Analisis de datos climaticos

Mostramos unas figuras relacionadas a datos climaticos, las cuales pueden ser utilizados para presentar en manuscritos.

data science
weather analysis
plot
Author
Affiliation

Franklin Santos

AgriTech Bolivia

Published

June 25, 2023

Data

library(readxl)
df <- read_excel("/Users/franklin/Documents/R/myblog/posts/weather_mizque/dato_diarios.xlsx")

analisis de temperatura

#Real data Mizque
library(metan)
Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2
|=========================================================|
| Multi-Environment Trial Analysis (metan) v1.18.0        |
| Author: Tiago Olivoto                                   |
| Type 'citation('metan')' to know how to cite metan      |
| Type 'vignette('metan_start')' for a short tutorial     |
| Visit 'https://bit.ly/pkgmetan' for a complete tutorial |
|=========================================================|
inspect(df, 2:14, plot = F)
# A tibble: 13 × 10
   Variable      Class Missing Levels Valid_n    Min Median    Max Outlier Text 
   <chr>         <chr> <chr>   <chr>    <int>  <dbl>  <dbl>  <dbl>   <dbl> <chr>
 1 "mes"         nume… No      -          485    1      5     12         0  <NA>
 2 "dia"         nume… No      -          485    1     16     31         0  <NA>
 3 "estacion"    char… No      0          485   NA     NA     NA        NA "Lin…
 4 "longitud"    nume… No      -          485  -65.4  -65.4  -65.4       0  <NA>
 5 "latitud"     nume… No      -          485  -18.0  -18.0  -18.0       0  <NA>
 6 "altura"      nume… No      -          485 2055   2055   2055         0  <NA>
 7 "Precipitaci… char… No      0          485   NA     NA     NA        NA "Lin…
 8 "\"Temperatu… char… No      0          485   NA     NA     NA        NA "Lin…
 9 "\"Temperatu… char… No      0          485   NA     NA     NA        NA "Lin…
10 "\"Temperatu… char… No      0          485   NA     NA     NA        NA "Lin…
11 "\"Humedad R… char… No      0          485   NA     NA     NA        NA "Lin…
12 "\"Humedad R… char… No      0          485   NA     NA     NA        NA "Lin…
13 "\"Humedad R… char… No      0          485   NA     NA     NA        NA "Lin…
Warning: Considering the levels of factors, .data should have 1 rows, but it
has 485. Use 'as_factor()' for coercing a variable to a factor.
Warning: Expected three or more factor variables. The data has only 0.
Warning: Possible text fragments in variable(s) estacion, Precipitación,
"Temperatura Máxima", "Temperatura Mínima", "Temperatura Media", "Humedad
Relativa Máxima", "Humedad Relativa Mínima", "Humedad Relativa Media".
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   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ forcats::as_factor()         masks metan::as_factor()
✖ tibble::column_to_rownames() masks metan::column_to_rownames()
✖ dplyr::filter()              masks stats::filter()
✖ dplyr::lag()                 masks stats::lag()
✖ dplyr::recode_factor()       masks metan::recode_factor()
✖ tibble::remove_rownames()    masks metan::remove_rownames()
✖ tidyr::replace_na()          masks metan::replace_na()
✖ tibble::rownames_to_column() masks metan::rownames_to_column()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
names(df)
 [1] "gestion"                     "mes"                        
 [3] "dia"                         "estacion"                   
 [5] "longitud"                    "latitud"                    
 [7] "altura"                      "Precipitación"              
 [9] "\"Temperatura Máxima\""      "\"Temperatura Mínima\""     
[11] "\"Temperatura Media\""       "\"Humedad Relativa Máxima\""
[13] "\"Humedad Relativa Mínima\"" "\"Humedad Relativa Media\""

Rename variables

newvar = c(PP = "Precipitación", 
           Tmax = "\"Temperatura Máxima\"",
           Tmin = "\"Temperatura Mínima\"",
           Tmean = "\"Temperatura Media\"",
           HRmax = "\"Humedad Relativa Máxima\"",
           HRmin = "\"Humedad Relativa Mínima\"",
           HRmean = "\"Humedad Relativa Media\"")
db = df%>%
  rename(any_of(newvar))%>%
  select(c(1,2,3, 8:14))%>%
  mutate_if(is.character, as.numeric)%>%
  mutate(mes = as.factor(mes), 
         gestion = as.factor(gestion))%>%
  mutate(mes=recode(mes, 
                    '1'='Ene', 
                    '2'='Feb',
                    '3'='Mar',
                    '4'='Abr',
                    '5'='May',
                    '6'='Jun',
                    '7'='Jul',
                    '8'='Ago',
                    '9'='Sep',
                    '10'='Oct',
                    '11'='Nov',
                    '12'='Dic'))
db  
# A tibble: 485 × 10
   gestion mes     dia    PP  Tmax  Tmin Tmean HRmax HRmin HRmean
   <fct>   <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
 1 2022    Ene       1   0.4  31.4  9.92  20.6  93.2  19.3   53.4
 2 2022    Ene       2   0    33.3 11.7   19.7  96.6  18.0   69.6
 3 2022    Ene       3   5.1  29.6 14.4   21.0  88.7  27.2   58.7
 4 2022    Ene       4   0    30.1 14.1   20.1  90.8  23.6   66.3
 5 2022    Ene       5   1.5  21.9 13.4   16.7  94.6  47.8   81.0
 6 2022    Ene       6   4.5  25.5 14.4   19.8  94.3  35.9   65.8
 7 2022    Ene       7   0    27.3 14.7   20.4  95.6  39.9   67.3
 8 2022    Ene       8  24.2  25.3 15.3   19.0  91.3  49.6   74.5
 9 2022    Ene       9   0    26.9 15.5   20.2  92.7  38.5   70.6
10 2022    Ene      10   0    24   14.5   18.5  91.9  47.2   73.0
# ℹ 475 more rows

