#Cargamos librerías que usaremos en los ejercicios
library(tidyverse)
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library(ggplot2)
library(psych)
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library(rapportools)
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library(knitr)
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El package de R datasets (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html)
pone a nuestra disposición una serie de conjuntos de datos con los que
poder trabajar como, por ejemplo, iris, cars o Titanic. Escoged un
conjunto de datos. Deberéis:
• Buscar un resumen estadístico de las variables del dataset Iris y
Orange.
• Generar una tabla de frecuencias absolutas y una tabla de frecuencias relativas con el dataset Iris. ¿Todas las tablas generadas tienen sentido para vosotros?
• Generar una tabla de frecuencias absolutas con cada una de las variables del conjunto de datos Orange. ¿Todas las tablas generadas tienen sentido para vosotros?
• Generar una tabla de doble entrada entre las variables Tree y Age de Orange.
#Parte 1 - Escogemos el conjunto iris
data("iris")
view(iris)
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
data("Orange")
view(Orange)
summary(Orange)
## Tree age circumference
## 3:7 Min. : 118.0 Min. : 30.0
## 1:7 1st Qu.: 484.0 1st Qu.: 65.5
## 5:7 Median :1004.0 Median :115.0
## 2:7 Mean : 922.1 Mean :115.9
## 4:7 3rd Qu.:1372.0 3rd Qu.:161.5
## Max. :1582.0 Max. :214.0
print(summary(iris))
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
print(summary(Orange))
## Tree age circumference
## 3:7 Min. : 118.0 Min. : 30.0
## 1:7 1st Qu.: 484.0 1st Qu.: 65.5
## 5:7 Median :1004.0 Median :115.0
## 2:7 Mean : 922.1 Mean :115.9
## 4:7 3rd Qu.:1372.0 3rd Qu.:161.5
## Max. :1582.0 Max. :214.0
#Parte 2 - # Tablas de frecuencia absolutas
kable(table(iris$Sepal.Length), caption = "Frecuencia de Sepal.Length")
| Var1 | Freq |
|---|---|
| 4.3 | 1 |
| 4.4 | 3 |
| 4.5 | 1 |
| 4.6 | 4 |
| 4.7 | 2 |
| 4.8 | 5 |
| 4.9 | 6 |
| 5 | 10 |
| 5.1 | 9 |
| 5.2 | 4 |
| 5.3 | 1 |
| 5.4 | 6 |
| 5.5 | 7 |
| 5.6 | 6 |
| 5.7 | 8 |
| 5.8 | 7 |
| 5.9 | 3 |
| 6 | 6 |
| 6.1 | 6 |
| 6.2 | 4 |
| 6.3 | 9 |
| 6.4 | 7 |
| 6.5 | 5 |
| 6.6 | 2 |
| 6.7 | 8 |
| 6.8 | 3 |
| 6.9 | 4 |
| 7 | 1 |
| 7.1 | 1 |
| 7.2 | 3 |
| 7.3 | 1 |
| 7.4 | 1 |
| 7.6 | 1 |
| 7.7 | 4 |
| 7.9 | 1 |
kable(table(iris$Sepal.Width), caption = "Frecuencia de Sepal.Width")
| Var1 | Freq |
|---|---|
| 2 | 1 |
| 2.2 | 3 |
| 2.3 | 4 |
| 2.4 | 3 |
| 2.5 | 8 |
| 2.6 | 5 |
| 2.7 | 9 |
| 2.8 | 14 |
| 2.9 | 10 |
| 3 | 26 |
| 3.1 | 11 |
| 3.2 | 13 |
| 3.3 | 6 |
| 3.4 | 12 |
| 3.5 | 6 |
| 3.6 | 4 |
| 3.7 | 3 |
| 3.8 | 6 |
| 3.9 | 2 |
| 4 | 1 |
| 4.1 | 1 |
| 4.2 | 1 |
| 4.4 | 1 |
kable(table(iris$Petal.Length), caption = "Frecuencia de Petal.Length")
| Var1 | Freq |
|---|---|
| 1 | 1 |
| 1.1 | 1 |
| 1.2 | 2 |
| 1.3 | 7 |
| 1.4 | 13 |
| 1.5 | 13 |
| 1.6 | 7 |
| 1.7 | 4 |
| 1.9 | 2 |
| 3 | 1 |
| 3.3 | 2 |
| 3.5 | 2 |
| 3.6 | 1 |
| 3.7 | 1 |
| 3.8 | 1 |
| 3.9 | 3 |
| 4 | 5 |
| 4.1 | 3 |
| 4.2 | 4 |
| 4.3 | 2 |
| 4.4 | 4 |
| 4.5 | 8 |
| 4.6 | 3 |
| 4.7 | 5 |
| 4.8 | 4 |
| 4.9 | 5 |
| 5 | 4 |
| 5.1 | 8 |
| 5.2 | 2 |
| 5.3 | 2 |
| 5.4 | 2 |
| 5.5 | 3 |
| 5.6 | 6 |
| 5.7 | 3 |
| 5.8 | 3 |
| 5.9 | 2 |
| 6 | 2 |
| 6.1 | 3 |
| 6.3 | 1 |
| 6.4 | 1 |
| 6.6 | 1 |
| 6.7 | 2 |
| 6.9 | 1 |
kable(table(iris$Petal.Width), caption = "Frecuencia de Petal.Width")
| Var1 | Freq |
|---|---|
| 0.1 | 5 |
| 0.2 | 29 |
| 0.3 | 7 |
| 0.4 | 7 |
| 0.5 | 1 |
| 0.6 | 1 |
| 1 | 7 |
| 1.1 | 3 |
| 1.2 | 5 |
| 1.3 | 13 |
| 1.4 | 8 |
| 1.5 | 12 |
| 1.6 | 4 |
| 1.7 | 2 |
| 1.8 | 12 |
| 1.9 | 5 |
| 2 | 6 |
| 2.1 | 6 |
| 2.2 | 3 |
| 2.3 | 8 |
| 2.4 | 3 |
| 2.5 | 3 |
kable(table(iris$Species), caption = "Frecuencia de Species")
| Var1 | Freq |
|---|---|
| setosa | 50 |
| versicolor | 50 |
| virginica | 50 |
# Tablas de frecuencias relativas
kable(prop.table(table(iris$Sepal.Length)), caption = "Proporción de Sepal.Length")
| Var1 | Freq |
|---|---|
| 4.3 | 0.0066667 |
| 4.4 | 0.0200000 |
| 4.5 | 0.0066667 |
| 4.6 | 0.0266667 |
| 4.7 | 0.0133333 |
| 4.8 | 0.0333333 |
| 4.9 | 0.0400000 |
| 5 | 0.0666667 |
| 5.1 | 0.0600000 |
| 5.2 | 0.0266667 |
| 5.3 | 0.0066667 |
| 5.4 | 0.0400000 |
| 5.5 | 0.0466667 |
| 5.6 | 0.0400000 |
| 5.7 | 0.0533333 |
| 5.8 | 0.0466667 |
| 5.9 | 0.0200000 |
| 6 | 0.0400000 |
| 6.1 | 0.0400000 |
| 6.2 | 0.0266667 |
| 6.3 | 0.0600000 |
| 6.4 | 0.0466667 |
| 6.5 | 0.0333333 |
| 6.6 | 0.0133333 |
| 6.7 | 0.0533333 |
| 6.8 | 0.0200000 |
| 6.9 | 0.0266667 |
| 7 | 0.0066667 |
| 7.1 | 0.0066667 |
| 7.2 | 0.0200000 |
| 7.3 | 0.0066667 |
| 7.4 | 0.0066667 |
| 7.6 | 0.0066667 |
| 7.7 | 0.0266667 |
| 7.9 | 0.0066667 |
kable(prop.table(table(iris$Sepal.Width)), caption = "Proporción de Sepal.Width")
| Var1 | Freq |
|---|---|
| 2 | 0.0066667 |
| 2.2 | 0.0200000 |
| 2.3 | 0.0266667 |
| 2.4 | 0.0200000 |
| 2.5 | 0.0533333 |
| 2.6 | 0.0333333 |
| 2.7 | 0.0600000 |
| 2.8 | 0.0933333 |
| 2.9 | 0.0666667 |
| 3 | 0.1733333 |
| 3.1 | 0.0733333 |
| 3.2 | 0.0866667 |
| 3.3 | 0.0400000 |
| 3.4 | 0.0800000 |
| 3.5 | 0.0400000 |
| 3.6 | 0.0266667 |
| 3.7 | 0.0200000 |
| 3.8 | 0.0400000 |
| 3.9 | 0.0133333 |
| 4 | 0.0066667 |
| 4.1 | 0.0066667 |
| 4.2 | 0.0066667 |
| 4.4 | 0.0066667 |
kable(prop.table(table(iris$Petal.Length)), caption = "Proporción de Petal.Length")
| Var1 | Freq |
|---|---|
| 1 | 0.0066667 |
| 1.1 | 0.0066667 |
| 1.2 | 0.0133333 |
| 1.3 | 0.0466667 |
| 1.4 | 0.0866667 |
| 1.5 | 0.0866667 |
| 1.6 | 0.0466667 |
| 1.7 | 0.0266667 |
| 1.9 | 0.0133333 |
| 3 | 0.0066667 |
| 3.3 | 0.0133333 |
| 3.5 | 0.0133333 |
| 3.6 | 0.0066667 |
| 3.7 | 0.0066667 |
| 3.8 | 0.0066667 |
| 3.9 | 0.0200000 |
| 4 | 0.0333333 |
| 4.1 | 0.0200000 |
| 4.2 | 0.0266667 |
| 4.3 | 0.0133333 |
| 4.4 | 0.0266667 |
| 4.5 | 0.0533333 |
| 4.6 | 0.0200000 |
| 4.7 | 0.0333333 |
| 4.8 | 0.0266667 |
| 4.9 | 0.0333333 |
| 5 | 0.0266667 |
| 5.1 | 0.0533333 |
| 5.2 | 0.0133333 |
| 5.3 | 0.0133333 |
| 5.4 | 0.0133333 |
| 5.5 | 0.0200000 |
| 5.6 | 0.0400000 |
| 5.7 | 0.0200000 |
| 5.8 | 0.0200000 |
| 5.9 | 0.0133333 |
| 6 | 0.0133333 |
| 6.1 | 0.0200000 |
| 6.3 | 0.0066667 |
| 6.4 | 0.0066667 |
| 6.6 | 0.0066667 |
| 6.7 | 0.0133333 |
| 6.9 | 0.0066667 |
kable(prop.table(table(iris$Petal.Width)), caption = "Proporción de Petal.Width")
| Var1 | Freq |
|---|---|
| 0.1 | 0.0333333 |
| 0.2 | 0.1933333 |
| 0.3 | 0.0466667 |
| 0.4 | 0.0466667 |
| 0.5 | 0.0066667 |
| 0.6 | 0.0066667 |
| 1 | 0.0466667 |
| 1.1 | 0.0200000 |
| 1.2 | 0.0333333 |
| 1.3 | 0.0866667 |
| 1.4 | 0.0533333 |
| 1.5 | 0.0800000 |
| 1.6 | 0.0266667 |
| 1.7 | 0.0133333 |
| 1.8 | 0.0800000 |
| 1.9 | 0.0333333 |
| 2 | 0.0400000 |
| 2.1 | 0.0400000 |
| 2.2 | 0.0200000 |
| 2.3 | 0.0533333 |
| 2.4 | 0.0200000 |
| 2.5 | 0.0200000 |
Para variables como species sí puede tener sentido ya que es una variable categórica, por lo que las frecuencias absolutas y relativas nos dicen cuántos casos hay de cada especie.
