Modulo Analitica Descriptiva y Predictiva
Ana Di Nezio
2024-06-18
Sprint 2 Adicional
Analítica descriptiva en un reto en el ámbito de las FINANZAS
Objetivo de la actividad
Con el objetivo de ampliar las aplicaciones prácticas en analítica descriptiva sobre la temática vista en la clase 2, se propone realizar este sprint opcional sobre un conjunto de datos que encontramos en Kaggle en el ámbito de las FINANZAS.
Preparacion del entorno de trabajo
Se cargan las librerias y los datos a trabajar.
# Cargar las librerias
# Librerías para manipulación y modelado de datos
library(tidyr) # Manipulación de datos
library(tidyverse) # Conjunto de paquetes para manipulación de datos
library(dplyr) # Manipulación de datos con funciones verbosas
library(ggplot2) # Creación de gráficos con ggplot2
library(plotly) # Gráficos interactivos
library(readr) # Lectura de datos
# Librerías para estadísticas descriptivas y exploratorias
library(pastecs) # Estadísticas descriptivas
library(Hmisc) # Herramientas y procedimientos misceláneos
library(expss) # Análisis de datos categóricos
# Cargar los datos
data <- read_csv("/Users/anadinezio/Downloads/ADP_Clase 2/Finance_data.csv")Exploracion de los datos con analitica descriptiva basica
# Analitica exploratoria basica
# View(data). lo comento solo por propositos de markdown
summary(data)## gender age Investment_Avenues Mutual_Funds
## Length:40 Min. :21.00 Length:40 Min. :1.00
## Class :character 1st Qu.:25.75 Class :character 1st Qu.:2.00
## Mode :character Median :27.00 Mode :character Median :2.00
## Mean :27.80 Mean :2.55
## 3rd Qu.:30.00 3rd Qu.:3.00
## Max. :35.00 Max. :7.00
## Equity_Market Debentures Government_Bonds Fixed_Deposits
## Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:5.00 1st Qu.:4.00 1st Qu.:2.750
## Median :4.000 Median :6.50 Median :5.00 Median :3.500
## Mean :3.475 Mean :5.75 Mean :4.65 Mean :3.575
## 3rd Qu.:4.000 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :6.000 Max. :7.00 Max. :7.00 Max. :7.000
## PPF Gold Stock_Marktet Factor
## Min. :1.000 Min. :2.000 Length:40 Length:40
## 1st Qu.:1.000 1st Qu.:6.000 Class :character Class :character
## Median :1.000 Median :6.000 Mode :character Mode :character
## Mean :2.025 Mean :5.975
## 3rd Qu.:2.250 3rd Qu.:7.000
## Max. :6.000 Max. :7.000
## Objective Purpose Duration Invest_Monitor
## Length:40 Length:40 Length:40 Length:40
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Expect Avenue What are your savings objectives?
## Length:40 Length:40 Length:40
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Reason_Equity Reason_Mutual Reason_Bonds Reason_FD
## Length:40 Length:40 Length:40 Length:40
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Source
## Length:40
## Class :character
## Mode :character
##
##
## print(data)## # A tibble: 40 × 24
## gender age Investment_Avenues Mutual_Funds Equity_Market Debentures
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Female 34 Yes 1 2 5
