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Basics in R

Learning Objectives:

Familiarize with the RStudio interface.
Learn basic R Operations.
How to understand and create functions.
Dealing with packages.
Importing external data.
Basic data manipulation.

Open RStudio and take a few minutes to explore each pane and its functionality.

1. Familiarize with the RStudio Interface

In this section, we’ll get to know the RStudio interface and understand its different components:

Layout: The RStudio interface consists of several panes that help you manage your workflow efficiently.
Panes: These include the Source Pane, Console, Environment/History Pane, and the Files/Plots/Packages/Help/Viewer Pane.
Console: The Console is where you can directly execute R commands.
Script Editor: The Script Editor is used for writing and editing scripts, which can be executed in parts or as a whole.
Environment: The Environment Pane shows all the objects (data, functions, etc.) that you’ve created during your session.

2. Basic R Operations

Let’s start by performing some basic operations in R:

Arithmetic:

addition, subtraction, multiplication, division etc.

# Basic Arithmetic
sum <- 5 + 3 
difference <- 5 - 3
product <- 5 * 3
quotient <- 5 / 3
exponent <- 5 ^ 3
modulus <- 5 %% 3 # Remainder from division
int_division <-5 %/% 3 # Integer Division

# Display results
sum

[1] 8

difference

[1] 2

product

[1] 15

quotient

[1] 1.666667

exponent

[1] 125

modulus

[1] 2

int_division

[1] 1

Variable Assignment:

You can assign values to variables for reuse.

# Variable Assignment
x <- 10 
y <- 20
z <- x + y # x & y are now saved in your environment for reuse
z

[1] 30

Data Types: - Include Numeric, Character, Boolean, Integer, and Double types of data.

n <- numeric(6)
typeof(n) # a function used to return the type of the data stored

[1] "double"

c <- "c"
typeof(c)

[1] "character"

# Integer values are whole numbers that can be positive or negative
i <- integer(5)
typeof(i)

[1] "integer"

# Boolean Values consists of data hat has one of two values (e.g., 1 or 0 / True or False)
t <- TRUE # can store as letter "T"
f <- FALSE # can store as letter "F"
typeof(t)

[1] "logical"

typeof(f) # logical = boolean

[1] "logical"

Data Formats:

vectors,
lists,
data frames

# Vectors are arrays of data elements each of the SAME TYPE.
vec <- c(1, 2, 3, 4, 5)

# Lists contain multiple items that can be of different data types like numbers characters and also could contain stored vectors or data frames.
lst <- list(name = "John", age = 25)

# Data Frames are a tabular (2-dimensional) data structure that can store values of any data type.
df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))

vec

[1] 1 2 3 4 5

lst

$name
[1] "John"

$age
[1] 25

df

   Name Age
1 Alice  25
2   Bob  30

Exercise 1: Try creating your own variables and performing different operations. Experiment with different data types.

Exercise 2: Use the function str(x) to check the structure of each data type.

Answer

str(vec)

 num [1:5] 1 2 3 4 5

str(lst)

List of 2
 $ name: chr "John"
 $ age : num 25

str(df)

'data.frame':   2 obs. of  2 variables:
 $ Name: chr  "Alice" "Bob"
 $ Age : num  25 30

3. Functions in R

Understanding and creating functions is fundamental in R:

Functions are reusable blocks of code that perform a specific task.

Creating Simple Functions: Here’s how you can create a simple function in R.

# Creating a Function
add_numbers <- function(a, b) {  
  return(a + b)  # takes two arguments (a and b) and returns their sum.
}

# Using the Function: Here we are calling the function 'add_numbers' with 10 and 15 as inputs.
add_numbers(10, 15)

[1] 25

Exercise 3: Create a function that takes a number as input and returns the square of that number.

Answer

sq <- function(n1,n2) {
  first_step <- n1 + n2
  return(first_step^2) # also can use the function sqrt(first_step)
}

sq(2,3)

[1] 25

4. Installing and loading Packages:

R packages are collections of functions and datasets developed by the R community:
There are pre-loaded packages in Rstudio that can be used and called without installation (e.g., dplyr)
CRAN: CRAN (Comprehensive R Archive Network) is the main repository for R packages.

