BIOSTATS 640 Spring 2018 Introduction to R Data Description. 1. Start of Session. a. Preliminaries... b. Install Packages c. Attach Packages...

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1 BIOSTATS 640 Spring 2018 Introduction to R and R-Studio Data Description Page 1. Start of Session. a. Preliminaries... b. Install Packages c. Attach Packages Load R Data.. a. Load R data frames... b. Check 3. SINGLE VARIABLE - Discrete. a. Numerical. b. Graphical 4. SINGLE VARIABLE - Continuous. a. Numerical. b. Graphical 5. TWO VARIABLES One Continuous, One Discrete. a. Numerical. b. Graphical 6. TWO VARIABLES BOTH Continuous... a. XY Scatter Plot R Data Description II Spring 2018.docx Page 1 of 13

2 1. Start of Session # 1. START OF SESSION # 1a. Preliminaries # setwd( ) to set working directory # Tips: 1) R wants forward slashes and 2) must enclose in quotes setwd("/users/cbigelow/desktop/") # 1b. Clear workspace rm(list=ls()) # 1c. Turn OFF scientific notation options(scipen=1000) # 1b. Install packages (one time) # Tip - package name MUST be enclosed in quotes # Note - You'll get lots of stuff appearing in your console window. # Dear Reader - I have commented out these installations because I already have them. # install.packages("mosaic") # install.packages("desctools") # install.packages("psych") # install.packages("ggplot2") # 1c. Attach packages to be used in this session (1x per session) library(mosaic) library(desctools) library(psych) library(ggplot2) R Data Description II Spring 2018.docx Page 2 of 13

3 2. Load R Data #2a. LOAD R dataframes # Tip - Be sure you have downloaded data and placed it on desktop # At right click on ENVIRONMENT tab to confirm all is well. setwd("/users/cbigelow/desktop/") load(file="larvae.rdata") load(file="ivf.rdata") # 2b. Check # str(dataframe) to check structure of dataframe str(ivf) ## '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 "14 Feb :55" ## - 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)" "g estational age (weeks)"... ## - attr(*, "version")= int 12 ## - attr(*, "label.table")=list of 1 ##..$ sex: Named int 1 2 ##....- attr(*, "names")= chr "male" "female" # head(dataframe) to display first 6 rows head(ivf) ## id matage hyp gestwks sex bweight ## female 2410 ## female 2977 ## female 2100 ## male 3270 ## female 2620 ## male 3260 # tail(dataframe) to display last 6 rows tail(ivf) ## id matage hyp gestwks sex bweight ## male 2972 ## female 2850 ## male 3182 ## female 3048 ## female 3183 ## male 2920 R Data Description II Spring 2018.docx Page 3 of 13

4 # summary(dataframe) to get quick descriptives on every variable summary(ivf) ## id matage hyp gestwks ## Min. : 1 Min. :23.00 Min. : Min. :24.69 ## 1st Qu.:161 1st Qu.: st Qu.: st Qu.:38.01 ## Median :321 Median :34.00 Median : Median :39.15 ## Mean :321 Mean :33.97 Mean : Mean :38.69 ## 3rd Qu.:481 3rd Qu.: rd Qu.: rd Qu.:40.15 ## Max. :641 Max. :43.00 Max. : Max. :42.35 ## NA's :2 ## sex bweight ## male :326 Min. : 630 ## female:315 1st Qu.:2850 ## Median :3200 ## Mean :3129 ## 3rd Qu.:3550 ## Max. :4650 ## R Data Description II Spring 2018.docx Page 4 of 13

5 3. SINGLE VARIABLE - DISCRETE # 3. SINGLE VARIABLE - DISCRETE # 3a. Numerical # 3a.i. Frequency and relative frequency table - brute force # Step 1: Create columns of table n <- length(ivf$sex) sex_freq <- table(ivf$sex) sex_relfreq <- sex_freq/n sex_cum <- cumsum(sex_freq) sex_cumrel <- cumsum(sex_relfreq) # Step 2: cbind(,,,) to combine the columns into a table sextable <- cbind(sex_freq, sex_relfreq, sex_cum, sex_cumrel) # Step 3: colnames(" ", " ", " ") to label columns of table colnames(sextable) <- c("freq", "Rel Freq", "Cum Freq", "Cum Rel Freq") # Step 4: Display table with just 4 digits after the decimal round(sextable,digits=4) ## Freq Rel Freq Cum Freq Cum Rel Freq ## male ## female # 3a.ii Frequency and relative frequency table # Command Freq() in package=desctools # Tip - Turn off scientific notation first options(scipen=1000) Freq(ivf$sex) ## level freq perc cumfreq cumperc ## 1 male % % ## 2 female % % # 3b. Graphical # 3b.1. Bar Graph of counts # Command ggplot() + geom_bar() in package=ggplot2 # Basic ggplot(data=ivf, aes(x=factor(hyp))) + geom_bar() R Data Description II Spring 2018.docx Page 5 of 13

6 # With aesthetics: I recommend building step by step, then displaying! p <- ggplot(data=ivf, aes(x=factor(hyp))) p1 <- p + geom_bar(color="black", fill="blue") p2 <- p1 + xlab("hypertension") p3 <- p2 + ylab("frequency") p4 <- p3 + ggtitle("bar Graph of Hypertension") p5 <- p4 + theme_bw() p5 # 3b.2 Bar Graph of percents p6 <- ggplot(data=ivf, aes(x=hyp)) p7 <- p6 + geom_bar(aes(y = (..count..)/sum(..count..)), color="black", fill="blue") p8 <- p7 + scale_y_continuous(labels=scales::percent) p9 <- p8 + xlab("hypertension") p10 <-p9 + ylab("relative Frequency") p11 <- p10 + ggtitle("bar Graph of Hypertension") p12 <- p11 + theme_bw() p12 ## Warning: Removed 2 rows containing non-finite values (stat_count). R Data Description II Spring 2018.docx Page 6 of 13