## 2 Female 23 Yes 4 3 2
## 3 Male 30 Yes 3 6 4
## 4 Male 22 Yes 2 1 3
## 5 Female 24 No 2 1 3
## 6 Female 24 No 7 5 4
## 7 Female 27 Yes 3 6 4
## 8 Male 21 Yes 2 3 7
## 9 Male 35 Yes 2 4 7
## 10 Male 31 Yes 1 3 7
## # ℹ 30 more rows
## # ℹ 18 more variables: Government_Bonds <dbl>, Fixed_Deposits <dbl>, PPF <dbl>,
## # Gold <dbl>, Stock_Marktet <chr>, Factor <chr>, Objective <chr>,
## # Purpose <chr>, Duration <chr>, Invest_Monitor <chr>, Expect <chr>,
## # Avenue <chr>, `What are your savings objectives?` <chr>,
## # Reason_Equity <chr>, Reason_Mutual <chr>, Reason_Bonds <chr>,
## # Reason_FD <chr>, Source <chr>head(data)## # A tibble: 6 × 24
## gender age Investment_Avenues Mutual_Funds Equity_Market Debentures
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Female 34 Yes 1 2 5
## 2 Female 23 Yes 4 3 2
## 3 Male 30 Yes 3 6 4
## 4 Male 22 Yes 2 1 3
## 5 Female 24 No 2 1 3
## 6 Female 24 No 7 5 4
## # ℹ 18 more variables: Government_Bonds <dbl>, Fixed_Deposits <dbl>, PPF <dbl>,
## # Gold <dbl>, Stock_Marktet <chr>, Factor <chr>, Objective <chr>,
## # Purpose <chr>, Duration <chr>, Invest_Monitor <chr>, Expect <chr>,
## # Avenue <chr>, `What are your savings objectives?` <chr>,
## # Reason_Equity <chr>, Reason_Mutual <chr>, Reason_Bonds <chr>,
## # Reason_FD <chr>, Source <chr>tail(data)## # A tibble: 6 × 24
## gender age Investment_Avenues Mutual_Funds Equity_Market Debentures
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Male 27 Yes 2 3 6
## 2 Male 30 Yes 1 4 6
## 3 Male 30 Yes 2 4 7
## 4 Male 25 Yes 5 4 7
## 5 Male 31 Yes 2 4 7
## 6 Male 29 Yes 4 3 5
## # ℹ 18 more variables: Government_Bonds <dbl>, Fixed_Deposits <dbl>, PPF <dbl>,
## # Gold <dbl>, Stock_Marktet <chr>, Factor <chr>, Objective <chr>,
## # Purpose <chr>, Duration <chr>, Invest_Monitor <chr>, Expect <chr>,
## # Avenue <chr>, `What are your savings objectives?` <chr>,
## # Reason_Equity <chr>, Reason_Mutual <chr>, Reason_Bonds <chr>,
## # Reason_FD <chr>, Source <chr>str(data)## spc_tbl_ [40 × 24] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ gender : chr [1:40] "Female" "Female" "Male" "Male" ...
## $ age : num [1:40] 34 23 30 22 24 24 27 21 35 31 ...
## $ Investment_Avenues : chr [1:40] "Yes" "Yes" "Yes" "Yes" ...
## $ Mutual_Funds : num [1:40] 1 4 3 2 2 7 3 2 2 1 ...
## $ Equity_Market : num [1:40] 2 3 6 1 1 5 6 3 4 3 ...
## $ Debentures : num [1:40] 5 2 4 3 3 4 4 7 7 7 ...
## $ Government_Bonds : num [1:40] 3 1 2 7 6 6 2 4 5 4 ...
## $ Fixed_Deposits : num [1:40] 7 5 5 6 4 3 5 6 3 5 ...
## $ PPF : num [1:40] 6 6 1 4 5 1 1 1 1 2 ...
## $ Gold : num [1:40] 4 7 7 5 7 2 7 5 6 6 ...
## $ Stock_Marktet : chr [1:40] "Yes" "No" "Yes" "Yes" ...
## $ Factor : chr [1:40] "Returns" "Locking Period" "Returns" "Returns" ...
## $ Objective : chr [1:40] "Capital Appreciation" "Capital Appreciation" "Capital Appreciation" "Income" ...
## $ Purpose : chr [1:40] "Wealth Creation" "Wealth Creation" "Wealth Creation" "Wealth Creation" ...
## $ Duration : chr [1:40] "1-3 years" "More than 5 years" "3-5 years" "Less than 1 year" ...
## $ Invest_Monitor : chr [1:40] "Monthly" "Weekly" "Daily" "Daily" ...
## $ Expect : chr [1:40] "20%-30%" "20%-30%" "20%-30%" "10%-20%" ...
## $ Avenue : chr [1:40] "Mutual Fund" "Mutual Fund" "Equity" "Equity" ...
## $ What are your savings objectives?: chr [1:40] "Retirement Plan" "Health Care" "Retirement Plan" "Retirement Plan" ...