plot temperature

library(ggridges)

my_theme <- 
  theme_bw() +
  theme(legend.title = element_blank(),
        axis.title = element_text(color = "black"),
        axis.text = element_text(color = "black"),
        panel.grid.minor = element_blank())

p1 = ggplot(
  data = db,
  aes(x = `Tmean`,
      y = `mes`,
      fill = after_stat(x))
) +
  geom_density_ridges_gradient(scale = 1.5, size = 0.1, 
                               rel_min_height = 0.001) +
  scale_fill_viridis_c(name = "Temp (ºC)", 
                       direction = 1, option = "H") +
  my_theme +
  theme(legend.position = c(0.95, 0.4))+
  labs(x="Temperatura promedio (ºC)", y = "Meses")

p2 = ggplot(
  data = db,
  aes(x = `Tmin`,
      y = `mes`,
      fill = after_stat(x))
) +
  geom_density_ridges_gradient(scale = 1.5, size = 0.1, 
                               rel_min_height = 0.001) +
  scale_fill_viridis_c(name = "Temp (ºC)", 
                       direction = 1, option = "H")+
  my_theme+
  theme(legend.position = c(0.9, 0.77))+
  labs(x="Temperatura mínima (ºC)", y = "Meses")


p3 = ggplot(
  data = db,
  aes(x = `Tmax`,
      y = `mes`,
      fill = after_stat(x))
) +
  geom_density_ridges_gradient(scale = 1.5, size = 0.1, 
                               rel_min_height = 0.001) +
  scale_fill_viridis_c(name = "Temp (ºC)", 
                       direction = 1, option = "H") +
  my_theme+
  theme(legend.position = "")+
  labs(x="Temperatura maxima (ºC)", y = "Meses")

arrange_ggplot((p1),
               (p2+p3),
               nrow = 2,
               tag_levels = "a")
Picking joint bandwidth of 0.801
Picking joint bandwidth of 1.11
Picking joint bandwidth of 1.2

plot precipitation

pp =db%>%
  mutate(PP = na_if(PP, 0.0))%>%
  ggplot(aes(x = mes,y = PP,
             colour = mes
             )) +
  geom_boxplot(outlier.shape = NA)+
  geom_jitter()+
  my_theme+
  theme(legend.position = "")+
  labs(x="Meses", y = "Precipitacion (mm)")

Humedad relativa plot

fechas = df%>%
  rename(any_of(newvar))%>%
  select(c(1,2,3, 8:14))%>%
  mutate_if(is.character, as.numeric)%>%
  mutate(fecha = make_datetime(gestion,mes,dia))%>%
  select(fecha,HRmin,HRmax, HRmean)
fechas
# A tibble: 485 × 4
   fecha               HRmin HRmax HRmean
   <dttm>              <dbl> <dbl>  <dbl>
 1 2022-01-01 00:00:00  19.3  93.2   53.4
 2 2022-01-02 00:00:00  18.0  96.6   69.6
 3 2022-01-03 00:00:00  27.2  88.7   58.7
 4 2022-01-04 00:00:00  23.6  90.8   66.3
 5 2022-01-05 00:00:00  47.8  94.6   81.0
 6 2022-01-06 00:00:00  35.9  94.3   65.8
 7 2022-01-07 00:00:00  39.9  95.6   67.3
 8 2022-01-08 00:00:00  49.6  91.3   74.5
 9 2022-01-09 00:00:00  38.5  92.7   70.6
10 2022-01-10 00:00:00  47.2  91.9   73.0
# ℹ 475 more rows
HR = fechas%>%
  ggplot(aes(x=fecha, y=HRmean))+
  geom_line()+
  my_theme+
  labs(x="Meses", y = "Humedad relativa promedio (%)")
HR

library(plotly)
p = fechas%>%
plot_ly(x=~fecha, y=~HRmean)%>%
  add_lines()
p
arrange_ggplot((pp),
               (p1+HR),
               nrow = 2,
               tag_levels = "a")
Warning: Removed 342 rows containing non-finite values (`stat_boxplot()`).
Warning: Removed 342 rows containing missing values (`geom_point()`).
Picking joint bandwidth of 0.801

Citation

BibTeX citation:
@online{santos2023,
  author = {Santos, Franklin},
  title = {Analisis de Datos Climaticos},
  date = {2023-06-25},
  url = {https://franklinsantosm.com/posts/weather_mizque},
  langid = {en}
}
For attribution, please cite this work as:
Santos, Franklin. 2023. “Analisis de Datos Climaticos.” June 25, 2023. https://franklinsantosm.com/posts/weather_mizque.