R session = environment + packages R F OR S AS US ERS Melinda - - PowerPoint PPT Presentation

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R session = environment + packages R F OR S AS US ERS Melinda - - PowerPoint PPT Presentation

Oct 16, 2023 235 likes •771 views

R session = environment + packages R F OR S AS US ERS Melinda Higgins, PhD Research Professor/Senior Biostatistician Emory University Why learn R? R is FREE . Free as in no cost and free as in open source licensing 1 R 's popularity is

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R session = environment + packages

R F OR S AS US ERS

Melinda Higgins, PhD

Research Professor/Senior Biostatistician Emory University

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R FOR SAS USERS

Why learn R?

R is FREE. Free as in no cost and free as in open source licensing R 's popularity is growing rapidly

Data science jobs for R have now surpassed those for SAS

R appears to now be more commonly reported in scholarly articles than SAS

The basic R installation is small (usually <100MB) Did I mention R is FREE?

http://r4stats.com/articles/popularity/

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R FOR SAS USERS

A computing session SAS vs R

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A computing session SAS vs R

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A computing session SAS vs R

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R FOR SAS USERS

A computing session SAS vs R

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A computing session SAS vs R

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A computing session SAS vs R

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Data and other objects

ls() lists all data and related objects loaded in R session's global environment

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Load data les

load() loads datasets in .RData binary format

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Global environment - new session

Usually there are no objects in the global environment at the beginning of a new R session.

ls() character(0)

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Load data

Abalone dataset Shellsh similar to clams, mussels or oysters Marine Research Lab, T asmania, Australia Use measurements to predict age

# Load the abalone dataset load("abalone.RData") # List the objects in memory ls() "abalone"

https://archive.ics.uci.edu/ml/datasets/abalone

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Getting help

help() provides access to documentation for any function or package installed

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help(ls)

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Settings and functionality

sessioninfo() provides details on computer system and packages loaded library() is used to load packages during your R session

T ens of thousands of R packages are available and increasing everyday

https://cran.r—project.org/web/packages/index.html

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R sessionInfo

sessionInfo() R version 3.4.3 (2017-11-30) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200) Matrix products: default locale: [1] LC_COLLATE=English_United States.1252 [2] LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base

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R sessionInfo

# Load the dplyr package and run sessionInfo again library(dplyr) sessionInfo() R version 3.4.3 (2017-11-30) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200) ... some output removed ... attached base packages: [1] stats graphics grDevices utils datasets methods base

  • ther attached base packages:

[1] dplyr_0.7.7

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Let's get started on your rst R session

R F OR S AS US ERS

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Descriptive statistics with R

R F OR S AS US ERS

Melinda Higgins, PhD

Research Professor/Senior Biostatistician Emory University

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R FOR SAS USERS

Loading external CSV datasets

Abalone dataset contains 9 measurements: length diameter height whole weight shucked weight shell weight viscera weight sex (infants, females, males) number of rings For 4177 abalones

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Loading external CSV datasets

abalone dataset available in CSV (comma separated value) format read_csv() function from readr package used to load CSV data

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The assign operator <- puts output from readr::read_csv into an object abalone

abalone is now saved in the global environment

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str(abalone) Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 4177 obs. of 9 variables: $ sex : chr "M" "M" "F" "M" ... $ length : num 0.455 0.35 0.53 0.44 0.33 0.425 0.53 0.545 ... $ diameter : num 0.365 0.265 0.42 0.365 0.255 0.3 0.415 0.425 ... $ height : num 0.095 0.09 0.135 0.125 0.08 0.095 0.15 0.125 ... $ wholeWeight : num 0.514 0.226 0.677 0.516 0.205 ... $ shuckedWeight: num 0.2245 0.0995 0.2565 0.2155 0.0895 ... $ visceraWeight: num 0.101 0.0485 0.1415 0.114 0.0395 ... $ shellWeight : num 0.15 0.07 0.21 0.155 0.055 0.12 0.33 0.26 ... $ rings : int 15 7 9 10 7 8 20 16 9 19 ...