7 4. SINGLE VARIABLE CONTINUOUS # 4. SINGLE VARIABLE - CONTINUOUS # 4a. Numerical # 4a.1. Five number summary # Command favstats() in package=mosaic favstats(~bweight, data=ivf) ## min Q1 median Q3 max mean sd n missing ## # 4a.2. Quantiles # Command quantile() in package=mosaic quantile(~bweight, data=ivf) ## 0% 25% 50% 75% 100% ## # 4a.3. Basic descriptives summary(ivf$bweight) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## # 4a.4. Detailed descriptives # Command describe( ) in package=psych describe(ivf$bweight) ## vars n mean sd median trimmed mad min max range skew ## X ## kurtosis se ## X # 4b. Graphical # 4b.1. Box plot # Command ggplot() + geom_boxplot() in package=ggplot2 # Basic ggplot(data=ivf, aes(x=1, y=matage)) + geom_boxplot() R Data Description II Spring 2018.docx Page 7 of 13

8 # With aesthetics - step by step p13 <- ggplot(data=ivf, aes(x=1, y=matage)) + geom_boxplot(color="black", fill="blue") p14 <- p13 + xlab(".") p15 <- p14 + ylab("maternal Age (years)") p16 <- p15 + ggtitle("box Plot of Maternal Age") p17 <- p16 + theme_bw() p17 # 4b.2 Histogram # Command ggplot() + geom_histogram() in package=ggplot2 # Basic ggplot(data=ivf, aes(x=matage)) + geom_histogram() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. R Data Description II Spring 2018.docx Page 8 of 13

9 # With aesthetics - step by step p18 <- ggplot(data=ivf, aes(x=matage)) + geom_histogram(color="black", fill="blue", binwidth=2) p19 <- p18 + xlab("maternal Age (years)") p20 <- p19 + ylab("frequency") p21 <- p20 + ggtitle("histogram of Maternal Age") p22 <- p21 + theme_bw() p22 R Data Description II Spring 2018.docx Page 9 of 13

10 5. TWO VARIABLES One Continuous, One Discrete # 5. TWO VARIABLES - ONE CONTINUOUS, ONE DISCRETE # 5a. Numerical # 5a.1. Detailed descriptives, by group # Command describeby() in package=psych # Tip - Declare your discrete group variable to be factor library(psych) ivf$sex <- as.factor(ivf$sex) describeby(ivf$bweight, group = ivf$sex,digits= 4) ## $male ## vars n mean sd median trimmed mad min max range skew ## X ## kurtosis se ## X ## ## $female ## vars n mean sd median trimmed mad min max range skew ## X ## kurtosis se ## X ## ## attr(,"call") ## by.default(data = x, INDICES = group, FUN = describe, type = type) # 5b. Graphical # 5b.1. Side-by-side box plot # Command ggplot() + geom_boxplot() in package=ggplot2 # Basic ggplot(data=ivf, aes(x=as.factor(sex), y=bweight)) + geom_boxplot() R Data Description II Spring 2018.docx Page 10 of 13

11 # With aesthetics - step by step p13 <- ggplot(data=ivf, aes(x=as.factor(sex), y=bweight)) + geom_boxplot(color="black", fill="blue") p14 <- p13 + xlab("sex") p15 <- p14 + ylab("birthweight (g)") p16 <- p15 + ggtitle("side-by Side Box Plot of Birthweight, by Sex") p17 <- p16 + theme_bw() p17 # 5b.2. Histogram, by group - Stacked # Command ggplot() + geom_histogram() + facet_wrap() in package=ggplot2 # Basic ivf$sex <- as.factor(ivf$sex) ggplot(data=ivf, aes(x=bweight)) + geom_histogram() + facet_grid(sex ~.) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. R Data Description II Spring 2018.docx Page 11 of 13

12 # With aesthetics - step by step p18 <- ggplot(data=ivf, aes(x=bweight, fill=sex)) + geom_histogram(color="black", fill="blue", binwidth=2 00) + facet_wrap(~sex, ncol=1) p19 <- p18 + xlab("birthweight (g)") p20 <- p19 + ylab("frequency") p21 <- p20 + ggtitle("histogram of Birthweight (g), by Sex") p22 <- p21 + theme_bw() p22 # 5b.3. Histogram, by group - Overlay # Command ggplot() + geom_histogram() in package=ggplot2 # With aesthetics - step by step p23 <- ggplot(data=ivf, aes(x=bweight, fill=sex)) + geom_histogram(position="identity", alpha=0.4, binwid th=200) p24 <- p23 + xlab("birthweight (g)") p25 <- p24 + ylab("frequency") p26 <- p25 + ggtitle("histogram of Birthweight (g), over Sex") p27 <- p26 + theme_bw() p27 R Data Description II Spring 2018.docx Page 12 of 13

13 6. TWO VARIABLES Both Continuous # 6. TWO VARIABLES - BOTH CONTINUOUS # 6a. XY Scatterplot # Command ggplot() + geom_point() in package=ggplot2 # Basic ggplot(data=ivf, aes(x=matage, y=bweight)) + geom_point() # With aesthetics - step by step p28 <- ggplot(data=ivf, aes(x=matage, y=bweight)) + geom_point(size=0.5, color="blue") p29 <- p28 + xlab("maternal Age (years") p30 <- p29 + ylab("birthweight (g)") p31 <- p30 + ggtitle("scatterplot of Birthweight in Relationship to Maternal Age") p32 <- p31 + theme_bw() p32 R Data Description II Spring 2018.docx Page 13 of 13