Tables are suitable for various purposes in R and provide a convenient way to work with structured data. I use tables here to view and explore my data in the following exercises.

Here is how I have started. Note: I have already installed the package “data.table”

library(“data.table”)

## Warning: package ‘data.table’ was built under R version 4.2.3

#Your data.frame is
assignment_data <- data.frame( Country = c(“France”,”Spain”,”Germany”,”Spain”,”Germany”, “France”,”Spain”,”France”,”Germany”,”France”),
                               age = c(44,27,30,38,40,35,52,48,45,37), salary = c(6000,5000,7000,4000,8000),
                               Purchased=c(“No”,”Yes”,”No”,”No”,”Yes”, “Yes”,”No”,”Yes”,”No”,”Yes”))
###  1. Generate simple table in R that consists of four rows: Country, age, salary and purchased
setDT(assignment_data)
my_table <- assignment_data[, .(Country, age, salary, Purchased)]
# Print the table
print(my_table)

##     Country age salary Purchased
##  1:  France  44   6000        No
##  2:   Spain  27   5000       Yes
##  3: Germany  30   7000        No
##  4:   Spain  38   4000        No
##  5: Germany  40   8000       Yes
##  6:  France  35   6000       Yes
##  7:   Spain  52   5000        No
##  8:  France  48   7000       Yes
##  9: Germany  45   4000        No
## 10:  France  37   8000       Yes

######################### end of exercise 1 ##############################/

### 2. Generate contingency table also know as r x C table using mtcars dataset.
# In this step, I  am using the `table()` function to create a contingency table named `assignment9`.
# The table shows how different gear options (“gears”: 3, 4, and 5) are distributed across  different cylinder options (“cyl”: 4, 6, and 8) in the `mtcars` dataset.
# The printed table displays the counts for each combination of gear  and cylinder categories.
assignment9 <- table(mtcars$gear, mtcars$cyl, dnn=c(“gears”, “cyl”))
print(assignment9)

##      cyl
## gears  4  6  8
##     3  1  2 12
##     4  8  4  0
##     5  2  1  2

#### 2.1 Add the addmargins() function to report on the sum totals of the rows and columns of assignment9 table
# I am using the `addmargins()` function to add sum totals to the contingency table.
# This helps me to see the total counts for each row and column.
addmargins(assignment9)                          

##      cyl
## gears  4  6  8 Sum
##   3    1  2 12  15
##   4    8  4  0  12
##   5    2  1  2   5
##   Sum 11  7 14  32

#### 2.2 Add prop.tables() function, and report on the proportional weight of each value in a assignment9 table.
# In this step, I apply the `prop.table()` function to the `assignment9` table.
# It calculates the proportional weight of each count in the table relative to the total count.
# The prop.table accepts a table, and a margin= argument, With no value specified for margin=,
# the sum of all the cells in the table will be 1.
prop.table(assignment9)

##      cyl
## gears       4       6       8
##     3 0.03125 0.06250 0.37500
##     4 0.25000 0.12500 0.00000
##     5 0.06250 0.03125 0.06250

#### 2.3 Add margin = 1 to the argument under prop.table() function, and report on the row proportions found in assignment9 table.
# Here, I refine the analysis by specifying `margin = 1` in the `prop.table()` function.
# With margin=1, each row of the resulting table will add to 1.
prop.table(assignment9, margin = 1)

##      cyl
## gears          4          6          8
##     3 0.06666667 0.13333333 0.80000000
##     4 0.66666667 0.33333333 0.00000000
##     5 0.40000000 0.20000000 0.40000000

# In summary, using R to generate a contingency table, add sum totals, calculate proportional weights, and explore row proportions enabled me
# to gain insights into the distribution of gear and cylinder combinations in the `mtcars` dataset.
# Contingency tables and these additional analyses are valuable tools for understanding relationships between categorical variables in data analysis.
######################### end of exercise 2 ###########################/