Installing and Loading Packages: To use additional functions, you might need to install and load packages.

# Installing a Package (Uncomment the line below if the package has been loaded previously)
#     install.packages("ggplot2")

# Loading the package "ggplot" a data visualtion package we will use later in the course:
library(ggplot2) # no quotations needed when loading a package from your library
ggplot

function (data = NULL, mapping = aes(), ..., environment = parent.frame()) 
{
    UseMethod("ggplot")
}
<bytecode: 0x1147aa0b0>
<environment: namespace:ggplot2>

Exercise 4: Install and load the dplyr package. Use it to manipulate a data frame of your choice.

Hint use the code browseVignettes(package = "dplyr") to see how you could use this package.

Answer

#     install.packages("dplyr")
#     ?dplyr
#     browseVignettes(package = "dplyr") --> see what you can do with dplyr
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# Manipulating data:

data <- data.frame(
  Name = c("Alice", "Bob", "Charlie", "David", "Eva"),  # Example data frame
  Age = c(23, 35, 45, 29, 31),
  Score = c(89, 72, 95, 88, 76)
) 

# 1. Select specific columns (Name and Score)
selected_data <- data %>%
  select(Name, Score)

# 2. Filter rows where Age is greater than 30
filtered_data <- data %>%
  filter(Age > 30)

# 3. Arrange the data by Score in descending order
arranged_data <- data %>%
  arrange(desc(Score))

[1] "Selected Data:"

     Name Score
1   Alice    89
2     Bob    72
3 Charlie    95
4   David    88
5     Eva    76

[1] "Filtered Data (Age > 30):"

     Name Age Score
1     Bob  35    72
2 Charlie  45    95
3     Eva  31    76

[1] "Arranged Data by Score (Descending):"

     Name Age Score
1 Charlie  45    95
2   Alice  23    89
3   David  29    88
4     Eva  31    76
5     Bob  35    72

5. Importing External Data:

Data analysis often involves importing data from external files:
R can read data from various formats like CSV, Excel, etc.
It is important to pay attention to the extension the file uses (e.g., csv = comman seperated values)

# Reading CSV Files:
# df_csv <- read.csv("path/to/your/file.csv")

# Reading Excel Files (requires the readxl package)
#   install.packages("readxl)
#   library(readxl)
# df_excel <- read_excel("path/to/your/file.xlsx")

TIP: If you look at the top left of Rstudio you can import the dataset manually using the “Import Dataset” option.

Exercise 5: Try reading a sample CSV file into R. Practice using different data formats if available.

Answer

# df_csv <- read.csv("path/to/sample/file/sample.csv", sep = ",")
# df_table <- read.table("path/to/sample/file/sample.tsv, sep = "\t"")
# df_table <- read.excel("path/to/sample/file/sample.xlsx)

# If file is a text (.txt) file, remember to check what the extension is before reading it in.

6. Indexing:

I have stored a data frame (with example data not shown) named “df”

df <- data.frame(city, abb, region, pop, total)
head(df) # shows the first 'n' rows

           city abb  region      pop  total
1         Cairo  CA   North 22183200 125700
2      Kinshasa  KI Central 16315534  80500
3         Lagos  LA Central 15387639  66900
4        Luanda  LU   South  8952496  51700
5 Dar es Salaam  DS   South  7404689  36400
6      Khartoum  KH   North  6160327  45700

Indexing is the process of selecting elements using their indices (i.e., positions in the data format)

Indexing ROWS and COLUMNS by POSITION:

# Select 1st Row:
first_row <- df[1, ]  # Selects the entire first row
first_row

   city abb region      pop  total
1 Cairo  CA  North 22183200 125700

# Select 2nd column:
second_column <- df[, 2]  # Selects the entire second column ('abb')
second_column

 [1] "CA" "KI" "LA" "LU" "DS" "KH" "JO" "AB" "AD" "NA" "YA" "CB" "AN" "KA" "KU"
[16] "DA" "OU" "LS" "AL" "BA" "BR" "MO" "TU" "CO" "LO" "MA" "MN" "HA" "ND" "NO"
[31] "NI" "FR" "LI" "KG" "AC" "TR" "BU" "AS" "BG" "LB"

