options(repos = list(CRAN="http://cran.rstudio.com/"))
install.packages("dplyr")
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install.packages('riem')
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library(riem)
install.packages('tidyverse')
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library(tidyverse)
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install.packages('lubridate')
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library(lubridate)
install.packages('ggplot2')
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library(ggplot2)
install.packages('plotly')
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library(plotly)
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# Paso 2 Buscar la red (paÃs).
view(riem_networks())
# Paso 3 Buscar la estación (ciudad) y copiar ID
view(riem_stations('MX__ASOS'))
#Paso 4 Obtener informacion
monterrey<- riem_measures("MMMY")
# Paso 5 Agregar Temperatura en grados centigrados
monterrey$tmpc<- (monterrey$tmpf -32)/1.8
str(monterrey)
## tibble [82,440 × 33] (S3: tbl_df/tbl/data.frame)
## $ station : chr [1:82440] "MMMY" "MMMY" "MMMY" "MMMY" ...
## $ valid : POSIXct[1:82440], format: "2014-01-01 00:16:00" "2014-01-01 00:49:00" ...
## $ lon : num [1:82440] -100 -100 -100 -100 -100 ...
## $ lat : num [1:82440] 25.8 25.8 25.8 25.8 25.8 ...
## $ tmpf : num [1:82440] 48.2 48.2 48.2 46.4 46.4 46.4 46.4 46.4 46.4 46.4 ...
## $ dwpf : num [1:82440] 46.4 46.4 46.4 46.4 46.4 44.6 44.6 44.6 44.6 44.6 ...
## $ relh : num [1:82440] 93.5 93.5 93.5 100 100 ...
## $ drct : num [1:82440] 0 120 120 120 110 100 110 130 60 0 ...
## $ sknt : num [1:82440] 0 3 5 6 5 5 4 3 3 0 ...
## $ p01i : num [1:82440] 0 0 0 0 0 0 0 0 0 0 ...
## $ alti : num [1:82440] 30.3 30.3 30.3 30.3 30.3 ...
## $ mslp : num [1:82440] NA NA NA NA NA ...
## $ vsby : num [1:82440] 4 3 1 0.25 0.12 0.12 0.06 0.06 0.06 0.12 ...
## $ gust : num [1:82440] NA NA NA NA NA NA NA NA NA NA ...
## $ skyc1 : chr [1:82440] "SCT" "SCT" "SCT" "VV " ...
## $ skyc2 : chr [1:82440] "BKN" "BKN" "BKN" NA ...
## $ skyc3 : chr [1:82440] "OVC" "OVC" "OVC" NA ...
## $ skyc4 : chr [1:82440] NA NA NA NA ...
## $ skyl1 : num [1:82440] 700 300 200 200 100 100 100 100 100 100 ...
## $ skyl2 : num [1:82440] 1200 400 300 NA NA NA NA NA NA NA ...
## $ skyl3 : num [1:82440] 4000 900 500 NA NA NA NA NA NA NA ...
## $ skyl4 : num [1:82440] NA NA NA NA NA NA NA NA NA NA ...
## $ wxcodes : chr [1:82440] NA "BR" "BR" "FG" ...
## $ ice_accretion_1hr: logi [1:82440] NA NA NA NA NA NA ...
## $ ice_accretion_3hr: logi [1:82440] NA NA NA NA NA NA ...
## $ ice_accretion_6hr: logi [1:82440] NA NA NA NA NA NA ...
## $ peak_wind_gust : logi [1:82440] NA NA NA NA NA NA ...
## $ peak_wind_drct : logi [1:82440] NA NA NA NA NA NA ...
## $ peak_wind_time : logi [1:82440] NA NA NA NA NA NA ...
## $ feel : num [1:82440] 48.2 47.2 45.6 42.9 43.5 ...
## $ metar : chr [1:82440] "MMMY 010016Z 00000KT 4SM SCT007 BKN012 OVC040 09/08 A3028 RMK 8/5// BR" "MMMY 010049Z 12003KT 3SM BR SCT003 BKN004 OVC009 09/08 A3028 RMK 8/5// -DZ OCNL" "MMMY 010116Z 12005KT 1SM BR SCT002 BKN003 OVC005 09/08 A3028 RMK 8/6// -DZ OCNL" "MMMY 010120Z 12006KT 1/4SM FG VV002 08/08 A3029 RMK 8//// BC FG MOV SE/NW" ...
## $ snowdepth : logi [1:82440] NA NA NA NA NA NA ...
## $ tmpc : num [1:82440] 9 9 9 8 8 8 8 8 8 8 ...
# Paso 6 Filtrar informacion - Ejemplo Enero a Marzo 2023
este_año <- subset(monterrey, valid >= as.POSIXct('2023-01-01 00:00') & valid <= as.POSIXct('2023-03-10 07:00'))
# Paso 7 Graficar temperatura en 2023
plot(este_año$valid,este_año$tmpc)
# Paso 8 Promediar información por dÃa
este_año <- este_año %>%
mutate(date=ymd_hms(valid), date = as.Date(date)) %>%
group_by(date) %>%
summarize_if(is.numeric, ~mean(.,na.rm = TRUE))
# Paso 9 Graficar la temperatura en 2023
plot(este_año$date,este_año$tmpc, type="l", main="Temperatura promedio en monterrey", xlab="Fecha", ylab="´Centigrados")
