E1U1
Daniel Quintana
23/10/2020
Nombre: Daniel Antonio Quintana Valenzuela Matrícula: 00000216722 Fecha: 23/10/2020
Respuestas amplias y muy bien argumentadas / elaboradas / específicas.
1.- ¿Qué es la estadística y que aplicaciones tiene en ingeniería (según su ingeniería)?
Es una ciencia y una rama de las matemáticas a través de la cual se recolecta, analiza, describe y estudia una serie de datos a fin de establecer comparaciones o variabilidades que permitan comprender un fenómeno en particular. La estadística se vale, en gran medida, de la observación para la recolección de datos que posteriormente serán analizados y comparados a fin de obtener un resultado.
Estadistica
La importancia de la estadística en la ingeniería se basa en la participación de la industria en el aumento de la calidad, ya que las técnicas estadísticas pueden emplearse para describir y comprender la variabilidad, que es el resultado de cambios en las condiciones bajo las que se hacen las observaciones.
RStudio es un entorno de desarrollo integrado para el lenguaje de programación R, dedicado a la computación estadística y gráficos. Incluye una consola, editor de sintaxis que apoya la ejecución de código, así como herramientas para el trazado, la depuración y la gestión del espacio de trabajo.
RStudio
2.- Enliste y define los tipos de variables usados en estadística, de 2 ejemplos de cada uno. Defina distribución de frecuencia y explique que es la distribución normal.
Variable cualitativa Artículo principal: Variable cualitativa. Las variables cualitativas son aquellas características o cualidades que no pueden ser calculadas con números, sino que son clasificadas con palabras.
Este tipo de variable, a su vez, se divide en:
Cualitativa nominal: aquellas variables que no siguen ningún orden en específico. Por ejemplo, los colores, tales como el negro, naranja o amarillo.
Cualitativa ordinal: aquellas que siguen un orden o jerarquía. Por ejemplo, el nivel socioeconómico alto, medio o bajo.
Cualitativa binaria: variables que permiten tan solo dos resultados. Por ejemplo, sí o no; hombre o mujer.
Variable cuantitativa
Artículo principal: Variable cuantitativa.
Las variables cuantitativas son aquellas características o cualidades que sí pueden expresarse o medirse a través de números.
Este tipo de variable, a su vez, se divide en:
Cuantitativa discreta: aquella variable que utiliza valores enteros y no finitos. Por ejemplo, la cantidad de familiares que tiene una persona, tal como 2, 3, 4 o más.
Cuantitativa continua: aquella variable que utiliza valores finitos y objetivos, y suele caracterizarse por utilizar valores decimales. Por ejemplo, el peso de una persona, tal como 64.3 kg, 72.3 kg, etc.
Usando los datos de calidad del aire contenidos en este apartado:
ftp://132.248.8.31/caire/erno/L1/hora Son datos de Hermosillo, Sonora
¿Que es pm10? Las PM10 se pueden definir como aquellas partículas sólidas o líquidas de polvo, cenizas, hollín, partículas metálicas, cemento o polen, dispersas en la atmósfera, y cuyo diámetro varía entre 2,5 y 10 µm (1 micrómetro corresponde la milésima parte de 1 milímetro). Primero que todo vamos a extraer los datos,setear la carpeta de probabilidad y estadistica he instalar las librerias necesarias para la elaboracion del examen
¿Que es el oxigeno? Elemento químico gaseoso, símbolo O, número atómico 8 y peso atómico 15.9994. Es de gran interés por ser el elemento esencial en los procesos de respiración de la mayor parte de las células vivas y en los procesos de combustión. Es el elemento más abundante en la corteza terrestre. Cerca de una quinta parte (en volumen) del aire es oxígeno. Existen equipos capaces de concentrar el oxígeno del aire. Son los llamados generadores o concentradores de oxígeno, que son los utilizados en los bares de oxígeno. El oxígeno gaseoso no combinado suele existir en forma de moléculas diatómicas, O2, pero también existe en forma triatómica, O3, llamada ozono. El oxígeno se separa del aire por licuefacción y destilación fraccionada. Las principales aplicaciones del oxígeno en orden de importancia son: 1) fundición, refinación y fabricación de acero y otros metales 2) manufactura de productos químicos por oxidación controlada 3) propulsión de cohetes 4) apoyo a la vida biológica y medicina,minería, producción y fabricación de productos de piedra y vidrio.
Read more: https://www.lenntech.es/periodica/elementos/o.htm#ixzz6bllqY7FU
library(pacman)
p_load("readr","DT","prettydoc","fdth","modeest","plotly","tidyverse","gganimate","gifski","lubridate")
setwd("~/Tareas uni/Probabilidad y estadistica")
OxigenoJulio2019 <- read_csv("OxigenoJulio2019.csv")##
## -- Column specification ------------------------------------------------------------------------------------------------------------------------
## cols(
## Time = col_character(),
## O3 = col_double(),
## PM10 = col_double()
## )OxigenoJulio2020 <- read_csv("OxigenoJulio2020.csv")##
## -- Column specification ------------------------------------------------------------------------------------------------------------------------
## cols(
## Time = col_character(),
## O3 = col_double(),
## PM10 = col_double()
## )OxigenoUnion <- read_csv("junio2019yjunio2020.csv")##
## -- Column specification ------------------------------------------------------------------------------------------------------------------------
## cols(
## Time = col_character(),
## O32019 = col_double(),
## PM102019 = col_double(),
## PM102020 = col_double(),
## O32020 = col_double()
## )datatable(OxigenoJulio2019)datatable(OxigenoJulio2020)datatable(OxigenoUnion)#Solo de Oxigeno
O3Julio2019 <- OxigenoJulio2019$O3
O3Julio2020 <- OxigenoJulio2020$O3
#Solo de contaminacion de ambiente pm10
pm10Julio2019 <- OxigenoJulio2019$PM10
pm10Julio2020 <- OxigenoJulio2020$PM10Explicare lo que quiero hacer lo que quiero hacer es comparar la mitad de este año con la mitad del año pasado a ver que tanta contaminacion hubo y oxigeno
#Oxigeno de julio 2019
distOxigenoJulio2019 <- fdt(OxigenoJulio2019, breaks = "Sturges")
dist## function (x, method = "euclidean", diag = FALSE, upper = FALSE,
## p = 2)
## {
## if (!is.na(pmatch(method, "euclidian")))
## method <- "euclidean"
## METHODS <- c("euclidean", "maximum", "manhattan", "canberra",
## "binary", "minkowski")
## method <- pmatch(method, METHODS)
## if (is.na(method))
## stop("invalid distance method")
## if (method == -1)
## stop("ambiguous distance method")
## x <- as.matrix(x)
## N <- nrow(x)
## attrs <- if (method == 6L)
## list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag,
## Upper = upper, method = METHODS[method], p = p, call = match.call(),
## class = "dist")
## else list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag,
## Upper = upper, method = METHODS[method], call = match.call(),
## class = "dist")
## .Call(C_Cdist, x, method, attrs, p)
## }
## <bytecode: 0x000000001be7f960>
## <environment: namespace:stats>#Oxigeno de julio 2020
distOxigenoJulio2020 <- fdt(OxigenoJulio2020, breaks = "Sturges")
dist## function (x, method = "euclidean", diag = FALSE, upper = FALSE,