Para variables numéricas solo tendría sentido si agrupamos por rangos ya que hacer una tabla de frecuencias con valores exactos generara una tabla con muchos valores únicos y frecuencias muy bajas
#Parte 3
kable(table(Orange))
| Tree | age | circumference | Freq |
|---|---|---|---|
| 3 | 118 | 30 | 1 |
| 1 | 118 | 30 | 1 |
| 5 | 118 | 30 | 1 |
| 2 | 118 | 30 | 0 |
| 4 | 118 | 30 | 0 |
| 3 | 484 | 30 | 0 |
| 1 | 484 | 30 | 0 |
| 5 | 484 | 30 | 0 |
| 2 | 484 | 30 | 0 |
| 4 | 484 | 30 | 0 |
| 3 | 664 | 30 | 0 |
| 1 | 664 | 30 | 0 |
| 5 | 664 | 30 | 0 |
| 2 | 664 | 30 | 0 |
| 4 | 664 | 30 | 0 |
| 3 | 1004 | 30 | 0 |
| 1 | 1004 | 30 | 0 |
| 5 | 1004 | 30 | 0 |
| 2 | 1004 | 30 | 0 |
| 4 | 1004 | 30 | 0 |
| 3 | 1231 | 30 | 0 |
| 1 | 1231 | 30 | 0 |
| 5 | 1231 | 30 | 0 |
| 2 | 1231 | 30 | 0 |
| 4 | 1231 | 30 | 0 |
| 3 | 1372 | 30 | 0 |
| 1 | 1372 | 30 | 0 |
| 5 | 1372 | 30 | 0 |
| 2 | 1372 | 30 | 0 |
| 4 | 1372 | 30 | 0 |
| 3 | 1582 | 30 | 0 |
| 1 | 1582 | 30 | 0 |
| 5 | 1582 | 30 | 0 |
| 2 | 1582 | 30 | 0 |
| 4 | 1582 | 30 | 0 |
| 3 | 118 | 32 | 0 |
| 1 | 118 | 32 | 0 |
| 5 | 118 | 32 | 0 |
| 2 | 118 | 32 | 0 |
| 4 | 118 | 32 | 1 |
| 3 | 484 | 32 | 0 |
| 1 | 484 | 32 | 0 |
| 5 | 484 | 32 | 0 |
| 2 | 484 | 32 | 0 |
| 4 | 484 | 32 | 0 |
| 3 | 664 | 32 | 0 |
| 1 | 664 | 32 | 0 |
| 5 | 664 | 32 | 0 |
| 2 | 664 | 32 | 0 |
| 4 | 664 | 32 | 0 |
| 3 | 1004 | 32 | 0 |
| 1 | 1004 | 32 | 0 |
| 5 | 1004 | 32 | 0 |
| 2 | 1004 | 32 | 0 |
| 4 | 1004 | 32 | 0 |
| 3 | 1231 | 32 | 0 |
| 1 | 1231 | 32 | 0 |
| 5 | 1231 | 32 | 0 |
| 2 | 1231 | 32 | 0 |
| 4 | 1231 | 32 | 0 |
| 3 | 1372 | 32 | 0 |
| 1 | 1372 | 32 | 0 |
| 5 | 1372 | 32 | 0 |
| 2 | 1372 | 32 | 0 |
| 4 | 1372 | 32 | 0 |
| 3 | 1582 | 32 | 0 |
| 1 | 1582 | 32 | 0 |
| 5 | 1582 | 32 | 0 |
| 2 | 1582 | 32 | 0 |
| 4 | 1582 | 32 | 0 |
| 3 | 118 | 33 | 0 |
| 1 | 118 | 33 | 0 |
| 5 | 118 | 33 | 0 |
| 2 | 118 | 33 | 1 |
| 4 | 118 | 33 | 0 |
| 3 | 484 | 33 | 0 |
| 1 | 484 | 33 | 0 |
| 5 | 484 | 33 | 0 |
| 2 | 484 | 33 | 0 |
| 4 | 484 | 33 | 0 |
| 3 | 664 | 33 | 0 |
| 1 | 664 | 33 | 0 |
| 5 | 664 | 33 | 0 |
| 2 | 664 | 33 | 0 |
| 4 | 664 | 33 | 0 |
| 3 | 1004 | 33 | 0 |
| 1 | 1004 | 33 | 0 |
| 5 | 1004 | 33 | 0 |
| 2 | 1004 | 33 | 0 |
| 4 | 1004 | 33 | 0 |
| 3 | 1231 | 33 | 0 |
| 1 | 1231 | 33 | 0 |
| 5 | 1231 | 33 | 0 |
| 2 | 1231 | 33 | 0 |
| 4 | 1231 | 33 | 0 |
| 3 | 1372 | 33 | 0 |
| 1 | 1372 | 33 | 0 |
| 5 | 1372 | 33 | 0 |
| 2 | 1372 | 33 | 0 |
| 4 | 1372 | 33 | 0 |
| 3 | 1582 | 33 | 0 |
| 1 | 1582 | 33 | 0 |
| 5 | 1582 | 33 | 0 |
| 2 | 1582 | 33 | 0 |
| 4 | 1582 | 33 | 0 |
| 3 | 118 | 49 | 0 |
| 1 | 118 | 49 | 0 |
| 5 | 118 | 49 | 0 |
| 2 | 118 | 49 | 0 |
| 4 | 118 | 49 | 0 |
| 3 | 484 | 49 | 0 |
| 1 | 484 | 49 | 0 |
| 5 | 484 | 49 | 1 |
| 2 | 484 | 49 | 0 |
| 4 | 484 | 49 | 0 |
| 3 | 664 | 49 | 0 |
| 1 | 664 | 49 | 0 |
| 5 | 664 | 49 | 0 |
| 2 | 664 | 49 | 0 |
| 4 | 664 | 49 | 0 |
| 3 | 1004 | 49 | 0 |
| 1 | 1004 | 49 | 0 |
| 5 | 1004 | 49 | 0 |
| 2 | 1004 | 49 | 0 |
| 4 | 1004 | 49 | 0 |
| 3 | 1231 | 49 | 0 |
| 1 | 1231 | 49 | 0 |
| 5 | 1231 | 49 | 0 |
| 2 | 1231 | 49 | 0 |
| 4 | 1231 | 49 | 0 |
| 3 | 1372 | 49 | 0 |
| 1 | 1372 | 49 | 0 |
| 5 | 1372 | 49 | 0 |
| 2 | 1372 | 49 | 0 |
| 4 | 1372 | 49 | 0 |
| 3 | 1582 | 49 | 0 |
| 1 | 1582 | 49 | 0 |
| 5 | 1582 | 49 | 0 |
| 2 | 1582 | 49 | 0 |
| 4 | 1582 | 49 | 0 |
| 3 | 118 | 51 | 0 |
| 1 | 118 | 51 | 0 |
| 5 | 118 | 51 | 0 |
| 2 | 118 | 51 | 0 |
| 4 | 118 | 51 | 0 |
| 3 | 484 | 51 | 1 |
| 1 | 484 | 51 | 0 |
| 5 | 484 | 51 | 0 |
| 2 | 484 | 51 | 0 |
| 4 | 484 | 51 | 0 |
| 3 | 664 | 51 | 0 |
| 1 | 664 | 51 | 0 |
| 5 | 664 | 51 | 0 |
| 2 | 664 | 51 | 0 |
| 4 | 664 | 51 | 0 |
| 3 | 1004 | 51 | 0 |
| 1 | 1004 | 51 | 0 |
| 5 | 1004 | 51 | 0 |
| 2 | 1004 | 51 | 0 |
| 4 | 1004 | 51 | 0 |
| 3 | 1231 | 51 | 0 |
| 1 | 1231 | 51 | 0 |
| 5 | 1231 | 51 | 0 |
| 2 | 1231 | 51 | 0 |
| 4 | 1231 | 51 | 0 |
| 3 | 1372 | 51 | 0 |
| 1 | 1372 | 51 | 0 |
| 5 | 1372 | 51 | 0 |
| 2 | 1372 | 51 | 0 |
| 4 | 1372 | 51 | 0 |
| 3 | 1582 | 51 | 0 |
| 1 | 1582 | 51 | 0 |
| 5 | 1582 | 51 | 0 |
| 2 | 1582 | 51 | 0 |
| 4 | 1582 | 51 | 0 |
| 3 | 118 | 58 | 0 |
| 1 | 118 | 58 | 0 |
| 5 | 118 | 58 | 0 |
| 2 | 118 | 58 | 0 |
| 4 | 118 | 58 | 0 |
| 3 | 484 | 58 | 0 |
| 1 | 484 | 58 | 1 |
| 5 | 484 | 58 | 0 |
| 2 | 484 | 58 | 0 |
| 4 | 484 | 58 | 0 |
| 3 | 664 | 58 | 0 |
| 1 | 664 | 58 | 0 |
| 5 | 664 | 58 | 0 |
| 2 | 664 | 58 | 0 |
| 4 | 664 | 58 | 0 |
| 3 | 1004 | 58 | 0 |
| 1 | 1004 | 58 | 0 |
| 5 | 1004 | 58 | 0 |
| 2 | 1004 | 58 | 0 |
| 4 | 1004 | 58 | 0 |
| 3 | 1231 | 58 | 0 |
| 1 | 1231 | 58 | 0 |
| 5 | 1231 | 58 | 0 |
| 2 | 1231 | 58 | 0 |
| 4 | 1231 | 58 | 0 |
| 3 | 1372 | 58 | 0 |
| 1 | 1372 | 58 | 0 |
| 5 | 1372 | 58 | 0 |
| 2 | 1372 | 58 | 0 |
| 4 | 1372 | 58 | 0 |
| 3 | 1582 | 58 | 0 |
| 1 | 1582 | 58 | 0 |
| 5 | 1582 | 58 | 0 |
| 2 | 1582 | 58 | 0 |
| 4 | 1582 | 58 | 0 |
| 3 | 118 | 62 | 0 |
| 1 | 118 | 62 | 0 |
| 5 | 118 | 62 | 0 |
| 2 | 118 | 62 | 0 |
| 4 | 118 | 62 | 0 |
| 3 | 484 | 62 | 0 |
| 1 | 484 | 62 | 0 |
| 5 | 484 | 62 | 0 |
| 2 | 484 | 62 | 0 |
| 4 | 484 | 62 | 1 |
| 3 | 664 | 62 | 0 |
| 1 | 664 | 62 | 0 |
| 5 | 664 | 62 | 0 |
| 2 | 664 | 62 | 0 |
| 4 | 664 | 62 | 0 |
| 3 | 1004 | 62 | 0 |
| 1 | 1004 | 62 | 0 |
| 5 | 1004 | 62 | 0 |
| 2 | 1004 | 62 | 0 |
| 4 | 1004 | 62 | 0 |
| 3 | 1231 | 62 | 0 |
| 1 | 1231 | 62 | 0 |
| 5 | 1231 | 62 | 0 |
| 2 | 1231 | 62 | 0 |
| 4 | 1231 | 62 | 0 |
| 3 | 1372 | 62 | 0 |
| 1 | 1372 | 62 | 0 |
| 5 | 1372 | 62 | 0 |
| 2 | 1372 | 62 | 0 |
| 4 | 1372 | 62 | 0 |
| 3 | 1582 | 62 | 0 |
| 1 | 1582 | 62 | 0 |
| 5 | 1582 | 62 | 0 |
| 2 | 1582 | 62 | 0 |
| 4 | 1582 | 62 | 0 |
| 3 | 118 | 69 | 0 |
| 1 | 118 | 69 | 0 |
| 5 | 118 | 69 | 0 |
| 2 | 118 | 69 | 0 |
| 4 | 118 | 69 | 0 |
| 3 | 484 | 69 | 0 |
| 1 | 484 | 69 | 0 |
| 5 | 484 | 69 | 0 |
| 2 | 484 | 69 | 1 |
| 4 | 484 | 69 | 0 |
| 3 | 664 | 69 | 0 |
| 1 | 664 | 69 | 0 |
| 5 | 664 | 69 | 0 |
| 2 | 664 | 69 | 0 |
| 4 | 664 | 69 | 0 |
| 3 | 1004 | 69 | 0 |
| 1 | 1004 | 69 | 0 |
| 5 | 1004 | 69 | 0 |
| 2 | 1004 | 69 | 0 |
| 4 | 1004 | 69 | 0 |
| 3 | 1231 | 69 | 0 |
| 1 | 1231 | 69 | 0 |
| 5 | 1231 | 69 | 0 |
| 2 | 1231 | 69 | 0 |
| 4 | 1231 | 69 | 0 |
| 3 | 1372 | 69 | 0 |
| 1 | 1372 | 69 | 0 |
| 5 | 1372 | 69 | 0 |
| 2 | 1372 | 69 | 0 |
| 4 | 1372 | 69 | 0 |
| 3 | 1582 | 69 | 0 |
| 1 | 1582 | 69 | 0 |
| 5 | 1582 | 69 | 0 |
| 2 | 1582 | 69 | 0 |
| 4 | 1582 | 69 | 0 |
| 3 | 118 | 75 | 0 |
| 1 | 118 | 75 | 0 |
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| 5 | 1582 | 203 | 0 |
| 2 | 1582 | 203 | 1 |
| 4 | 1582 | 203 | 0 |
| 3 | 118 | 209 | 0 |
| 1 | 118 | 209 | 0 |
| 5 | 118 | 209 | 0 |
| 2 | 118 | 209 | 0 |
| 4 | 118 | 209 | 0 |
| 3 | 484 | 209 | 0 |
| 1 | 484 | 209 | 0 |
| 5 | 484 | 209 | 0 |
| 2 | 484 | 209 | 0 |
| 4 | 484 | 209 | 0 |
| 3 | 664 | 209 | 0 |
| 1 | 664 | 209 | 0 |
| 5 | 664 | 209 | 0 |
| 2 | 664 | 209 | 0 |
| 4 | 664 | 209 | 0 |
| 3 | 1004 | 209 | 0 |
| 1 | 1004 | 209 | 0 |
| 5 | 1004 | 209 | 0 |
| 2 | 1004 | 209 | 0 |
| 4 | 1004 | 209 | 0 |
| 3 | 1231 | 209 | 0 |
| 1 | 1231 | 209 | 0 |
| 5 | 1231 | 209 | 0 |
| 2 | 1231 | 209 | 0 |
| 4 | 1231 | 209 | 0 |
| 3 | 1372 | 209 | 0 |
| 1 | 1372 | 209 | 0 |
| 5 | 1372 | 209 | 0 |
| 2 | 1372 | 209 | 0 |
| 4 | 1372 | 209 | 1 |
| 3 | 1582 | 209 | 0 |
| 1 | 1582 | 209 | 0 |
| 5 | 1582 | 209 | 0 |
| 2 | 1582 | 209 | 0 |
| 4 | 1582 | 209 | 0 |
| 3 | 118 | 214 | 0 |
| 1 | 118 | 214 | 0 |
| 5 | 118 | 214 | 0 |
| 2 | 118 | 214 | 0 |
| 4 | 118 | 214 | 0 |
| 3 | 484 | 214 | 0 |
| 1 | 484 | 214 | 0 |
| 5 | 484 | 214 | 0 |
| 2 | 484 | 214 | 0 |
| 4 | 484 | 214 | 0 |
| 3 | 664 | 214 | 0 |
| 1 | 664 | 214 | 0 |
| 5 | 664 | 214 | 0 |
| 2 | 664 | 214 | 0 |
| 4 | 664 | 214 | 0 |
| 3 | 1004 | 214 | 0 |
| 1 | 1004 | 214 | 0 |
| 5 | 1004 | 214 | 0 |
| 2 | 1004 | 214 | 0 |
| 4 | 1004 | 214 | 0 |
| 3 | 1231 | 214 | 0 |
| 1 | 1231 | 214 | 0 |
| 5 | 1231 | 214 | 0 |
| 2 | 1231 | 214 | 0 |
| 4 | 1231 | 214 | 0 |
| 3 | 1372 | 214 | 0 |
| 1 | 1372 | 214 | 0 |
| 5 | 1372 | 214 | 0 |
| 2 | 1372 | 214 | 0 |
| 4 | 1372 | 214 | 0 |
| 3 | 1582 | 214 | 0 |
| 1 | 1582 | 214 | 0 |
| 5 | 1582 | 214 | 0 |
| 2 | 1582 | 214 | 0 |
| 4 | 1582 | 214 | 1 |
kable(table(Orange$Tree))
| Var1 | Freq |
|---|---|
| 3 | 7 |
| 1 | 7 |
| 5 | 7 |
| 2 | 7 |
| 4 | 7 |
kable(table(Orange$age))
| Var1 | Freq |
|---|---|
| 118 | 5 |
| 484 | 5 |
| 664 | 5 |
| 1004 | 5 |
| 1231 | 5 |
| 1372 | 5 |
| 1582 | 5 |
