analisis minimas

library(readr)
library(xts)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo
dataset <- read_csv("dataset.csv")
## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   junin.temp_min = col_double(),
##   temperatureLow = col_double()
## )
colnames(dataset) <- c("date","estacion","darksky")
dataset_ts <- xts(dataset[,-1],order.by = dataset$date)
library(ggfortify)
## Loading required package: ggplot2
autoplot(dataset_ts)
## Warning: Removed 1 rows containing missing values (geom_path).

library(ggfortify)
#plot(dataset_ts)
plot(dataset_ts,legend.loc="bottomright")

Diferencias en valores menores a cero

library(reshape2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
## 
##     first, last
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dataset_ts %>%
  as_tibble() %>%
  filter(estacion <= 0) %>%
  melt() %>%
  ggplot(aes(x=variable,y=value)) + geom_boxplot()
## Warning: Calling `as_tibble()` on a vector is discouraged, because the behavior is likely to change in the future. Use `tibble::enframe(name = NULL)` instead.
## This warning is displayed once per session.
## No id variables; using all as measure variables

dataset_ts %>%
  as_tibble() %>%
  filter(estacion <= 0) %>%
  tally()
## # A tibble: 1 x 1
##       n
##   <int>
## 1    36
dataset_ts %>%
  as_tibble() %>%
  mutate(diferencia = estacion - darksky) %>%
  as.ts() %>%
  plot.ts()

Histograma que muestre la distribución de las diferencias de valores entre el real (la estación) y el valor del modelo meteorologico o sistema.

Observamos que existe diferencia entre la medición in-situ, en este caso mediante una estación meteorológica en INTA EEA Junin, y la estimada por modelos meteorológicos globales.

dataset_ts %>%
  as_tibble() %>%
  mutate(diferencia = darksky - estacion) %>%
  select(diferencia) %>%
ggplot( aes(x=diferencia)) + 
  geom_histogram(binwidth=1)
## Warning: Removed 1 rows containing non-finite values (stat_bin).