## p = 2)
## {
## if (!is.na(pmatch(method, "euclidian")))
## method <- "euclidean"
## METHODS <- c("euclidean", "maximum", "manhattan", "canberra",
## "binary", "minkowski")
## method <- pmatch(method, METHODS)
## if (is.na(method))
## stop("invalid distance method")
## if (method == -1)
## stop("ambiguous distance method")
## x <- as.matrix(x)
## N <- nrow(x)
## attrs <- if (method == 6L)
## list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag,
## Upper = upper, method = METHODS[method], p = p, call = match.call(),
## class = "dist")
## else list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag,
## Upper = upper, method = METHODS[method], call = match.call(),
## class = "dist")
## .Call(C_Cdist, x, method, attrs, p)
## }
## <bytecode: 0x000000001be7f960>
## <environment: namespace:stats>Ordenar los datos
- visuzalización de reconocimiento Esto con una gráfica sencilla para conocer cómo se comportan los datos para ordenarlos ###Histograma y poligonos de distribucion de frecuencia de julio 2019 y julio 2020
plot(distOxigenoJulio2019,type = "fh")
plot(distOxigenoJulio2019,type = "rfh")
plot(distOxigenoJulio2019,type = "cfh")
plot(distOxigenoJulio2019,type = "fp")
plot(distOxigenoJulio2019,type = "rfp")
plot(distOxigenoJulio2019,type = "cfp")
plot(distOxigenoJulio2020,type = "fh")
plot(distOxigenoJulio2020,type = "rfh")
plot(distOxigenoJulio2020,type = "cfh")
plot(distOxigenoJulio2020,type = "fp")
plot(distOxigenoJulio2020,type = "rfp")
plot(distOxigenoJulio2020,type = "cfp")
##Medidas de tendencia de o3 (moda,media,mediana) de julio 2019 y julio 2020
summary(O3Julio2019)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.30 18.07 25.55 26.28 34.13 71.49boxplot(O3Julio2019)
mfv(O3Julio2019)## [1] 12.41 16.74 23.65 26.88 29.82summary(O3Julio2020)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.69 15.73 22.12 23.86 30.94 60.06boxplot(O3Julio2020)
mfv(O3Julio2020)## [1] 8.30 8.74 9.91 10.03 11.28 11.42 11.95 13.46 13.59 13.68 13.82 14.34
## [13] 14.39 14.66 15.74 16.27 16.32 16.52 16.74 16.81 17.29 17.39 18.17 18.37
## [25] 18.88 19.22 19.40 19.44 19.51 19.60 19.80 20.66 20.74 20.77 21.48 23.37
## [37] 23.84 24.00 24.97 26.21 26.62 27.42 27.63 27.89 28.33 28.35 28.51 28.70
## [49] 28.82 29.45 29.50 30.16 30.65 31.07 31.78 32.24 32.41 33.04 34.26 34.41
## [61] 37.29 40.72 49.65##Medidas de tendencia de pm10 (moda,media,mediana) de julio 2019 y julio 2020
summary(pm10Julio2019)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -14.99 14.22 22.30 25.91 31.03 952.99boxplot(pm10Julio2019)
mfv(pm10Julio2019)## [1] 15.06 23.86summary(pm10Julio2020)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -35.44 13.16 21.24 24.34 32.10 112.70boxplot(pm10Julio2020)
mfv(pm10Julio2020)## [1] 14.75 25.34###Medidas de dispercion de o3 de julio 2019 y julio 2020
sd(O3Julio2019)## [1] 10.63907var(O3Julio2019)## [1] 113.1898plot(O3Julio2019)
sd(O3Julio2020)## [1] 10.86695var(O3Julio2020)## [1] 118.0907plot(O3Julio2020)
###Medidas de dispercion de pm10 de julio 2019 y julio 2020
sd(pm10Julio2019)## [1] 39.00793var(pm10Julio2019)## [1] 1521.619plot(pm10Julio2019)
sd(pm10Julio2020)## [1] 17.43223var(pm10Julio2020)## [1] 303.8827plot(pm10Julio2020)
##Distribucion de probabilidad
frecuencia con la cual se repiten los valores del conjunto de julio 2019 pero de o3
sort(O3Julio2019)## [1] 1.30 2.77 3.11 4.04 4.73 5.15 5.55 5.85 5.86 6.21 6.42 7.46
## [13] 7.57 7.85 8.00 8.19 8.21 9.07 9.13 9.29 9.31 9.43 9.61 9.69
## [25] 10.03 10.10 10.12 10.24 10.27 10.36 10.37 10.44 10.49 10.51 10.52 10.53
## [37] 10.58 10.93 10.94 10.96 11.01 11.03 11.03 11.18 11.30 11.33 11.43 11.50
## [49] 11.52 11.62 11.71 11.87 11.94 11.96 12.00 12.01 12.13 12.14 12.15 12.16
## [61] 12.17 12.18 12.23 12.26 12.38 12.41 12.41 12.41 12.45 12.50 12.51 12.51
## [73] 12.52 12.54 12.61 12.68 12.71 12.78 12.81 12.81 12.83 12.85 12.95 12.98
## [85] 13.04 13.08 13.17 13.18 13.38 13.41 13.50 13.51 13.51 13.54 13.56 13.57
## [97] 13.66 13.69 13.77 13.77 13.78 13.82 13.85 13.90 13.91 13.95 13.98 14.06
## [109] 14.10 14.11 14.24 14.33 14.39 14.46 14.47 14.49 14.51 14.55 14.58 14.63
## [121] 14.80 14.92 14.94 14.99 15.06 15.15 15.26 15.27 15.30 15.34 15.36 15.38
## [133] 15.40 15.40 15.41 15.47 15.58 15.60 15.61 15.79 15.82 15.84 15.97 15.98
## [145] 16.19 16.23 16.36 16.38 16.39 16.41 16.43 16.46 16.55 16.58 16.60 16.61
## [157] 16.65 16.74 16.74 16.74 16.75 16.79 16.79 16.83 16.91 16.94 16.94 16.98
## [169] 17.12 17.19 17.24 17.27 17.28 17.31 17.31 17.35 17.36 17.36 17.37 17.38
## [181] 17.62 17.84 17.90 17.96 18.04 18.05 18.07 18.13 18.17 18.29 18.33 18.34
## [193] 18.46 18.46 18.48 18.51 18.56 18.57 18.59 18.67 18.69 18.71 18.73 18.74
## [205] 18.76 18.80 18.88 18.94 18.96 18.99 18.99 19.06 19.07 19.08 19.18 19.21
## [217] 19.28 19.31 19.37 19.40 19.46 19.52 19.56 19.60 19.61 19.64 19.67 19.68
## [229] 19.69 19.78 19.79 19.87 19.91 19.98 19.99 20.01 20.03 20.13 20.15 20.16
## [241] 20.16 20.18 20.19 20.20 20.31 20.32 20.36 20.39 20.41 20.41 20.46 20.56
## [253] 20.60 20.62 20.66 20.67 20.71 20.82 20.84 20.92 20.95 20.97 21.02 21.06
## [265] 21.07 21.13 21.22 21.23 21.26 21.36 21.40 21.43 21.49 21.56 21.56 21.57
## [277] 21.71 21.72 21.73 21.82 21.86 21.87 21.96 21.97 21.98 22.05 22.07 22.08
## [289] 22.09 22.11 22.11 22.15 22.20 22.25 22.27 22.27 22.37 22.41 22.45 22.45
## [301] 22.51 22.56 22.58 22.60 22.69 22.79 22.82 22.85 22.88 22.96 22.97 22.99
## [313] 22.99 23.02 23.03 23.11 23.16 23.18 23.20 23.20 23.21 23.25 23.32 23.38
## [325] 23.38 23.39 23.40 23.47 23.49 23.50 23.54 23.61 23.62 23.65 23.65 23.65
## [337] 23.66 23.66 23.69 23.73 23.76 23.80 23.84 23.84 23.85 24.03 24.05 24.08
## [349] 24.10 24.12 24.14 24.23 24.24 24.29 24.40 24.42 24.48 24.52 24.53 24.63
## [361] 24.64 24.82 24.84 24.95 24.97 25.10 25.16 25.29 25.30 25.33 25.41 25.55
## [373] 25.56 25.57 25.57 25.67 25.70 25.79 25.79 25.81 25.84 25.94 25.96 25.96
## [385] 25.98 26.00 26.01 26.02 26.03 26.11 26.17 26.19 26.23 26.40 26.42 26.45
## [397] 26.52 26.58 26.58 26.59 26.59 26.62 26.66 26.88 26.88 26.88 26.98 26.99
## [409] 27.01 27.06 27.15 27.27 27.39 27.46 27.46 27.51 27.65 27.66 27.71 27.73
## [421] 27.81 28.04 28.05 28.09 28.10 28.16 28.28 28.29 28.41 28.42 28.48 28.49
## [433] 28.55 28.69 28.71 28.75 28.77 28.81 28.85 28.97 29.03 29.07 29.12 29.18
## [445] 29.20 29.22 29.26 29.27 29.29 29.31 29.38 29.39 29.44 29.46 29.47 29.52
## [457] 29.60 29.68 29.74 29.75 29.82 29.82 29.82 29.84 29.92 29.92 30.04 30.08
## [469] 30.24 30.28 30.28 30.30 30.32 30.38 30.47 30.49 30.51 30.52 30.59 30.65
## [481] 30.73 30.73 30.77 30.80 30.95 31.00 31.13 31.14 31.30 31.38 31.43 31.44
## [493] 31.46 31.50 31.58 31.64 31.68 31.70 31.71 31.76 31.80 32.01 32.04 32.09
## [505] 32.11 32.12 32.14 32.19 32.20 32.20 32.27 32.30 32.31 32.35 32.42 32.43
## [517] 32.52 32.54 32.64 32.68 32.71 32.81 32.83 32.95 33.02 33.05 33.05 33.09
## [529] 33.15 33.16 33.20 33.20 33.23 33.23 33.24 33.25 33.27 33.28 33.33 33.35
## [541] 33.44 33.45 33.47 33.53 33.54 33.56 33.57 33.63 33.65 33.66 33.82 33.87
## [553] 33.88 33.89 33.89 34.04 34.10 34.13 34.14 34.15 34.21 34.21 34.22 34.22
## [565] 34.25 34.25 34.34 34.36 34.42 34.53 34.54 34.55 34.56 34.57 34.64 34.73
## [577] 34.77 34.84 34.93 34.96 35.06 35.12 35.13 35.17 35.18 35.40 35.42 35.50
## [589] 35.60 35.63 35.67 35.89 35.91 36.02 36.02 36.07 36.27 36.31 36.36 36.38
## [601] 36.41 36.44 36.46 36.50 36.73 36.76 36.80 36.83 36.88 36.88 36.93 37.05
## [613] 37.23 37.54 37.55 37.60 37.65 37.65 37.73 37.79 37.90 37.90 37.93 37.94
## [625] 37.94 38.07 38.08 38.12 38.14 38.15 38.17 38.30 38.30 38.35 38.37 38.39
## [637] 38.41 38.43 38.48 38.57 38.61 38.69 38.77 38.78 38.89 39.01 39.05 39.16
## [649] 39.18 39.20 39.33 39.34 39.34 39.44 39.45 39.51 39.51 39.57 39.82 39.99
## [661] 40.01 40.07 40.17 40.22 40.26 40.35 40.37 40.39 40.40 40.49 40.50 40.72
## [673] 40.82 40.82 41.01 41.03 41.13 41.19 41.22 41.24 41.29 41.38 41.38 41.41
## [685] 41.50 41.50 41.73 41.75 41.75 41.77 41.83 41.84 41.97 42.07 42.14 42.17
## [697] 42.25 42.62 42.68 42.80 42.83 42.85 42.94 42.98 43.03 43.11 43.16 43.27
## [709] 43.47 43.63 43.75 44.26 44.35 44.52 44.71 44.75 44.94 44.97 45.67 45.69
## [721] 46.56 46.94 47.02 47.17 47.38 47.75 47.76 48.11 48.41 48.70 48.75 49.61
## [733] 49.82 49.85 49.85 50.17 50.42 50.56 50.84 51.27 51.61 53.36 55.20 71.49table(O3Julio2019)## O3Julio2019
## 1.3 2.77 3.11 4.04 4.73 5.15 5.55 5.85 5.86 6.21 6.42 7.46 7.57
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 7.85 8 8.19 8.21 9.07 9.13 9.29 9.31 9.43 9.61 9.69 10.03 10.1
