Working with R
Lecture 6
What is R?
R is a free software environment for statistical computing and graphics.
History
- 1976: In Bell Labs
Slanguage was created - 1988: Commercial version
S-PLUSappeared - 1993: Fork
Rappeared - ~2000:
Rgetting more and more popular
Pure R
RStudio IDE
CRAN & packages
Free software “tradition”:
- CTAN: Comprehensive TeX archive network
- CRAN: Comprehensive R archive network
- …
Another sources:
- Bioconductor
- GitHub
Main IDE frames
Editor
Create and edit scripts
In regular R: separate editors could be used.
Ctrl + Enter to execute selected part
Console
Main window:
> a <- 5 * 5
Environment
All variables and types
+ Viewer
Help & plots
In regular R:
- help inside console
- plots in separate windows
Primitives
# help on function
?lm
# math functions
2 + 3
10 / 3
sqrt(9)
# assigning
a <- 'Hello, world!'
Adding packages
install.packages("ggplot2")
library(ggplot2)
require(ggplot2)
Working directory
getwd()
[1] "/Users/quatsch/Documents/RIA_lectures"
# Set required WD
setwd('C:/Documents/my_R_project')
# Show files in current WD
dir()
# View raw file
file.show()
Loading tables
read.table(fname)
read.csv(fname)
read.csv2(fname)
read.delim(fname)
read.delim2(fname)
require(xlsx)
read.xlsx(fname, sheetName)
Writing files
write.table
write.csv
write.csv2
Demo data
head(cars, 5)
speed dist
1 4 2
2 4 10
3 7 4
4 7 22
5 8 16
iris
mtcars
Titanic
# etc...
First graphic
plot(cars)
hist(cars$speed)
Basic data types
- Numeric
10.5/ Integer10/ Complex1 + 2i - Factor
factor(c('m', 'v', 'm', 'v', 'v'), levels = c('m', 'v'))
[1] m v m v v
Levels: m v
- logical
True (T) / False (F) - character
Missing & special data
# NA
vec <- c(3, 10, 8, NA, 5, 6)
mean(vec)
[1] NA
mean(vec, na.rm=T)
[1] 6.4
Missing & special data
# Additional: NaN, Inf, and -Inf
pi/0
[1] Inf
0/0
[1] NaN
as.logical(0/0)
[1] NA
Converting
vec <- factor(c('1982', '1983', '1982', '1984', '1985'))
# Wrong
as.numeric(vec)
[1] 1 2 1 3 4
# Right
as.numeric(as.character(vec))
[1] 1982 1983 1982 1984 1985
Sequences - Vector
# concatenate
vec <- c(TRUE, 1, 0.5, 'item')
# vector elements should be of the same class
class(vec)
[1] "character"
# slicing
vec[3:4]
[1] "0.5" "item"
Sequences - Matrix
v <- 1:9
v
[1] 1 2 3 4 5 6 7 8 9
dim(v) <- c(3,3)
v
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
Sequences - List
test.list <- list(i1 = TRUE, i2 = 1, i3 = 0.5, i4 = 'item')
# every element is independent
class(test.list)
[1] "list"
# slicing
test.list[4]
$i4
[1] "item"
test.list$i4
[1] "item"
Sequences - Data Frame
Imagine list of multiple vectors (columns):
df <- data.frame(var1 = c('f', 'm', 'm', 'f'),
var2 = c(1982, 1982, 1983, 1985),
var3 = c(TRUE, FALSE, FALSE, FALSE))
df
var1 var2 var3
1 f 1982 TRUE
2 m 1982 FALSE
3 m 1983 FALSE
4 f 1985 FALSE
Data Frames Slicing
nrow(iris)
[1] 150
iris.filtered <- iris[iris$Species == 'setosa', ]
nrow(iris.filtered)
[1] 50
head(iris[, 'Species'])
[1] setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica
Vectorized computations
df <- data.frame(var1 = c(1, 2, 3, 4, 5, 6),
var2 = c(10, 20, 30, 40, 50, 60))
df$var3 <- df$var1 + df$var2
df$var4 <- df$var1 * df$var2
df
var1 var2 var3 var4
1 1 10 11 10
2 2 20 22 40
3 3 30 33 90
4 4 40 44 160
5 5 50 55 250
6 6 60 66 360
apply VS for
#head(trees, 3)
apply(trees, 2, mean)
Girth Height Volume
13.25 76.00 30.17
for(row in names(trees)){
print(paste(row, mean(trees[, row])))
}
[1] "Girth 13.2483870967742"
[1] "Height 76"
[1] "Volume 30.1709677419355"
Basic functions for stat analysis
mean()
median()
sd()
var() # sd() ^ 2
Your own functions
square <- function(vec)
{
vec <- vec ^ 2
return(vec)
}
my.vec <- c(1,2,3,4,5)
square(my.vec)
[1] 1 4 9 16 25