3. 3
Coercion
All elements of a vector must be the same type, so when we
attempt to combine different types they will be coerced to the
most flexible type.
Types from least to most flexible are:
.
Logical
Integer
Double/ Numeric
Character
4. 4
Coercion
When a logical vector is coerced to an integer or double, TRUE
becomes 1 and FALSE becomes 0
x <- c(FALSE, FALSE, TRUE); as.numeric(x)
Total number of TRUEs
sum(x)
Proportion that are TRUE
mean(x)
5. 5
Coercion in R
✓ To forcefully coerce a variable class into other, following
functions are used
as.numeric(), as.logical(), etc.
In Python, we call it ‘typecasting’
https://youtu.be/FJ6IkFycCdA
6. 6
Data Structure in R
✓ Vector
The basic one dimensional data structure in R is the vector
✓ List
Lists are generic vectors which can contain any data type or
data structure
✓ Matrix
The basic two dimensional data structure in R is the matrix
Note: A variable with a single value is known as scalar. In R a
scalar is a vector of length 1
7. 7
Data Structure in R
✓ Factor
A factor is a vector that can contain only predefined values, and
is used to store categorical data
✓ Data Frame
A data frame is a list of equal-length vectors. This makes it a 2-
dimensional structure, so it shares properties of both the matrix
and the list.
✓ Array
An array is an n-dimensional data structure. Matrix is an special
case of array for 2 dimensions.
We will discuss ‘tibble’ from tidyverse in next lession
8. 8
Vectors in R
To create vectors in R using concatenation function
num_var <- c(1, 2, 4.5)
Use the L suffix to get an integer rather than a double
int_var <- c(13L, 0L, 10L)
Use TRUE and FALSE (or T and F) to create logical vectors
log_var <- c(TRUE, FALSE, T, F)
Use double or single quotation to create character vector
chr_var <- c(“abc", “123")
Vectors can also be created by using sequence or scan function
9. 9
Vectors in R
To name a vector
# Assigning names directly
x <- c(Mon = 37, Tue = 41.4, Wed = 43.2)
# Using names() function
x <- c(78, 86, 89); names(x) <- c(“chem", “phy", “math")
# Using setNames() function
x <- setNames(1:3, c("a", "b", "c"))
10. 10
Vector Subsetting
x = c(11,42,23,14,55);
names(x) = c('ajay', 'ravi', 'john', 'anjali', 'namrata'); x
x[2]; x[1:3]; x[5]; x[7]
# x[n] gives 'nth' element of vector x, there are only 6 elements,
so x[7] is NA
x['ajay']; x[c('ravi', 'namrata’)]
# To select elements by names
11. 11
List in R
Lists are different from vectors because their elements can be of
any type, including lists.
We can construct lists by using list() instead of c()
x <- list(1:4, "abc", c(T, T, F), c(2.3, 5.9))
13. 13
Matrix in R
To create matrix in R
x = matrix(1:9, nrow = 3, ncol = 3)
x = matrix (1:9, 3, 3) # Alternate way
To create a matrix by using by row
z = matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE)
# By default byrow is FALSE, so matrix is created by column
a <- matrix(1:9, byrow=TRUE, nrow=3) # Alternate way
14. 14
Matrix in R
To create matrix by using cbind() command
one <- c(1,0,0)
two <- c(0,1,0)
three <- c(0,0,1)
b <- cbind(one, two, three)
To create a matrix by using rbind() command
c <- rbind(one, two, three)
15. 15
Matrix in R
To assign names to columns and rows of matrix
x = cbind(c(78, 85, 95), c(99, 91, 85), c(67, 62, 63))
colnames(x) = c(“Jan", ‘Feb', “Mar“)
rownames(x) = c(“product1”, ‘product2’, ‘product3’)
Other useful commands
dim(x); head(x); nrow(x); ncol(x); attributes(x)
rowSums(x); colSums(x)
16. 16
Matrix Subsetting
To find sub matrices of a given matrix
x <- matrix(1:6, 2, 3)
x[1, 2] # Element of first row, second column [single element]
x[2, 1] # Element of second row, first column [single element]
x[2, ] # Matrix of all the elements of second row [matrix]
x[, 1] # Matrix of all the elements of first column [matrix]
x[1:2, 3] # Elements of first & second row for third column only
17. 17
Matrix Subsetting
To find sub matrices of a given matrix
x <- matrix(1:6, 2, 3)
By default, when a single element of a matrix is retrieved, it is returned
as a vector of length 1 rather than a 1 × 1 matrix.
This behaviour can be turned off by setting drop = FALSE.
x[1, 2] # Single element
x[1, 2, drop = FALSE] # Matrix of one row & one column
18. 18
Matrix Subsetting
To find sub matrices of a given matrix
x <- matrix(1:6, 2, 3)
Similarly, sub-setting a single column or a single row results in a
vector, not a matrix (by default).
