Introducción

La realización del siguiente informe tiene como objetivo la aplicación de los conceptos estadísticos tales como tablas de frecuencias simple y agrupada, gráficos de series temporales, diagramas de tallo y hojas, gráfico circular, gráfico de barras clásico, agrupado y apilado, medidas de tendencia central, diagramas de caja y bigote, entre otros; en el software RStudio, utilizando el lenguaje Markdown. Los datos para su elaboración fueron tomados del Catálogo Global de Deslizamiento de Tierra, siendo el área de estudio los países suramericanos.

Datos

library(readr)
library(knitr)
df <- read_csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
## Rows: 1693 Columns: 23
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (16): date, time, continent_code, country_name, country_code, state/prov...
## dbl  (7): id, population, distance, latitude, longitude, injuries, fatalities
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Países de Sudamérica

Los países sudamericanos han sido afectados por el desprendimientos de tierra debido a la alta precipitación. Tales países son: Colombia, Perú, Venezuela, Ecuador y Brazil. En el siguiente apartado se presentarán distintas gráficas capaces de ayudar a sintetizar una serie de datos tomados del GLC para una mejor comprensión.

Tabla de Frecuencia Simple (Colombia)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Colombia")
library(questionr)
## Warning: package 'questionr' was built under R version 4.1.1
table <- questionr::freq(pais$state.province, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)
n % val% %cum val%cum
Antioquia 19 19.8 19.8 19.8 19.8
Santander 11 11.5 11.5 31.2 31.2
Caldas 10 10.4 10.4 41.7 41.7
Cundinamarca 7 7.3 7.3 49.0 49.0
Huila 6 6.2 6.2 55.2 55.2
Cauca 5 5.2 5.2 60.4 60.4
Nariño 5 5.2 5.2 65.6 65.6
Norte de Santander 5 5.2 5.2 70.8 70.8
Risaralda 4 4.2 4.2 75.0 75.0
Tolima 4 4.2 4.2 79.2 79.2
Valle del Cauca 4 4.2 4.2 83.3 83.3
Boyacá 3 3.1 3.1 86.5 86.5
Córdoba 3 3.1 3.1 89.6 89.6
Magdalena 3 3.1 3.1 92.7 92.7
Caquetá 2 2.1 2.1 94.8 94.8
Bolívar 1 1.0 1.0 95.8 95.8
Meta 1 1.0 1.0 96.9 96.9
Putumayo 1 1.0 1.0 97.9 97.9
Quindío 1 1.0 1.0 99.0 99.0
Sucre 1 1.0 1.0 100.0 100.0 
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="Orange", fill="Green") +
  xlab("Colombia") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Tabla de Frecuencia Simple (Peru)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Peru")
library(questionr)

table <- questionr::freq(pais$state.province, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)
n % val% %cum val%cum
Ancash 5 35.7 35.7 35.7 35.7
Huanuco 4 28.6 28.6 64.3 64.3
San Martín 3 21.4 21.4 85.7 85.7
La Libertad 2 14.3 14.3 100.0 100.0 
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="Orange", fill="Green") +
    xlab("Peru") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Tabla de Frecuencia Simple (Venezuela)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Venezuela")
library(questionr)

table <- questionr::freq(pais$state.province, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)
n % val% %cum val%cum
Distrito Federal 12 60 60 60 60
Miranda 4 20 20 80 80
Vargas 2 10 10 90 90
Aragua 1 5 5 95 95
Falcón 1 5 5 100 100 
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="Orange", fill="Green") +
    xlab("Venezuela") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Tabla de Frecuencia Simple (Ecuador)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Ecuador")
library(questionr)