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10.12 10.24 10.27 10.36 10.37 10.44 10.49 10.51 10.52 10.53 10.58 10.93 10.94
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 10.96 11.01 11.03 11.18 11.3 11.33 11.43 11.5 11.52 11.62 11.71 11.87 11.94
## 1 1 2 1 1 1 1 1 1 1 1 1 1
## 11.96 12 12.01 12.13 12.14 12.15 12.16 12.17 12.18 12.23 12.26 12.38 12.41
## 1 1 1 1 1 1 1 1 1 1 1 1 3
## 12.45 12.5 12.51 12.52 12.54 12.61 12.68 12.71 12.78 12.81 12.83 12.85 12.95
## 1 1 2 1 1 1 1 1 1 2 1 1 1
## 12.98 13.04 13.08 13.17 13.18 13.38 13.41 13.5 13.51 13.54 13.56 13.57 13.66
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 13.69 13.77 13.78 13.82 13.85 13.9 13.91 13.95 13.98 14.06 14.1 14.11 14.24
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 14.33 14.39 14.46 14.47 14.49 14.51 14.55 14.58 14.63 14.8 14.92 14.94 14.99
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15.06 15.15 15.26 15.27 15.3 15.34 15.36 15.38 15.4 15.41 15.47 15.58 15.6
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 15.61 15.79 15.82 15.84 15.97 15.98 16.19 16.23 16.36 16.38 16.39 16.41 16.43
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 16.46 16.55 16.58 16.6 16.61 16.65 16.74 16.75 16.79 16.83 16.91 16.94 16.98
## 1 1 1 1 1 1 3 1 2 1 1 2 1
## 17.12 17.19 17.24 17.27 17.28 17.31 17.35 17.36 17.37 17.38 17.62 17.84 17.9
## 1 1 1 1 1 2 1 2 1 1 1 1 1
## 17.96 18.04 18.05 18.07 18.13 18.17 18.29 18.33 18.34 18.46 18.48 18.51 18.56
## 1 1 1 1 1 1 1 1 1 2 1 1 1
## 18.57 18.59 18.67 18.69 18.71 18.73 18.74 18.76 18.8 18.88 18.94 18.96 18.99
## 1 1 1 1 1 1 1 1 1 1 1 1 2
## 19.06 19.07 19.08 19.18 19.21 19.28 19.31 19.37 19.4 19.46 19.52 19.56 19.6
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19.61 19.64 19.67 19.68 19.69 19.78 19.79 19.87 19.91 19.98 19.99 20.01 20.03
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 20.13 20.15 20.16 20.18 20.19 20.2 20.31 20.32 20.36 20.39 20.41 20.46 20.56
## 1 1 2 1 1 1 1 1 1 1 2 1 1
## 20.6 20.62 20.66 20.67 20.71 20.82 20.84 20.92 20.95 20.97 21.02 21.06 21.07
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 21.13 21.22 21.23 21.26 21.36 21.4 21.43 21.49 21.56 21.57 21.71 21.72 21.73
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 21.82 21.86 21.87 21.96 21.97 21.98 22.05 22.07 22.08 22.09 22.11 22.15 22.2
## 1 1 1 1 1 1 1 1 1 1 2 1 1
## 22.25 22.27 22.37 22.41 22.45 22.51 22.56 22.58 22.6 22.69 22.79 22.82 22.85
## 1 2 1 1 2 1 1 1 1 1 1 1 1
## 22.88 22.96 22.97 22.99 23.02 23.03 23.11 23.16 23.18 23.2 23.21 23.25 23.32
## 1 1 1 2 1 1 1 1 1 2 1 1 1
## 23.38 23.39 23.4 23.47 23.49 23.5 23.54 23.61 23.62 23.65 23.66 23.69 23.73
## 2 1 1 1 1 1 1 1 1 3 2 1 1
## 23.76 23.8 23.84 23.85 24.03 24.05 24.08 24.1 24.12 24.14 24.23 24.24 24.29
## 1 1 2 1 1 1 1 1 1 1 1 1 1
## 24.4 24.42 24.48 24.52 24.53 24.63 24.64 24.82 24.84 24.95 24.97 25.1 25.16
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 25.29 25.3 25.33 25.41 25.55 25.56 25.57 25.67 25.7 25.79 25.81 25.84 25.94
## 1 1 1 1 1 1 2 1 1 2 1 1 1
## 25.96 25.98 26 26.01 26.02 26.03 26.11 26.17 26.19 26.23 26.4 26.42 26.45
## 2 1 1 1 1 1 1 1 1 1 1 1 1
## 26.52 26.58 26.59 26.62 26.66 26.88 26.98 26.99 27.01 27.06 27.15 27.27 27.39
## 1 2 2 1 1 3 1 1 1 1 1 1 1
## 27.46 27.51 27.65 27.66 27.71 27.73 27.81 28.04 28.05 28.09 28.1 28.16 28.28
## 2 1 1 1 1 1 1 1 1 1 1 1 1
## 28.29 28.41 28.42 28.48 28.49 28.55 28.69 28.71 28.75 28.77 28.81 28.85 28.97
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 29.03 29.07 29.12 29.18 29.2 29.22 29.26 29.27 29.29 29.31 29.38 29.39 29.44
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 29.46 29.47 29.52 29.6 29.68 29.74 29.75 29.82 29.84 29.92 30.04 30.08 30.24
## 1 1 1 1 1 1 1 3 1 2 1 1 1
## 30.28 30.3 30.32 30.38 30.47 30.49 30.51 30.52 30.59 30.65 30.73 30.77 30.8
## 2 1 1 1 1 1 1 1 1 1 2 1 1
## 30.95 31 31.13 31.14 31.3 31.38 31.43 31.44 31.46 31.5 31.58 31.64 31.68
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 31.7 31.71 31.76 31.8 32.01 32.04 32.09 32.11 32.12 32.14 32.19 32.2 32.27
## 1 1 1 1 1 1 1 1 1 1 1 2 1
## 32.3 32.31 32.35 32.42 32.43 32.52 32.54 32.64 32.68 32.71 32.81 32.83 32.95
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 33.02 33.05 33.09 33.15 33.16 33.2 33.23 33.24 33.25 33.27 33.28 33.33 33.35
## 1 2 1 1 1 2 2 1 1 1 1 1 1
## 33.44 33.45 33.47 33.53 33.54 33.56 33.57 33.63 33.65 33.66 33.82 33.87 33.88
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 33.89 34.04 34.1 34.13 34.14 34.15 34.21 34.22 34.25 34.34 34.36 34.42 34.53
## 2 1 1 1 1 1 2 2 2 1 1 1 1
## 34.54 34.55 34.56 34.57 34.64 34.73 34.77 34.84 34.93 34.96 35.06 35.12 35.13
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 35.17 35.18 35.4 35.42 35.5 35.6 35.63 35.67 35.89 35.91 36.02 36.07 36.27
## 1 1 1 1 1 1 1 1 1 1 2 1 1
## 36.31 36.36 36.38 36.41 36.44 36.46 36.5 36.73 36.76 36.8 36.83 36.88 36.93
## 1 1 1 1 1 1 1 1 1 1 1 2 1
## 37.05 37.23 37.54 37.55 37.6 37.65 37.73 37.79 37.9 37.93 37.94 38.07 38.08
## 1 1 1 1 1 2 1 1 2 1 2 1 1
## 38.12 38.14 38.15 38.17 38.3 38.35 38.37 38.39 38.41 38.43 38.48 38.57 38.61
## 1 1 1 1 2 1 1 1 1 1 1 1 1
## 38.69 38.77 38.78 38.89 39.01 39.05 39.16 39.18 39.2 39.33 39.34 39.44 39.45
## 1 1 1 1 1 1 1 1 1 1 2 1 1
## 39.51 39.57 39.82 39.99 40.01 40.07 40.17 40.22 40.26 40.35 40.37 40.39 40.4
## 2 1 1 1 1 1 1 1 1 1 1 1 1
## 40.49 40.5 40.72 40.82 41.01 41.03 41.13 41.19 41.22 41.24 41.29 41.38 41.41
## 1 1 1 2 1 1 1 1 1 1 1 2 1
## 41.5 41.73 41.75 41.77 41.83 41.84 41.97 42.07 42.14 42.17 42.25 42.62 42.68
## 2 1 2 1 1 1 1 1 1 1 1 1 1
## 42.8 42.83 42.85 42.94 42.98 43.03 43.11 43.16 43.27 43.47 43.63 43.75 44.26
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 44.35 44.52 44.71 44.75 44.94 44.97 45.67 45.69 46.56 46.94 47.02 47.17 47.38
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 47.75 47.76 48.11 48.41 48.7 48.75 49.61 49.82 49.85 50.17 50.42 50.56 50.84
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 51.27 51.61 53.36 55.2 71.49
## 1 1 1 1 1frecuencia con la cual se repiten los valores del conjunto de julio 2020 pero de o3
sort(O3Julio2020)## [1] 3.69 4.01 4.06 4.62 4.73 4.81 4.84 4.87 4.92 5.35 5.43 5.58
