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Basics in R

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
5 + 3 # Sum

[1] 8

5 - 3 # Difference

[1] 2

5 * 3 # Product

[1] 15

5 / 3 # Quotient

[1] 1.666667

5 ^ 3 # Exponent

[1] 125

5 %% 3 # Remainder from division

[1] 2

Variable Assignment:

You can assign values to variables for reuse.

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

[1] 30

Data Types: - Numeric, Character, Boolean, Integer, and Double

# 6 = numeric
# "c" = character
# -5 = integer
# TRUE = boolean

Data Formats:

vectors,
lists,
data frames

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

[1] 1 2 3 4 5

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

$name
[1] "John"

$age
[1] 25

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

   Name Age
1 Alice  25
2   Bob  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

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")


The downloaded binary packages are in
    /var/folders/p9/7vjxs6dd7p70vybzdhy_0hw00000gn/T//RtmpUm5NEJ/downloaded_packages

# 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

5. Saving

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

# Can store columns of different data types

city <- c("Cairo", "Kinshasa", "Lagos", "Luanda", "Dar es Salaam", "Khartoum",
          "Johannesburg", "Abidjan", "Addis Ababa", "Nairobi", "Yaoundé", 
          "Casablanca", "Antananarivo", "Kampala", "Kumasi" , "Dakar", 
          "Ouagadougou", "Lusaka", "Algiers", "Bamako", "Brazzaville",
          "Mogadishu", "Tunis", "Conakry", "Lomé", "Matola", "Monrovia",
          "Harare", "N'Djamena", "Nouakchott", "Niamey", "Freetown", 
          "Lilongwe", "Kigali", "Abomey-Calavi", "Tripoli", "Bujumbura",
          "Asmara", "Bangui", "Libreville")

abb <- c("CA", "KI", "LA", "LU", "DS", "KH", "JO", "AB", "AD", "NA", "YA",
         "CB", "AN", "KA", "KU", "DA", "OU", "LS", "AL", "BA", "BR", "MO",
         "TU", "CO", "LO", "MA", "MN", "HA", "ND", "NO", "NI", "FR", "LI", 
         "KG", "AC", "TR", "BU", "AS", "BG", "LB")

region <- c("North", "Central", "Central", "South", "South", "North",
            "South", "Central", "Central", "Central", "Central",
            "North", "South", "Central", "Central", "Central",
            "Central", "South", "North", "North", "South",
            "Central", "North", "Central", "Central", "South",
            "Central", "South", "North", "North", "North",
            "Central", "South", "Central", "Central", "North",
            "South", "Central", "Central", "Central")

region <- factor(region, levels = c("North", "Central" , "South"))

population <- c(22183200, 16315534, 15387639, 8952496, 7404689, 
         6160327, 6065354, 5515790, 5227794, 5118844, 
         4336670, 3840396, 3699900, 3651919, 3630326, 
         3326001, 3055788, 3041789, 2853959, 2816943, 
         2552813, 2497463, 2435961, 2048525, 1925517, 
         1796872, 1622582, 1557740, 1532588, 1431539, 
         1383909, 1272145, 1222325, 1208296, 1188736, 
         1175830, 1139265, 1034872, 933176, 856854)

total <- c(125700, 80500, 66900, 51700, 36400, 45700, 31000, 
           37600, 41300, 28600, 24600, 25000, 9300, 11800,
           14200, 23200, 21900, 32100, 29300, 9700, 5300,
           6500, 13500, 20700, 35100, 11600, 3600, 11100,
           9700, 2100, 12000, 9300, 6300, 2200, 8400, 
           6700, 2700, 3200,1200, 700)

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

           city abb  region population  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

write.csv(df,"output/data.csv")

6. 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 <- read.csv("output/data.csv")

# Reading Excel Files (requires the readxl package)
install.packages("readxl")


The downloaded binary packages are in
    /var/folders/p9/7vjxs6dd7p70vybzdhy_0hw00000gn/T//RtmpUm5NEJ/downloaded_packages

library(readxl)
# df_excel <- read_excel("path/to/your/file.xlsx")

7. Indexing:

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

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

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

Indexing Using COLUMN Names:

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

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

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

Indexing with dplyr:

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

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

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

8. 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 Matrix
matrix_example <- matrix(1:9, nrow = 3, byrow = TRUE)

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

Subsetting Data: Extract subsets of data based on conditions.

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

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

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

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

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

# Using dplyr for Data Manipulation:

# Add a New Column: City Area
df <- df %>%
  mutate(rank = 1:40)

# 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)

Exercise: :

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)

  X          city abb  region population  total area rank
1 1         Cairo  CA   North   26841672 125700  500    1
2 2      Kinshasa  KI Central   19741796  80500  600    2
3 3         Lagos  LA Central   18619043  66900  700    3
4 4        Luanda  LU   South   10832520  51700  800    4
5 5 Dar es Salaam  DS   South    8959674  36400  900    5
6 6      Khartoum  KH   North    7453996  45700  500    6

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     readxl_1.4.3    ggplot2_3.5.1   workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.5      jsonlite_1.8.9    compiler_4.3.3    promises_1.3.0   
 [5] tidyselect_1.2.1  Rcpp_1.0.14       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_1.1.5       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_3.0.2       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        cellranger_1.1.0 
[45] whisker_0.4.1     colorspace_2.1-1  fansi_1.0.6       rmarkdown_2.29   
[49] httr_1.4.7        tools_4.3.3       pkgconfig_2.0.3   htmltools_0.5.8.1

Introduction

Stefano Cacciatore

February 18, 2025