kable(table(Orange$circumference))
| Var1 | Freq |
|---|---|
| 30 | 3 |
| 32 | 1 |
| 33 | 1 |
| 49 | 1 |
| 51 | 1 |
| 58 | 1 |
| 62 | 1 |
| 69 | 1 |
| 75 | 1 |
| 81 | 1 |
| 87 | 1 |
| 108 | 1 |
| 111 | 1 |
| 112 | 1 |
| 115 | 2 |
| 120 | 1 |
| 125 | 1 |
| 139 | 1 |
| 140 | 1 |
| 142 | 2 |
| 145 | 1 |
| 156 | 1 |
| 167 | 1 |
| 172 | 1 |
| 174 | 1 |
| 177 | 1 |
| 179 | 1 |
| 203 | 2 |
| 209 | 1 |
| 214 | 1 |
#Parte 4
kable(table(Orange$Tree, Orange$age))
| 118 | 484 | 664 | 1004 | 1231 | 1372 | 1582 | |
|---|---|---|---|---|---|---|---|
| 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Al igual que en el caso anterior, estas tablas de frecuencias tienen sentido si estamos trabajando con variables categóricas o numéricas discretas con pocos valores, para variables continuas podría resultar interesante si previamente hemos agrupado los datos
Copiad y ejecutad el código siguiente de los dos vectores:
vect1 <- c(1,2,1,2,1,2,1,2,1,2,1,1,1,1,2,2,1,1,2,1)
vect2 <- c(1,1,2,2,2,1,2,1,1,2,1,2,1,1,1,2,1,1,1,1)
Responded a los apartados siguientes:
Usando el vect1 creamos un nuevo vector llamado Bajo_peso que sea un factor con dos niveles. Las etiquetas corresponden a 1 = Bajo peso y 2 = Peso normal.
Usando el vect2 creamos un nuevo vector llamado Fumador que sea un factor con dos niveles. Las etiquetas corresponden a 1 = Fuma y 2 = No fuma.
Creamos una tabla de contingencia con las dos variables anteriores con el nombre Tabla.
Miramos la relación de las variables anteriores con la prueba del ji cuadrado.
Miramos también cómo resulta el test de Fisher.
#A)
Bajo_peso <- factor(vect1, levels=c(1,2), labels=c("Bajo peso", "Peso normal"))
print(Bajo_peso)
## [1] Bajo peso Peso normal Bajo peso Peso normal Bajo peso Peso normal
## [7] Bajo peso Peso normal Bajo peso Peso normal Bajo peso Bajo peso
## [13] Bajo peso Bajo peso Peso normal Peso normal Bajo peso Bajo peso
## [19] Peso normal Bajo peso
## Levels: Bajo peso Peso normal
#B)
Fumador <- factor(vect2, levels=c(1,2), labels=c("Fuma", "No fuma"))
print(Fumador)
## [1] Fuma Fuma No fuma No fuma No fuma Fuma No fuma Fuma Fuma
## [10] No fuma Fuma No fuma Fuma Fuma Fuma No fuma Fuma Fuma
## [19] Fuma Fuma
## Levels: Fuma No fuma
#C)
resultado <-chisq.test(Bajo_peso, Fumador)
## Warning in chisq.test(Bajo_peso, Fumador): Chi-squared approximation may be
## incorrect
print(resultado)
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: Bajo_peso and Fumador
## X-squared = 0, df = 1, p-value = 1
#D)
resulado_fisher <-fisher.test(Bajo_peso, Fumador)
print(resulado_fisher)
##
## Fisher's Exact Test for Count Data
##
## data: Bajo_peso and Fumador
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1200513 10.9278345
## sample estimates:
## odds ratio
## 1.189031
Activad el paquete de datos airquality del paquete datasets y
generad los siguientes gráficos:
• Un gráfico de dispersión de la variable Ozone de color azul y con
almohadillas (#) en vez de puntos. • Un gráfico de caja de color rojo
con la variable Temp con el título Temperatura (en grados
Farenheit).
Activad el paquete de datos airmiles del paquete datasets y
generad los siguientes gráficos:
• Un gráfico de líneas de la serie de datos airmiles con el título Datos
de pasajeros en vuelos comerciales (en miles) y de color azul
(cadetblue2) y la etiqueta del eje x con Miles de pasajeros.
• Un histograma de la serie de datos airmiles de color marrón
(chocolate2).
Representad los cuatro gráficos en una única imagen donde los veamos juntos.
#Para representar todos los gráficos en una sola imagen creamos una matriz de 2x2 antes de los gráficos
par(mfrow=c(2,2))
#A.1) Gráfico de dispersión, con pch conseguimos cambiar de circulos a almohadillas
data("airquality")
plot(airquality$Ozone, col="blue", pch=35)
#A.2) Gráfico de caja, con main= ponemos el titulo
boxplot(airquality$Temp, col = "red", main="Temperatura (en grados Farenheit)")
#B.1) Gráfico de lineas. con xlab= ponemos la etiqueta al eje x
data("airmiles")
plot(airmiles, col="cadetblue", xlab="Miles de pasarejos", main="Datos de pasajeros en vuelos comerciales (en miles)")
#B.2) Gráfico de histograma
hist(airmiles, col = "chocolate2")
Intentad reproducir el ejemplo 7 de este LAB con unos datos simulados por vosotros. No hace falta que sean parecidos, pero es necesario que podáis hacer diferentes gráficos estadísticos con el comando plot().
set.seed(123)
x1 <- rnorm(1500) #simulamos una variable x1
y1 <- x1 + rnorm(1500) #simulamos una variable y1
plot(x1, y1, main="Gráfico de dispersión", col= c("lightblue", "red"), pch = c(1, 19))
boxplot(x1, y1, main="Boxplot", col= c("lightblue", "red"), xlab = "Grupos")
plot(density(x1), col="lightblue")
Para poder practicar la creación de gráficos con ggplot2, vamos a crear seis gráficos con diferentes características y con diferentes conjuntos de datos de paquetes trabajados anteriormente.
library(ggplot2)
ggplot(data = airquality, aes(x = Wind, y = Ozone)) +
geom_point() +
geom_smooth(method = "lm", color = "red")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 37 rows containing missing values or values outside the scale range
## (`geom_point()`).
#Con col=factor().... y shape=factor() lo que hacemos es convertir la variable entre () a factor, y codificar el color y la forma en base a dicha variable
ggplot(data = airquality, aes(x= Solar.R, y= Temp, col=factor(Month), shape=factor(Month)))+
geom_point(size=2)
## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
library(MASS)
## Warning: package 'MASS' was built under R version 4.4.3
##
## Adjuntando el paquete: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
ggplot(data = birthwt, aes(age))+
geom_histogram(fill="lightblue", col="black")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
library(MASS)
ggplot(data = birthwt, aes(age))+
geom_histogram(fill="lightblue", col="black")+
facet_grid(~smoke)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Cread un gráfico con unos datos extraídos del paquete Datasets de R y guardadlo como imagen (.jpg) con el nombre migrafic1 y, también, como documento en PDF con el nombre migrafic2. Haced una captura de pantalla del fichero generado.
jpeg("migrafic1.jpeg")
ggplot(data = iris, aes(x=Petal.Length, y=Petal.Width, col = Species))+
geom_point(size=1.5)
dev.off
## function (which = dev.cur())
## {
## if (which == 1)
## stop("cannot shut down device 1 (the null device)")
## .External(C_devoff, as.integer(which))
## dev.cur()
## }
## <bytecode: 0x000001933ef30ae8>
## <environment: namespace:grDevices>
pdf("migrafic2.pdf")
ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width, colour = Species))+
geom_point(size=1.5)
Para practicar la regresión lineal simple usaremos el conjunto de datos Orange que se encuentra en la librería tidyverse y que tiene información sobre tres variables (árbol, edad en días desde que se sembró el árbol y circunferencia del tronco en centímetros) de 35 naranjos.
• Queremos saber qué valor de circunferencia tendrá un árbol seiscientos días después de plantarlo. Cread para ello un modelo lineal y practicad la regresión lineal paso a paso con los pasos que habéis visto en este laboratorio.
library(tidyverse)
#Seguimos los diferentes pasos indicados para la regresión
data("Orange")
view(Orange)
#Cargamos los datos y hacemos una inspección visual y estadística de los datos
summary(Orange)
## Tree age circumference
## 3:7 Min. : 118.0 Min. : 30.0
## 1:7 1st Qu.: 484.0 1st Qu.: 65.5
## 5:7 Median :1004.0 Median :115.0
## 2:7 Mean : 922.1 Mean :115.9
## 4:7 3rd Qu.:1372.0 3rd Qu.:161.5
## Max. :1582.0 Max. :214.0
grafico_visual<-pairs(Orange)
print(grafico_visual)
## NULL
#Hacemos un estudio de correlación
correlacion <-cor.test(Orange$circumference, Orange$age)
print(correlacion)
##
## Pearson's product-moment correlation
##
## data: Orange$circumference and Orange$age
## t = 12.9, df = 33, p-value = 1.931e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8342364 0.9557955
## sample estimates:
## cor
## 0.9135189
#Planteamos el modelo
regresion <- lm(circumference ~ age, data = Orange)
resumen_modelo <-summary(regresion)
print(resumen_modelo)
##
## Call:
## lm(formula = circumference ~ age, data = Orange)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.310 -14.946 -0.076 19.697 45.111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.399650 8.622660 2.018 0.0518 .