## [13] 5.82 5.98 6.07 6.36 6.56 6.74 6.89 6.94 7.01 7.11 7.26 7.63
## [25] 7.67 7.80 8.22 8.29 8.30 8.30 8.32 8.37 8.50 8.62 8.74 8.74
## [37] 8.83 8.95 8.96 9.02 9.16 9.17 9.27 9.32 9.45 9.59 9.64 9.66
## [49] 9.69 9.71 9.73 9.79 9.91 9.91 10.01 10.03 10.03 10.25 10.27 10.40
## [61] 10.57 10.70 10.74 10.76 10.77 10.89 10.95 11.04 11.06 11.08 11.09 11.16
## [73] 11.19 11.21 11.23 11.26 11.28 11.28 11.29 11.33 11.37 11.42 11.42 11.52
## [85] 11.55 11.61 11.69 11.74 11.76 11.81 11.86 11.91 11.92 11.95 11.95 12.19
## [97] 12.22 12.24 12.28 12.42 12.45 12.61 12.67 12.77 12.93 12.96 12.98 12.99
## [109] 13.00 13.07 13.09 13.10 13.13 13.14 13.18 13.19 13.31 13.32 13.35 13.37
## [121] 13.46 13.46 13.47 13.59 13.59 13.62 13.67 13.68 13.68 13.82 13.82 13.85
## [133] 13.96 13.97 13.98 14.01 14.02 14.03 14.08 14.11 14.12 14.20 14.34 14.34
## [145] 14.35 14.39 14.39 14.48 14.58 14.59 14.60 14.62 14.64 14.66 14.66 14.72
## [157] 14.76 14.78 14.80 14.84 14.85 14.94 14.95 14.97 15.09 15.13 15.15 15.16
## [169] 15.18 15.22 15.23 15.24 15.30 15.36 15.40 15.41 15.42 15.44 15.46 15.50
## [181] 15.51 15.53 15.64 15.65 15.67 15.70 15.74 15.74 15.76 15.86 15.89 15.92
## [193] 15.98 15.99 16.04 16.06 16.07 16.15 16.27 16.27 16.29 16.31 16.32 16.32
## [205] 16.33 16.38 16.46 16.47 16.50 16.52 16.52 16.53 16.58 16.60 16.70 16.74
## [217] 16.74 16.81 16.81 16.85 16.87 16.90 16.93 16.96 16.98 17.02 17.10 17.12
## [229] 17.13 17.15 17.20 17.21 17.23 17.24 17.25 17.27 17.29 17.29 17.30 17.32
## [241] 17.36 17.37 17.38 17.39 17.39 17.40 17.42 17.45 17.53 17.59 17.65 17.68
## [253] 17.82 17.83 17.84 17.85 17.92 17.95 17.98 18.00 18.06 18.12 18.13 18.14
## [265] 18.17 18.17 18.31 18.33 18.36 18.37 18.37 18.39 18.40 18.48 18.49 18.50
## [277] 18.52 18.53 18.59 18.62 18.63 18.71 18.72 18.80 18.83 18.88 18.88 18.90
## [289] 18.92 18.98 18.99 19.06 19.09 19.15 19.17 19.22 19.22 19.23 19.28 19.29
## [301] 19.31 19.32 19.40 19.40 19.43 19.44 19.44 19.48 19.50 19.51 19.51 19.58
## [313] 19.60 19.60 19.61 19.71 19.75 19.78 19.80 19.80 19.92 19.94 20.00 20.03
## [325] 20.05 20.08 20.20 20.41 20.45 20.55 20.59 20.60 20.65 20.66 20.66 20.67
## [337] 20.68 20.69 20.70 20.74 20.74 20.77 20.77 20.80 20.82 20.84 20.85 21.00
## [349] 21.02 21.05 21.15 21.16 21.19 21.21 21.25 21.28 21.35 21.40 21.47 21.48
## [361] 21.48 21.53 21.55 21.72 21.76 21.81 21.83 21.85 21.86 21.90 21.93 22.10
## [373] 22.14 22.19 22.20 22.24 22.28 22.30 22.35 22.39 22.43 22.44 22.47 22.58
## [385] 22.63 22.64 22.66 22.67 22.69 22.74 22.77 22.82 22.84 22.86 22.87 22.90
## [397] 23.03 23.27 23.28 23.37 23.37 23.43 23.46 23.52 23.56 23.60 23.64 23.67
## [409] 23.68 23.70 23.72 23.77 23.84 23.84 23.85 23.90 23.95 23.96 24.00 24.00
## [421] 24.06 24.10 24.21 24.26 24.27 24.35 24.43 24.45 24.48 24.61 24.62 24.63
## [433] 24.71 24.73 24.77 24.83 24.90 24.97 24.97 25.03 25.04 25.12 25.17 25.19
## [445] 25.29 25.34 25.36 25.41 25.46 25.61 25.62 25.64 25.75 25.82 25.85 25.87
## [457] 25.91 25.95 25.98 25.99 26.01 26.06 26.13 26.21 26.21 26.37 26.38 26.41
## [469] 26.48 26.60 26.62 26.62 26.73 26.78 26.79 26.93 27.02 27.05 27.07 27.09
## [481] 27.11 27.14 27.29 27.30 27.40 27.42 27.42 27.44 27.63 27.63 27.68 27.86
## [493] 27.89 27.89 27.90 27.93 27.96 27.98 28.06 28.09 28.21 28.33 28.33 28.35
## [505] 28.35 28.40 28.51 28.51 28.56 28.57 28.60 28.61 28.70 28.70 28.76 28.77
## [517] 28.82 28.82 28.83 28.84 29.01 29.04 29.07 29.10 29.15 29.28 29.37 29.45
## [529] 29.45 29.48 29.50 29.50 29.56 29.59 29.60 29.67 29.72 29.74 29.81 29.91
## [541] 29.95 29.97 30.13 30.16 30.16 30.18 30.19 30.29 30.37 30.38 30.41 30.44
## [553] 30.54 30.64 30.65 30.65 30.71 30.91 31.03 31.07 31.07 31.09 31.12 31.22
## [565] 31.23 31.35 31.39 31.40 31.41 31.57 31.61 31.69 31.78 31.78 31.79 31.80
## [577] 31.86 31.88 31.90 31.92 32.00 32.08 32.17 32.20 32.24 32.24 32.25 32.37
## [589] 32.41 32.41 32.42 32.65 32.71 32.72 32.82 32.86 32.96 33.02 33.04 33.04
## [601] 33.06 33.18 33.33 33.40 33.61 33.64 33.97 34.00 34.06 34.07 34.08 34.15
## [613] 34.24 34.26 34.26 34.27 34.30 34.41 34.41 34.42 34.46 34.56 34.63 34.70
## [625] 34.73 34.77 34.83 34.92 35.09 35.12 35.17 35.29 35.38 35.55 35.63 35.68
## [637] 35.73 35.94 35.98 35.99 36.01 36.03 36.25 36.37 36.38 36.52 36.58 36.68
## [649] 36.71 37.03 37.13 37.15 37.19 37.20 37.29 37.29 37.33 37.42 37.54 37.59
## [661] 38.08 38.09 38.19 38.26 38.33 38.40 38.52 38.53 38.59 38.67 38.71 39.00
## [673] 39.14 39.24 39.38 39.53 40.00 40.07 40.15 40.21 40.39 40.52 40.55 40.56
## [685] 40.72 40.72 40.73 41.35 41.47 41.48 41.51 41.97 42.06 42.34 42.48 42.50
## [697] 42.54 42.76 42.84 43.07 43.14 43.25 43.31 43.33 43.63 43.69 43.94 44.00
## [709] 44.33 44.36 44.65 44.71 44.77 44.96 45.08 45.31 45.68 45.76 46.06 46.27
## [721] 46.42 46.54 47.38 47.81 47.97 48.06 48.23 48.37 49.65 49.65 50.44 51.62
## [733] 51.97 52.96 53.05 54.57 54.89 55.30 55.42 56.14 57.67 59.04 59.19 60.06table(O3Julio2020)## O3Julio2020
## 3.69 4.01 4.06 4.62 4.73 4.81 4.84 4.87 4.92 5.35 5.43 5.58 5.82
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 5.98 6.07 6.36 6.56 6.74 6.89 6.94 7.01 7.11 7.26 7.63 7.67 7.8
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 8.22 8.29 8.3 8.32 8.37 8.5 8.62 8.74 8.83 8.95 8.96 9.02 9.16
## 1 1 2 1 1 1 1 2 1 1 1 1 1
## 9.17 9.27 9.32 9.45 9.59 9.64 9.66 9.69 9.71 9.73 9.79 9.91 10.01
## 1 1 1 1 1 1 1 1 1 1 1 2 1
## 10.03 10.25 10.27 10.4 10.57 10.7 10.74 10.76 10.77 10.89 10.95 11.04 11.06
## 2 1 1 1 1 1 1 1 1 1 1 1 1
## 11.08 11.09 11.16 11.19 11.21 11.23 11.26 11.28 11.29 11.33 11.37 11.42 11.52
## 1 1 1 1 1 1 1 2 1 1 1 2 1
## 11.55 11.61 11.69 11.74 11.76 11.81 11.86 11.91 11.92 11.95 12.19 12.22 12.24
## 1 1 1 1 1 1 1 1 1 2 1 1 1
## 12.28 12.42 12.45 12.61 12.67 12.77 12.93 12.96 12.98 12.99 13 13.07 13.09
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 13.1 13.13 13.14 13.18 13.19 13.31 13.32 13.35 13.37 13.46 13.47 13.59 13.62
## 1 1 1 1 1 1 1 1 1 2 1 2 1
## 13.67 13.68 13.82 13.85 13.96 13.97 13.98 14.01 14.02 14.03 14.08 14.11 14.12
## 1 2 2 1 1 1 1 1 1 1 1 1 1
## 14.2 14.34 14.35 14.39 14.48 14.58 14.59 14.6 14.62 14.64 14.66 14.72 14.76
## 1 2 1 2 1 1 1 1 1 1 2 1 1
## 14.78 14.8 14.84 14.85 14.94 14.95 14.97 15.09 15.13 15.15 15.16 15.18 15.22
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15.23 15.24 15.3 15.36 15.4 15.41 15.42 15.44 15.46 15.5 15.51 15.53 15.64
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 15.65 15.67 15.7 15.74 15.76 15.86 15.89 15.92 15.98 15.99 16.04 16.06 16.07
## 1 1 1 2 1 1 1 1 1 1 1 1 1
## 16.15 16.27 16.29 16.31 16.32 16.33 16.38 16.46 16.47 16.5 16.52 16.53 16.58
## 1 2 1 1 2 1 1 1 1 1 2 1 1
## 16.6 16.7 16.74 16.81 16.85 16.87 16.9 16.93 16.96 16.98 17.02 17.1 17.12
## 1 1 2 2 1 1 1 1 1 1 1 1 1
## 17.13 17.15 17.2 17.21 17.23 17.24 17.25 17.27 17.29 17.3 17.32 17.36 17.37
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 17.38 17.39 17.4 17.42 17.45 17.53 17.59 17.65 17.68 17.82 17.83 17.84 17.85
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 17.92 17.95 17.98 18 18.06 18.12 18.13 18.14 18.17 18.31 18.33 18.36 18.37
## 1 1 1 1 1 1 1 1 2 1 1 1 2
## 18.39 18.4 18.48 18.49 18.5 18.52 18.53 18.59 18.62 18.63 18.71 18.72 18.8
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 18.83 18.88 18.9 18.92 18.98 18.99 19.06 19.09 19.15 19.17 19.22 19.23 19.28
## 1 2 1 1 1 1 1 1 1 1 2 1 1
## 19.29 19.31 19.32 19.4 19.43 19.44 19.48 19.5 19.51 19.58 19.6 19.61 19.71
## 1 1 1 2 1 2 1 1 2 1 2 1 1
## 19.75 19.78 19.8 19.92 19.94 20 20.03 20.05 20.08 20.2 20.41 20.45 20.55
## 1 1 2 1 1 1 1 1 1 1 1 1 1
## 20.59 20.6 20.65 20.66 20.67 20.68 20.69 20.7 20.74 20.77 20.8 20.82 20.84
## 1 1 1 2 1 1 1 1 2 2 1 1 1
## 20.85 21 21.02 21.05 21.15 21.16 21.19 21.21 21.25 21.28 21.35 21.4 21.47
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 21.48 21.53 21.55 21.72 21.76 21.81 21.83 21.85 21.86 21.9 21.93 22.1 22.14
## 2 1 1 1 1 1 1 1 1 1 1 1 1
## 22.19 22.2 22.24 22.28 22.3 22.35 22.39 22.43 22.44 22.47 22.58 22.63 22.64
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 22.66 22.67 22.69 22.74 22.77 22.82 22.84 22.86 22.87 22.9 23.03 23.27 23.28
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 23.37 23.43 23.46 23.52 23.56 23.6 23.64 23.67 23.68 23.7 23.72 23.77 23.84
## 2 1 1 1 1 1 1 1 1 1 1 1 2
## 23.85 23.9 23.95 23.96 24 24.06 24.1 24.21 24.26 24.27 24.35 24.43 24.45
## 1 1 1 1 2 1 1 1 1 1 1 1 1
## 24.48 24.61 24.62 24.63 24.71 24.73 24.77 24.83 24.9 24.97 25.03 25.04 25.12
## 1 1 1 1 1 1 1 1 1 2 1 1 1
## 25.17 25.19 25.29 25.34 25.36 25.41 25.46 25.61 25.62 25.64 25.75 25.82 25.85
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 25.87 25.91 25.95 25.98 25.99 26.01 26.06 26.13 26.21 26.37 26.38 26.41 26.48
