R Basics: Quick and Easy

1. What’is R and why learning R?

Read more: What’is R and why learning R?

2. Installing R and RStudio

• Install R and RStudio on windows
• Install R and RStudio for MAC OSX
• Install R and RStudio on Linux

Read more: Installing R and RStudio

3. Running RStudio and setting up your working directory

• Use R outside RStudio
• Use R inside RStudio
  • Launch RStudio under Windows, MAC OSX and Linux
  • Set up your working directory
    • Change your working directory
    • Set up a default working directory
• Close your R/RStudio session
• Functions: setwd(), getwd()

Read more: Running RStudio and setting up your working directory

4. R programming basics

• Basic arithmetic operations: + (addition), - (subtraction), * (multiplication), / (division), ^ (exponentiation)

7 + 4 # => 11
7 - 4 # => 3
7 / 2 # => 3.5
7 * 2 # => 14

• Basic arithmetic functions:
  • Logarithms and exponentials: log2(x), log10(x), exp(x)
  • Trigonometric functions: cos(x), sin(x), tan(x), acos(x), asin(x), atan(x)
  • Other mathematical functions: abs(x): absolute value; sqrt(x): square root.

log2(4) # => 2
abs(-4) # => 4
sqrt(4) # => 2

• Assigning values to variables:

lemon_price <- 2

• Basic data types: numeric, character and logical

my_age <- 28 # Numeric variable
my_name <- "Nicolas" # Character variable
#  Are you a data scientist?: (yes/no) <=> (TRUE/FALSE)
is_datascientist <- TRUE # logical variable

• Vectors: a combination of multiple values (numeric, character or logical)
  • Create a vector: c() for concatenate
  • Case of missing values: NA (not available) and NaN (not a number)
  • Get a subset of a vector: my_vector[i] to get the ith element
  • Calculations with vectors: max(x), min(x), range(x), length(x), sum(x), mean(x), prod(x): product of the elements in x, sd(x): standard deviation, var(x): variance, sort(x)

# Create a numeric vector
friend_ages <- c(27, 25, 29, 26)
mean(friend_ages) # => 26.75
max(friend_ages) # => 29

• Matrices: like an Excel sheet containing multiple rows and columns. Combination of multiple vectors with the same types (numeric, character or logical).
  • Create and naming matrix: matrix(), cbind(), rbind(), rownames(), colnames()
  • Check and convert: is.matrix(), as.matrix()
  • Transpose a matrix: t()
  • Dimensions of a matrix: ncol(), nrow(), dim()
  • Get a subset of a matrix: my_data[row, col]
  • Calculations with numeric matrices: rowSums(), colSums(), rowMeans(), colMeans(), apply()

     col1 col2 col3
row1    5    2    7
row2    6    4    3
row3    7    5    4
row4    8    9    8
row5    9    8    7

• Factors: grouping variables in your data
  • Create a factor: factor(), levels()
  • Check and convert: is.factor(x), as.factor(x)
  • Calculations with factors:
    • Number of elements in each category: summary(), table()
    • Compute some statistics by groups (for example, mean by groups): tapply()

# Create a factor
friend_groups <- factor(c("grp1", "grp2", "grp1", "grp2"))
levels(friend_groups) # => "grp1", "grp2"

[1] "grp1" "grp2"

# Compute the mean age by groups
friend_ages <- c(27, 25, 29, 26)
tapply(friend_ages, friend_groups, mean)

grp1 grp2 
28.0 25.5 

• Data frames: like a matrix but can have columns with different types
  • Create a data frame: data.frame()
  • Check and convert: is.data.frame(), as.data.frame()
  • Transpose a data frame: t()
  • Subset a data frame: my_data[row, col], subset(), attach() and detach()
  • Extend a data frame: $, cbind(), rbind()
  • Calculations with numeric data frames: rowSums(), colSums(), rowMeans(), colMeans(), apply()

     name age height married
1 Nicolas  27    180    TRUE
2 Thierry  25    170   FALSE
3 Bernard  29    185    TRUE
4  Jerome  26    169    TRUE

• Lists: collection of objects, which can be vectors, matrices, data frames,
  • Create a list: list()
  • Subset a list
  • Extend a list

my_family <- list(
  mother = "Veronique", 
  father = "Michel",
  sisters = c("Alicia", "Monica"),
  sister_age = c(12, 22)
  )
# Print
my_family

$mother
[1] "Veronique"
$father
[1] "Michel"
$sisters
[1] "Alicia" "Monica"
$sister_age
[1] 12 22

Read more: R programming basics

5. Getting help with functions in R programming

• Getting help on a specific function: help(mean), example(mean)
• General help about R: help_start()
• Others functions: apropos() and help.search()

Read more: Getting help with functions in R programming

6. Installing and using R packages

• What is R packages?

• Installing R packages
  • Install a package from CRAN: install.packages()
  • Install a package from Bioconductor: biocLite()
  • Install a package from GitHub: devtools::install_github()
  • View the list of installed packages: installed.packages()
  • Folder containing installed packages: .libPaths()

• Load and use an R package: library()

• View loaded R packages: search()

• Unload an R package: detach(pkg_name, unload = TRUE)

• Remove installed packages: remove.packages()

• Update installed packages: update.packages()

Read more: Installing and using R packages

7. R Built-in data sets

• List of pre-loaded data
• Loading a built-in R data
• Most used R built-in data sets
  • mtcars: Motor Trend Car Road Tests
  • iris
  • ToothGrowth
  • PlantGrowth
  • USArrests

Read more: R Built-in data sets