table <- questionr::freq(pais$state.province, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)
n % val% %cum val%cum
Pichincha 9 30.0 30.0 30.0 30.0
Loja 4 13.3 13.3 43.3 43.3
Azuay 2 6.7 6.7 50.0 50.0
Carchi 2 6.7 6.7 56.7 56.7
Esmeraldas 2 6.7 6.7 63.3 63.3
Manabi 2 6.7 6.7 70.0 70.0
Zamora-Chinchipe 2 6.7 6.7 76.7 76.7
Cotopaxi 1 3.3 3.3 80.0 80.0
Guayas 1 3.3 3.3 83.3 83.3
Morona-Santiago 1 3.3 3.3 86.7 86.7
Napo 1 3.3 3.3 90.0 90.0
Santo Domingo de los Tsáchilas 1 3.3 3.3 93.3 93.3
Sucumbios 1 3.3 3.3 96.7 96.7
Tungurahua 1 3.3 3.3 100.0 100.0 
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="Orange", fill="Green") +
    xlab("Ecuador") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Tabla de Frecuencia Simple (Brazil)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Brazil")
library(questionr)

table <- questionr::freq(pais$state.province, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)
n % val% %cum val%cum
Pará 2 50 50 50 50
Amapá 1 25 25 75 75
Maranhão 1 25 25 100 100 
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="Orange", fill="Green") +
    xlab("Venezuela") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Tabla de Frecuencia Agrupada (Todos los paises de Sudamerica)

distance <- c(0.00003, 0.00442, 0.11421, 0.13147, 0.14776, 0.1702, 0.30587, 0.35649, 0.37809, 0.38844, 0.44753, 0.47714, 0.55865, 0.60599, 0.62022, 0.64094, 0.64469, 0.68544, 0.70558, 0.7283, 0.74201, 0.79694, 0.80432, 0.85976, 0.93184, 1.01848, 1.01932, 1.04062, 1.04263, 1.07765, 1.08964, 1.16036, 1.23724, 1.27637, 1.33829, 1.35196, 1.4794, 1.53032, 1.56942, 1.7085, 1.73101, 1.82885, 1.84941,  2.04898,  2.07081,  2.18776,  2.28425,  2.36822,  2.43089,  2.53047, 2.55282,  2.55507,  2.69644,  2.81891,  2.89809,  2.92493,  2.95706,  2.99929,  3.06383,  3.09014, 3.1406,  3.14201,  3.26788,   3.4989,   3.6052,  3.65044,  3.70678,  3.72195,  3.81445,  3.87793,  4.09028,  4.19867,  4.25486,  4.29197,  4.39517,  4.58994,  4.95353,   5.0696,   5.1217,   5.1765, 5.6405,  5.74106,  6.04235,  6.08628,  6.16385,  6.44532,  6.65506,  6.77672,  6.84683,   6.9613, 7.28959,  7.67919,  7.70237,  7.78677,  7.85369,  7.87303,  7.89319,  7.90754,  7.98838,  8.11953, 8.18229,  8.30406,  8.45736,  8.46579,  8.56086,  8.58891,  8.81287,  8.89799,  9.21217,  9.23778,  9.64894,  9.65157, 10.16196,  10.1804, 10.36239, 10.47204, 10.55986, 11.11685, 11.19714, 11.55916, 11.91442, 12.61362, 12.70296, 13.21139, 14.28266, 14.62503, 15.04256, 15.16116, 15.42607, 15.82404, 15.84114, 16.34404, 16.94642, 16.97776, 17.31514, 17.34318, 17.48659, 17.57187, 18.88784, 18.91189, 19.81345, 19.85816, 20.25692, 20.31227, 21.26652, 22.53724, 23.49217, 23.97854, 24.48479, 25.51411, 25.82923, 26.18676, 26.72137, 26.89879, 28.29459, 28.50569, 30.81169, 33.94603, 45.69792, 46.77007, 50.21741, 51.84125, 61.75306)
n_sturges = 1 + log(length(distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)

