal to percent confidence interval: sample estimates: p 0.49 # TWO 0/1 Discrete Variables: 2 Sample Test of Equality of Proportions hypsex <-table(ivfdata$hyp,ivfdata$sex) hypsex male female chisq.test(hypsex) # More Detailed TWO Sample Test of Equality of Proportions # using command CrossTable() in package gmodels CrossTable(ivfdata$hyp,ivfdata$sex, digits=2, expected=true,dnn=c("hypertension", "Sex")) Cell Contents N Expected N Chi-square contribution N / Row Total N / Col Total N / Table Total \R session 1 Spring 2017.docx Page 22 of 23

23 Total Observations in Table: 639 Sex Hypertension male female Row Total Column Total Statistics for All Table Factors Pearson's Chi-squared test Chi^2 = 2.4 d.f. = 1 p = 0.12 Pearson's Chi-squared test with Yates' continuity correction Chi^2 = 2 d.f. = 1 p = 0.15 chisq.test(hypsex, correct=false) Pearson's Chi-squared test data: hypsex X-squared = 2.4, df = 1, p-value = \R session 1 Spring 2017.docx Page 23 of 23

A Little Stata Session 1

A Little Stata Session 1 Following is a very basic introduction to Stata. I highly recommend the tutorial available at: http://www.ats.ucla.edu/stat/stata/default.htm When you bring up Stata, you will