0.1 R

http://r-project.org
Open-source, statistical programming language; widely used in academia, finance, pharma, . . .
Core language, ‘base’ and > 4000 contributed packages
Interactive sessions, scripts, packages

0.2 Useful Functions in base R

dir, list.files
List files
read.table, scan
Read Data into R
c, factor, data.frame, matrix
Create vectors , data.frame and matrices to store data
summary, table, xtabs
Summarize or cross-tabulate data.
plot
Plot data to visualize it
match, %in%, which
find elements of one vector in another.
split, cut
Split or cut vectors.
strsplit, grep, sub
Operate on character vectors.
lapply, sapply, mapply
Apply function to elements of lists.
t.test, lm, anova
Compare two or several groups.
dist , hclust
Cluster Data
biocLite, install.packages
Install packages in R from online repository
traceback, debug, browser
debug errors

0.3 Getting help in R

?data.frame
methods(lm), methods(class=class(fit))
?“plot”
help(package=“Biostrings”)
vignette(package=“GenomicRanges”)
StackOverflow; R-help mailing list

0.4 Data types in R

Vectors
logical, integer, numeric, character, . . .
list() - contains other vectors (recursive)
factor(), NA - statistical concepts
Can be named - c(Seattle=1, Portland=2)
matrix(), array() - a vector with a ‘dim’ attribute.
data.frame()
like spreadsheets; list of equal length vectors.
Homogenous types within a column, heterogenous types across columns.
Other classes
more complicated arrangement of vectors.
Examples
- the value returned by lm();
- the DNAStringSet class used to hold DNA sequences.
plain, ‘accessor’, ‘generic’, and ‘method’ functions
Packages
base, recommended, contributed.

0.5 R programming concepts

Functions - built-in (e.g., rnorm()); user-defined

mean(1:10)

## [1] 5.5

rnorm(1:10)

##  [1] -0.33290892 -1.03732032  0.00358368 -0.45695476  1.00505058  0.67227284 -0.90495556 -1.45142584
##  [9]  0.22424728 -0.20679577

summary(rnorm(1:10))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.9524 -0.4189  0.4298  0.3124  0.6072  2.2260

Subsetting - logical, numeric, character;

data(iris) 

# find those rows where petal.width is exactly 0.2
iris[iris$Petal.Width==0.2,]

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 11          5.4         3.7          1.5         0.2  setosa
## 12          4.8         3.4          1.6         0.2  setosa
## 15          5.8         4.0          1.2         0.2  setosa
## 21          5.4         3.4          1.7         0.2  setosa
## 23          4.6         3.6          1.0         0.2  setosa
## 25          4.8         3.4          1.9         0.2  setosa
## 26          5.0         3.0          1.6         0.2  setosa
## 28          5.2         3.5          1.5         0.2  setosa
## 29          5.2         3.4          1.4         0.2  setosa
## 30          4.7         3.2          1.6         0.2  setosa
## 31          4.8         3.1          1.6         0.2  setosa
## 34          5.5         4.2          1.4         0.2  setosa
## 35          4.9         3.1          1.5         0.2  setosa
## 36          5.0         3.2          1.2         0.2  setosa
## 37          5.5         3.5          1.3         0.2  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 40          5.1         3.4          1.5         0.2  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 47          5.1         3.8          1.6         0.2  setosa
## 48          4.6         3.2          1.4         0.2  setosa
## 49          5.3         3.7          1.5         0.2  setosa
## 50          5.0         3.3          1.4         0.2  setosa

# find those rows where sepal.length is less than 4.5
iris[iris$Sepal.Length < 4.5,]

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 9           4.4         2.9          1.4         0.2  setosa
## 14          4.3         3.0          1.1         0.1  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 43          4.4         3.2          1.3         0.2  setosa

# find all rows belonging to setosa
setosa_iris = iris[iris$Species=="setosa",]
dim(setosa_iris)

## [1] 50  5

head(setosa_iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Iteration - over vector elements, lapply(), mapply(), apply()

