1. Required Packages for this Analysis

# CODE TO LOAD PACKAGES
library(tidyverse)
library(AER)

2. Dataset

I have chosen to work with the AFFAIRS data set from the AES package.

3. Description:

Infidelity data, known as Fair’s Affairs. Cross-section data from a survey conducted by Psychology Today in 1969.

4. First 10 Rows

The first 10 rows of the dataset look like the following.

# CODE TO DISPLAY FIRST 10 ROWS
data("Affairs")
head(Affairs,10)

##    affairs gender age yearsmarried children religiousness education occupation
## 4        0   male  37        10.00       no             3        18          7
## 5        0 female  27         4.00       no             4        14          6
## 11       0 female  32        15.00      yes             1        12          1
## 16       0   male  57        15.00      yes             5        18          6
## 23       0   male  22         0.75       no             2        17          6
## 29       0 female  32         1.50       no             2        17          5
## 44       0 female  22         0.75       no             2        12          1
## 45       0   male  57        15.00      yes             2        14          4
## 47       0 female  32        15.00      yes             4        16          1
## 49       0   male  22         1.50       no             4        14          4
##    rating
## 4       4
## 5       4
## 11      4
## 16      5
## 23      3
## 29      5
## 44      3
## 45      4
## 47      2
## 49      5

5. Number of Observations and Variables

# CODE TO DISPLAY NUMBER OF OBSERVATIONS
nrow(Affairs)

## [1] 601

# CODE TO DISPLAY NUMBER OF VARIABLES
ncol(Affairs)

## [1] 9

6. View of types of data

Below is a visual of the types of data in my dataset.

# CODE TO GENERATE VIEW OF DATA TYPES
library(visdat)
vis_dat(Affairs)

Numeric Variables

7. Basic Statistcs

Below are the basic descriptive statistics of the data variable.

# CODE TO GENERATE BASIC DECRIPTIVE STATISTICS

mean(Affairs$age)

## [1] 32.48752

sd(Affairs$age)

## [1] 9.288762

min(Affairs$age)

## [1] 17.5

max(Affairs$age)

## [1] 57

summary(Affairs$age)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   17.50   27.00   32.00   32.49   37.00   57.00

8. Graph of one variable

# CODE TO GENERATE PLOT OF ONE VARIABLE WITH APPORPORIATE TITLES
ggplot(data=Affairs, mapping=aes(x=age)) + 
  geom_bar() +
  labs(title="affairs ", 
       subtitle="affairs",
       x = " age ")

9. Description of Graph

The distribution is normal distribution graph right skewed .The graph shows affairs data along with the age , the count is highest around age 20 to 30 year .

Relationship between variables.

Below is an anlysis showing affairs count withrespect to age

10. Graph of two variables

# CODE TO GENERATE PLOT FROM TWO VARIRABLES (in color)
ggplot(data=Affairs,mapping = aes(x=age,y= yearsmarried,
color=children))+
  geom_boxplot()+
  labs(title="Vis
       ",
       subtitle="data in context with age and years of marriage
          ", 
       x = "age ", y = " yearsmarried",
       caption = "Source: Psychology Today in 1969.")

11. Description of Graph

we can see from the graph that around age 40 to 50 have children instead of age 20 to 30 year

Fair’s Extramarital Affairs Data

NIKKI SHAH