## $ Reason_Equity : chr [1:40] "Capital Appreciation" "Dividend" "Capital Appreciation" "Dividend" ...
## $ Reason_Mutual : chr [1:40] "Better Returns" "Better Returns" "Tax Benefits" "Fund Diversification" ...
## $ Reason_Bonds : chr [1:40] "Safe Investment" "Safe Investment" "Assured Returns" "Tax Incentives" ...
## $ Reason_FD : chr [1:40] "Fixed Returns" "High Interest Rates" "Fixed Returns" "High Interest Rates" ...
## $ Source : chr [1:40] "Newspapers and Magazines" "Financial Consultants" "Television" "Internet" ...
## - attr(*, "spec")=
## .. cols(
## .. gender = col_character(),
## .. age = col_double(),
## .. Investment_Avenues = col_character(),
## .. Mutual_Funds = col_double(),
## .. Equity_Market = col_double(),
## .. Debentures = col_double(),
## .. Government_Bonds = col_double(),
## .. Fixed_Deposits = col_double(),
## .. PPF = col_double(),
## .. Gold = col_double(),
## .. Stock_Marktet = col_character(),
## .. Factor = col_character(),
## .. Objective = col_character(),
## .. Purpose = col_character(),
## .. Duration = col_character(),
## .. Invest_Monitor = col_character(),
## .. Expect = col_character(),
## .. Avenue = col_character(),
## .. `What are your savings objectives?` = col_character(),
## .. Reason_Equity = col_character(),
## .. Reason_Mutual = col_character(),
## .. Reason_Bonds = col_character(),
## .. Reason_FD = col_character(),
## .. Source = col_character()
## .. )
## - attr(*, "problems")=<externalptr>glimpse(data)## Rows: 40
## Columns: 24
## $ gender <chr> "Female", "Female", "Male", "Male"…
## $ age <dbl> 34, 23, 30, 22, 24, 24, 27, 21, 35…
## $ Investment_Avenues <chr> "Yes", "Yes", "Yes", "Yes", "No", …
## $ Mutual_Funds <dbl> 1, 4, 3, 2, 2, 7, 3, 2, 2, 1, 2, 2…
## $ Equity_Market <dbl> 2, 3, 6, 1, 1, 5, 6, 3, 4, 3, 4, 5…
## $ Debentures <dbl> 5, 2, 4, 3, 3, 4, 4, 7, 7, 7, 7, 7…
## $ Government_Bonds <dbl> 3, 1, 2, 7, 6, 6, 2, 4, 5, 4, 5, 6…
## $ Fixed_Deposits <dbl> 7, 5, 5, 6, 4, 3, 5, 6, 3, 5, 3, 3…
## $ PPF <dbl> 6, 6, 1, 4, 5, 1, 1, 1, 1, 2, 1, 1…
## $ Gold <dbl> 4, 7, 7, 5, 7, 2, 7, 5, 6, 6, 6, 4…
## $ Stock_Marktet <chr> "Yes", "No", "Yes", "Yes", "No", "…
## $ Factor <chr> "Returns", "Locking Period", "Retu…
## $ Objective <chr> "Capital Appreciation", "Capital A…
## $ Purpose <chr> "Wealth Creation", "Wealth Creatio…
## $ Duration <chr> "1-3 years", "More than 5 years", …
## $ Invest_Monitor <chr> "Monthly", "Weekly", "Daily", "Dai…
## $ Expect <chr> "20%-30%", "20%-30%", "20%-30%", "…
## $ Avenue <chr> "Mutual Fund", "Mutual Fund", "Equ…
## $ `What are your savings objectives?` <chr> "Retirement Plan", "Health Care", …
## $ Reason_Equity <chr> "Capital Appreciation", "Dividend"…
## $ Reason_Mutual <chr> "Better Returns", "Better Returns"…
## $ Reason_Bonds <chr> "Safe Investment", "Safe Investmen…
## $ Reason_FD <chr> "Fixed Returns", "High Interest Ra…
## $ Source <chr> "Newspapers and Magazines", "Finan…# Revisión de Nulos
rowSums(is.na(data)) # filas## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39] 0 0colSums(is.na(data)) # columnas## gender age
## 0 0
## Investment_Avenues Mutual_Funds
## 0 0
## Equity_Market Debentures
## 0 0
## Government_Bonds Fixed_Deposits
## 0 0
## PPF Gold
## 0 0
## Stock_Marktet Factor
## 0 0
## Objective Purpose
## 0 0
## Duration Invest_Monitor
## 0 0
## Expect Avenue
## 0 0
## What are your savings objectives? Reason_Equity
## 0 0
## Reason_Mutual Reason_Bonds
## 0 0
## Reason_FD Source
## 0 0# Tablas simples
table(data$`gender`)##
## Female Male
## 15 25table(data$`age`)##
## 21 22 23 24 25 26 27 28 29 30 31 32 34 35
## 2 1 1 3 3 4 7 2 5 3 4 1 2 2table(data$Mutual_Funds)##
## 1 2 3 4 5 7
## 4 21 9 3 2 1table(data$`Equity_Market`)##
## 1 2 3 4 5 6
## 2 5 12 16 3 2table(data$`Purpose`)##
## Returns Savings for Future Wealth Creation
## 2 6 32table(data$Objective)##
## Capital Appreciation Growth Income
## 26 11 3table(data$`Expect`)##
## 10%-20% 20%-30% 30%-40%
## 3 32 5table(data$`Source`)##
## Financial Consultants Internet Newspapers and Magazines
## 16 4 14
## Television
## 6table(data$Reason_Bonds)##
## Assured Returns Safe Investment Tax Incentives
## 26 13 1# Histogramas de variables numericas
hist (data$`age`)