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 26.6 26.62 26.73 26.78 26.79 26.93 27.02 27.05 27.07 27.09 27.11 27.14 27.29
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 27.3 27.4 27.42 27.44 27.63 27.68 27.86 27.89 27.9 27.93 27.96 27.98 28.06
## 1 1 2 1 2 1 1 2 1 1 1 1 1
## 28.09 28.21 28.33 28.35 28.4 28.51 28.56 28.57 28.6 28.61 28.7 28.76 28.77
## 1 1 2 2 1 2 1 1 1 1 2 1 1
## 28.82 28.83 28.84 29.01 29.04 29.07 29.1 29.15 29.28 29.37 29.45 29.48 29.5
## 2 1 1 1 1 1 1 1 1 1 2 1 2
## 29.56 29.59 29.6 29.67 29.72 29.74 29.81 29.91 29.95 29.97 30.13 30.16 30.18
## 1 1 1 1 1 1 1 1 1 1 1 2 1
## 30.19 30.29 30.37 30.38 30.41 30.44 30.54 30.64 30.65 30.71 30.91 31.03 31.07
## 1 1 1 1 1 1 1 1 2 1 1 1 2
## 31.09 31.12 31.22 31.23 31.35 31.39 31.4 31.41 31.57 31.61 31.69 31.78 31.79
## 1 1 1 1 1 1 1 1 1 1 1 2 1
## 31.8 31.86 31.88 31.9 31.92 32 32.08 32.17 32.2 32.24 32.25 32.37 32.41
## 1 1 1 1 1 1 1 1 1 2 1 1 2
## 32.42 32.65 32.71 32.72 32.82 32.86 32.96 33.02 33.04 33.06 33.18 33.33 33.4
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 33.61 33.64 33.97 34 34.06 34.07 34.08 34.15 34.24 34.26 34.27 34.3 34.41
## 1 1 1 1 1 1 1 1 1 2 1 1 2
## 34.42 34.46 34.56 34.63 34.7 34.73 34.77 34.83 34.92 35.09 35.12 35.17 35.29
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 35.38 35.55 35.63 35.68 35.73 35.94 35.98 35.99 36.01 36.03 36.25 36.37 36.38
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 36.52 36.58 36.68 36.71 37.03 37.13 37.15 37.19 37.2 37.29 37.33 37.42 37.54
## 1 1 1 1 1 1 1 1 1 2 1 1 1
## 37.59 38.08 38.09 38.19 38.26 38.33 38.4 38.52 38.53 38.59 38.67 38.71 39
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 39.14 39.24 39.38 39.53 40 40.07 40.15 40.21 40.39 40.52 40.55 40.56 40.72
## 1 1 1 1 1 1 1 1 1 1 1 1 2
## 40.73 41.35 41.47 41.48 41.51 41.97 42.06 42.34 42.48 42.5 42.54 42.76 42.84
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 43.07 43.14 43.25 43.31 43.33 43.63 43.69 43.94 44 44.33 44.36 44.65 44.71
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 44.77 44.96 45.08 45.31 45.68 45.76 46.06 46.27 46.42 46.54 47.38 47.81 47.97
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 48.06 48.23 48.37 49.65 50.44 51.62 51.97 52.96 53.05 54.57 54.89 55.3 55.42
## 1 1 1 2 1 1 1 1 1 1 1 1 1
## 56.14 57.67 59.04 59.19 60.06
## 1 1 1 1 1frecuencia con la cual se repiten los valores del conjunto de julio 2019 pero de pm10
sort(pm10Julio2019)## [1] -14.99 -10.28 -9.26 -9.02 -8.13 -7.85 -7.06 -3.96 -3.81 -2.83
## [11] -1.71 -1.53 -1.24 -0.75 -0.73 -0.71 -0.22 -0.12 0.99 1.06
## [21] 1.48 1.52 1.57 1.62 1.92 2.08 2.22 2.71 2.86 3.19
## [31] 3.37 3.43 3.52 3.55 3.59 3.63 3.86 3.88 4.00 4.01
## [41] 4.30 4.53 4.62 4.80 4.83 4.84 5.15 5.31 5.54 5.83
## [51] 5.88 5.94 5.96 5.97 6.19 6.30 6.64 6.64 6.73 7.13
## [61] 7.16 7.26 7.29 7.29 7.33 7.37 7.42 7.49 7.60 7.77
## [71] 7.81 8.09 8.09 8.15 8.26 8.26 8.27 8.37 8.52 8.60
## [81] 8.61 8.65 8.72 8.74 8.76 8.77 8.77 8.95 9.02 9.06
## [91] 9.07 9.21 9.27 9.33 9.35 9.36 9.57 9.58 9.62 9.67
## [101] 9.68 9.84 9.89 9.93 9.96 10.02 10.25 10.28 10.36 10.38
## [111] 10.43 10.56 10.59 10.69 10.70 10.73 10.74 10.82 10.90 10.98
## [121] 10.98 11.04 11.07 11.26 11.35 11.45 11.58 11.62 11.63 11.68
## [131] 11.70 11.71 11.81 11.81 11.82 11.83 11.85 11.92 11.93 12.02
## [141] 12.08 12.21 12.24 12.25 12.27 12.30 12.39 12.40 12.40 12.42
## [151] 12.44 12.46 12.49 12.49 12.55 12.65 12.74 12.75 12.81 12.86
## [161] 12.90 12.91 12.97 12.97 13.07 13.09 13.11 13.21 13.32 13.42
## [171] 13.48 13.51 13.55 13.58 13.61 13.64 13.69 13.70 13.70 13.76
## [181] 13.97 13.99 14.07 14.11 14.13 14.21 14.22 14.25 14.33 14.43
## [191] 14.45 14.48 14.50 14.65 14.65 14.67 14.80 14.89 14.90 14.92
## [201] 14.98 15.01 15.06 15.06 15.06 15.07 15.13 15.19 15.20 15.27
## [211] 15.28 15.29 15.34 15.41 15.55 15.57 15.59 15.65 15.66 15.74
## [221] 15.75 15.76 15.77 15.80 15.88 15.90 15.93 15.93 16.02 16.09
## [231] 16.15 16.18 16.18 16.24 16.32 16.39 16.44 16.44 16.46 16.47
## [241] 16.49 16.57 16.58 16.61 16.64 16.70 16.70 16.80 16.91 16.93
## [251] 16.93 16.95 17.05 17.08 17.15 17.17 17.20 17.25 17.26 17.26
## [261] 17.33 17.35 17.44 17.45 17.47 17.55 17.63 17.64 17.64 17.75
## [271] 17.81 17.83 17.91 17.91 17.93 18.01 18.06 18.08 18.09 18.14
## [281] 18.21 18.21 18.22 18.29 18.29 18.35 18.36 18.44 18.46 18.51
## [291] 18.63 18.63 18.70 18.72 18.73 18.74 18.76 18.77 18.77 18.78
## [301] 18.80 18.81 18.84 18.93 18.99 19.03 19.11 19.14 19.14 19.19
## [311] 19.32 19.33 19.35 19.39 19.43 19.49 19.51 19.53 19.55 19.64
## [321] 19.69 19.71 19.77 19.78 19.82 19.86 19.86 19.98 20.01 20.04
## [331] 20.06 20.08 20.15 20.19 20.25 20.34 20.37 20.40 20.50 20.51
## [341] 20.52 20.55 20.77 20.81 20.84 20.85 20.91 20.98 21.01 21.09
## [351] 21.17 21.22 21.23 21.24 21.29 21.34 21.39 21.42 21.51 21.56
## [361] 21.59 21.60 21.61 21.63 21.94 22.00 22.05 22.08 22.15 22.15
## [371] 22.28 22.29 22.30 22.30 22.41 22.45 22.46 22.47 22.49 22.51
## [381] 22.55 22.59 22.61 22.63 22.66 22.79 22.80 22.82 22.86 22.90
## [391] 22.90 22.97 23.01 23.05 23.05 23.10 23.13 23.16 23.23 23.26
## [401] 23.27 23.40 23.42 23.49 23.55 23.65 23.66 23.67 23.72 23.76
## [411] 23.86 23.86 23.86 23.91 23.92 24.08 24.08 24.09 24.13 24.14
## [421] 24.18 24.20 24.26 24.35 24.35 24.37 24.42 24.46 24.57 24.60
## [431] 24.69 24.69 24.76 24.82 24.82 24.85 24.89 24.90 24.92 24.94
## [441] 24.97 24.98 25.07 25.11 25.14 25.14 25.16 25.18 25.25 25.26
## [451] 25.26 25.27 25.30 25.37 25.37 25.44 25.48 25.50 25.52 25.58
## [461] 25.60 25.65 25.66 25.72 25.78 25.87 25.90 26.05 26.07 26.07
## [471] 26.12 26.21 26.30 26.36 26.42 26.47 26.55 26.56 26.60 26.60
## [481] 26.67 26.70 26.77 26.79 27.03 27.04 27.05 27.09 27.11 27.15
## [491] 27.28 27.33 27.38 27.39 27.45 27.49 27.64 27.66 27.66 27.67
## [501] 27.80 27.80 27.81 27.87 27.90 27.91 27.93 27.95 28.02 28.11
## [511] 28.28 28.30 28.34 28.35 28.36 28.38 28.55 28.56 28.64 28.67
## [521] 28.75 28.80 28.94 28.95 28.96 29.01 29.02 29.04 29.07 29.11
## [531] 29.13 29.17 29.28 29.37 29.43 29.47 29.48 29.55 29.59 29.67
## [541] 29.70 29.81 29.85 29.86 29.93 29.93 29.95 29.97 30.15 30.23
## [551] 30.41 30.45 30.68 30.78 30.93 30.94 30.98 31.01 31.08 31.09
## [561] 31.11 31.23 31.30 31.31 31.32 31.38 31.70 31.71 31.81 31.82
## [571] 31.84 31.85 31.86 31.93 32.02 32.02 32.05 32.13 32.22 32.27
## [581] 32.29 32.33 32.46 32.59 32.68 32.79 32.85 32.91 32.94 33.09
## [591] 33.21 33.22 33.23 33.40 33.43 33.46 33.47 33.48 33.54 33.56
## [601] 33.70 33.71 33.85 33.99 34.00 34.29 34.48 34.56 34.65 34.66
## [611] 34.73 34.87 34.95 34.99 35.16 35.22 35.26 35.39 35.44 35.49
## [621] 35.52 35.62 35.80 35.83 35.90 36.09 36.13 36.18 36.19 36.19
## [631] 36.23 36.32 36.32 36.33 36.53 36.60 36.68 36.70 36.72 36.83
## [641] 36.87 37.03 37.17 37.21 37.65 37.89 38.18 38.42 38.56 38.98
## [651] 39.05 39.59 39.67 39.74 39.78 39.79 39.97 40.09 40.19 40.23
## [661] 40.46 41.56 41.70 41.76 41.84 42.01 42.12 42.26 42.40 42.51
## [671] 42.63 42.79 42.87 42.88 42.91 42.95 43.10 43.22 43.28 43.58
## [681] 43.65 43.67 43.87 44.14 44.37 44.39 44.44 44.56 44.61 45.29
## [691] 45.83 46.23 46.54 46.74 46.77 46.86 47.63 47.69 48.47 48.52
## [701] 49.49 49.80 49.82 49.85 49.96 50.28 50.71 51.31 51.56 52.66
## [711] 54.08 54.11 56.10 56.64 56.95 57.02 57.65 57.82 59.36 61.72
## [721] 61.99 62.33 62.96 66.44 75.14 75.37 76.32 79.51 80.36 82.19
## [731] 82.78 84.48 85.82 88.35 98.39 99.53 100.48 110.34 118.98 121.18
## [741] 121.41 126.15 265.14 