
Paso 1.Instalar paquetes y llamar librerias
#install.packages("riem")
library(riem)
#installed.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
#install.packages("lubridate")
library(lubridate)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("plotly")
library(plotly)
##
## Attaching package: 'plotly'
##
## 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
Paso 2. Buscar la red (país) Ejemplo: México. y copiar CODE
#View(riem_networks())
Paso 3. buscar la estación (ciudad) Ejemplo: Monterrey. y copiar
ID
#View(riem_stations("MX__ASOS"))
Paso 5. Agregar temperatura en grados centigrados
monterrey$tmpc = (monterrey$tmpf - 32)/1.8
#str(monterrey)
#summary(monterrey)
Paso 7. Graficar temperatura en 2023
plot(este_año$valid, este_año$tmpc)

Paso 9. Graficar temperatura en 2023
plot(este_año$date, este_año$tmpc, type ="l", main = "Temperatura promedo en Monterrey", xlab ="Fecha", ylab = "Grados C°")

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