# drop the column containing characters i.e., Species 
iris <- iris[,!( names(iris) %in% "Species")]
dim(iris)

## [1] 150   4

# find the mean of the first 4 numerical columns 
lapply(iris, mean) # simpler: colMeans(iris)

## $Sepal.Length
## [1] 5.843333
## 
## $Sepal.Width
## [1] 3.057333
## 
## $Petal.Length
## [1] 3.758
## 
## $Petal.Width
## [1] 1.199333

# simplify the result 
sapply(iris, mean)

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
##     5.843333     3.057333     3.758000     1.199333

# find the mean for each row. 
apply(iris, 1 , mean) #simpler : rowMeans(iris)

##   [1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700 2.500 2.325 2.125 2.800
##  [16] 3.000 2.750 2.575 2.875 2.675 2.675 2.675 2.350 2.650 2.575 2.450 2.600 2.600 2.550 2.425
##  [31] 2.425 2.675 2.725 2.825 2.425 2.400 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675 2.800
##  [46] 2.375 2.675 2.350 2.675 2.475 4.075 3.900 4.100 3.275 3.850 3.575 3.975 2.900 3.850 3.300
##  [61] 2.875 3.650 3.300 3.775 3.350 3.900 3.650 3.400 3.600 3.275 3.925 3.550 3.800 3.700 3.725
##  [76] 3.850 3.950 4.100 3.725 3.200 3.200 3.150 3.400 3.850 3.600 3.875 4.000 3.575 3.500 3.325
##  [91] 3.425 3.775 3.400 2.900 3.450 3.525 3.525 3.675 2.925 3.475 4.525 3.875 4.525 4.150 4.375
## [106] 4.825 3.400 4.575 4.200 4.850 4.200 4.075 4.350 3.800 4.025 4.300 4.200 5.100 4.875 3.675
## [121] 4.525 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025 4.250 3.925 3.925
## [136] 4.775 4.425 4.200 3.900 4.375 4.450 4.350 3.875 4.550 4.550 4.300 3.925 4.175 4.325 3.950

0.6 R as a Statistical Computing Environment

# define a vector
x <- rnorm(1000) 

# vectorized calculation
y <- x + rnorm(1000, sd=.8) 

# object construction
df <- data.frame(x=x, y=y)
 
# linear model
fit <- lm(y ~ x, df)

0.7 Visualizing Data in R

par(mfrow=c(1,2))
plot(y ~ x, df, cex.lab=2)
abline(fit, col="red", lwd=2)

library(ggplot2)
ggplot(df, aes(x, y)) + 
   geom_point() +
   stat_smooth(method="lm")

0.8 `sessionInfo()`

sessionInfo()

## R version 3.2.1 (2015-06-18)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.2 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_1.0.1   BiocStyle_1.7.4
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.11.6      codetools_0.2-14 digest_0.6.8     MASS_7.3-43      grid_3.2.1      
##  [6] plyr_1.8.3       gtable_0.1.2     formatR_1.2      magrittr_1.5     scales_0.2.5    
## [11] evaluate_0.7     stringi_0.5-5    reshape2_1.4.1   rmarkdown_0.7    labeling_0.3    
## [16] proto_0.3-10     tools_3.2.1      stringr_1.0.0    munsell_0.4.2    yaml_2.1.13     
## [21] colorspace_1.2-6 htmltools_0.2.6  knitr_1.10.5

Introduction to R

Sonali Arora

Contents

0.1 R

0.2 Useful Functions in base R

0.3 Getting help in R

0.4 Data types in R

0.5 R programming concepts

0.6 R as a Statistical Computing Environment

0.7 Visualizing Data in R

0.8 `sessionInfo()`

Introduction to R

Sonali Arora

Contents

0.1 R

0.2 Useful Functions in base R

0.3 Getting help in R

0.4 Data types in R

0.5 R programming concepts

0.6 R as a Statistical Computing Environment

0.7 Visualizing Data in R

0.8 sessionInfo()

0.8 `sessionInfo()`