n_clases = 0
if (n_sturgesc%%2 == 0) {
  n_clases = n_sturgesf
} else {
  
  n_clases = n_sturgesc
}
R = max(distance) - min(distance)
w = ceiling(R/n_clases)
bins <- seq(min(distance), max(distance) + w, by = w)
bins
##  [1]  0.00003  7.00003 14.00003 21.00003 28.00003 35.00003 42.00003 49.00003
##  [9] 56.00003 63.00003
distance <- cut(distance, bins)
Freq_table <- transform(table(distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)
distance Freq Rel_Freq Cum_Freq
(3e-05,7] 89 0.5493827 89
(7,14] 34 0.2098765 123
(14,21] 20 0.1234568 143
(21,28] 10 0.0617284 153
(28,35] 4 0.0246914 157
(35,42] 0 0.0000000 157
(42,49] 2 0.0123457 159
(49,56] 2 0.0123457 161
(56,63] 1 0.0061728 162 
df <- data.frame(x = Freq_table$distance, y = Freq_table$Freq)
knitr::kable(df)
x y
(3e-05,7] 89
(7,14] 34
(14,21] 20
(21,28] 10
(28,35] 4
(35,42] 0
(42,49] 2
(49,56] 2
(56,63] 1

Gráfico de Series Temporales (Colombia)

library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Colombia")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Fecha")

Gráfico de Series Temporales (Ecuador)

library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Ecuador")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Fecha")

Gráfico de Series Temporales (Peru)

library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Peru")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Fecha")

Gráfico de Series Temporales (Venezuela)

library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Venezuela")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Fecha")

Gráfico de Series Temporales (Brazil)

library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Brazil")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Fecha")

Diagrama de Tallo y Hojas

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
df_C <- subset(df, continent_code == "SA") 
df <- data.frame(distance= c(0.00003, 0.00442, 0.11421, 0.13147, 0.14776, 0.1702, 0.30587, 0.35649, 0.37809, 0.38844, 0.44753, 0.47714, 0.55865, 0.60599, 0.62022, 0.64094, 0.64469, 0.68544, 0.70558, 0.7283, 0.74201, 0.79694, 0.80432, 0.85976, 0.93184, 1.01848, 1.01932, 1.04062, 1.04263, 1.07765, 1.08964, 1.16036, 1.23724, 1.27637, 1.33829, 1.35196, 1.4794, 1.53032, 1.56942, 1.7085, 1.73101, 1.82885, 1.84941,  2.04898,  2.07081,  2.18776,  2.28425,  2.36822,  2.43089,  2.53047, 2.55282,  2.55507,  2.69644,  2.81891,  2.89809,  2.92493,  2.95706,  2.99929,  3.06383,  3.09014, 3.1406,  3.14201,  3.26788,   3.4989,   3.6052,  3.65044,  3.70678,  3.72195,  3.81445,  3.87793,  4.09028,  4.19867,  4.25486,  4.29197,  4.39517,  4.58994,  4.95353,   5.0696,   5.1217,   5.1765, 5.6405,  5.74106,  6.04235,  6.08628,  6.16385,  6.44532,  6.65506,  6.77672,  6.84683,   6.9613, 7.28959,  7.67919,  7.70237,  7.78677,  7.85369,  7.87303,  7.89319,  7.90754,  7.98838,  8.11953, 8.18229,  8.30406,  8.45736,  8.46579,  8.56086,  8.58891,  8.81287,  8.89799,  9.21217,  9.23778,  9.64894,  9.65157, 10.16196,  10.1804, 10.36239, 10.47204, 10.55986, 11.11685, 11.19714, 11.55916, 11.91442, 12.61362, 12.70296, 13.21139, 14.28266, 14.62503, 15.04256, 15.16116, 15.42607, 15.82404, 15.84114, 16.34404, 16.94642, 16.97776, 17.31514, 17.34318, 17.48659, 17.57187, 18.88784, 18.91189, 19.81345, 19.85816, 20.25692, 20.31227, 21.26652, 22.53724, 23.49217, 23.97854, 24.48479, 25.51411, 25.82923, 26.18676, 26.72137, 26.89879, 28.29459, 28.50569, 30.81169, 33.94603, 45.69792, 46.77007, 50.21741, 51.84125, 61.75306))
stem(df_C$distance)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   0 | 00000000000011111111111111111111111112222222222223333333333333333444
##   0 | 55555666666777778888888888888999999
##   1 | 000000111223334
##   1 | 555566677777899
##   2 | 000013344
##   2 | 6667789
##   3 | 14
##   3 | 
##   4 | 
##   4 | 67
##   5 | 02
##   5 | 
##   6 | 2