# Select the element in the 3rd row and 4th column:
specific_element <- df[3, 4]  # Selects the population ('pop') of the third city ('Lagos')
specific_element

[1] 15387639

Indexing Using COLUMN Names:

# Select the 'city' column:
city_column <- df$city  # Selects the 'city' column
city_column

 [1] "Cairo"         "Kinshasa"      "Lagos"         "Luanda"       
 [5] "Dar es Salaam" "Khartoum"      "Johannesburg"  "Abidjan"      
 [9] "Addis Ababa"   "Nairobi"       "Yaoundé"       "Casablanca"   
[13] "Antananarivo"  "Kampala"       "Kumasi"        "Dakar"        
[17] "Ouagadougou"   "Lusaka"        "Algiers"       "Bamako"       
[21] "Brazzaville"   "Mogadishu"     "Tunis"         "Conakry"      
[25] "Lomé"          "Matola"        "Monrovia"      "Harare"       
[29] "N'Djamena"     "Nouakchott"    "Niamey"        "Freetown"     
[33] "Lilongwe"      "Kigali"        "Abomey-Calavi" "Tripoli"      
[37] "Bujumbura"     "Asmara"        "Bangui"        "Libreville"

# Select the first row using column names:
first_row_city_pop <- df[1, c("city", "pop")]  # Selects the 'city' and 'pop' columns for the first row
first_row_city_pop

   city      pop
1 Cairo 22183200

# Logical Indexing:
large_cities <- df[df$pop > 10000000, ]  # Returns all rows where 'pop' is greater than 10 million
large_cities

      city abb  region      pop  total
1    Cairo  CA   North 22183200 125700
2 Kinshasa  KI Central 16315534  80500
3    Lagos  LA Central 15387639  66900

Indexing with dplyr:

# Using dplyr you can perform similar operations with clearer syntax:
library(dplyr)

# Select specific columns
selected_df <- df %>%
  select(city, pop)  # Selects 'city' and 'pop' columns

selected_df

            city      pop
1          Cairo 22183200
2       Kinshasa 16315534
3          Lagos 15387639
4         Luanda  8952496
5  Dar es Salaam  7404689
6       Khartoum  6160327
7   Johannesburg  6065354
8        Abidjan  5515790
9    Addis Ababa  5227794
10       Nairobi  5118844
11       Yaoundé  4336670
12    Casablanca  3840396
13  Antananarivo  3699900
14       Kampala  3651919
15        Kumasi  3630326
16         Dakar  3326001
17   Ouagadougou  3055788
18        Lusaka  3041789
19       Algiers  2853959
20        Bamako  2816943
21   Brazzaville  2552813
22     Mogadishu  2497463
23         Tunis  2435961
24       Conakry  2048525
25          Lomé  1925517
26        Matola  1796872
27      Monrovia  1622582
28        Harare  1557740
29     N'Djamena  1532588
30    Nouakchott  1431539
31        Niamey  1383909
32      Freetown  1272145
33      Lilongwe  1222325
34        Kigali  1208296
35 Abomey-Calavi  1188736
36       Tripoli  1175830
37     Bujumbura  1139265
38        Asmara  1034872
39        Bangui   933176
40    Libreville   856854

# Filter rows based on a condition
filtered_df <- df %>%
  filter(pop > 10000000)  # Filters for cities with population greater than 10 million

filtered_df

      city abb  region      pop  total
1    Cairo  CA   North 22183200 125700
2 Kinshasa  KI Central 16315534  80500
3    Lagos  LA Central 15387639  66900