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R FOR SAS USERS # Display dimensions of abalone dataset dim(abalone) 4177 9 # Elements or variables in abalone dataset names(abalone) "sex" "length" "diameter" "height" "wholeWeight" "shuckedWeight" "visceraWeight" "shellWeight" "rings"

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Dataset contents and variable types

head() and tail() show top and bottom 6 rows respectively by default

Change the number of rows shown by adding a second argument to the function

# Show bottom 7 rows of abalone tail(abalone, 7) # A tibble: 7 x 9 sex length diameter height wholeWeight shuckedWeight visceraWeight shellWeight rings <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 M 0.55 0.43 0.13 0.840 0.316 0.196 0.240 10 2 M 0.56 0.43 0.155 0.868 0.4 0.172 0.229 8 3 F 0.565 0.45 0.165 0.887 0.37 0.239 0.249 11 4 M 0.59 0.44 0.135 0.966 0.439 0.214 0.260 10 5 M 0.6 0.475 0.205 1.18 0.526 0.288 0.308 9 6 F 0.625 0.485 0.15 1.09 0.531 0.261 0.296 10 7 M 0.71 0.555 0.195 1.95 0.946 0.376 0.495 12

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Working with data using dplyr approach

In this course, you will use these dplyr functions:

%>% is a pipe operator from the magrittr package included with dplyr arrange() will sort the data by one or more variables pull(x) will pull one column x variable out of the dataset select(x,y,z) will select more than one variable out of the dataset

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dplyr arrange function and pipe %>% approach

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dplyr arrange function and pipe %>% approach

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dplyr arrange function and pipe %>% approach

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dplyr arrange function and pipe %>% approach

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Arrange abalones by diameter

# Arrange abalone dataset by diameter dimension abalone %>% arrange(diameter) # A tibble: 4,177 x 9 sex length diameter height wholeWeight shuckedWeight visceraWeight shellWeight rings <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 I 0.075 0.055 0.01 0.002 0.001 0.0005 0.0015 1 2 I 0.11 0.09 0.03 0.008 0.0025 0.002 0.003 3 3 I 0.13 0.095 0.035 0.0105 0.005 0.0065 0.0035 4 4 I 0.13 0.1 0.03 0.013 0.0045 0.003 0.004 3 5 I 0.15 0.1 0.025 0.015 0.0045 0.004 0.005 2 6 I 0.155 0.105 0.05 0.0175 0.005 0.0035 0.005 4 7 I 0.14 0.105 0.035 0.014 0.0055 0.0025 0.004 3 8 I 0.17 0.105 0.035 0.034 0.012 0.0085 0.005 4 9 I 0.14 0.105 0.035 0.0145 0.005 0.0035 0.005 4 10 M 0.155 0.11 0.04 0.0155 0.0065 0.003 0.005 3

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Extract one variable from abalone

Let's extract shuckedWeight from abalone using pull() from dplyr

# Pull out shuckedWeight variable from abalone abalone %>% pull(shuckedWeight) [1] 0.2245 0.0995 0.2565 0.2155 0.0895 0.1410 0.2370 0.2940 0.2165 0.3145 0.1940 0.1675 [13] 0.2175 0.2725 0.1675 0.2580 0.0950 0.1880 0.0970 0.1705 0.0955 0.0800 0.4275 0.3180 [25] 0.5130 0.3825 0.3945 0.3560 0.3940 0.3930 0.3935 0.6055 0.5515 0.8150 0.6330 0.2270 [37] 0.5305 0.2370 0.3810 0.1340 0.1865 0.3620 0.0315 0.0255 0.0175 0.0875 0.2930 0.1775 [49] 0.0755 0.3545 0.2385 0.1335 0.2595 0.2105 0.1730 0.2565 0.1920 0.2765 0.0420 0.2460 [61] 0.1800 0.3050 0.3020 0.1705 0.2340 0.2340 0.3540 0.4160 0.2135 0.0630 0.2640 0.1405 [73] 0.4800 0.4740 0.4810 0.4425 0.3625 0.3630 0.2820 0.4695 0.3845 0.5105 0.3960 0.4080 [85] 0.3800 0.3390 0.4825 0.3305 0.2205 0.3135 0.3410 0.3070 0.4015 0.5070 0.5880 0.5755 [97] 0.2690 0.2140 0.2010 0.2775 0.1050 0.3280 0.3160 0.3105 0.4975 0.2910 0.2935 0.2610 ...remaining output removed...