## age 0.106770 0.008277 12.900 1.93e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.74 on 33 degrees of freedom
## Multiple R-squared: 0.8345, Adjusted R-squared: 0.8295
## F-statistic: 166.4 on 1 and 33 DF, p-value: 1.931e-14
#Graficamos el modelo
plot(Orange$age, Orange$circumference)
abline(regresion)
#Evaluamos residuos
residuos <-rstandard(regresion)
valores_ajustados <- fitted(regresion)
plot(valores_ajustados, residuos)
qqnorm(residuos)
qqline(residuos)
#Hacemos la predicción para un arbol con 600 dias
nuevos_datos <- data.frame(age = 600)
pred_circunferencia <- predict(regresion, nuevos_datos)
print(pred_circunferencia)
## 1
## 81.46185
Repetid los gráficos (aquellos que podáis) de la regresión lineal simple del ejercicio anterior pero ahora con ggplot2
#Primero evaluamos la graficamente la correlación entre ambas variables
ggplot(data = Orange) + geom_point(aes(x = age, y = circumference))
#Añadimos la recta de correlación
ggplot(data = Orange) + geom_point(aes(x = age, y = circumference)) + geom_smooth(aes(x = age, y = circumference), method = "lm", se = TRUE)
## `geom_smooth()` using formula = 'y ~ x'
Activad el conjunto de datos PlantGrowth del paquete datasets de R. Este archivo tiene los recultados de un experimento para comparar los rendimientos medios por el peso seco de las plantas (weight) obtenidos bajo un control y dos condiciones de tratamiento diferentes (group factor). • ¿Creéis que hay diferencias entre tratamientos?
#Cargamos los datos y hacemos un resumen x grupo
view(PlantGrowth)
describeBy(PlantGrowth$weight, PlantGrowth$group)
##
## Descriptive statistics by group
## group: ctrl
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 10 5.03 0.58 5.15 5 0.72 4.17 6.11 1.94 0.23 -1.12 0.18
## ------------------------------------------------------------
## group: trt1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 10 4.66 0.79 4.55 4.62 0.53 3.59 6.03 2.44 0.47 -1.1 0.25
## ------------------------------------------------------------
## group: trt2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 10 5.53 0.44 5.44 5.5 0.36 4.92 6.31 1.39 0.48 -1.16 0.14
pairs(PlantGrowth)
#Para ver si hay diferencias entre los tratamientos, primero lo graficamos como boxplot
boxplot(PlantGrowth$weight ~ PlantGrowth$group)
Simplemente por inspección visual no parece haber diferencias entre el control y el tratamiento 1 y tratamiento 2 Aunque puede que haya diferencias entre tratamiento 1 y tratamiento 2
• Se cumplen las condiciones para poder aplicar una ANOVA. ¿Qué pruebas os planteáis?
#comprobamos la presencia de outliers (aunque se pueden ver en el boxplot)
tapply(PlantGrowth$weight, PlantGrowth$group, function(x) {
boxplot.stats(x)$out
})
## $ctrl
## numeric(0)
##
## $trt1
## [1] 6.03
##
## $trt2
## numeric(0)
#Comprobamos la normalidad y la homocedasticidad de las varianzas
by(PlantGrowth$weight, PlantGrowth$group, shapiro.test)
## PlantGrowth$group: ctrl
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95668, p-value = 0.7475
##
## ------------------------------------------------------------
## PlantGrowth$group: trt1
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.93041, p-value = 0.4519
##
## ------------------------------------------------------------
## PlantGrowth$group: trt2
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.94101, p-value = 0.5643
bartlett.test(PlantGrowth$weight, PlantGrowth$group)
##
## Bartlett test of homogeneity of variances
##
## data: PlantGrowth$weight and PlantGrowth$group
## Bartlett's K-squared = 2.8786, df = 2, p-value = 0.2371
#Se cumple la normalidad de las muestras y la homocedasticidad de las varianzas
#Realizamos ANOVA
model_anova <- aov(weight ~ group, data = PlantGrowth)
summary(model_anova)
## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 3.766 1.8832 4.846 0.0159 *
## Residuals 27 10.492 0.3886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(model_anova)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = weight ~ group, data = PlantGrowth)
##
## $group
## diff lwr upr p adj
## trt1-ctrl -0.371 -1.0622161 0.3202161 0.3908711
## trt2-ctrl 0.494 -0.1972161 1.1852161 0.1979960
## trt2-trt1 0.865 0.1737839 1.5562161 0.0120064
Estos resultados muestran que hay diferencias entre el tratamiento 2 y el 1, pero no así en el resto de comparaciones entre grupos
Buscad información del paquete Plotly para la creación de gráficos interactivos y generad un histograma o un gráfico de barras interactivo. Explicad qué se puede hacer con este gráfico.
library(plotly)
## Warning: package 'plotly' was built under R version 4.4.2
##
## Adjuntando el paquete: 'plotly'
## The following object is masked from 'package:MASS':
##
## select
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Histograma con colores por especie
plot_ly(data = iris,
x = ~Sepal.Length,
color = ~Species,
type = "histogram",
opacity = 0.5,
colors = c("#E41A1C", "#377EB8", "#4DAF4A"),
barmode = "overlay") %>%
layout(title = "Distribución del Largo del Sépalo por Especie",
xaxis = list(title = "Largo del Sépalo (cm)"),
yaxis = list(title = "Frecuencia"))
## Warning: 'histogram' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'autobinx', 'autobiny', 'bingroup', 'cliponaxis', 'constraintext', 'cumulative', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'histfunc', 'histnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'nbinsx', 'nbinsy', 'offsetgroup', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textsrc', 'texttemplate', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'xaxis', 'xbins', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'yaxis', 'ybins', 'ycalendar', 'yhoverformat', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'histogram' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'autobinx', 'autobiny', 'bingroup', 'cliponaxis', 'constraintext', 'cumulative', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'histfunc', 'histnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'nbinsx', 'nbinsy', 'offsetgroup', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textsrc', 'texttemplate', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'xaxis', 'xbins', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'yaxis', 'ybins', 'ycalendar', 'yhoverformat', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
## Warning: 'histogram' objects don't have these attributes: 'barmode'
## Valid attributes include:
## '_deprecated', 'alignmentgroup', 'autobinx', 'autobiny', 'bingroup', 'cliponaxis', 'constraintext', 'cumulative', 'customdata', 'customdatasrc', 'error_x', 'error_y', 'histfunc', 'histnorm', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'insidetextanchor', 'insidetextfont', 'legendgroup', 'legendgrouptitle', 'legendrank', 'marker', 'meta', 'metasrc', 'name', 'nbinsx', 'nbinsy', 'offsetgroup', 'opacity', 'orientation', 'outsidetextfont', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textangle', 'textfont', 'textposition', 'textsrc', 'texttemplate', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'visible', 'x', 'xaxis', 'xbins', 'xcalendar', 'xhoverformat', 'xsrc', 'y', 'yaxis', 'ybins', 'ycalendar', 'yhoverformat', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'
Plotly es una librería de R que permite crear gráficos interactivos y dinámicos para visualización de datos. Al ser interactivo permite hacer zoom, desplazarse por el gráfico, poder ver datos al posicionar el cursor encima de partes del gráfico y también descargarlos en jpeg.
A partir de unos datos bioclínicos o biosanitarios que escojáis y que importéis a R, explicad sus variables (mínimo de ocho variables) y también:
Para este ejercicio he empleado el siguiente conjunto de datos Características y carga del covid agudo y v
#Empleamos el conjunto de datos: Characteristics and burden of acute COVID-19 and long-COVID
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
covid <- read_excel("Data.xlsx")
View(covid)
• Realizad un resumen estadístico completo del dataset y explicad los resultados. Se trata de una base de datos sobre las características y la carga del covid agudo y covid persistente. La base de datos incluye datos de 416 participantes y mediciones de 254 variables (la mayoría de caracter numérico). Sin embargo, la gran mayoría de las variables presentan valores nulos o NA.
#Dado el tamaño de la base de datos, podemos comenzar con ver la estructura y dimensiones:
str(covid)
## tibble [416 × 254] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:416] 1 2 3 4 5 6 7 8 9 10 ...
## $ age : num [1:416] NA 29.5 NA NA 23.7 ...
## $ duration_since_disease_by_10052022 : num [1:416] NA 18.34 1.88 NA 1.95 ...
## $ origin : num [1:416] NA 1 1 NA 4 NA 1 NA 1 NA ...
## $ origin_other : chr [1:416] NA NA NA NA ...
## $ gender : num [1:416] NA 1 1 NA 1 NA 1 NA 1 NA ...
## $ height : num [1:416] NA 172 186 NA 176 NA 160 NA 163 NA ...
## $ weight : num [1:416] NA 87 78 NA 62 NA 70 NA 59 NA ...
## $ BMI : num [1:416] NA 29.4 22.5 NA 20 ...
## $ comorbidities : num [1:416] NA 1 1 NA 1 NA NA NA 1 NA ...
## $ com_dementia : logi [1:416] NA NA NA NA NA NA ...
## $ com_parkinson : logi [1:416] NA NA NA NA NA NA ...
## $ com_epilepsy : logi [1:416] NA NA NA NA NA NA ...
## $ com_MS : logi [1:416] NA NA NA NA NA NA ...
## $ com_stroke : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ com_hypertonia : num [1:416] NA NA NA NA NA NA 1 NA NA NA ...
## $ com_hypertonia_new : num [1:416] NA 0 0 NA 0 NA 1 NA 0 NA ...
## $ com_diabetes : num [1:416] NA NA NA NA NA NA 1 NA NA NA ...
## $ com_copd : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ com_cardiac_insufficiency : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ com_cancer : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ com_cancer_type : chr [1:416] NA NA NA NA ...
## $ com_other : chr [1:416] NA NA NA NA ...
## $ education : num [1:416] NA 8 9 NA NA NA 3 NA 8 NA ...
## $ education_other : chr [1:416] NA NA NA NA ...
## $ education_years : num [1:416] NA 19 20 NA NA NA NA NA 18 NA ...
## $ current_job_Arbeiter : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ current_job_Angestellter : num [1:416] NA 1 1 NA NA NA NA NA 1 NA ...
## $ current_job_Student : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ current_job_Schüler : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ current_job_Pensionist : num [1:416] NA NA NA NA NA NA 1 NA NA NA ...
## $ current_job_Arbeitsloser : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ current_job_Selbstständiger : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ current_job_other : chr [1:416] NA NA NA NA ...
## $ changes_work_covid : num [1:416] NA 2 2 NA 2 NA NA NA 2 NA ...
## $ changes_work_covid_hours_reduced : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ changes_work_covid_no_work_anymore : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ changes_work_covid_job_change : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ changes_work_covid_sick_leave : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ changes_work_covid_other : chr [1:416] NA NA NA NA ...
## $ financial_losses : num [1:416] NA 2 2 NA 2 NA NA NA 2 NA ...
## $ covid_infection_acute_duration : num [1:416] NA 5 2 NA 3 NA 2 NA 4 NA ...
## $ covid_infection_acute_duration_other : chr [1:416] NA NA NA NA ...
## $ course_disease_severe : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ course_disease_mild : num [1:416] NA 1 1 NA 1 NA 1 NA NA NA ...
## $ course_disease_asymptomatic : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ hospitalization : num [1:416] NA 2 2 NA 2 NA NA NA 2 NA ...
## $ hospitalization_cov19_or_ICU : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ covid_hospitalization_duration : num [1:416] NA NA NA NA NA NA NA NA NA NA ...