952.99table(pm10Julio2019)## pm10Julio2019
## -14.99 -10.28 -9.26 -9.02 -8.13 -7.85 -7.06 -3.96 -3.81 -2.83 -1.71
## 1 1 1 1 1 1 1 1 1 1 1
## -1.53 -1.24 -0.75 -0.73 -0.71 -0.22 -0.12 0.99 1.06 1.48 1.52
## 1 1 1 1 1 1 1 1 1 1 1
## 1.57 1.62 1.92 2.08 2.22 2.71 2.86 3.19 3.37 3.43 3.52
## 1 1 1 1 1 1 1 1 1 1 1
## 3.55 3.59 3.63 3.86 3.88 4 4.01 4.3 4.53 4.62 4.8
## 1 1 1 1 1 1 1 1 1 1 1
## 4.83 4.84 5.15 5.31 5.54 5.83 5.88 5.94 5.96 5.97 6.19
## 1 1 1 1 1 1 1 1 1 1 1
## 6.3 6.64 6.73 7.13 7.16 7.26 7.29 7.33 7.37 7.42 7.49
## 1 2 1 1 1 1 2 1 1 1 1
## 7.6 7.77 7.81 8.09 8.15 8.26 8.27 8.37 8.52 8.6 8.61
## 1 1 1 2 1 2 1 1 1 1 1
## 8.65 8.72 8.74 8.76 8.77 8.95 9.02 9.06 9.07 9.21 9.27
## 1 1 1 1 2 1 1 1 1 1 1
## 9.33 9.35 9.36 9.57 9.58 9.62 9.67 9.68 9.84 9.89 9.93
## 1 1 1 1 1 1 1 1 1 1 1
## 9.96 10.02 10.25 10.28 10.36 10.38 10.43 10.56 10.59 10.69 10.7
## 1 1 1 1 1 1 1 1 1 1 1
## 10.73 10.74 10.82 10.9 10.98 11.04 11.07 11.26 11.35 11.45 11.58
## 1 1 1 1 2 1 1 1 1 1 1
## 11.62 11.63 11.68 11.7 11.71 11.81 11.82 11.83 11.85 11.92 11.93
## 1 1 1 1 1 2 1 1 1 1 1
## 12.02 12.08 12.21 12.24 12.25 12.27 12.3 12.39 12.4 12.42 12.44
## 1 1 1 1 1 1 1 1 2 1 1
## 12.46 12.49 12.55 12.65 12.74 12.75 12.81 12.86 12.9 12.91 12.97
## 1 2 1 1 1 1 1 1 1 1 2
## 13.07 13.09 13.11 13.21 13.32 13.42 13.48 13.51 13.55 13.58 13.61
## 1 1 1 1 1 1 1 1 1 1 1
## 13.64 13.69 13.7 13.76 13.97 13.99 14.07 14.11 14.13 14.21 14.22
## 1 1 2 1 1 1 1 1 1 1 1
## 14.25 14.33 14.43 14.45 14.48 14.5 14.65 14.67 14.8 14.89 14.9
## 1 1 1 1 1 1 2 1 1 1 1
## 14.92 14.98 15.01 15.06 15.07 15.13 15.19 15.2 15.27 15.28 15.29
## 1 1 1 3 1 1 1 1 1 1 1
## 15.34 15.41 15.55 15.57 15.59 15.65 15.66 15.74 15.75 15.76 15.77
## 1 1 1 1 1 1 1 1 1 1 1
## 15.8 15.88 15.9 15.93 16.02 16.09 16.15 16.18 16.24 16.32 16.39
## 1 1 1 2 1 1 1 2 1 1 1
## 16.44 16.46 16.47 16.49 16.57 16.58 16.61 16.64 16.7 16.8 16.91
## 2 1 1 1 1 1 1 1 2 1 1
## 16.93 16.95 17.05 17.08 17.15 17.17 17.2 17.25 17.26 17.33 17.35
## 2 1 1 1 1 1 1 1 2 1 1
## 17.44 17.45 17.47 17.55 17.63 17.64 17.75 17.81 17.83 17.91 17.93
## 1 1 1 1 1 2 1 1 1 2 1
## 18.01 18.06 18.08 18.09 18.14 18.21 18.22 18.29 18.35 18.36 18.44
## 1 1 1 1 1 2 1 2 1 1 1
## 18.46 18.51 18.63 18.7 18.72 18.73 18.74 18.76 18.77 18.78 18.8
## 1 1 2 1 1 1 1 1 2 1 1
## 18.81 18.84 18.93 18.99 19.03 19.11 19.14 19.19 19.32 19.33 19.35
## 1 1 1 1 1 1 2 1 1 1 1
## 19.39 19.43 19.49 19.51 19.53 19.55 19.64 19.69 19.71 19.77 19.78
## 1 1 1 1 1 1 1 1 1 1 1
## 19.82 19.86 19.98 20.01 20.04 20.06 20.08 20.15 20.19 20.25 20.34
## 1 2 1 1 1 1 1 1 1 1 1
## 20.37 20.4 20.5 20.51 20.52 20.55 20.77 20.81 20.84 20.85 20.91
## 1 1 1 1 1 1 1 1 1 1 1
## 20.98 21.01 21.09 21.17 21.22 21.23 21.24 21.29 21.34 21.39 21.42
## 1 1 1 1 1 1 1 1 1 1 1
## 21.51 21.56 21.59 21.6 21.61 21.63 21.94 22 22.05 22.08 22.15
## 1 1 1 1 1 1 1 1 1 1 2
## 22.28 22.29 22.3 22.41 22.45 22.46 22.47 22.49 22.51 22.55 22.59
## 1 1 2 1 1 1 1 1 1 1 1
## 22.61 22.63 22.66 22.79 22.8 22.82 22.86 22.9 22.97 23.01 23.05
## 1 1 1 1 1 1 1 2 1 1 2
## 23.1 23.13 23.16 23.23 23.26 23.27 23.4 23.42 23.49 23.55 23.65
## 1 1 1 1 1 1 1 1 1 1 1
## 23.66 23.67 23.72 23.76 23.86 23.91 23.92 24.08 24.09 24.13 24.14
## 1 1 1 1 3 1 1 2 1 1 1
## 24.18 24.2 24.26 24.35 24.37 24.42 24.46 24.57 24.6 24.69 24.76
## 1 1 1 2 1 1 1 1 1 2 1
## 24.82 24.85 24.89 24.9 24.92 24.94 24.97 24.98 25.07 25.11 25.14
## 2 1 1 1 1 1 1 1 1 1 2
## 25.16 25.18 25.25 25.26 25.27 25.3 25.37 25.44 25.48 25.5 25.52
## 1 1 1 2 1 1 2 1 1 1 1
## 25.58 25.6 25.65 25.66 25.72 25.78 25.87 25.9 26.05 26.07 26.12
## 1 1 1 1 1 1 1 1 1 2 1
## 26.21 26.3 26.36 26.42 26.47 26.55 26.56 26.6 26.67 26.7 26.77
## 1 1 1 1 1 1 1 2 1 1 1
## 26.79 27.03 27.04 27.05 27.09 27.11 27.15 27.28 27.33 27.38 27.39
## 1 1 1 1 1 1 1 1 1 1 1
## 27.45 27.49 27.64 27.66 27.67 27.8 27.81 27.87 27.9 27.91 27.93
## 1 1 1 2 1 2 1 1 1 1 1
## 27.95 28.02 28.11 28.28 28.3 28.34 28.35 28.36 28.38 28.55 28.56
## 1 1 1 1 1 1 1 1 1 1 1
## 28.64 28.67 28.75 28.8 28.94 28.95 28.96 29.01 29.02 29.04 29.07
## 1 1 1 1 1 1 1 1 1 1 1
## 29.11 29.13 29.17 29.28 29.37 29.43 29.47 29.48 29.55 29.59 29.67
## 1 1 1 1 1 1 1 1 1 1 1
## 29.7 29.81 29.85 29.86 29.93 29.95 29.97 30.15 30.23 30.41 30.45
## 1 1 1 1 2 1 1 1 1 1 1
## 30.68 30.78 30.93 30.94 30.98 31.01 31.08 31.09 31.11 31.23 31.3
## 1 1 1 1 1 1 1 1 1 1 1
## 31.31 31.32 31.38 31.7 31.71 31.81 31.82 31.84 31.85 31.86 31.93
## 1 1 1 1 1 1 1 1 1 1 1
## 32.02 32.05 32.13 32.22 32.27 32.29 32.33 32.46 32.59 32.68 32.79
## 2 1 1 1 1 1 1 1 1 1 1
## 32.85 32.91 32.94 33.09 33.21 33.22 33.23 33.4 33.43 33.46 33.47
## 1 1 1 1 1 1 1 1 1 1 1
## 33.48 33.54 33.56 33.7 33.71 33.85 33.99 34 34.29 34.48 34.56
## 1 1 1 1 1 1 1 1 1 1 1
## 34.65 34.66 34.73 34.87 34.95 34.99 35.16 35.22 35.26 35.39 35.44
## 1 1 1 1 1 1 1 1 1 1 1
## 35.49 35.52 35.62 35.8 35.83 35.9 36.09 36.13 36.18 36.19 36.23
## 1 1 1 1 1 1 1 1 1 2 1
## 36.32 36.33 36.53 36.6 36.68 36.7 36.72 36.83 36.87 37.03 37.17
## 2 1 1 1 1 1 1 1 1 1 1
## 37.21 37.65 37.89 38.18 38.42 38.56 38.98 39.05 39.59 39.67 39.74
## 1 1 1 1 1 1 1 1 1 1 1
## 39.78 39.79 39.97 40.09 40.19 40.23 40.46 41.56 41.7 41.76 41.84
## 1 1 1 1 1 1 1 1 1 1 1
## 42.01 42.12 42.26 42.4 42.51 42.63 42.79 42.87 42.88 42.91 42.95
## 1 1 1 1 1 1 1 1 1 1 1
## 43.1 43.22 43.28 43.58 43.65 43.67 43.87 44.14 44.37 44.39 44.44
## 1 1 1 1 1 1 1 1 1 1 1
## 44.56 44.61 45.29 45.83 46.23 46.54 46.74 46.77 46.86 47.63 47.69
## 1 1 1 1 1 1 1 1 1 1 1
## 48.47 48.52 49.49 49.8 49.82 49.85 49.96 50.28 50.71 51.31 51.56
## 1 1 1 1 1 1 1 1 1 1 1
## 52.66 54.08 54.11 56.1 56.64 56.95 57.02 57.65 57.82 59.36 61.72
## 1 1 1 1 1 1 1 1 1 1 1
## 61.99 62.33 62.96 66.44 75.14 75.37 76.32 79.51 80.36 82.19 82.78
## 1 1 1 1 1 1 1 1 1 1 1
## 84.48 85.82 88.35 98.39 99.53 100.48 110.34 118.98 121.18 121.41 126.15
## 1 1 1 1 1 1 1 1 1 1 1
## 265.14 952.99
## 1 1frecuencia con la cual se repiten los valores del conjunto de julio 2020 pero de pm10
sort(pm10Julio2020)## [1] -35.44 -26.71 -11.92 -10.58 -10.07 -9.45 -8.10 -6.73 -4.30 -3.13
## [11] -3.00 -2.90 -1.89 -1.59 -1.51 -1.33 -1.24 -1.06 -0.82 -0.78
## [21] -0.49 -0.20 -0.16 -0.15 0.18 0.33 0.45 1.18 1.34 1.43
## [31] 1.84 1.99 2.03 2.10 2.10 2.20 2.36 2.46 2.47 2.59
## [41] 2.66 3.12 3.24 3.42 3.42 3.72 3.78 3.86 3.89 3.94
## [51] 4.03 4.04 4.27 4.66 4.72 4.76 5.11 5.20 5.30 5.39
## [61] 5.67 5.79 5.95 6.13 6.24 6.52 6.56 6.62 6.73 6.76
## [71] 6.81 6.82 7.05 7.09 7.13 7.15 7.15 7.16 7.16 7.27
## [81] 7.36 7.40 7.48 7.50 7.61 7.62 7.76 7.80 7.84 7.86
## [91] 7.89 7.89 7.92 8.00 8.03 8.04 8.10 8.11 8.14 8.24
## [101] 8.35 8.51 8.52 8.65 8.71 8.74 8.86 8.88 9.06 9.06
## [111] 9.07 9.22 9.36 9.39 9.39 9.40 9.50 9.54 9.58 9.58
## [121] 9.62 9.65 9.79 9.81 9.86 9.89 10.00 10.08 10.08 10.09
## [131] 10.10 10.12 10.17 10.17 10.25 10.31 10.37 10.42 10.46 10.51
## [141] 10.74 10.76 10.88 11.02 11.16 11.21 11.33 11.49 11.55 11.57
## [151] 11.64 11.64 11.67 11.70 11.87 11.87 12.02 12.03 12.04 12.13
## [161] 12.21 12.22 12.23 12.24 12.27 12.29 12.38 12.49 12.50 12.52
## [171] 12.60 12.70 12.72 12.74 12.79 12.80 12.82 12.91 12.94 12.95
## [181] 12.95 12.98 12.99 12.99 13.07 13.15 13.16 13.38 13.45 13.50
## [191] 13.54 13.65 13.69 13.78 13.81 13.98 14.18 14.20 14.22 14.24
## [201] 14.25 14.26 14.29 14.41 14.48 14.52 14.54 14.58 14.61 14.74
## [211] 14.75 14.75 14.75 14.82 14.86 14.91 14.93 14.96 14.99 15.01
## [221] 15.02 15.08 15.09 15.14 15.24 15.25 15.27 15.31 15.34 15.39
## [231] 15.40 15.42 15.42 15.45 15.50 15.61 15.63 15.66 15.68 15.82
## [241] 15.84 15.89 15.91 15.93 16.01 16.02 16.04 16.04 16.07 16.08
## [251] 16.09 16.14 16.14 16.15 16.17 