Exercise 6: Given the following dataframe

df <- data.frame(
  city = c("Cairo", "Kinshasa", "Lagos", "Luanda", "Dar es Salaam"),
  abb = c("CA", "KI", "LA", "LU", "DS"),
  region = factor(c("North", "Central", "Central", "South", "South"), 
                  levels = c("North", "Central", "South")),
  pop = c(22183200, 16315534, 15387639, 8952496, 7404689),
  total = c(125700, 80500, 66900, 51700, 36400)
)

head(df)

           city abb  region      pop  total
1         Cairo  CA   North 22183200 125700
2      Kinshasa  KI Central 16315534  80500
3         Lagos  LA Central 15387639  66900
4        Luanda  LU   South  8952496  51700
5 Dar es Salaam  DS   South  7404689  36400

a. Select the Population of the First City (Cairo):

b. Select the Abbreviation for the Third City

c. Select the Entire Row for the Fourth City

d. Select the ‘city’ and ‘pop’ Columns for the Second and Fifth Cities

e. Filter for Cities in the ‘South’ Region

f. Using dplyr, Select the city and total Columns

Answer

# a. Population of the First City
df[1, "pop"]

[1] 22183200

# b. Abbreviation for the Third City
df[3, "abb"]

[1] "LA"

# c. Entire Row for the Fourth City
df[4, ]

    city abb region     pop total
4 Luanda  LU  South 8952496 51700

# d. 'city' and 'pop' Columns for the Second and Fifth Cities
df[c(2, 5), c("city", "pop")]

           city      pop
2      Kinshasa 16315534
5 Dar es Salaam  7404689

# e. Filter for Cities in the 'South' Region
df[df$region == "South", ]

           city abb region     pop total
4        Luanda  LU  South 8952496 51700
5 Dar es Salaam  DS  South 7404689 36400

# f. Using dplyr, Select the 'city' and 'total' Columns
library(dplyr)
df %>%
  select(city, total)

           city  total
1         Cairo 125700
2      Kinshasa  80500
3         Lagos  66900
4        Luanda  51700
5 Dar es Salaam  36400

7. Data Manipulation Basics

Basic data manipulation is key to preparing data for analysis.

Overview of Data Classes: Learn about data frames, matrices, and lists.

Data Frames: Rectangular tables with rows and columns, where columns can be of different types.
Matrices: Rectangular tables with rows and columns, where all elements must be of the same type.
Lists: Collections of elements that can be of different types, including other lists.

# Creating a Data Frame
df <- data.frame(
  city = c("Cairo", "Kinshasa", "Lagos", "Luanda", "Dar es Salaam"),
  population = c(22183200, 16315534, 15387639, 8952496, 7404689),
  region = c("North", "Central", "Central", "South", "South")
)
head(df)

           city population  region
1         Cairo   22183200   North
2      Kinshasa   16315534 Central
3         Lagos   15387639 Central
4        Luanda    8952496   South
5 Dar es Salaam    7404689   South

# Creating a Matrix
matrix_example <- matrix(1:9, nrow = 3, byrow = TRUE)
head(matrix_example)

     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6
[3,]    7    8    9

# Creating a List
list_example <- list(
  numbers = 1:5,
  text = c("A", "B", "C"),
  data_frame = df
)
print(list_example)

$numbers
[1] 1 2 3 4 5

$text
[1] "A" "B" "C"

$data_frame
           city population  region
1         Cairo   22183200   North
2      Kinshasa   16315534 Central
3         Lagos   15387639 Central
4        Luanda    8952496   South
5 Dar es Salaam    7404689   South

Subsetting Data: Extract subsets of data based on conditions.

# Extract cities with population greater than 10 million
large_cities <- df[df$population > 10000000, ]
print(large_cities)

      city population  region
1    Cairo   22183200   North
2 Kinshasa   16315534 Central
3    Lagos   15387639 Central

# Extract cities in the 'South' region
south_cities <- df[df$region == "South", ]
print(south_cities)

           city population region
4        Luanda    8952496  South
5 Dar es Salaam    7404689  South

# Select Specific Columns
city_population <- df[, c("city", "population")]
print(city_population)

           city population
1         Cairo   22183200
2      Kinshasa   16315534
3         Lagos   15387639
4        Luanda    8952496
5 Dar es Salaam    7404689

Basic Transformations: Perform simple data transformations such as adding new columns.

