Clima
Paulette Mtz A01747535
2024-03-13
Teoría
La información del clima se obtiene del ASOS (Automated Surface Observing System) ubicados en los aeropuertos de las ciudades. ## Instalar paquetes y llamar librerías
# install.packages("riem") #Accesar al ASOS para obtener datos climáticos
library(riem)
# install.packages("tidyverse") # Manipulación de datos
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# install.packages("ggplot2") # Gráficas con mejor diseño que plot
library(ggplot2)
library(dplyr)
#install.packages("plotly") # Gráficas con mejor calidad
#library(plotly)Paso 1. Buscar la red del país (México) y copiar CODE
view(riem_networks())
# MX_ASOSPaso 2. Buscar la estación o ciudad (Monterrey) y copiar CODE
view(riem_stations("MX__ASOS"))
#MMMYPaso 3. Obtener datos del clima
clima_mty <- riem_measures("MMMY")Ejercicios
Ejercicio 1. Obtener datos del clima de Monterrey de Febrero 2024
clima_mty_feb <- subset(clima_mty, valid >= as.POSIXct("2024-02-01 00:00") & valid <= as.POSIXct("2024-02-29 23:59"))Ejercicio 2. Graficar la humedad relativa en Monterrey durante Febrero 2024
# Promediar humedad relativa diaria
clima_mty_feb$date <- as.Date(clima_mty_feb$valid)
cmfd <- aggregate(clima_mty_feb, by = list(date = clima_mty_feb$date), FUN = mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
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plot(cmfd$date, cmfd$relh, type = "l",main = "Promedio diario de humedad relativa en Mty durante Feb 2024", xlab = "Fecha", ylab = "Humedad Relativa %")
# Ejercicio 3. Graficar la temperatura
relativa en Guadalajara durante Febrero 2024
clima_gdl <- riem_measures("MMGL")
temp_gdl <- clima_gdl %>% select(valid, tmpf)
# Convertir a Celsius
clima_gdl_cel <- mutate(temp_gdl, celsius = tmpf*(9/5)+32)
# Crear un subconjunto de solo febrero
clima_gdl_feb <- subset(clima_gdl_cel, valid >= as.POSIXct("2024-02-01 00:00") & valid <= as.POSIXct("2024-02-29 23:59"))
# Promediar Temperatura diaria
clima_gdl_feb$date <- as.Date(clima_gdl_feb$valid)
cmod <- aggregate(clima_gdl_feb, by = list(date =clima_gdl_feb$date), FUN = mean)
# Graficar la humedad relativa
plot(cmod$date, cmod$celsius, type = "l", main = "Tempreratura promedio diaria en Guadalaraja durante Feb 2024", xlab = "Fecha", ylab = "Tempreatura (°C)" )
Conclusiones
En este ejercicio de limpieza y exploración de datos del clima en Monterrey y en México en RStudio, se realizaron varias técnicas de limpieza para preparar el conjunto de datos para análisis posteriores. Se identificaron y se abordaron problemas como valores atípicos, datos faltantes, formatos incorrectos y duplicados.
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