## $ symptoms_fatigue_burden : num [1:416] NA 5 5 NA NA NA NA NA 4 NA ...
## $ symptoms_poor_memory_burden : num [1:416] NA NA 3 NA 2 NA 3 NA 4 NA ...
## $ symptoms_limitation_mental_performance_burden : num [1:416] NA NA 2 NA 2 NA 3 NA 5 NA ...
## $ symptoms_fever_burden : num [1:416] NA NA NA NA 4 NA NA NA 1 NA ...
## $ symptoms_loss_of_appetite_burden : num [1:416] NA 4 NA NA 4 NA NA NA 5 NA ...
## $ symptoms_rattling_breathing_burden : num [1:416] NA 3 NA NA 2 NA NA NA 5 NA ...
## $ symptoms_runny_nose_burden : num [1:416] NA 3 4 NA 3 NA NA NA 1 NA ...
## $ symptoms_increased_temperature_burden : num [1:416] NA NA NA NA 4 NA NA NA 1 NA ...
## $ symptoms_dry_cough_burden : num [1:416] NA 5 1 NA 4 NA NA NA 5 NA ...
## $ symptoms_vomiting_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_sore_throat_burden : num [1:416] NA NA NA NA 1 NA NA NA 1 NA ...
## $ symptoms_diarrhoea_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_bloody_cough_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_chills_or_sweating_burden : num [1:416] NA 3 NA NA 2 NA NA NA 3 NA ...
## $ symptoms_altered_taste_smell_burden : num [1:416] NA 5 NA NA 4 NA 3 NA 5 NA ...
## $ symptoms_sneeze_burden : num [1:416] NA 2 NA NA 3 NA NA NA 1 NA ...
## $ symptoms_difficulty_breathing_normal_oxy_sat_burden : num [1:416] NA 4 NA NA 2 NA NA NA 5 NA ...
## $ symptoms_tightness_chest_burden : num [1:416] NA 3 NA NA NA NA NA NA 5 NA ...
## $ symptoms_shortness_of_breath_burden : num [1:416] NA 3 NA NA NA NA NA NA 5 NA ...
## $ symptoms_cough_mucus_burden : num [1:416] NA 3 NA NA 5 NA NA NA 4 NA ...
## $ symptoms_nausea_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_burning_chest_pain_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_disorientation_confusion_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_muscle_pain_burden : num [1:416] NA 2 NA NA NA NA NA NA 3 NA ...
## $ symptoms_dizziness_burden : num [1:416] NA 3 NA NA NA NA NA NA 5 NA ...
## $ symptoms_low_temp_burden : num [1:416] NA NA NA NA NA NA NA NA 4 NA ...
## $ symptoms_exhaustion_burden : num [1:416] NA 5 NA NA 3 NA 3 NA 5 NA ...
## $ symptoms_stomach_pain_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_other_sleep_symptoms_burden : num [1:416] NA NA NA NA NA NA NA NA 4 NA ...
## $ symptoms_headache_burden : num [1:416] NA 5 NA NA 3 NA NA NA 5 NA ...
## $ symptoms_covidzeh_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_hallucinatoin_burden : num [1:416] NA NA NA NA 2 NA NA NA 1 NA ...
## $ symptoms_bone_pain_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_tachycardia_burden : num [1:416] NA 2 NA NA NA NA NA NA 4 NA ...
## $ symptoms_sleepapnoea_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_insomnia_burden : num [1:416] NA NA NA NA NA NA NA NA 4 NA ...
## $ symptoms_slurred_speech_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_joint_pain_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_heart_palpations_burden : num [1:416] NA 4 NA NA NA NA NA NA 5 NA ...
## $ symptoms_other_temp_deviations_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_other_eye_sympt_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_muscle_cramps_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_disturbed_neurological_sensations_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_peeling_skin_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_heartburn_reflux_burden : num [1:416] NA NA NA NA NA NA NA NA 2 NA ...
## $ symptoms_skin_rash_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_constipation_burden : num [1:416] NA NA NA NA NA NA NA NA 2 NA ...
## $ symptoms_bladder_control_problems_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## $ symptoms_hearloss_burden : num [1:416] NA NA NA NA NA NA NA NA 3 NA ...
## $ symptoms_bradycardia_burden : num [1:416] NA NA NA NA NA NA NA NA 1 NA ...
## [list output truncated]
dim(covid)
## [1] 416 254
summary(covid)
## id age duration_since_disease_by_10052022
## Min. : 1.0 Min. :16.34 Min. : 0.2736
## 1st Qu.:104.8 1st Qu.:34.34 1st Qu.: 2.8034
## Median :208.5 Median :44.34 Median :12.1011
## Mean :208.5 Mean :44.18 Mean :10.5355
## 3rd Qu.:312.2 3rd Qu.:54.34 3rd Qu.:17.3250
## Max. :416.0 Max. :79.70 Max. :26.4913
## NA's :99 NA's :141
## origin origin_other gender height
## Min. :1.000 Length:416 Min. :1.000 Min. :123.0
## 1st Qu.:1.000 Class :character 1st Qu.:1.000 1st Qu.:165.0
## Median :1.000 Mode :character Median :1.000 Median :170.0
## Mean :1.267 Mean :1.261 Mean :171.1
## 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:177.0
## Max. :5.000 Max. :2.000 Max. :200.0
## NA's :79 NA's :83 NA's :88
## weight BMI comorbidities com_dementia com_parkinson
## Min. : 43.00 Min. :15.42 Min. :1 Mode:logical Mode:logical
## 1st Qu.: 62.00 1st Qu.:21.80 1st Qu.:1 NA's:416 NA's:416
## Median : 73.00 Median :24.65 Median :1
## Mean : 76.33 Mean :25.96 Mean :1
## 3rd Qu.: 85.00 3rd Qu.:28.37 3rd Qu.:1
## Max. :178.00 Max. :62.50 Max. :1
## NA's :83 NA's :89 NA's :199
## com_epilepsy com_MS com_stroke com_hypertonia com_hypertonia_new
## Mode:logical Mode:logical Min. :1 Min. :1 Min. :0.00000
## NA's:416 NA's:416 1st Qu.:1 1st Qu.:1 1st Qu.:0.00000
## Median :1 Median :1 Median :0.00000
## Mean :1 Mean :1 Mean :0.08824
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:0.00000
## Max. :1 Max. :1 Max. :1.00000
## NA's :414 NA's :386 NA's :76
## com_diabetes com_copd com_cardiac_insufficiency com_cancer
## Min. :1 Min. :1 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :1 Median :1 Median :1 Median :1
## Mean :1 Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1 Max. :1
## NA's :406 NA's :412 NA's :411 NA's :410
## com_cancer_type com_other education education_other
## Length:416 Length:416 Min. : 2.00 Length:416
## Class :character Class :character 1st Qu.: 6.00 Class :character
## Mode :character Mode :character Median : 7.00 Mode :character
## Mean : 6.91
## 3rd Qu.: 8.00
## Max. :11.00
## NA's :81
## education_years current_job_Arbeiter current_job_Angestellter
## Min. : 6.50 Min. :1 Min. :1
## 1st Qu.:12.00 1st Qu.:1 1st Qu.:1
## Median :15.00 Median :1 Median :1
## Mean :14.55 Mean :1 Mean :1
## 3rd Qu.:18.00 3rd Qu.:1 3rd Qu.:1
## Max. :25.00 Max. :1 Max. :1
## NA's :252 NA's :400 NA's :177
## current_job_Student current_job_Schüler current_job_Pensionist
## Min. :1 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :1 Median :1 Median :1
## Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1
## NA's :394 NA's :411 NA's :393
## current_job_Arbeitsloser current_job_Selbstständiger current_job_other
## Min. :1 Min. :1 Length:416
## 1st Qu.:1 1st Qu.:1 Class :character
## Median :1 Median :1 Mode :character
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :409 NA's :387
## changes_work_covid changes_work_covid_hours_reduced
## Min. :1.00 Min. :1
## 1st Qu.:1.00 1st Qu.:1
## Median :2.00 Median :1
## Mean :1.59 Mean :1
## 3rd Qu.:2.00 3rd Qu.:1
## Max. :2.00 Max. :1
## NA's :92 NA's :385
## changes_work_covid_no_work_anymore changes_work_covid_job_change
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :404 NA's :406
## changes_work_covid_sick_leave changes_work_covid_other financial_losses
## Min. :1 Length:416 Min. :1.000
## 1st Qu.:1 Class :character 1st Qu.:1.000
## Median :1 Mode :character Median :2.000
## Mean :1 Mean :1.595
## 3rd Qu.:1 3rd Qu.:2.000
## Max. :1 Max. :2.000
## NA's :339 NA's :100
## covid_infection_acute_duration covid_infection_acute_duration_other
## Min. :1.000 Length:416
## 1st Qu.:2.000 Class :character
## Median :3.000 Mode :character
## Mean :3.415
## 3rd Qu.:4.000
## Max. :9.000
## NA's :117
## course_disease_severe course_disease_mild course_disease_asymptomatic
## Min. :1 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :1 Median :1 Median :1
## Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1
## NA's :357 NA's :194 NA's :398
## hospitalization hospitalization_cov19_or_ICU covid_hospitalization_duration
## Min. :1.000 Min. :1.000 Min. :1
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1
## Median :2.000 Median :1.000 Median :1
## Mean :1.922 Mean :1.222 Mean :2
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:3
## Max. :2.000 Max. :2.000 Max. :7
## NA's :120 NA's :398 NA's :398
## symptoms_fatigue_burden symptoms_poor_memory_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :3.000
## Mean :3.961 Mean :2.793
## 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :134 NA's :189
## symptoms_limitation_mental_performance_burden symptoms_fever_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.500
## Mean :3.018 Mean :2.583
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :188 NA's :198
## symptoms_loss_of_appetite_burden symptoms_rattling_breathing_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :3.041 Mean :2.377
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :197 NA's :257
## symptoms_runny_nose_burden symptoms_increased_temperature_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000
## Mean :2.722 Mean :2.727
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :193 NA's :218
## symptoms_dry_cough_burden symptoms_vomiting_burden symptoms_sore_throat_burden
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.000
## Median :4.000 Median :1.000 Median :3.000
## Mean :3.409 Mean :1.619 Mean :2.874
## 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000
## NA's :186 NA's :311 NA's :210
## symptoms_diarrhoea_burden symptoms_bloody_cough_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000
## Mean :2.286 Mean :1.344
## 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :5.000 Max. :4.000
## NA's :276 NA's :355
## symptoms_chills_or_sweating_burden symptoms_altered_taste_smell_burden
## Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:3.00
## Median :3.000 Median :5.00
## Mean :3.378 Mean :3.97
## 3rd Qu.:4.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.00
## NA's :199 NA's :219
## symptoms_sneeze_burden symptoms_difficulty_breathing_normal_oxy_sat_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000
## Mean :2.694 Mean :3.184
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :243 NA's :215
## symptoms_tightness_chest_burden symptoms_shortness_of_breath_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :4.000
## Mean :3.335 Mean :3.591
## 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000
## NA's :210 NA's :201
## symptoms_cough_mucus_burden symptoms_nausea_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.500 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :2.789 Mean :2.481
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :269 NA's :287
## symptoms_burning_chest_pain_burden symptoms_disorientation_confusion_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :2.774 Mean :2.303
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :292 NA's :294
## symptoms_muscle_pain_burden symptoms_dizziness_burden symptoms_low_temp_burden
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000
## Median :4.000 Median :3.000 Median :1.000
## Mean :3.535 Mean :3.292 Mean :1.828
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:2.500
## Max. :5.000 Max. :5.000 Max. :5.000
## NA's :199 NA's :224 NA's :329
## symptoms_exhaustion_burden symptoms_stomach_pain_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:1.000
## Median :4.000 Median :1.000
## Mean :4.125 Mean :1.953
## 3rd Qu.:5.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :161 NA's :310
## symptoms_other_sleep_symptoms_burden symptoms_headache_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000
## Median :3.000 Median :4.000
## Mean :3.205 Mean :3.763
## 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000
## NA's :265 NA's :197
## symptoms_covidzeh_burden symptoms_hallucinatoin_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.000
## Median :1.00 Median :1.000
## Mean :1.75 Mean :1.328
## 3rd Qu.:2.25 3rd Qu.:1.000
## Max. :5.00 Max. :5.000
## NA's :348 NA's :355
## symptoms_bone_pain_burden symptoms_tachycardia_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :3.000
## Mean :3.117 Mean :2.617
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :288 NA's :296