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Compute mean and median shucked weight

# Compute mean shuckedWeight abalone %>% pull(shuckedWeight) %>% mean() 0.3593675 # Compute median shuckedWeight abalone %>% pull(shuckedWeight) %>% median() 0.336

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Select two variables from abalone

# Select two variables length and height abalone %>% select(length, height) # A tibble: 4,177 x 2 length height <dbl> <dbl> 1 0.455 0.095 2 0.35 0.09 3 0.53 0.135 4 0.44 0.125 5 0.33 0.08 6 0.425 0.095 7 0.53 0.15 8 0.545 0.125 # ... with 4,169 more rows

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Get summary statistics of length and height

summary() outputs min, max, mean, median and 25th and 75th quartiles

# Get summary stats of length and height abalone %>% select(length, height) %>% summary() length height

  • Min. :0.075 Min. :0.0000

1st Qu.:0.450 1st Qu.:0.1150 Median :0.545 Median :0.1400 Mean :0.524 Mean :0.1395 3rd Qu.:0.615 3rd Qu.:0.1650

  • Max. :0.815 Max. :1.1300
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Let's go nd out about abalones

R F OR S AS US ERS

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Graphical visualizations in R using ggplot2

R F OR S AS US ERS

Melinda Higgins, PhD

Research Professor/Senior Biostatistician Emory University

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ggplot2 package

ggplot2 is a powerful graphics package for R

"GG" in ggplot stands for the "grammar of graphics"

ggplot2 uses a layering approach to build graphics

One or more geometric objects are added to the base graphics layer

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Layers - base layer

# Create plot for x=sex and y=diameter ggplot(data = abalone, aes(sex, diameter))

Dene base layer with ggplot() Set data = abalone Set aes to sex and diameter No graphical objects in the plot yet x-axis is ready for sex y-axis is ready for diameter grid is laid out

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Layers - add boxplot geom

# Add boxplot geometric object or geom ggplot(data = abalone, aes(sex, diameter)) + geom_boxplot()

Plus operator + adds layer Boxplot geom_boxplot() added Result is series of boxplots Abalone diameters by sex

F females, I infants, and M males

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Layers - add a theme

# Add black white theme ggplot(data = abalone, aes(sex, diameter)) + geom_boxplot() + theme_bw()

Add "theme" layer using theme_bw() Removes grey background Draws black box around the plot

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Change boxplot geom to violin geom

# Change to geom_violin() ggplot(data = abalone, aes(sex, diameter)) + geom_violin() + theme_bw() geom_violin replaces geom_boxplot

Creates a shape similar to a violin Reects data density distribution Simple change to make new gure

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Single variable histogram

# Make histogram of shuckedWeight ggplot(abalone, aes(shuckedWeight)) + geom_histogram()

Create a histogram for one variable One variable = one aesthetic Add geom_histogram() Set aes() to shuckedWeight Default colors need changing

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Histogram add colors

# Make lines black and fill light blue ggplot(abalone, aes(shuckedWeight)) + geom_histogram(color = "black", fill = "lightblue")

Change graphical parameters Set color of bin lines Set fill color for bins Each option is set inside the () Histogram looks much better

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Histogram add title and axis labels

# Add x, y axis labels and title ggplot(abalone, aes(shuckedWeight)) + geom_histogram(color = "black", fill = "lightblue") + xlab("Shucked Weight") + ylab("Frequency Counts") + ggtitle("Shucked Weights Histogram")

Add better labels for axes and title Use xlab() and ylab() for axes Use ggtitle() for title This gure is ready to publish!

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Make scatterplot

# Make scatterplot with geom_point() ggplot(abalone, aes(rings, shellWeight)) + geom_point()

A scatterplot aes needs two variables

geom_point() adds the points

Scatterplot of shell weights by rings

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Scatterplot add smoothed t line

# Add smoothed fit line ggplot(abalone, aes(rings, shellWeight)) + geom_point() + geom_smooth()

T

  • scatterplot, add geom_smooth() line

Includes shaded condence area

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Create panels by another variable

# Add panels using facet_wrap() ggplot(abalone, aes(rings, shellWeight)) + geom_point() + geom_smooth() + facet_wrap(vars(sex))

Add one more layer to scatterplot Create panels for each abalone sex Add facet_wrap() layer

vars(sex) denes variable for panels

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Rest of course

Chapter 1 nishes with brief introduction to graphics

ggplot2 graphical skills foundation

visualize abalone measurements by sex Chapter 2 teaches data wrangling skills clean up abalone dataset Chapter 3 teaches data exploration methods descriptive statistics, correlations and comparison tests Chapter 4 teaches modeling and results presentation predict abalone ages by measurements explore models by sex

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Let's make some plots for abalones

R F OR S AS US ERS