16.31 16.37 16.37 16.38 16.47
## [261] 16.48 16.53 16.54 16.58 16.61 16.62 16.66 16.67 16.72 16.85
## [271] 16.92 16.96 17.06 17.11 17.15 17.32 17.33 17.38 17.39 17.44
## [281] 17.51 17.53 17.53 17.57 17.62 17.65 17.68 17.73 17.76 17.78
## [291] 17.80 17.84 17.87 17.95 17.97 18.00 18.07 18.08 18.13 18.14
## [301] 18.23 18.23 18.39 18.43 18.48 18.50 18.52 18.52 18.55 18.57
## [311] 18.66 18.71 18.79 18.79 18.82 18.85 18.89 18.96 18.96 18.97
## [321] 19.00 19.08 19.10 19.10 19.12 19.12 19.21 19.23 19.27 19.31
## [331] 19.46 19.52 19.53 19.54 19.55 19.60 19.60 19.62 19.65 19.66
## [341] 19.68 19.72 19.80 19.88 19.92 19.94 19.96 19.99 19.99 20.03
## [351] 20.06 20.08 20.08 20.37 20.42 20.48 20.58 20.63 20.68 20.69
## [361] 20.69 20.72 20.85 20.86 20.90 21.01 21.04 21.09 21.12 21.15
## [371] 21.23 21.24 21.24 21.42 21.46 21.47 21.61 21.64 21.75 21.76
## [381] 21.88 21.91 21.97 22.03 22.03 22.22 22.22 22.28 22.31 22.32
## [391] 22.32 22.34 22.36 22.38 22.39 22.42 22.44 22.48 22.55 22.65
## [401] 22.70 22.72 22.82 22.82 22.83 22.83 22.87 22.88 22.89 22.98
## [411] 23.01 23.02 23.06 23.14 23.15 23.24 23.35 23.36 23.51 23.53
## [421] 23.54 23.60 23.65 23.84 23.95 23.99 24.03 24.06 24.27 24.30
## [431] 24.31 24.32 24.41 24.49 24.51 24.54 24.55 24.60 24.60 24.62
## [441] 24.72 24.88 24.91 25.15 25.18 25.22 25.31 25.34 25.34 25.34
## [451] 25.44 25.48 25.50 25.55 25.56 25.59 25.59 25.61 25.63 25.67
## [461] 25.77 25.78 25.81 25.83 25.85 25.94 26.01 26.15 26.18 26.26
## [471] 26.27 26.28 26.39 26.50 26.52 26.55 26.59 26.67 26.78 26.79
## [481] 26.80 26.85 26.86 26.91 27.00 27.04 27.19 27.26 27.31 27.32
## [491] 27.35 27.36 27.39 27.42 27.51 27.56 27.57 27.62 27.63 27.63
## [501] 27.75 27.78 27.80 27.87 27.93 27.95 27.97 28.11 28.23 28.26
## [511] 28.54 28.57 28.69 28.70 28.70 29.04 29.06 29.18 29.19 29.24
## [521] 29.40 29.48 29.49 29.49 29.62 29.63 29.68 29.81 29.83 30.17
## [531] 30.19 30.27 30.28 30.33 30.38 30.57 30.82 30.96 31.00 31.01
## [541] 31.03 31.07 31.08 31.14 31.16 31.21 31.24 31.28 31.37 31.39
## [551] 31.49 31.60 31.68 31.99 32.02 32.03 32.06 32.10 32.10 32.23
## [561] 32.24 32.28 32.44 32.44 32.46 32.47 32.52 32.67 32.68 32.69
## [571] 33.09 33.14 33.24 33.33 33.39 33.70 33.71 33.74 33.74 33.84
## [581] 34.21 34.38 34.54 34.54 34.70 34.89 35.01 35.08 35.12 35.21
## [591] 35.23 35.29 35.39 35.40 35.85 35.90 35.94 36.12 36.12 36.19
## [601] 36.28 36.29 36.42 36.57 36.60 36.65 36.65 36.67 36.72 36.75
## [611] 36.84 36.87 36.89 36.95 37.06 37.26 37.27 37.33 37.34 37.42
## [621] 37.58 37.77 37.79 37.87 37.96 38.05 38.06 38.12 38.18 38.25
## [631] 38.36 38.40 38.70 38.82 39.06 39.15 39.17 39.32 39.38 39.42
## [641] 39.43 39.49 40.36 40.41 40.51 40.72 40.99 41.18 41.39 41.58
## [651] 41.78 42.19 42.20 42.48 42.69 42.78 42.83 42.91 42.95 43.06
## [661] 43.28 43.29 43.75 44.86 44.93 45.52 45.57 45.61 45.63 45.74
## [671] 45.76 46.13 46.14 46.33 46.36 46.50 46.60 46.62 46.65 46.76
## [681] 46.89 47.06 47.71 47.76 47.94 47.96 48.05 48.62 48.62 48.80
## [691] 48.86 49.64 50.82 51.24 51.42 51.52 51.56 51.61 52.50 52.77
## [701] 53.09 53.28 54.06 54.37 54.47 54.52 55.10 56.33 56.38 57.13
## [711] 57.19 58.19 58.39 58.72 58.87 60.12 62.52 62.65 63.59 64.42
## [721] 65.66 66.64 66.97 68.77 70.44 71.54 73.00 75.54 77.09 77.62
## [731] 81.25 81.56 82.24 83.79 85.51 85.62 88.96 91.02 91.55 91.76
## [741] 93.54 97.46 100.12 112.70table(pm10Julio2020)## pm10Julio2020
## -35.44 -26.71 -11.92 -10.58 -10.07 -9.45 -8.1 -6.73 -4.3 -3.13 -3
## 1 1 1 1 1 1 1 1 1 1 1
## -2.9 -1.89 -1.59 -1.51 -1.33 -1.24 -1.06 -0.82 -0.78 -0.49 -0.2
## 1 1 1 1 1 1 1 1 1 1 1
## -0.16 -0.15 0.18 0.33 0.45 1.18 1.34 1.43 1.84 1.99 2.03
## 1 1 1 1 1 1 1 1 1 1 1
## 2.1 2.2 2.36 2.46 2.47 2.59 2.66 3.12 3.24 3.42 3.72
## 2 1 1 1 1 1 1 1 1 2 1
## 3.78 3.86 3.89 3.94 4.03 4.04 4.27 4.66 4.72 4.76 5.11
## 1 1 1 1 1 1 1 1 1 1 1
## 5.2 5.3 5.39 5.67 5.79 5.95 6.13 6.24 6.52 6.56 6.62
## 1 1 1 1 1 1 1 1 1 1 1
## 6.73 6.76 6.81 6.82 7.05 7.09 7.13 7.15 7.16 7.27 7.36
## 1 1 1 1 1 1 1 2 2 1 1
## 7.4 7.48 7.5 7.61 7.62 7.76 7.8 7.84 7.86 7.89 7.92
## 1 1 1 1 1 1 1 1 1 2 1
## 8 8.03 8.04 8.1 8.11 8.14 8.24 8.35 8.51 8.52 8.65
## 1 1 1 1 1 1 1 1 1 1 1
## 8.71 8.74 8.86 8.88 9.06 9.07 9.22 9.36 9.39 9.4 9.5
## 1 1 1 1 2 1 1 1 2 1 1
## 9.54 9.58 9.62 9.65 9.79 9.81 9.86 9.89 10 10.08 10.09
## 1 2 1 1 1 1 1 1 1 2 1
## 10.1 10.12 10.17 10.25 10.31 10.37 10.42 10.46 10.51 10.74 10.76
## 1 1 2 1 1 1 1 1 1 1 1
## 10.88 11.02 11.16 11.21 11.33 11.49 11.55 11.57 11.64 11.67 11.7
## 1 1 1 1 1 1 1 1 2 1 1
## 11.87 12.02 12.03 12.04 12.13 12.21 12.22 12.23 12.24 12.27 12.29
## 2 1 1 1 1 1 1 1 1 1 1
## 12.38 12.49 12.5 12.52 12.6 12.7 12.72 12.74 12.79 12.8 12.82
## 1 1 1 1 1 1 1 1 1 1 1
## 12.91 12.94 12.95 12.98 12.99 13.07 13.15 13.16 13.38 13.45 13.5
## 1 1 2 1 2 1 1 1 1 1 1
## 13.54 13.65 13.69 13.78 13.81 13.98 14.18 14.2 14.22 14.24 14.25
## 1 1 1 1 1 1 1 1 1 1 1
## 14.26 14.29 14.41 14.48 14.52 14.54 14.58 14.61 14.74 14.75 14.82
## 1 1 1 1 1 1 1 1 1 3 1
## 14.86 14.91 14.93 14.96 14.99 15.01 15.02 15.08 15.09 15.14 15.24
## 1 1 1 1 1 1 1 1 1 1 1
## 15.25 15.27 15.31 15.34 15.39 15.4 15.42 15.45 15.5 15.61 15.63
## 1 1 1 1 1 1 2 1 1 1 1
## 15.66 15.68 15.82 15.84 15.89 15.91 15.93 16.01 16.02 16.04 16.07
## 1 1 1 1 1 1 1 1 1 2 1
## 16.08 16.09 16.14 16.15 16.17 16.31 16.37 16.38 16.47 16.48 16.53
## 1 1 2 1 1 1 2 1 1 1 1
## 16.54 16.58 16.61 16.62 16.66 16.67 16.72 16.85 16.92 16.96 17.06
## 1 1 1 1 1 1 1 1 1 1 1
## 17.11 17.15 17.32 17.33 17.38 17.39 17.44 17.51 17.53 17.57 17.62
## 1 1 1 1 1 1 1 1 2 1 1
## 17.65 17.68 17.73 17.76 17.78 17.8 17.84 17.87 17.95 17.97 18
## 1 1 1 1 1 1 1 1 1 1 1
## 18.07 18.08 18.13 18.14 18.23 18.39 18.43 18.48 18.5 18.52 18.55
## 1 1 1 1 2 1 1 1 1 2 1
## 18.57 18.66 18.71 18.79 18.82 18.85 18.89 18.96 18.97 19 19.08
## 1 1 1 2 1 1 1 2 1 1 1
## 19.1 19.12 19.21 19.23 19.27 19.31 19.46 19.52 19.53 19.54 19.55
## 2 2 1 1 1 1 1 1 1 1 1
## 19.6 19.62 19.65 19.66 19.68 19.72 19.8 19.88 19.92 19.94 19.96
## 2 1 1 1 1 1 1 1 1 1 1
## 19.99 20.03 20.06 20.08 20.37 20.42 20.48 20.58 20.63 20.68 20.69
## 2 1 1 2 1 1 1 1 1 1 2
## 20.72 20.85 20.86 20.9 21.01 21.04 21.09 21.12 21.15 21.23 21.24
## 1 1 1 1 1 1 1 1 1 1 2
## 21.42 21.46 21.47 21.61 21.64 21.75 21.76 21.88 21.91 21.97 22.03
## 1 1 1 1 1 1 1 1 1 1 2
## 22.22 22.28 22.31 22.32 22.34 22.36 22.38 22.39 22.42 22.44 22.48
## 2 1 1 2 1 1 1 1 1 1 1
## 22.55 22.65 22.7 22.72 22.82 22.83 22.87 22.88 22.89 22.98 23.01
## 1 1 1 1 2 2 1 1 1 1 1
## 23.02 23.06 23.14 23.15 23.24 23.35 23.36 23.51 23.53 23.54 23.6
## 1 1 1 1 1 1 1 1 1 1 1
## 23.65 23.84 23.95 23.99 24.03 24.06 24.27 24.3 24.31 24.32 24.41
## 1 1 1 1 1 1 1 1 1 1 1
## 24.49 24.51 24.54 24.55 24.6 24.62 24.72 24.88 24.91 25.15 25.18
## 1 1 1 1 2 1 1 1 1 1 1
## 25.22 25.31 25.34 25.44 25.48 25.5 25.55 25.56 25.59 25.61 25.63
## 1 1 3 1 1 1 1 1 2 1 1
## 25.67 25.77 25.78 25.81 25.83 25.85 25.94 26.01 26.15 26.18 26.26
## 1 1 1 1 1 1 1 1 1 1 1
## 26.27 26.28 26.39 26.5 26.52 26.55 26.59 26.67 26.78 26.79 26.8
## 1 1 1 1 1 1 1 1 1 1 1
## 26.85 26.86 26.91 27 27.04 27.19 27.26 27.31 27.32 27.35 27.36
## 1 1 1 1 1 1 1 1 1 1 1
## 27.39 27.42 27.51 27.56 27.57 27.62 27.63 27.75 27.78 27.8 27.87
## 1 1 1 1 1 1 2 1 1 1 1
## 27.93 27.95 27.97 28.11 28.23 28.26 28.54 28.57 28.69 28.7 29.04
## 1 1 1 1 1 1 1 1 1 2 1
## 29.06 29.18 29.19 29.24 29.4 29.48 29.49 29.62 29.63 29.68 29.81
## 1 1 1 1 1 1 2 1 1 1 1
## 29.83 30.17 30.19 30.27 30.28 30.33 30.38 30.57 30.82 30.96 31
## 1 1 1 1 1 1 1 1 1 1 1
## 31.01 31.03 31.07 31.08 31.14 31.16 31.21 31.24 31.28 31.37 31.39
## 1 1 1 1 1 1 1 1 1 1 1
## 