R

THE BASIC CONCEPTS OF R

• Functions
• Comments
• Variables

# assignment variable
variable_1 <- "This is variable"
variable_2 <- 3

• Data types
• Vectors: A group of data elements of the same type stored in a sequence in R

# vec_1 is our vector
vec_1 <- c(2, 4, 7, 3.5, 54.5)

• Pipes: A tool in R for expressing a sequence of multiple operations, represented with "%>%"

DATA STRUCTURE

• Vectors
• Data frames
• Matrices
• Arrays

VECTORS

• There are 2 types of vectors: atomic vectors and lists
• vector is a group of data elements of the same type, stored in a sequence in R. You cannot have a vector that contains both logicals and numerics
• There are six types of vectors: logical, integer, double, character(contains strings), complex, and raw.

Type	Description	Example
Logical	True/False	TRUE
Integer	Positive and negative whole values	3
Double	Decimal values	101.751
Character	String/character values	"Coding"

• Creating vectors:

  • Using the c()(combine function)
  • For example, you can use the c() function to store numeric data in a vector
    c(2.5, 48.5, 101.5
  • To create a vector of integers using the c() function, you must place the letter "L" directly after each number
    c(1L, 5L, 15L)
  • You can also create a vector containing characters or logicals
    c("Sara", "Lee", "Anna")
c(TRUE, FALSE, FALSE)

• Determining the properties of vector:

  • Every vector you create will have two key properties: type and length
  • You can determine what type of vector you are working with using the typeof() function
    typeof(c("a","b"))
#> [1] "character"
    Notice that the output of the typeof function in this example is "character". Similarly, if you use the typeof function on a vector with integer values, then the output will include "integer" instead:
    typeof(c(1L, 5L))
#> [1] "integer"
  • You can determine the length of an existing vector-meaning the number of elements it contains- by using the length() function.
    x <- c(33.5, 57.75, 120.05)
length(x)
#> [1] 3
  • You can also check if a vector is a specific type by using an is function: is.logical(), is.double(), is.integer(), is.character()

• Creating Lists

  • Lists are different from atomic vectors because their elements can be of any type - like dates, data frames, vectors, matrices, and more. Lists can contain other lists.
  • You can create a list with the list() function.
    list("a",1L, 1.5, TRUE)
list(list(list(1 , 3, 5)))

• Determining the structure of lists

  • If you want to find out that types of elements a list contains, you can use the str() function.
    str(list("a", 1L, 1.5, TRUE)
    Our result is:
  • Let's use the str() function to discover the structure of our second example.
    z <- list(list(list(1 , 3, 5)))
  • Run str(z)
    Our result is:
    The indentation of the $ symbols reflect the nested structure of this list. Here, there are 3 levels (so there is a list within a list within a list)

• Naming list:

  • Lists, like vectors, can be named:
    list('Chicago' = 1, 'New York' = 2, 'Los Angeles' = 3 )

DATE AND TIME

• Types:

  • A date ("2016-08-19")
  • A time within a date ("20:11:23 UTC")
  • And a date-time. This is a date plus a time ("2018-03-29 18:13:55 UTC")
  • today() function gives you a date
  • now() function gives you a current date-time

• Converting from strings:

  • ymd("January 20th, 2021") gives you the date in yyyy-mm-dd format.
  • ymd_hms("01/20/2021 08:01") gives you the date in date-time format.
  • as_date() converts a date-time to a date.

• Data frames

  • Data frames are the most common way of storing and analyzing data in R, so it's important to understand what they are and how to create them. A data frame is a collection of columns containing data, similar to a spreadsheet or SQL table. Each column has a name that represents a variable and includes one observation per row. Data frames summarize data and organize it into a format that is easy to read and use.
    
    There are a few key things to keep in mind when working with data frames:

  • Data frames can include many different types of data, including numeric, logical, or character.

  • Data frames can have only one element in each cell.

  • Each column should be named.

  • Each column should consist of elements of the data data type.
    z <- data.frame(x = c(1,2,3), y = c(1.5, 2.5, 7.5)) gives you a 2 column.

  • You can extract the element from the second row and the fist column, use the z[2,1]

  • Create a file: Use file.create("filename.csv")

  • Copy a file: Use file.copy("filename.txt", "destination_folder")

  • Delete file: Use unlink("some_file.csv")

  • Matrices:

    • A matrix is a two-dimensional collection of data elements. This means it has both rows and columns. By contrast, a vector is a one-dimensional, sequence of data elements. But like vectors, matrices can only contain a single data type. For example, you can't have both logicals and numerics in a matrix.
    • Use the matrix() function, then add at least one matrix dimension. You can choose to specify the number of rows ore the number of column by using the code nrow = or ncol =.
      matrix(c(3:8), nrow = 2)
matrix(c(3:8), ncol = 2)

facet_wrap() is a function used to create subplots, which are individual plots that represent a specific part of a broader data set.
geom_point() function is used in the ggplot2 package to create scatter plots or plots with individual points

ggplot(data, aes(x = <x-variable), y =<y-variable>)+
	geom_point()

Common arguments:
mapping: Used to map aesthetic (aes(x, y, color, size)) color: sets the color of points size: sets the size of points shape: changes the shape (circles, triangles) alpha`: adjusts the transparency of points

