Base personal

library(tidyverse)
base_datos <- read_csv("Libro3.csv")
base_datos

Estadísticas descriptivas

data_frame_mod <- transform(
base_datos,Estado_civil = as.factor(Estado_civil), Cliente = as.factor(Cliente))
summary(data_frame_mod)
##       Edad          Celular              Estado_civil          Cliente  
##  Min.   :22.87   Min.   :2.007e+09   casado    :36    Aprobatorio  :44  
##  1st Qu.:31.24   1st Qu.:4.416e+09   divorciado:17    Notable      :23  
##  Median :37.11   Median :6.117e+09   separado  :10    Sobresaliente:22  
##  Mean   :37.78   Mean   :6.010e+09   soltero   :23    suspenso     :11  
##  3rd Qu.:44.90   3rd Qu.:7.329e+09   viudo     :14                      
##  Max.   :52.46   Max.   :9.987e+09
library(arsenal)
base_datos %>% 
  select(Cliente, Estado_civil) %>% 
  table() %>% 
  freqlist() %>% 
  summary() 
## 
## 
## |Cliente       |Estado_civil | Freq| Cumulative Freq| Percent| Cumulative Percent|
## |:-------------|:------------|----:|---------------:|-------:|------------------:|
## |Aprobatorio   |casado       |   16|              16|   16.00|              16.00|
## |              |divorciado   |    7|              23|    7.00|              23.00|
## |              |separado     |    3|              26|    3.00|              26.00|
## |              |soltero      |   10|              36|   10.00|              36.00|
## |              |viudo        |    8|              44|    8.00|              44.00|
## |Notable       |casado       |    8|              52|    8.00|              52.00|
## |              |divorciado   |    2|              54|    2.00|              54.00|
## |              |separado     |    4|              58|    4.00|              58.00|
## |              |soltero      |    5|              63|    5.00|              63.00|
## |              |viudo        |    4|              67|    4.00|              67.00|
## |Sobresaliente |casado       |    9|              76|    9.00|              76.00|
## |              |divorciado   |    5|              81|    5.00|              81.00|
## |              |separado     |    2|              83|    2.00|              83.00|
## |              |soltero      |    6|              89|    6.00|              89.00|
## |suspenso      |casado       |    3|              92|    3.00|              92.00|
## |              |divorciado   |    3|              95|    3.00|              95.00|
## |              |separado     |    1|              96|    1.00|              96.00|
## |              |soltero      |    2|              98|    2.00|              98.00|
## |              |viudo        |    2|             100|    2.00|             100.00|

Gráficos de variables

ggplot(data = data_frame_mod,
       mapping = aes(x = Celular)) +
  geom_histogram()

ggplot(data = data_frame_mod,
       mapping = aes(Cliente)) +
  geom_bar()

ggplot(data = data_frame_mod,
       mapping = aes(y = Edad)) +
  geom_boxplot()

library(lessR)
count <- data_frame_mod$Estado_civil
tabla_genero <- table(count)
PieChart(tabla_genero, hole = 0, values = "%", data = count,
         , main = "")
## >>> Note: tabla_genero is not in a data frame (table)
## >>> Note: tabla_genero is not in a data frame (table)

## >>> suggestions
## PieChart(tabla_genero, hole=0)  # traditional pie chart
## PieChart(tabla_genero, values="%")  # display %'s on the chart
## PieChart(tabla_genero)  # bar chart
## Plot(tabla_genero)  # bubble plot
## Plot(tabla_genero, values="count")  # lollipop plot 
## 
## --- tabla_genero --- 
## 
##                casado  divorciado  separado  soltero  viudo     Total 
## Frequencies:       36          17        10       23     14       100 
## Proportions:    0.360       0.170     0.100    0.230  0.140     1.000 
## 
## Chi-squared test of null hypothesis of equal probabilities 
##   Chisq = 20.500, df = 4, p-value = 0.000