Análisis de datos climáticos de la E.E. Kallutaca

Breve análisis de datos climáticos de la Estación Experimental de Kallutaca, durante la campaña agrícola 2019-2020.

Load library

library(tidyverse)
library(readxl)

Import data

kallutaca <- read_xlsx("Weather_Kallutaca.xlsx",
                       sheet = "Hoja1")

kallutaca

# A tibble: 259 x 16
    Year Month   Day Date                Mean_temp High_t Time_h Low_t
   <dbl> <chr> <dbl> <dttm>                  <dbl>  <dbl> <chr>  <dbl>
 1  2019 Aug       1 2019-08-01 00:00:00       6.1   17.4 3:00p   -5.1
 2  2019 Aug       2 2019-08-02 00:00:00       4.8   15.4 2:00p   -5.9
 3  2019 Aug       3 2019-08-03 00:00:00       3.7   13.7 4:00p   -6.4
 4  2019 Aug       4 2019-08-04 00:00:00       2.9   16.8 2:00p  -11.1
 5  2019 Aug       5 2019-08-05 00:00:00       4.5   16.4 1:00p   -7.5
 6  2019 Aug       6 2019-08-06 00:00:00       5.6   18.2 2:00p   -7  
 7  2019 Aug       7 2019-08-07 00:00:00       6.4   18   2:00p   -5.3
 8  2019 Aug       8 2019-08-08 00:00:00       5.9   16.9 2:00p   -5.1
 9  2019 Aug       9 2019-08-09 00:00:00       5.1   15.3 1:00p   -5.1
10  2019 Aug      10 2019-08-10 00:00:00       6.1   14.6 2:00p   -2.3
# … with 249 more rows, and 8 more variables: Time_l <chr>,
#   Heat_Deg_Days <dbl>, Cool_Deg_Days <dbl>, Rain <dbl>,
#   AVG_Wind_Speed <dbl>, High_w <dbl>, Time_w <chr>, Dom_Dir <chr>

Pivot data

fsg <- kallutaca %>%
  pivot_longer(
    cols = c("Mean_temp", "High_t", "Low_t", "Rain"), 
    names_to = "Var_weat", 
    values_to = "weather"
  )

Plots

library(plotly)
## High temperature
p <- ggplot(kallutaca, aes(Time_h, High_t)) +
  geom_boxplot(colour = "blue")+
  geom_jitter(colour = "blue")

fig <- ggplotly(p)

fig

## Law temperature
p1 <- ggplot(kallutaca, aes(Time_l, Low_t)) +
  geom_boxplot(colour = "red")+
  geom_jitter(colour = "red")
fig1 <- ggplotly(p1)

fig1

## High wind
p2 <- ggplot(kallutaca, aes(Time_w, High_w)) +
  geom_violin(colour = "purple")+
  geom_jitter(colour = "purple")
fig2 <- ggplotly(p2)

fig2

## Dominance direction wind
p3 <- ggplot(kallutaca, aes(Dom_Dir, High_w)) +
  geom_violin(colour = "blue")+
  geom_jitter(colour = "blue")
fig3 <- ggplotly(p3)

fig3

Rain Plot and temperature

p4 <- ggplot(fsg, aes(x=Date, y=weather, color = Var_weat)) + 
  geom_point(size=1) +
  geom_line() + 
  xlab("Months") + 
  ylab("")

fig4 <- ggplotly(p4)
fig4

fs <- fsg %>%
  ggplot(aes(Date, weather, color = Var_weat)) +
  geom_line() +
  facet_wrap(~Var_weat, ncol = 1) +
  labs(x = "Month", y = "")

fig5 <- ggplotly(fs)
fig5

Pearson Correlation

library(metan)
## select variable
corrd <- kallutaca %>%
  select(Mean_temp, High_t, Low_t, Rain, AVG_Wind_Speed, High_w)
corrd

# A tibble: 259 x 6
   Mean_temp High_t Low_t  Rain AVG_Wind_Speed High_w
       <dbl>  <dbl> <dbl> <dbl>          <dbl>  <dbl>
 1       6.1   17.4  -5.1     0            0.3    5.4
 2       4.8   15.4  -5.9     0            0.6    8.9
 3       3.7   13.7  -6.4     0            1.1   12.1
 4       2.9   16.8 -11.1     0            0.3    7.2
 5       4.5   16.4  -7.5     0            0.4    7.2
 6       5.6   18.2  -7       0            0.1    5.8
 7       6.4   18    -5.3     0            0.4    8  
 8       5.9   16.9  -5.1     0            0.6    7.2
 9       5.1   15.3  -5.1     0            0.6    9.4
10       6.1   14.6  -2.3     0            0.4    5.8
# … with 249 more rows

## Correlation plot
coef2 <- corr_coef(corrd)
plot(coef2)