## symptoms_sleepapnoea_burden symptoms_insomnia_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:1.00 1st Qu.:2.000
## Median :2.00 Median :3.000
## Mean :2.16 Mean :3.224
## 3rd Qu.:3.00 3rd Qu.:4.000
## Max. :5.00 Max. :5.000
## NA's :335 NA's :264
## symptoms_slurred_speech_burden symptoms_joint_pain_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000
## Median :2.000 Median :4.000
## Mean :2.063 Mean :3.562
## 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000
## NA's :337 NA's :247
## symptoms_heart_palpations_burden symptoms_other_temp_deviations_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :1.000
## Mean :3.245 Mean :1.861
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :269 NA's :351
## symptoms_other_eye_sympt_burden symptoms_muscle_cramps_burden
## Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:1.00
## Median :3.000 Median :2.00
## Mean :2.536 Mean :2.33
## 3rd Qu.:3.000 3rd Qu.:3.00
## Max. :5.000 Max. :5.00
## NA's :304 NA's :319
## symptoms_disturbed_neurological_sensations_burden symptoms_peeling_skin_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:2.00 1st Qu.:1.000
## Median :3.00 Median :1.000
## Mean :2.74 Mean :1.826
## 3rd Qu.:3.25 3rd Qu.:3.000
## Max. :5.00 Max. :5.000
## NA's :312 NA's :347
## symptoms_heartburn_reflux_burden symptoms_skin_rash_burden
## Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:1.00
## Median :2.000 Median :2.00
## Mean :2.046 Mean :2.16
## 3rd Qu.:3.000 3rd Qu.:3.00
## Max. :5.000 Max. :5.00
## NA's :329 NA's :335
## symptoms_constipation_burden symptoms_bladder_control_problems_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :1.781 Mean :1.753
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000
## NA's :343 NA's :343
## symptoms_hearloss_burden symptoms_bradycardia_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :1.783 Mean :1.538
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000
## NA's :347 NA's :351
## symptoms_nerve_pain_burden symptoms_hearing_impairment_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :2.781 Mean :1.932
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :311 NA's :342
## symptoms_tremor_burden symptoms_dermographism_burden symtpoms_petechiae_burden
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :1.000
## Mean :2.308 Mean :1.667 Mean :1.717
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000 Max. :5.000
## NA's :322 NA's :362 NA's :356
## symptoms_skin_abnormalities_allergies_burden
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :2.183
## 3rd Qu.:3.000
## Max. :5.000
## NA's :345
## symptoms_discomfort_after_exertion_burden symptoms_tinnitus_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000
## Median :4.000 Median :3.000
## Mean :3.816 Mean :2.611
## 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :237 NA's :321
## symptoms_visual_disturbances_burden symptoms_menstrual_disorders_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.362 Mean :2.224
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :322 NA's :349
## symptoms_anaphylactic_reaction_burden symptoms_protruding_veins_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :1.269 Mean :1.781
## 3rd Qu.:1.000 3rd Qu.:2.250
## Max. :4.000 Max. :5.000
## NA's :364 NA's :352
## symptoms_new_allergies_burden symptoms_weight_loss_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :2.000
## Mean :1.454 Mean :2.381
## 3rd Qu.:1.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :361 NA's :303
## covid_vaccinated_before_inf covid_vaccination_after_inf
## Min. :0.0000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:1.000
## Median :0.0000 Median :1.000
## Mean :0.4527 Mean :1.442
## 3rd Qu.:1.0000 3rd Qu.:2.000
## Max. :1.0000 Max. :2.000
## NA's :120 NA's :131
## vaccine_1st_vaccination vaccine_1st_vaccination_comment
## Min. :1 Length:416
## 1st Qu.:1 Class :character
## Median :1 Mode :character
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :291
## vaccine_2nd_vaccination vaccine_2nd_vaccination_comment
## Min. :1 Length:416
## 1st Qu.:1 Class :character
## Median :1 Mode :character
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :293
## vaccine_3rd_vaccination vaccine_3rd_vaccination_comment sick_leave_days
## Min. :1 Length:416 Min. : 0.00
## 1st Qu.:1 Class :character 1st Qu.: 10.00
## Median :1 Mode :character Median : 16.00
## Mean :1 Mean : 44.38
## 3rd Qu.:1 3rd Qu.: 41.00
## Max. :1 Max. :450.00
## NA's :311 NA's :169
## long_covid_symptoms_after_infection_days
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.219
## 3rd Qu.:3.000
## Max. :5.000
## NA's :151
## long_covid_symptoms_after_infection_days_other lc_symptoms_fatigue_burden
## Length:416 Min. :1.000
## Class :character 1st Qu.:4.000
## Mode :character Median :4.000
## Mean :4.117
## 3rd Qu.:5.000
## Max. :5.000
## NA's :152
## lc_symptoms_poor_memory_burden
## Min. :1.000
## 1st Qu.:3.000
## Median :3.000
## Mean :3.304
## 3rd Qu.:4.000
## Max. :5.000
## NA's :192
## lc_symptoms_limitation_mental_performance_burden lc_symptoms_fever_burden
## Min. :1.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :3.426 Mean :1.892
## 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000
## NA's :207 NA's :351
## lc_symptoms_loss_of_appetite_burden lc_symptoms_rattling_breathing_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.538 Mean :2.043
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :312 NA's :347
## lc_symptoms_runny_nose_burden lc_symptoms_increased_temperature_burden
## Min. :1.000 Min. :1
## 1st Qu.:1.000 1st Qu.:1
## Median :2.000 Median :1
## Mean :2.247 Mean :2
## 3rd Qu.:3.000 3rd Qu.:3
## Max. :5.000 Max. :5
## NA's :319 NA's :355
## lc_symptoms_dry_cough_burden lc_symptoms_vomiting_burden
## Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:1.00
## Median :3.000 Median :1.00
## Mean :2.901 Mean :1.87
## 3rd Qu.:4.000 3rd Qu.:2.75
## Max. :5.000 Max. :5.00
## NA's :295 NA's :370
## lc_symptoms_sore_throat_burden lc_symptoms_diarrhoea_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.000
## Median :2.00 Median :2.000
## Mean :2.28 Mean :2.397
## 3rd Qu.:3.00 3rd Qu.:3.000
## Max. :5.00 Max. :5.000
## NA's :316 NA's :343
## lc_symptoms_bloody_cough_burden lc_symptoms_chills_or_sweating_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :1.000 Median :3.000
## Mean :1.227 Mean :2.922
## 3rd Qu.:1.000 3rd Qu.:4.000
## Max. :3.000 Max. :5.000
## NA's :394 NA's :301
## lc_symptoms_altered_taste_smell_burden lc_symptoms_sneeze_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :4.000 Median :2.000
## Mean :3.438 Mean :2.296
## 3rd Qu.:5.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :288 NA's :328
## lc_symptoms_difficulty_breathing_normal_oxy_sat_burden
## Min. :1.000
## 1st Qu.:3.000
## Median :3.000
## Mean :3.247
## 3rd Qu.:4.000
## Max. :5.000
## NA's :266
## lc_symptoms_tightness_chest_burden lc_symptoms_shortness_of_breath_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:2.00 1st Qu.:2.000
## Median :3.00 Median :3.000
## Mean :3.16 Mean :3.298
## 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :5.00 Max. :5.000
## NA's :247 NA's :225
## lc_symptoms_cough_mucus_burden lc_symptoms_nausea_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.494 Mean :2.459
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :339 NA's :342
## lc_symptoms_burning_chest_pain_burden
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :2.924
## 3rd Qu.:4.000
## Max. :5.000
## NA's :324
## lc_symptoms_disorientation_confusion_burden lc_symptoms_muscle_pain_burden
## Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:3.00
## Median :2.000 Median :3.00
## Mean :2.571 Mean :3.37
## 3rd Qu.:3.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.00
## NA's :311 NA's :281
## lc_symptoms_dizziness_burden lc_symptoms_low_temp_burden
## Min. :1.00 Min. :1.000
## 1st Qu.:2.00 1st Qu.:1.000
## Median :3.00 Median :2.000
## Mean :3.06 Mean :1.938
## 3rd Qu.:4.00 3rd Qu.:3.000
## Max. :5.00 Max. :5.000
## NA's :248 NA's :368
## lc_symptoms_exhaustion_burden lc_symptoms_stomach_pain_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000
## Median :4.000 Median :2.000
## Mean :4.031 Mean :2.355
## 3rd Qu.:5.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :189 NA's :354
## lc_symptoms_other_sleep_symptoms_burden lc_symptoms_headache_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :3.000
## Mean :3.439 Mean :3.381
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :302 NA's :256
## lc_symptoms_covidzeh_burden lc_symptoms_hallucinatoin_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :2.064 Mean :1.533
## 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :5.000 Max. :4.000
## NA's :385 NA's :386
## lc_symptoms_bone_pain_burden lc_symptoms_tachycardia_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000
## Mean :3.025 Mean :2.989
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :336 NA's :328
## lc_symptoms_sleepapnoea_burden lc_symptoms_insomnia_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000
## Mean :2.333 Mean :3.263
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :371 NA's :264
## lc_symptoms_slurred_speech_burden lc_symptoms_joint_pain_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000
## Median :2.000 Median :3.000
## Mean :2.318 Mean :3.328
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :350 NA's :294
## lc_symptoms_heart_palpations_burden lc_symptoms_other_temp_deviations_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000
## Median :3.000 Median :3.000
## Mean :3.336 Mean :2.439
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :291 NA's :375
## lc_symptoms_other_eye_sympt_burden lc_symptoms_muscle_cramps_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :2.500 Median :3.000
## Mean :2.585 Mean :2.838
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :334 NA's :342
## lc_symptoms_disturbed_neurological_sensations_burden
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :3.021
## 3rd Qu.:4.000
## Max. :5.000
## NA's :321
## lc_symptoms_peeling_skin_burden lc_symptoms_heartburn_reflux_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.053 Mean :2.357
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :378 NA's :360
## lc_symptoms_skin_rash_burden lc_symptoms_constipation_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.255 Mean :2.143
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :365 NA's :367
## lc_symptoms_bladder_control_problems_burden lc_symptoms_hearloss_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.389 Mean :2.079
## 3rd Qu.:3.750 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :362 NA's :378
## lc_symptoms_bradycardia_burden lc_symptoms_nerve_pain_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000
## Mean :1.731 Mean :3.108
## 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :4.000 Max. :5.000
## NA's :390 NA's :342
## lc_symptoms_hearing_impairment_burden lc_symptoms_tremor_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.239 Mean :2.377
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :370 NA's :347
## lc_symptoms_dermographism_burden lc_symtpoms_petechiae_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :2.207 Mean :1.852
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :387 NA's :389
## lc_symptoms_skin_abnormalities_allergies_burden
## Min. :1.000
## 1st Qu.:2.000
## Median :2.000
## Mean :2.579
## 3rd Qu.:3.000
## Max. :5.000
## NA's :359
## lc_symptoms_discomfort_after_exertion_burden lc_symptoms_tinnitus_burden
## Min. :2.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :3.000
## Mean :3.925 Mean :3.032
## 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## NA's :243 NA's :353
## lc_symptoms_visual_disturbances_burden lc_symptoms_menstrual_disorders_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.500
## Median :2.000 Median :2.000
## Mean :2.573 Mean :2.529
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :334 NA's :365
## lc_symptoms_anaphylactic_reaction_burden lc_symptoms_protruding_veins_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :2.500
## Mean :1.737 Mean :2.316
## 3rd Qu.:1.500 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
## NA's :397 NA's :378
## lc_symptoms_new_allergies_burden lc_symptoms_weight_loss_burden
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.151 Mean :2.418
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :5.000
## NA's :383 NA's :361
## relation_between_LongCovid_symptoms investigation_longcovid_symptoms
## Length:416 Min. :1.000
## Class :character 1st Qu.:1.000
## Mode :character Median :1.000
## Mean :1.377
## 3rd Qu.:2.000
## Max. :2.000
## NA's :156
## investigation_longcovid_symptoms_CT
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :359
