STAA 566 - Maps

Summary

The lending data comes from 5 years of investment in a crowd-lending platform, the purpuse of creating this tables is to have a scorecard to decide how to invest based on real data of the of the credit’s behavior classified by the Credit Score.

The Credit Score can be A for a person who has a very good credit history in the Mexican Bureau of Credit, to G being the lower score. Of course, credits with better score have a lower interest rate. This game is about manage the risk.

Data Source :

Personal information about investment from a crowd-lending platform known as Prestadero.

www.prestadero.com

Formating:

R Code

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.4.1 
✔ readr   2.1.2      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(gt)



lending <- read_csv('lending.csv', show_col_types = FALSE)



lending$capital_int <- lending$interes_p+lending$interes_m+lending$capital_p

#grouping by score


lending_t1 <- lending %>% filter(year <= 2019 & status %in% c("PAGADO","LIBERADO"))   %>% select( score, amt_inv, interes_rate, capital_p,  capital_int) %>% group_by(score) %>% summarise(credits=n(), interest_avg =mean(interes_rate),ammount_inv=sum(amt_inv), cap_int =sum(capital_int)) %>% dplyr::mutate(profit_pct =  cap_int/ ammount_inv - 1) %>% gt() 

#formatting

#tittles and footnotes
lending_t1 <-   lending_t1 %>%tab_header(
    title = md("**Performance of Investment by Credit Score**"),
    subtitle =md( "*Credit Score* provided by Credit Bureau")
  ) %>%  tab_source_note(
    source_note = "Source: Crowd-Lending investment, credits granted between 2017 and 2019."
  )

#
lending_t1 <-  lending_t1 %>% cols_label( score = md("**Credit \n Score**"), credits = md("**Credits**"), interest_avg = md("**Interest Rate**\n(Average)"), ammount_inv = md("**Ammount Invested**"), cap_int = md("**Capital + Interest**"), profit_pct = md("**Profit Percentage**"))

lending_t1

Performance of Investment by Credit Score
Credit Score provided by Credit Bureau
Credit Score	Credits	Interest Rate (Average)	Ammount Invested	Capital + Interest	Profit Percentage
A	3	10.23333	2500.00	2233.595	-0.10656184
B	35	13.32857	16750.00	19191.686	0.14577228
C	107	15.99346	45497.39	53698.737	0.18025984
D	142	18.99155	52974.24	59546.300	0.12406143
E	58	21.84828	18866.05	20185.792	0.06995317
F	26	24.93846	9250.00	8852.453	-0.04297807
G	13	27.59231	4250.00	3686.879	-0.13249915
Source: Crowd-Lending investment, credits granted between 2017 and 2019.