This behaviour can be turned off by setting drop = FALSE.
x[1, ] # Single row
x[1, , drop = FALSE] # Matrix of one row & one column
19. 19
Matrix Subsetting
To find sub matrices of a given matrix
x = cbind(c(78, 85, 95), c(99, 91, 85), c(67, 62, 63))
x[ , 2]
x[ ,2:3]
x[ 2, 3]
x[1:2, 3]
20. 20
Factors in R
They are used for handling categorical variable, e.g., the ones
that are nominal or ordered categorical variables.
For example,
Male, Female Nominal categorical
Low, Medium, High Ordinal categorical
21. 21
Factors in R
To create a factor in R using factor()
gender_vector <- c("Male", "Female", "Female", "Male", "Male")
factor_gender_vector <- factor(gender_vector)
Also, try levels(factor_gender_vector)
To change the levels of factor
levels(factor_gender_vector) = c(("F", "M"))
Other useful commands
summary(factor_gender_vector); table(factor_gender_vector)
22. 22
Data frames in R
A data frame is the most common way of storing data in R, and if used
systematically makes data analysis easier.
✓ Similar to tables (databases), dataset (Excel/SAS/SPSS) etc.
✓ Consists of columns of different types; More general than a matrix
✓ Columns – Variables; Rows – Observations
✓ Convenient to hold all the data required for a data analysis
✓ They are represented as a special type of list where every element of
the list has to have the same length
✓ Data frames also have a special attribute called row.names
23. 23
Data frames in R
✓ Data frames are, well, tables (like in any spreadsheet program).
✓ In data frames variables are typically in the columns, and cases in
the rows.
✓ Columns can have mixed types of data; some can contain
numeric, yet others text.
✓ If all columns would contain only character or numerical data,
then the data can also be saved in a matrix (those are faster to
operate on).
We will also discuss ‘tibble’ in the course.
24. 24
Data frames in R
To create a data frame in R
Example_1:
df <- data.frame(x = 1:3, y = c("a", "b", "c"))
Example_2:
height <- c(180,175,190)
weight <- c(75,82,88)
name <- c("Anil","Ankit","Sunil")
data <- data.frame(name, heigth, weight)
25. 25
Data frames in R
To combine data frames in R
Example_1: using cbind()
df <- data.frame(x = 1:3, y = c("a", "b", "c"))
cbind(df, data.frame(z = 3:1))
Example_2: using rbind()
rbind(df, data.frame(x = 10, y = "z"))
26. 26
Data frames in R
To combine data frames in R
Example_1: using cbind()
df <- data.frame(x = 1:3, y = c("a", "b", "c"))
cbind(df, data.frame(z = 3:1))
Example_2: using rbind()
rbind(df, data.frame(x = 10, y = "z"))
27. 27
Data Type Conversions
Use is.foo to test for data type foo.
Returns TRUE or FALSE
Use as.foo to explicitly convert it.
Examples:
is.numeric(), is.character(), is.vector(), is.matrix(), is.data.frame()
as.numeric(), as.character(), as.vector(), as.matrix(), as.data.frame)
28. 28
Handling of missing values
X <- c(1:8,NA)
✓ Removing missing vlaues
mean(X, na.rm = T) or mean(X ,na.rm=TRUE)
✓ To check for the location of missing values within a vector
which(is.na(X))
✓ To assign this a large number, say, 999
X[which(is.na(X))] = 999
For more code: follow me on GitHub
29. 29
Handling of missing values
x <- c(1, 2, NA, 4, NA, 5)
✓ Identify missing values
bad <- is.na(x)
✓ To remove missing values
x[!bad]
30. 30
Handling of missing values
x <- c(1, 2, NA, 4, NA, 5); y <- c("a", "b", NA, "d", "e", NA)
df = data.frame(x,y)
✓ To take the subset of data frame with no missing value
good = complete.cases(x,y); good
✓ To take the subset of vector x with no missing value
x[good]
✓ To take the subset of vector y with no missing value
y[good]
31. Books
31
✓ Crowley, M. J. (2007). The R Book. Chichester, New
England: John Wiley & Sons, Ltd.
✓ An Introduction to R by W. N. Venables, D. M. Smith and
the R Core Team
✓ R in a Nutshell by Joseph Adler: O’Reilly
✓ Teetor, P. (2011). R cookbook. Sebastopol, CA: O’Reilly
Media Inc.
32. Books
32
✓ Bio Statistics - https://www.middleprofessor.com/files/applied-
biostatistics_bookdown/_book/
✓ Advanced R - https://adv-r.hadley.nz/
✓ Data Visualization - https://rkabacoff.github.io/datavis/
✓ R for Data Science - https://r4ds.had.co.nz/index.html
✓ Data Exploration & Analysis -
https://bookdown.org/mikemahoney218/IDEAR/
✓ https://bookdown.org/mikemahoney218/LectureBook/
35. 35
Reach Out to Me
http://stats.stackexchange.com/users/79100/learner
https://www.researchgate.net/profile/Nisha_Arora2/contributions
https://www.quora.com/profile/Nisha-Arora-9
https://github.com/arora123/
https://www.youtube.com/channel/UCniyhvrD_8AM2jXki3eEErw
https://www.linkedin.com/in/drnishaarora/
Dr.aroranisha@gmail.com