## investigation_longcovid_symptoms_CT_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_MRI
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :354
## investigation_longcovid_symptoms_MRI_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_Xray
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :330
## investigation_longcovid_symptoms_Xray_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_LungFunctionTest
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :298
## investigation_longcovid_symptoms_LungFunctionTest_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_NeuroTest
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :349
## investigation_longcovid_symptoms_NeuroTest_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_NeuroPsychTest
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :374
## investigation_longcovid_symptoms_NeuroPsychTest_comment
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_other
## Length:416
## Class :character
## Mode :character
##
##
##
##
## investigation_longcovid_symptoms_other_comment long_covid_outpatient_dep
## Length:416 Min. :1.000
## Class :character 1st Qu.:2.000
## Mode :character Median :2.000
## Mean :1.823
## 3rd Qu.:2.000
## Max. :2.000
## NA's :156
## long_covid_reha Long_Covid_Symptoms_worse_after_physical_activity
## Min. :1.000 Min. :1
## 1st Qu.:2.000 1st Qu.:1
## Median :2.000 Median :1
## Mean :1.781 Mean :1
## 3rd Qu.:2.000 3rd Qu.:1
## Max. :2.000 Max. :1
## NA's :151 NA's :230
## Long_Covid_Symptoms_better_after_physical_activity
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :394
## Long_Covid_Symptoms_worse_after_mental_activity
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :278
## Long_Covid_Symptoms_better_after_mental_activity
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :404
## Long_Covid_Symptoms_fluctuating Long_Covid_Symptoms_getting_better_overall
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :274 NA's :347
## Long_Covid_Symptoms_getting_worse_overall Long_Covid_Symptoms_staying_the_same
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :395 NA's :341
## Long_Covid_Symptoms_other Control_over_breathing Oxygen_therapy_at_home
## Length:416 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:1.000 1st Qu.:2.000
## Mode :character Median :1.000 Median :2.000
## Mean :1.413 Mean :1.989
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000
## NA's :174 NA's :147
## Alteration_stress_resistance_since_covid
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :1.125
## 3rd Qu.:1.000
## Max. :2.000
## NA's :152
## Alteration_stress_resistance_since_covid_specification
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :1.018
## 3rd Qu.:1.000
## Max. :2.000
## NA's :188
## Alteration_mood_since_covid Alteration_mood_since_covid_specification
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :1.000 Median :2.000
## Mean :1.279 Mean :1.956
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000
## NA's :154 NA's :235
## Need_help Need_help_from_organisation Need_help_from_relatives
## Min. :1.000 Min. :1 Min. :1
## 1st Qu.:2.000 1st Qu.:1 1st Qu.:1
## Median :2.000 Median :1 Median :1
## Mean :1.767 Mean :1 Mean :1
## 3rd Qu.:2.000 3rd Qu.:1 3rd Qu.:1
## Max. :2.000 Max. :1 Max. :1
## NA's :154 NA's :412 NA's :355
## Need_help_from_organisation_specification
## Length:416
## Class :character
## Mode :character
##
##
##
##
## Need_help_from_relatives_specification more_addictive_substances_or_drugs
## Length:416 Min. :1.000
## Class :character 1st Qu.:1.000
## Mode :character Median :2.000
## Mean :1.554
## 3rd Qu.:2.000
## Max. :2.000
## NA's :156
## more_addictive_substances_or_drugs_alcohol
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :410
## more_addictive_substances_or_drugs_illegal_drugs
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :415
## more_addictive_substances_or_drugs_nicotin
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :410
## more_addictive_substances_or_drugs_meds
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :310
## more_addictive_substances_or_drugs_other Alteration_sexuality
## Length:416 Min. :1.000
## Class :character 1st Qu.:1.000
## Mode :character Median :2.000
## Mean :1.619
## 3rd Qu.:2.000
## Max. :2.000
## NA's :185
## Alteration_sexuality_specification exercises_against_symptoms_breathing
## Length:416 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :291
## exercises_against_symptoms_movement exercises_against_symptoms_cognitive
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :278 NA's :330
## exercises_against_symptoms_nothing exercises_against_symptoms_other
## Min. :1 Length:416
## 1st Qu.:1 Class :character
## Median :1 Mode :character
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :352
## what_believe_would_help_breathing_exercises
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :299
## what_believe_would_help_movement_exercises
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :272
## what_believe_would_help_cognitive_exercises
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :287
## what_believe_would_help_other_exercises considered_training_no
## Length:416 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :329
## considered_training_cognitive considered_training_physical
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :327 NA's :278
## considered_training_other motivation_to_train_better_physical_health
## Length:416 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :212
## motivation_to_train_better_mental_health
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :262
## motivation_to_train_interested_learning_new_things
## Min. :1
## 1st Qu.:1
## Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :358
## motivation_to_train_training_from_home motivation_to_train_self_paced
## Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1
## Median :1 Median :1
## Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1
## NA's :311 NA's :324
## motivation_to_train_social_contacts motivation_to_train_other
## Min. :1 Length:416
## 1st Qu.:1 Class :character
## Median :1 Mode :character
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :370
#Evaluamos posibles datos faltantes
nas_por_variable <- sort(colSums(is.na(covid)), decreasing = TRUE)
print(head(nas_por_variable[nas_por_variable > 0], 254))
## com_dementia
## 416
## com_parkinson
## 416
## com_epilepsy
## 416
## com_MS
## 416
## origin_other
## 415
## more_addictive_substances_or_drugs_illegal_drugs
## 415
## com_stroke
## 414
## Need_help_from_organisation_specification
## 414
## com_copd
## 412
## Need_help_from_organisation
## 412
## com_cardiac_insufficiency
## 411
## current_job_Schüler
## 411
## com_cancer
## 410
## com_cancer_type
## 410
## more_addictive_substances_or_drugs_alcohol
## 410
## more_addictive_substances_or_drugs_nicotin
## 410
## current_job_Arbeitsloser
## 409
## covid_infection_acute_duration_other
## 407
## more_addictive_substances_or_drugs_other
## 407
## com_diabetes
## 406
## changes_work_covid_job_change
## 406
## changes_work_covid_no_work_anymore
## 404
## Long_Covid_Symptoms_better_after_mental_activity
## 404
## current_job_Arbeiter
## 400
## education_other
## 398
## course_disease_asymptomatic
## 398
## hospitalization_cov19_or_ICU
## 398
## covid_hospitalization_duration
## 398
## lc_symptoms_anaphylactic_reaction_burden
## 397
## investigation_longcovid_symptoms_NeuroPsychTest_comment
## 397
## Long_Covid_Symptoms_getting_worse_overall
## 395
## current_job_Student
## 394
## current_job_other
## 394
## lc_symptoms_bloody_cough_burden
## 394
## Long_Covid_Symptoms_better_after_physical_activity
## 394
## current_job_Pensionist
## 393
## considered_training_other
## 393
## motivation_to_train_other
## 393
## changes_work_covid_other
## 392
## lc_symptoms_bradycardia_burden
## 390
## lc_symtpoms_petechiae_burden
## 389
## current_job_Selbstständiger
## 387
## lc_symptoms_dermographism_burden
## 387
## com_hypertonia
## 386
## lc_symptoms_hallucinatoin_burden
## 386
## investigation_longcovid_symptoms_other
## 386
## investigation_longcovid_symptoms_other_comment
## 386
## changes_work_covid_hours_reduced
## 385
## lc_symptoms_covidzeh_burden
## 385
## Long_Covid_Symptoms_other
## 385
## lc_symptoms_new_allergies_burden
## 383
## lc_symptoms_peeling_skin_burden
## 378
## lc_symptoms_hearloss_burden
## 378
## lc_symptoms_protruding_veins_burden
## 378
## long_covid_symptoms_after_infection_days_other
## 377
## investigation_longcovid_symptoms_NeuroTest_comment
## 377
## lc_symptoms_other_temp_deviations_burden
## 375
## investigation_longcovid_symptoms_NeuroPsychTest
## 374
## investigation_longcovid_symptoms_CT_comment
## 372
## lc_symptoms_sleepapnoea_burden
## 371
## lc_symptoms_vomiting_burden
## 370
## lc_symptoms_hearing_impairment_burden
## 370
## motivation_to_train_social_contacts
## 370
## investigation_longcovid_symptoms_MRI_comment
## 369
## lc_symptoms_low_temp_burden
## 368
## lc_symptoms_constipation_burden
## 367
## lc_symptoms_skin_rash_burden
## 365
## lc_symptoms_menstrual_disorders_burden
## 365
## symptoms_anaphylactic_reaction_burden
## 364
## symptoms_dermographism_burden
## 362
## lc_symptoms_bladder_control_problems_burden
## 362
## symptoms_new_allergies_burden
## 361
## lc_symptoms_weight_loss_burden
## 361
## Need_help_from_relatives_specification
## 361
## lc_symptoms_heartburn_reflux_burden
## 360
## lc_symptoms_skin_abnormalities_allergies_burden
## 359
## investigation_longcovid_symptoms_CT
## 359
## motivation_to_train_interested_learning_new_things
## 358
## course_disease_severe
## 357
## symtpoms_petechiae_burden
## 356
## symptoms_bloody_cough_burden
## 355
## symptoms_hallucinatoin_burden
## 355
## lc_symptoms_increased_temperature_burden
## 355
## Need_help_from_relatives
## 355
## lc_symptoms_stomach_pain_burden
## 354
## investigation_longcovid_symptoms_MRI
## 354
## investigation_longcovid_symptoms_Xray_comment
## 354
## lc_symptoms_tinnitus_burden
## 353
## symptoms_protruding_veins_burden
## 352
## investigation_longcovid_symptoms_LungFunctionTest_comment
## 352
## exercises_against_symptoms_nothing
## 352
## symptoms_other_temp_deviations_burden
## 351
## symptoms_bradycardia_burden
## 351
## lc_symptoms_fever_burden
## 351
## lc_symptoms_slurred_speech_burden
## 350
## symptoms_menstrual_disorders_burden
## 349
## investigation_longcovid_symptoms_NeuroTest
## 349
## symptoms_covidzeh_burden
## 348
## symptoms_peeling_skin_burden
## 347
## symptoms_hearloss_burden
## 347
## lc_symptoms_rattling_breathing_burden
## 347
## lc_symptoms_tremor_burden
## 347
## Long_Covid_Symptoms_getting_better_overall
## 347
## exercises_against_symptoms_other
## 346
## symptoms_skin_abnormalities_allergies_burden
## 345
## symptoms_constipation_burden
## 343
## symptoms_bladder_control_problems_burden
## 343
## lc_symptoms_diarrhoea_burden
## 343
## symptoms_hearing_impairment_burden
## 342
## lc_symptoms_nausea_burden
## 342
## lc_symptoms_muscle_cramps_burden
## 342
## lc_symptoms_nerve_pain_burden
## 342
## Long_Covid_Symptoms_staying_the_same
## 341
## Alteration_sexuality_specification
## 341
## changes_work_covid_sick_leave
## 339
## lc_symptoms_cough_mucus_burden
## 339
## symptoms_slurred_speech_burden
## 337
## lc_symptoms_bone_pain_burden
## 336
## symptoms_sleepapnoea_burden
## 335
## symptoms_skin_rash_burden
## 335
## lc_symptoms_other_eye_sympt_burden
## 334
## lc_symptoms_visual_disturbances_burden
## 334
## what_believe_would_help_other_exercises
## 333
## investigation_longcovid_symptoms_Xray
## 330
## exercises_against_symptoms_cognitive
## 330
## symptoms_low_temp_burden
## 329
## symptoms_heartburn_reflux_burden
## 329
## considered_training_no
## 329
## lc_symptoms_sneeze_burden
## 328
## lc_symptoms_tachycardia_burden
## 328
## considered_training_cognitive
## 327
## lc_symptoms_burning_chest_pain_burden
## 324
## motivation_to_train_self_paced
## 324
## com_other
## 322
## symptoms_tremor_burden
## 322
## symptoms_visual_disturbances_burden
## 322
## symptoms_tinnitus_burden
## 321
## lc_symptoms_disturbed_neurological_sensations_burden
## 321
## symptoms_muscle_cramps_burden
## 319