Diagrama de Pareto

df <- data.frame(Pais =
                   c("Colombia", "Ecuador", "Peru", "Venezuela", "Brazil"), 
                 Frecuencia = c(96, 30, 14, 20, 4))
knitr::kable(df)
Pais Frecuencia
Colombia 96
Ecuador 30
Peru 14
Venezuela 20
Brazil 4 
library(qcc)
## Warning: package 'qcc' was built under R version 4.1.1
## Package 'qcc' version 2.7
## Type 'citation("qcc")' for citing this R package in publications.
Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Pais 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores"
)

##            
## Pareto chart analysis for Frecuencia
##              Frequency  Cum.Freq. Percentage Cum.Percent.
##   Colombia   96.000000  96.000000  58.536585    58.536585
##   Ecuador    30.000000 126.000000  18.292683    76.829268
##   Venezuela  20.000000 146.000000  12.195122    89.024390
##   Peru       14.000000 160.000000   8.536585    97.560976
##   Brazil      4.000000 164.000000   2.439024   100.000000

Gráfico Circular

library(ggplot2)
library(dplyr)

data <- data.frame(Pais = 
                     c("Colombia", 
                       "Ecuador", 
                       "Peru", 
                       "Venezuela",
                       "Brazil"), 
                   Numeros_de_desplazamiento = c(96, 30, 14, 20, 4))
knitr::kable(data)
Pais Numeros_de_desplazamiento
Colombia 96
Ecuador 30
Peru 14
Venezuela 20
Brazil 4 
ggplot(data, aes(x = "", y = Numeros_de_desplazamiento, fill=Pais)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(Pais)) %>%
  mutate(prop = Numeros_de_desplazamiento / sum(data$Numeros_de_desplazamiento) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
## Loading required package: scales
## 
## Attaching package: 'scales'
## The following object is masked from 'package:readr':
## 
##     col_factor
ggplot(data, aes(x="", y = prop, fill=Pais)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() + 
  theme(legend.position="none") +
  
  geom_text(aes(y = ypos, label = percent(Numeros_de_desplazamiento/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

Medidas de tendencia central, Medidas de variabilidad, mMdidas de posicion

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

df_J <- subset(df, continent_code == "SA")

summary(df_J$distance)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.00003  1.80439  5.69078  9.25696 12.63595 61.75306

Diagramas de caja y bigotes para sudamerica

boxplot(df_J$distance, horizontal=TRUE, col='steelblue')

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble  3.1.3     v stringr 1.4.0
## v tidyr   1.1.3     v forcats 0.5.1
## v purrr   0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x scales::col_factor() masks readr::col_factor()
## x purrr::discard()     masks scales::discard()
## x dplyr::filter()      masks stats::filter()
## x dplyr::lag()         masks stats::lag()
library(hrbrthemes)
## Warning: package 'hrbrthemes' was built under R version 4.1.1
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(viridis)
## Warning: package 'viridis' was built under R version 4.1.1
## Loading required package: viridisLite
## 
## Attaching package: 'viridis'
## The following object is masked from 'package:scales':
## 
##     viridis_pal
df <- data.frame(df_J$distance)
df %>% ggplot(aes(x = "", y = df_J$distance)) +
  geom_boxplot(color="red", fill="orange", alpha=0.5) +
  theme_ipsum() +
  theme(legend.position="none", plot.title = element_text(size=11)) +
  ggtitle("Basic boxplot") +
  coord_flip() +
  xlab("") +
  ylab("")
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

Conclusion

En el siguiente informe se vio reflejado la adquisición de los temas tratados en clases mediante la elaboración de las diferentes tablas, gráficas y diagramas. También, logramos familiarizarnos con los algoritmos y códigos del lenguaje de programación Markdown a través del software RStudio. Además de esto, comprendimos como agrupar y separar los valores o variables necesitadas de un conjunto extenso de datos con el fin de obtener el diseño deseado según corresponda.