# Add a New Column: City Area (dummy data)
df$area <- c(606, 851, 1171, 300, 400)  # Example areas in square kilometers
print(df)

           city population  region area
1         Cairo   22183200   North  606
2      Kinshasa   16315534 Central  851
3         Lagos   15387639 Central 1171
4        Luanda    8952496   South  300
5 Dar es Salaam    7404689   South  400

# Modify Existing Column: Increase Population by 10%
df$population <- df$population * 1.10
print(df)

           city population  region area
1         Cairo   24401520   North  606
2      Kinshasa   17947087 Central  851
3         Lagos   16926403 Central 1171
4        Luanda    9847746   South  300
5 Dar es Salaam    8145158   South  400

# Using dplyr for Data Manipulation:

# Add a New Column: City Area
df <- df %>%
  mutate(area = c(606, 851, 1171, 300, 400))

# Modify Existing Column: Increase Population by 10%
df <- df %>%
  mutate(population = population * 1.10)

# Filter: Cities in the 'South' Region
south_cities_dplyr <- df %>%
  filter(region == "South")

# Select Specific Columns
city_population_dplyr <- df %>%
  select(city, population)

print(df)

           city population  region area
1         Cairo   26841672   North  606
2      Kinshasa   19741796 Central  851
3         Lagos   18619043 Central 1171
4        Luanda   10832520   South  300
5 Dar es Salaam    8959674   South  400

print(south_cities_dplyr)

           city population region area
1        Luanda   10832520  South  300
2 Dar es Salaam    8959674  South  400

print(city_population_dplyr)

           city population
1         Cairo   26841672
2      Kinshasa   19741796
3         Lagos   18619043
4        Luanda   10832520
5 Dar es Salaam    8959674

Exercise 7:

a. Data Classes: Extract the population of the 3rd city from the df data frame.

b. Subsetting Data -> Extract the elements from matrix_example that are greater than 5.

c. Basic Transformations: Add a new column area to df with values 500, 600, 700, 800, 900.

Answer

# 7a:
df[3, "population"]

[1] 18619043

# 7b:
matrix_example[matrix_example > 5]

[1] 7 8 6 9

# 7c:
df$area <- c(500, 600, 700, 800, 900)
head(df)

           city population  region area
1         Cairo   26841672   North  500
2      Kinshasa   19741796 Central  600
3         Lagos   18619043 Central  700
4        Luanda   10832520   South  800
5 Dar es Salaam    8959674   South  900

sessionInfo()

R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Africa/Johannesburg
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.1.4     ggplot2_3.5.1   workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.5      jsonlite_1.8.8    compiler_4.3.3    promises_1.3.0   
 [5] tidyselect_1.2.1  Rcpp_1.0.13       stringr_1.5.1     git2r_0.33.0     
 [9] callr_3.7.6       later_1.3.2       jquerylib_0.1.4   scales_1.3.0     
[13] yaml_2.3.10       fastmap_1.2.0     R6_2.5.1          generics_0.1.3   
[17] knitr_1.48        tibble_3.2.1      munsell_0.5.1     rprojroot_2.0.4  
[21] bslib_0.8.0       pillar_1.9.0      rlang_1.1.4       utf8_1.2.4       
[25] cachem_1.1.0      stringi_1.8.4     httpuv_1.6.15     xfun_0.46        
[29] getPass_0.2-4     fs_1.6.4          sass_0.4.9        cli_3.6.3        
[33] withr_3.0.1       magrittr_2.0.3    ps_1.7.7          grid_4.3.3       
[37] digest_0.6.36     processx_3.8.4    rstudioapi_0.16.0 lifecycle_1.0.4  
[41] vctrs_0.6.5       evaluate_0.24.0   glue_1.7.0        whisker_0.4.1    
[45] colorspace_2.1-1  fansi_1.0.6       rmarkdown_2.27    httr_1.4.7       
[49] tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.8.1

Data Analysis in R: Module 1

Stefano Cacciatore

September 05, 2024

Basics in R

Learning Objectives:

1. Familiarize with the RStudio Interface

2. Basic R Operations

3. Functions in R

4. Installing and loading Packages:

5. Importing External Data:

6. Indexing:

7. Data Manipulation Basics