## lc_symptoms_runny_nose_burden
## 319
## lc_symptoms_sore_throat_burden
## 316
## vaccine_3rd_vaccination_comment
## 313
## symptoms_disturbed_neurological_sensations_burden
## 312
## lc_symptoms_loss_of_appetite_burden
## 312
## symptoms_vomiting_burden
## 311
## symptoms_nerve_pain_burden
## 311
## vaccine_3rd_vaccination
## 311
## lc_symptoms_disorientation_confusion_burden
## 311
## motivation_to_train_training_from_home
## 311
## symptoms_stomach_pain_burden
## 310
## more_addictive_substances_or_drugs_meds
## 310
## symptoms_other_eye_sympt_burden
## 304
## symptoms_weight_loss_burden
## 303
## lc_symptoms_other_sleep_symptoms_burden
## 302
## lc_symptoms_chills_or_sweating_burden
## 301
## what_believe_would_help_breathing_exercises
## 299
## investigation_longcovid_symptoms_LungFunctionTest
## 298
## symptoms_tachycardia_burden
## 296
## vaccine_2nd_vaccination_comment
## 295
## lc_symptoms_dry_cough_burden
## 295
## symptoms_disorientation_confusion_burden
## 294
## lc_symptoms_joint_pain_burden
## 294
## vaccine_1st_vaccination_comment
## 293
## vaccine_2nd_vaccination
## 293
## symptoms_burning_chest_pain_burden
## 292
## vaccine_1st_vaccination
## 291
## lc_symptoms_heart_palpations_burden
## 291
## exercises_against_symptoms_breathing
## 291
## symptoms_bone_pain_burden
## 288
## lc_symptoms_altered_taste_smell_burden
## 288
## symptoms_nausea_burden
## 287
## what_believe_would_help_cognitive_exercises
## 287
## lc_symptoms_muscle_pain_burden
## 281
## Long_Covid_Symptoms_worse_after_mental_activity
## 278
## exercises_against_symptoms_movement
## 278
## considered_training_physical
## 278
## symptoms_diarrhoea_burden
## 276
## Long_Covid_Symptoms_fluctuating
## 274
## what_believe_would_help_movement_exercises
## 272
## symptoms_cough_mucus_burden
## 269
## symptoms_heart_palpations_burden
## 269
## lc_symptoms_difficulty_breathing_normal_oxy_sat_burden
## 266
## symptoms_other_sleep_symptoms_burden
## 265
## symptoms_insomnia_burden
## 264
## lc_symptoms_insomnia_burden
## 264
## relation_between_LongCovid_symptoms
## 264
## motivation_to_train_better_mental_health
## 262
## symptoms_rattling_breathing_burden
## 257
## lc_symptoms_headache_burden
## 256
## education_years
## 252
## lc_symptoms_dizziness_burden
## 248
## symptoms_joint_pain_burden
## 247
## lc_symptoms_tightness_chest_burden
## 247
## symptoms_sneeze_burden
## 243
## lc_symptoms_discomfort_after_exertion_burden
## 243
## symptoms_discomfort_after_exertion_burden
## 237
## Alteration_mood_since_covid_specification
## 235
## Long_Covid_Symptoms_worse_after_physical_activity
## 230
## lc_symptoms_shortness_of_breath_burden
## 225
## symptoms_dizziness_burden
## 224
## symptoms_altered_taste_smell_burden
## 219
## symptoms_increased_temperature_burden
## 218
## symptoms_difficulty_breathing_normal_oxy_sat_burden
## 215
## motivation_to_train_better_physical_health
## 212
## symptoms_sore_throat_burden
## 210
## symptoms_tightness_chest_burden
## 210
## lc_symptoms_limitation_mental_performance_burden
## 207
## symptoms_shortness_of_breath_burden
## 201
## comorbidities
## 199
## symptoms_chills_or_sweating_burden
## 199
## symptoms_muscle_pain_burden
## 199
## symptoms_fever_burden
## 198
## symptoms_loss_of_appetite_burden
## 197
## symptoms_headache_burden
## 197
## course_disease_mild
## 194
## symptoms_runny_nose_burden
## 193
## lc_symptoms_poor_memory_burden
## 192
## symptoms_poor_memory_burden
## 189
## lc_symptoms_exhaustion_burden
## 189
## symptoms_limitation_mental_performance_burden
## 188
## Alteration_stress_resistance_since_covid_specification
## 188
## symptoms_dry_cough_burden
## 186
## Alteration_sexuality
## 185
## current_job_Angestellter
## 177
## Control_over_breathing
## 174
## sick_leave_days
## 169
## symptoms_exhaustion_burden
## 161
## investigation_longcovid_symptoms
## 156
## long_covid_outpatient_dep
## 156
## more_addictive_substances_or_drugs
## 156
## Alteration_mood_since_covid
## 154
## Need_help
## 154
## lc_symptoms_fatigue_burden
## 152
## Alteration_stress_resistance_since_covid
## 152
## long_covid_symptoms_after_infection_days
## 151
## long_covid_reha
## 151
## Oxygen_therapy_at_home
## 147
## duration_since_disease_by_10052022
## 141
## symptoms_fatigue_burden
## 134
## covid_vaccination_after_inf
## 131
## hospitalization
## 120
## covid_vaccinated_before_inf
## 120
## covid_infection_acute_duration
## 117
## financial_losses
## 100
## age
## 99
## changes_work_covid
## 92
## BMI
## 89
## height
## 88
## gender
## 83
## weight
## 83
## education
## 81
## origin
## 79
## com_hypertonia_new
## 76
• Realizad cinco gráficos básicos con las variables, explicad su significado y guardadlos como imágenes (jpeg o bmp).
# Gráficos descriptivos de edad, BMI, duración de síntomas, origen y género
# 1. Edad por género - Boxplot
jpeg("01_edad_por_genero.jpg", width = 800, height = 600, quality = 90)
boxplot(covid$age ~ covid$gender,
col = c("lightblue", "orange"),
xlab = "Género",
ylab = "Edad (años)",
main = "Distribución de Edad por Género")
dev.off()
## png
## 2
# Estos datos muestran que no hay diferencias significativas en la edad entre géneros
# 2. BMI y duración de síntomas - Scatter plot
jpeg("02_bmi_vs_duracion_sintomas.jpg", width = 800, height = 600, quality = 90)
print(
ggplot(data = covid) +
geom_point(aes(x = duration_since_disease_by_10052022, y = BMI)) +
labs(x = "Duración síntomas",
y = "BMI",
title = "Relación entre BMI y duración de síntomas")
)
## Warning: Removed 151 rows containing missing values or values outside the scale range
## (`geom_point()`).
dev.off()
## png
## 2
# Esta imagen muestra la duración de los síntomas en relación al BMI de los participantes.
# No muestra una correlación pero sí parece que existen 3 patrones de duración de síntomas:
# aquellos con una duración menor a 10 meses, otros con una duración entre 12-20 y otros
# con una duración de 25 meses o más
# 3. Duración de síntomas y edad por género - Scatter plot con color
jpeg("03_edad_vs_duracion_por_genero.jpg", width = 800, height = 600, quality = 90)
print(
ggplot(data = covid) +
geom_point(aes(x = duration_since_disease_by_10052022, y = age,
color = as.factor(gender)), alpha = 0.6) +
labs(x = "Duración síntomas",
y = "Edad",
title = "Relación entre edad y duración de síntomas",
color = "Género")
)
## Warning: Removed 153 rows containing missing values or values outside the scale range
## (`geom_point()`).
dev.off()
## png
## 2
# Similar al caso anterior, en esta imagen vemos la relación entre la duración de síntomas
# y la edad, marcando además el género con diferentes colores. Por la dispersión, tampoco
# parece que haya una correlación clara entre ambas variables, y sí que vemos que por lo
# general hay más afectados de género masculino que femenino
# 4. Origen - Histograma
jpeg("04_origen_procedencia.jpg", width = 800, height = 600, quality = 90)
hist(covid$origin,
col = "lightblue",
xlab = "Origen",
main = "Procedencia de los participantes",
breaks = 10) # Ajusta el número de barras según necesites
dev.off()
## png
## 2
# Con este gráfico vemos que la mayoría de la muestra procede del mismo lugar de origen
# 5. Edad y género - Violin + Boxplot + Jitter
jpeg("05_distribucion_edad_por_genero.jpg", width = 800, height = 600, quality = 90)
covid_sin_na <- covid[!is.na(covid$gender) & !is.na(covid$age), ]
print(
ggplot(data = covid_sin_na, aes(x = as.factor(gender), y = age, fill = as.factor(gender))) +
geom_violin(alpha = 0.5) +
geom_boxplot(width = 0.2, fill = "white", alpha = 0.7) +
geom_jitter(width = 0.1, alpha = 0.3) +
labs(x = "Género",
y = "Edad",
title = "Distribución de Edad por Género",
fill = "Género") +
theme_minimal()
)
dev.off()
## png
## 2
# En este caso primero hemos eliminado los valores NA de las variables género y edad,
# y hemos realizado con ggplot un diagrama de violín + boxplot con los puntos individuales
# de cada observación (jitter). Además hemos usado la variable gender como factor ya que
# está codificada en la base de datos como numérica continua
• Realizad dos gráficos con el comando ggplot(), explicad su significado y guardadlos como imágenes (jpeg o bmp).
#GRÁFICO 1
#Preparar los datos: education como factor (1-11) y eliminar NA
covid_edu_bmi <- covid %>%
filter(!is.na(education) & !is.na(BMI)) %>%
mutate(education_factor = factor(education,
levels = 1:11,
labels = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11")))
# Crear y guardar el gráfico
jpeg("01_education_vs_bmi.jpg", width = 900, height = 600, quality = 90)
print(
ggplot(data = covid_edu_bmi, aes(x = education_factor, y = BMI)) +
geom_boxplot(fill = "lightblue", alpha = 0.7) +
geom_jitter(width = 0.2, alpha = 0.3, color = "darkblue") +
labs(x = "Nivel Educativo (1-11)",
y = "Índice de Masa Corporal (BMI)",
title = "Relación entre Nivel Educativo e Índice de Masa Corporal") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
)
dev.off()
## png
## 2
# GRÁFICO 2
jpeg("02_histograma_fatiga_por_genero.jpg", width = 900, height = 600, quality = 90)
covid_graf2 <- covid[!is.na(covid$symptoms_fatigue_burden) & !is.na(covid$gender), ]
print(
ggplot(data = covid_graf2, aes(x = symptoms_fatigue_burden, fill = as.factor(gender))) +
geom_histogram(binwidth = 1,
color = "white",
alpha = 0.7,
position = "stack") +
facet_wrap(~as.factor(gender), ncol = 1) +
labs(x = "Carga de fatiga",
y = "Frecuencia",
title = "Distribución de la Carga de Fatiga por Género",
subtitle = paste("n =", nrow(covid_graf2), "observaciones")) +
theme_minimal() +
theme(legend.position = "none",
strip.text = element_text(size = 12, face = "bold"))
)
dev.off()
## png
## 2
• Generad una regresión lineal entre dos de sus variables paso a paso y comentad los resultados obtenidos.
# Cargamos los datos y hacemos una inspección visual y estadística de los datos
view(covid)
summary(covid[, c("BMI", "duration_since_disease_by_10052022")])
## BMI duration_since_disease_by_10052022
## Min. :15.42 Min. : 0.2736
## 1st Qu.:21.80 1st Qu.: 2.8034
## Median :24.65 Median :12.1011
## Mean :25.96 Mean :10.5355
## 3rd Qu.:28.37 3rd Qu.:17.3250
## Max. :62.50 Max. :26.4913
## NA's :89 NA's :141
# Inspección visual
grafico_visual <- pairs(~ BMI + duration_since_disease_by_10052022, data = covid)
print(grafico_visual)
## NULL
# Hacemos un estudio de correlación, ignorando NA`s
correlacion <- cor.test(covid$BMI, covid$duration_since_disease_by_10052022,
use = "complete.obs")
print(correlacion)
##
## Pearson's product-moment correlation
##
## data: covid$BMI and covid$duration_since_disease_by_10052022
## t = -0.57136, df = 263, p-value = 0.5682
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15505028 0.08565256
## sample estimates:
## cor
## -0.03520947
# Planteamos el modelo
covid_sin_na <- covid[!is.na(covid$BMI) & !is.na(covid$duration_since_disease_by_10052022), ]
regresion <- lm(BMI ~ duration_since_disease_by_10052022, data = covid_sin_na)
resumen_modelo <- summary(regresion)
print(resumen_modelo)
##
## Call:
## lm(formula = BMI ~ duration_since_disease_by_10052022, data = covid_sin_na)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.325 -4.373 -1.277 2.547 36.482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.47540 0.68289 38.770 <2e-16 ***
## duration_since_disease_by_10052022 -0.02989 0.05231 -0.571 0.568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.628 on 263 degrees of freedom
## Multiple R-squared: 0.00124, Adjusted R-squared: -0.002558
## F-statistic: 0.3264 on 1 and 263 DF, p-value: 0.5682
# Graficamos el modelo
plot(covid$duration_since_disease_by_10052022, covid$BMI,
xlab = "Duración desde la enfermedad (días)",
ylab = "Índice de Masa Corporal (BMI)",
main = "Regresión: BMI vs Duración de enfermedad",
col = "steelblue")
abline(regresion, col = "red")
# Evaluamos residuos
# Eliminamos NA para los residuos
residuos <- rstandard(regresion)
valores_ajustados <- fitted(regresion)
# Gráfico de residuos vs valores ajustados
plot(valores_ajustados, residuos)
qqnorm(residuos)
qqline(residuos)
En este caso vemos que los resultados del análisis de correlación no indican un valor significativo (p=0.56), por lo que, cuando realiamos el modelo de regresión vemos los mismos resultados. Esto nos indica que no existe una correlación entre ambas variables. Además el supuesto de normalidad de los residuos tampoco se cumple como se observa con el diagrama QQ-plot