31.49 31.6 31.68 31.99 32.02 32.03 32.06 32.1 32.23 32.24 32.28
## 1 1 1 1 1 1 1 2 1 1 1
## 32.44 32.46 32.47 32.52 32.67 32.68 32.69 33.09 33.14 33.24 33.33
## 2 1 1 1 1 1 1 1 1 1 1
## 33.39 33.7 33.71 33.74 33.84 34.21 34.38 34.54 34.7 34.89 35.01
## 1 1 1 2 1 1 1 2 1 1 1
## 35.08 35.12 35.21 35.23 35.29 35.39 35.4 35.85 35.9 35.94 36.12
## 1 1 1 1 1 1 1 1 1 1 2
## 36.19 36.28 36.29 36.42 36.57 36.6 36.65 36.67 36.72 36.75 36.84
## 1 1 1 1 1 1 2 1 1 1 1
## 36.87 36.89 36.95 37.06 37.26 37.27 37.33 37.34 37.42 37.58 37.77
## 1 1 1 1 1 1 1 1 1 1 1
## 37.79 37.87 37.96 38.05 38.06 38.12 38.18 38.25 38.36 38.4 38.7
## 1 1 1 1 1 1 1 1 1 1 1
## 38.82 39.06 39.15 39.17 39.32 39.38 39.42 39.43 39.49 40.36 40.41
## 1 1 1 1 1 1 1 1 1 1 1
## 40.51 40.72 40.99 41.18 41.39 41.58 41.78 42.19 42.2 42.48 42.69
## 1 1 1 1 1 1 1 1 1 1 1
## 42.78 42.83 42.91 42.95 43.06 43.28 43.29 43.75 44.86 44.93 45.52
## 1 1 1 1 1 1 1 1 1 1 1
## 45.57 45.61 45.63 45.74 45.76 46.13 46.14 46.33 46.36 46.5 46.6
## 1 1 1 1 1 1 1 1 1 1 1
## 46.62 46.65 46.76 46.89 47.06 47.71 47.76 47.94 47.96 48.05 48.62
## 1 1 1 1 1 1 1 1 1 1 2
## 48.8 48.86 49.64 50.82 51.24 51.42 51.52 51.56 51.61 52.5 52.77
## 1 1 1 1 1 1 1 1 1 1 1
## 53.09 53.28 54.06 54.37 54.47 54.52 55.1 56.33 56.38 57.13 57.19
## 1 1 1 1 1 1 1 1 1 1 1
## 58.19 58.39 58.72 58.87 60.12 62.52 62.65 63.59 64.42 65.66 66.64
## 1 1 1 1 1 1 1 1 1 1 1
## 66.97 68.77 70.44 71.54 73 75.54 77.09 77.62 81.25 81.56 82.24
## 1 1 1 1 1 1 1 1 1 1 1
## 83.79 85.51 85.62 88.96 91.02 91.55 91.76 93.54 97.46 100.12 112.7
## 1 1 1 1 1 1 1 1 1 1 1*un analisis de regresion logistica entre el o3 de julio 2019 con el de o3 de julio 2020
colores <- NULL
colores[OxigenoUnion$O32019 >= 0] <- "green"
colores[OxigenoUnion$O32020 >= 0] <- "red"
plot(OxigenoUnion$O32019,OxigenoUnion$O32020, xlab = "O3Junio2019", ylab = "O3Junio2020", pch=21, bg=colores)
#Para poner anotacion en la graficas un cuadrito con datos se muestran abajo a la izquierda
legend("bottomright", c("% o3 julio 2019", "% o3 julio 2020"),pch =21, col=c("green","red") )
*un analisis de regresion logistica entre el pm10 de julio 2019 con el de pm10 de julio 2020
colores <- NULL
colores[OxigenoUnion$PM102019 >= 0] <- "green"
colores[OxigenoUnion$PM102020 >= 0] <- "red"
plot(OxigenoUnion$PM102019,OxigenoUnion$PM102020, xlab = "pm10Junio2019", ylab = "pm10Junio2020", pch=21, bg=colores)
#Para poner anotacion en la graficas un cuadrito con datos se muestran abajo a la izquierda
legend("bottomright", c("% o3 julio 2019", "% o3 julio 2020"),pch =21, col=c("green","red") )
Vamos a hacer una regresion logistica de o3 de julio 2019 y julio 2020
rego3 <- glm(OxigenoUnion$O32019 ~ OxigenoUnion$O32020, data = OxigenoUnion)
summary(rego3)##
## Call:
## glm(formula = OxigenoUnion$O32019 ~ OxigenoUnion$O32020, data = OxigenoUnion)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -23.392 -7.922 -0.739 7.492 44.640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.03866 0.64272 34.29 < 2e-16 ***
## OxigenoUnion$O32020 0.17414 0.02147 8.11 2.1e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 104.1145)
##
## Null deviance: 84100 on 743 degrees of freedom
## Residual deviance: 77253 on 742 degrees of freedom
## AIC: 5571.6
##
## Number of Fisher Scoring iterations: 2Vamos a hacer una regresion logistica de o3 de julio 2019 y julio 2020
regpm10 <- glm(OxigenoUnion$PM102019 ~ OxigenoUnion$PM102020, data = OxigenoUnion)
summary(regpm10)##
## Call:
## glm(formula = OxigenoUnion$PM102019 ~ OxigenoUnion$PM102020,
## data = OxigenoUnion)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -51.94 -11.17 -4.11 4.44 926.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.6339 3.4422 5.413 8.35e-08 ***
## OxigenoUnion$PM102020 0.3049 0.1313 2.322 0.0205 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1512.677)
##
## Null deviance: 1130563 on 743 degrees of freedom
## Residual deviance: 1122406 on 742 degrees of freedom
## AIC: 7562.7
##
## Number of Fisher Scoring iterations: 2- ordenar y transformar los datos para o3 de julio 2019
#Vector de Fecha
Fecha1 = seq(from = as.POSIXct("2019-07-01 00:00:00"),to = as.POSIXct("2019-07-31 23:00:00"), by = 'hour')
datos1 <- data.frame(Fecha1,O3Julio2019)ggplot(data = datos1) + geom_line(mapping = aes(x = Fecha1, y = O3Julio2019))+ ggtitle("xD")
- ordenar y transformar los datos para o3 de julio 2020
#Vector de Fecha
Fecha2 = seq(from = as.POSIXct("2020-07-01 00:00:00"),to = as.POSIXct("2020-07-31 23:00:00"), by = 'hour')
datoso32 <- data.frame(Fecha2,O3Julio2020)ggplot(data = datoso32) + geom_line(mapping = aes(x = Fecha2, y = O3Julio2020))+ ggtitle("xD")
- ordenar y transformar los datos para pm10 de julio 2019
#Vector de Fecha
Fecha3 = seq(from = as.POSIXct("2019-07-01 00:00:00"),to = as.POSIXct("2019-07-31 23:00:00"), by = 'hour')
datospm101 <- data.frame(Fecha3,pm10Julio2019)ggplot(data = datospm101) + geom_line(mapping = aes(x = Fecha3, y = O3Julio2019))+ ggtitle("xD")
- ordenar y transformar los datos para pm10 de julio 2020
#Vector de Fecha
Fecha4 = seq(from = as.POSIXct("2020-07-01 00:00:00"),to = as.POSIXct("2020-07-31 23:00:00"), by = 'hour')
datospm102 <- data.frame(Fecha4,pm10Julio2020)ggplot(data = datospm102) + geom_line(mapping = aes(x = Fecha4, y = pm10Julio2020))+ ggtitle("xD")
###Visualizar
head(OxigenoUnion)## # A tibble: 6 x 5
## Time O32019 PM102019 PM102020 O32020
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01/07/2019 00:00 16.4 24.5 10.0 25.2
## 2 01/07/2019 01:00 16.6 23.0 10.2 27.4
## 3 01/07/2019 02:00 12.8 20.8 11.2 12.4
## 4 01/07/2019 03:00 14.0 14.9 9.91 28.1
## 5 01/07/2019 04:00 12.5 19.0 7.67 25.6
## 6 01/07/2019 05:00 13.8 18.2 6.56 26.7##Analisis de correlacion *Matriz de diagramas de dispersión de o3 de julio 2019 y julio 2020
pairs(OxigenoUnion[2:5])
Gráfica de la recta de mínimos cuadrados de o3 de julio 2019 o julio 2020
plot (OxigenoUnion$O32019, OxigenoUnion$O32020, xlab="julio2019",ylab="julio2020")
abline(rego3)
### Gráfica de la recta de mínimos cuadrados de pm10 de julio 2019 o julio 2020
plot (OxigenoUnion$PM102019, OxigenoUnion$PM102020, xlab="julio2019",ylab="julio2020")
abline(regpm10)
Pregunta de rescate (opcional): Mini ensayo de mínimo media y máximo una cuartilla contestando a la pregunta: ¿la gente realmente quiere ser feliz o es una fabricada que nos vendieron? (Use datos para fundamentarse)
Si la gente realmente quiere ser feliz hace lo imposible para hacerse feliz ellos mismos ya sean con sus metas ya sean con sus trabajos ya sea ocn el dinero ya sea con el amor, ay muchas formas de representar la felicidad tantas que hay que tener que aplicar estadistica de a ver cual la gente es mas feliz seria interensante hacer eso no creo que la felicidad de venda por que por ejemplo si compras a alguien por dinero no es feliz esa persona ya que no lo esta consiguiendo por si misma bueno asi pensaria yo no se los demas incluso ahy algunos que comprados son felices pero con el tiempo no lo seran pero como dicen muchos la felicidad no se compra se logra
Plantee una hipótesis ¿Por qué un año está más contaminado que otro en ciertos periodos?
disminuyo la contaminacion por lo de la cuarentena al momento de hacer yo este examen me di cuenta de que julio del año pasado estaba mas contamido que el de ahora pero no por mucha diferencia no es tanta si comparabamos años enteros por cual no ay tanta diferencia la contamiacion de los carros disminuyo gracias a que por la cuarentena de calmo mas el uso del carro que igual forma si comparamos julio 2020 con octubre habia una diferencia ya que la gente empezo a salir mas