ECNM Discussion 2

Author

Bryan Calderon

library("AER")

Warning: package 'AER' was built under R version 4.3.3

Loading required package: car

Loading required package: carData

Loading required package: lmtest

Loading required package: zoo


Attaching package: 'zoo'

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

    as.Date, as.Date.numeric

Loading required package: sandwich

Loading required package: survival

Data set 1

Describing the data

The data is a Cross Sectional data frame showing the credit history for a sample of credit card applicants (2003). It includes a variety of interesting variables such as the applicants age, expenditures, income, and aplication approval status. I was very interested in understanding consumer spend by age & income as well as applicants approval status by age.

Variables:

card —> Factor. Was the application for a credit card accepted?
reports —> Number of major derogatory reports.
Age —> Age in years plus twelfths of a year.
income —> Yearly income (in USD 10,000).
share —> Ratio of monthly credit card expenditure to yearly income.
expenditure —> Average monthly credit card expenditure.
owner —> Factor. Does the individual own their home?
selfemp —> Factor. Is the individual self-employed?
dependents —> Number of dependents.
months —> Months living at current address.
Majorcards —> Number of major credit cards held.
active —> Number of active credit accounts.

Bringing in & Formatting Data

data("CreditCard")

card <- CreditCard

# Formatting data
card$income <- card$income * 10000
card$income <- round(card$income, digits = 0)
card$income <- format(card$income, big.mark = ",", scientific = FALSE)

card$expenditure <- round(card$expenditure, digits = 2)
card$age <- round(card$age + .01)

card$share <- round(card$share, digits = 4)

Creating a structured Table

library(knitr)

# Creating a table (First 10 rows)
kable(head(card, 10), 
      format = "html", 
      caption = "Credit Card (first 10 rows)")

Credit Card (first 10 rows)
card	age	income	share	expenditure	owner	selfemp	dependents	months	majorcards	active
yes	38	45,200	0.0333	124.98	yes	no	3	54	1	12
yes	33	24,200	0.0052	9.85	no	no	3	34	1	13
yes	34	45,000	0.0042	15.00	yes	no	4	58	1	5
yes	31	25,400	0.0652	137.87	no	no	0	25	1	7
yes	32	97,867	0.0671	546.50	yes	no	2	64	1	5
yes	23	25,000	0.0444	92.00	no	no	0	54	1	1
yes	28	39,600	0.0126	40.83	no	no	2	7	1	5
yes	29	23,700	0.0764	150.79	yes	no	0	77	1	3
yes	37	38,000	0.2456	777.82	yes	no	0	97	1	6
yes	28	32,000	0.0198	52.58	no	no	0	65	1	18

Creating a Scatter plot and bar chart

library(ggplot2)

ggplot(card, aes(x = age, y = share)) +
  geom_point(color = "navyblue") +
  labs(title = "Scatter plot of Age vs Share (expense / income)", 
       x = "Age", 
       y = "Share") +
  theme_minimal()

# Filter data to only rejected applicants
rejected_data <- card[card$card == "no", ]

# Create a bar chart
ggplot(rejected_data, aes(x = as.factor(age))) +
  geom_bar(fill = "red", color = "black") +
  labs(title = "Count of Rejected Credit Card Applications by Age",
       x = "Age",
       y = "Count of Rejected Applications") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Filter data to only accepted applicants
accepted_data <- card[card$card == "yes", ]

# Create a bar chart
ggplot(accepted_data, aes(x = as.factor(age))) +
  geom_bar(fill = "green", color = "black") +
  labs(title = "Count of Accepted Credit Card Applications by Age",
       x = "Age",
       y = "Count of Accepted Applications") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Creating a 2 way table

table(card$card, card$age)

     
       0  1 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
  no   0  1  0  2  4  7  7 18 15 19 14 12 15  9  9 12 12 10  9 14 14 13  9  9
  yes  1  5  2  8 15 27 43 44 45 75 43 45 57 40 38 36 40 33 31 32 25 25 22 26
     
      40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
  no   6  9  6  5  0  6  4  2  4  0  1  0  2  5  1  1  1  2  1  1  0  0  0  2
  yes 36 29 24 14 14 14 24 12 13 13  8  3 13  3  7  9  1  6  3  1  2  4  2  2
     
      64 65 66 67 70 72 74 80 84
  no   0  0  0  1  1  0  0  1  0
  yes  2  1  1  1  0  1  1  0  1

Data set 2

Describing the data

The data contains the net returns for US stock prices and their expected dividends, measured by the broad-based (NYSE and AMEX) value-weighted index of stock prices.

Type of Data —> it is structured as a multivariate time series data set (1931 - 2002)
Variables
- Returns —> Monthly return % minus risk free rate of US treasury bill. Return incorporates the benefit or loss from the change in price plus the dividend recieved.
- Dividends —> Distributions to shareholders over past 12 months divided by the price in the current month.

data("USStocksSW")
stock <- USStocksSW

# Creating a table (First 10 rows)
kable(head(stock, 10),
      format = "html",
      caption = "Stock returns (first 10 rows)")

Stock returns (first 10 rows)
returns	dividend
5.9650	-282.2329
10.3053	-293.2089
-6.8408	-287.8614
-10.4481	-278.2477
-14.3581	-265.4742
12.8503	-280.5102
-6.6559	-275.5950
0.0461	-278.4424
-34.2584	-247.1829
7.6799	-255.0321

#Creating a simple graph
plot(stock)

Understanding the type of data

Adjusting data from Multivariate time series to a data frame
- In this case the Class function tells us it is a multivariate time series while GGplot works more so with data frames.
- We didnt have an explicit column for time in the format GGplot expects and so we have to create one.

GGPlot - Identifying the Data structure

str(stock)

 Time-Series [1:864, 1:2] from 1931 to 2003: 5.96 10.31 -6.84 -10.45 -14.36 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:2] "returns" "dividend"

class(stock)

[1] "mts" "ts"

Creating a graph in GGplot

# Convert time series to a data frame
stock_df <- as.data.frame(stock)

# Create a new Date column by taking the time information (index)
stock_df$Date <- as.Date(time(stock), 
                         origin = "1970-01-01") # origin in 1970 as mentioned in class

# Formatting data
stock_df$returns <- round(stock_df$returns, digits = 2)
stock_df$dividend <- round(stock_df$dividend, digits = 2)

# Plot for "returns"
ggplot(stock_df, aes(x = Date, y = returns)) +
  geom_line(color = "navyblue") +
  labs(title = "US Stock Returns Over Time", 
       x = "Date", 
       y = "Returns") +
  theme_minimal()

# Plot for "dividends"
ggplot(stock_df, aes(x = Date, y = dividend)) +
  geom_line(color = "darkgreen") +
  labs(title = "US Stock Dividends Over Time", 
       x = "Date", 
       y = "Dividends") +
  theme_minimal()

Now we can also see we have a column for Date added

# Creating a table (First 10 rows)
kable(head(stock_df, 10), 
      format = "html", 
      caption = "Stock returns (first 10 rows)")

Stock returns (first 10 rows)
returns	dividend	Date
5.96	-282.23	1931-01-01
10.31	-293.21	1931-02-01
-6.84	-287.86	1931-03-01
-10.45	-278.25	1931-04-01
-14.36	-265.47	1931-05-01
12.85	-280.51	1931-06-01
-6.66	-275.60	1931-07-01
0.05	-278.44	1931-08-01
-34.26	-247.18	1931-09-01
7.68	-255.03	1931-10-01