• 8 core tidyverse packages:

  • ggplot2: Create a variety of data visualization by applying different visual properties to the data variables in R
  • tibble
  • tidyr: A package used for data cleaning to make tidy data
  • readr: Used for importing data (e.g read_csv(): Import a csv into R)
  • purrr
  • dplyr: Offers a consistent set of functions that help you complete some common data manipulation tasks (select(), filter())
  • stringr
  • forcats

• Use Pipes to nest code:

  • Pipe: A tool in R for expressing a sequence of multiple operations, represented with "%>%"
  • Nested: In programming, describes code that performs a particular function and is contained within code that performs a broader function.
  • When using pipe:
    • Add the pipe operator at the end of each line of the piped operation except the last one.
    • Check your code after you've programmed your pipe.
    • Revisit piped operations to check for parts of your code to fix

DATA FRAMES:

• Data frame: Is a collection of columns

  • Columns should be named
  • Data stored can be many different types, like numeric, factor, or character
  • Each column should contain the same number of data items

• head(): to view the first 6 rows of the data

• mutate(dataset, new_col=old_col+1): to make change to our data fram

• str(dataset): to see high level structure of each variable in the dataset

• colnames(dataset): to see all the column names

TIBBLE

• Use as_tibble(dataset) to turn a dataset into tibble format

DATA-IMPORT BASICS:

• The data()function: If you want to load a specific dataset, just enter its name in the parentheses of the data() function.

• The readr package:

  • .csv (comma separated values): Is a plain text file that contains a list of data. They mostly use commas to separate (or delimit) data, but sometimes they use other characters, like semicolons.
  • .tsv (tab separated values): stores a data table in which the columns of data are separated by tabs. For example, a database table or spreadsheet data.
  • .fwf (fixed width files): has a specific format that allows for the saving of textual data in an organized fashion.
  • .log: is a computer-generated file that records events from operating systems and other software programs.
  • readr functions:
    • read_csv(): comma-separated values files
    • read_tsv(): tab-separated values files
    • read_delim(): general delimited files
    • read_fwf(): fix-width files
    • read_table(): tabular files where columns are separated by white-space
    • read_log(): web log files

• **readxl functions:

  • Use read_excel() to read excel file
  • Use excel_sheet() to see the name of individual sheets

• pivot_longer() and pivot_wider() function to change data from wide to long and vice versa.

GGPLOT2

• Core concept:

  • Aesthetics: The visual property of an object in your plot
  • Geoms: the geometric object used to represent your data
  • Facets: let you display smaller groups, or subsets, of your data
  • Labels and annotations: text, title, subtitle

• Example:

ggplot(data= penguins) +geom_point(mapping= aes(x= flipper_length_mm, y= body_mass_g))

• ggplot(data=penguins): In ggplot2, you begin with a plot with the ggplot() function creates a coordinate system that you can add layers to. The first argument of the ggplot() function is the dataset to use in the plot. In this case, it's "penguins"

• Then you can add a "+" symbol to add a new layer to your plot. You complete your plot by adding one or more layers to ggplot()

• geom_point(): Next, you choose a geom by adding a geom function. The geom_point() function uses points to create scatterplots, the geom_bar function uses bars to create bar charts and so on. In this case, choose the geom_point() function to create a scatter plot of points.

• (mapping = aes(x = flipper_length_mm, y = body_mass_g): Each geom function in ggplot2 takes a mapping argument. This defines how variables in your dataset are mapped to visual properties. The mapping argument is always paired with the aes() function. The x and y arguments of the aes() function specify which variables to map to the x-axis and the y-axis of the coordinate system. In this case, you want to map the variable "flipper_length_mm" to the x-axis, and the variable "body_mass_g" to the y-axis.

• ?geom_point to get help from R Studio

• Common aesthetic for points
  - X
  - Y
  - Color
  - Shape
  - Size
  - Alpha

• Geom functions

  • geom_point

  • geom_bar: R counts how many times each x-value appears in the data, and then shows the counts on the y-axis

  • geom_line

  • geom_smooth
    - Loess smoothing: best for smoothing plots with less than 1000 points
    - Gam smoothing: generalized additive model smoothing, is useful for smoothing plots with a large number of points

  • geom_jitter: creates a scatterplot and then add a small amount of random noise to each point in the plot, which happens when the data points overlap with each other.

• Facet functions:

  • facet_wrap: create a separate plot for each categories of a variable
  • facet_grid: create a separate plot for 2 or more variable(s) then a separate plot for each categories of such variable(s)
  • Tilde ~ operator is used to define the relationship between dependent variable and independent variables in a statistical model formula. The variable on the left-hand side of tilde operator is the dependent variable and the variable(s) on the right-hand side of tilde is/are called the independent variable(s). So, tilde operator helps to define that dependent variable depends on the independent variable(s) that are on the right-hand side of tilde operator.

• labs(): function allows us to add label to our code
  e.g: labs(title='abc', subtitle='xyz')

• annotate() to add note or annotation to your chart

• Save a plot
  ggsave(example.png)

R MARKDOWN

• Refer to this to create a link: link