hist (data$`Mutual_Funds`)
hist (data$Equity_Market)
hist(data$Debentures)
hist(data$Government_Bonds)
hist(data$Fixed_Deposits)
hist(data$PPF)
hist(data$Gold)
Grafico de barras de variables categoricas
# Transformar variables categoricas
data$FACTOR <- as.factor(data$Factor)
data$GENDER <- as.factor(data$gender)
data$STOCK_MARKET <- as.factor(data$Stock_Marktet)
data$OBJECTIVE <- as.factor(data$Objective)
data$PURPOSE <- as.factor(data$Purpose)
data$DURATION <- as.factor(data$Duration)
data$INV_MONITOR <- as.factor(data$Invest_Monitor)
data$AVENUE <- as.factor(data$Avenue)
#Gráficos de barras de variables categóricas
plot(data$FACTOR)
plot(data$GENDER)
plot(data$STOCK_MARKET)
plot(data$OBJECTIVE)
plot(data$PURPOSE)
plot(data$DURATION)
plot(data$INV_MONITOR)
plot(data$AVENUE)
Pie charts de variables categoricas, cualitativas o cuantitativas discretas
#Diagramas de sectores variables categóricas, cualitativas o cuantitativas discretas
g <- ggplot(data, aes(x=TRUE, fill=GENDER)) + geom_bar(width=1)
g + coord_polar(theta = "y")
g <- ggplot(data, aes(x=TRUE, fill=STOCK_MARKET)) + geom_bar(width=1)
g + coord_polar(theta = "y")
g <- ggplot(data, aes(x=TRUE, fill=PURPOSE)) + geom_bar(width=1)
g + coord_polar(theta = "y")
g <- ggplot(data, aes(x=TRUE, fill=DURATION)) + geom_bar(width=1)
g + coord_polar(theta = "y")
Distribucion de edades por genero
# Age distribution by gender
ggplot(data, aes(x = age, color = GENDER)) +
geom_density(alpha = 0.5) +
labs(title = "Frequency of Age Range by Gender",
x = "Age",
y = "Density") 
Graficos de barra
# Age distribution by gender
ggplot(data, aes(x = age, color = GENDER)) +
geom_density(alpha = 0.5) +
labs(title = "Frequency of Age Range by Gender",
x = "Age",
y = "Density")
# Plot in a bar chart Investment purpose by gender
data %>%
count(GENDER, PURPOSE) %>%
ggplot(aes(x = GENDER, y = n, fill = PURPOSE)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Investment purpose by gender", x = "GENERO", y = "Purpose") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot in a bar chart Investment Objective by age
data %>%
count(OBJECTIVE, age) %>%
ggplot(aes(x = age, y = n, fill = OBJECTIVE)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Investment Objective by age ", x = "Age", y = "Objective") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot in a bar chart Investment Objective by gender
data %>%
count(OBJECTIVE, GENDER) %>%
ggplot(aes(x = GENDER, y = n, fill = OBJECTIVE)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Investment Objective by gender ", x = "Gender", y = "Objective") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot in a bar chart Investor age by gender
data %>%
count(age, GENDER) %>%
ggplot(aes(x = GENDER, y = n, fill = age)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Investor age by gender ", x = "Gender", y = "Age") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot in a bar chart Insvestment duration by gender
data %>%
count(DURATION, GENDER) %>%
ggplot(aes(x = GENDER, y = n, fill = DURATION)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Insvestment duration by gender", x = "Gender", y = "Duration") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Plot in a bar chart Insvestment duration by age
data %>%
count(age, DURATION) %>%
ggplot(aes(x = DURATION, y = n, fill = age)) +
geom_bar(stat = "identity", position = "stack") +
labs(title = "Insvestment duration by age", x = "Age", y = "Duration") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
CONCLUSIONES
-El perfil del inversor medio es hombre entre 25 y años, con objetivo
de apreciacion de capital (capital appreciation).
-El perfil de inversor mujer suele ser mas joven que el perfil
hombre.
-La mayoria de los inversores prefieren invertir en oro y debentures
(bonos u obligaciones).
-La duracion de la inversion suele ser de 1-3 años o 3-5 años.
-La mayoria de los inversores hacen un seguimiento mensual de la
inversion.
-Hay muy pocos inversores que inviertan por un año o mas de 5 años, y
los que asi lo hacen son los inversores mas jovenes con 22 y 23
años.
-De los datos analizados, solo las mujeres se han decantado por
inversiones a mas de 5 años.