README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
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)
```
# Meteorological data

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Daily meteorological data were downloaded from [TuTiempo.net](https://en.tutiempo.net)
for the following 4 climatic stations:

```{r echo = FALSE}
(selected_stations <- read.csv("data/stations.csv"))
```

Which, on the map, are located here in red, from left to right and from top to
bottom:

```{r echo = FALSE, message = FALSE, fig.retina = 2, fig.align = "center"}
library(sf)
library(dplyr)
laos <- readRDS("data-raw/gadm36_LAO_1_sf.rds")
stations <- readxl::read_excel("/Users/choisy/Dropbox/aaa/R_packages/tutiempo/data-raw/climatic stations.xlsx")
stations_sf <- stations %>% 
  select(-elevation, -from) %>% 
  na.exclude() %>% 
  st_as_sf(coords = c("longitude", "latitude"), crs = 4326)
selected_stations <- selected_stations %>% 
  select(-elevation) %>% 
  na.exclude() %>% 
  st_as_sf(coords = c("longitude", "latitude"), crs = 4326)
plot(st_geometry(laos))
plot(st_geometry(stations_sf), add = TRUE)
plot(st_geometry(selected_stations), col = "red", add = TRUE)
```

After cleaning (see [cleaning pipeline](https://ecomore2.github.io/meteo/make_data.html)),
the meteorological data as well and the climatic stations characteristics are
available here:

* [meteo.csv](https://raw.githubusercontent.com/ecomore2/meteo/master/data/meteo.csv) (905.5 KB)
* [stations.csv](https://raw.githubusercontent.com/ecomore2/meteo/master/data/stations.csv) (211 K)

From where they can be copied and pasted. They can also be downloaded directly
from R as so:

```{r eval = FALSE}
if (! "readr" %in% rownames(installed.packages())) install.packages("readr")
meteo <- readr::read_csv("https://raw.githubusercontent.com/ecomore2/meteo/master/data/meteo.csv",
                         col_types = "iDddddiddddllll")
stations <- readr::read_csv("https://raw.githubusercontent.com/ecomore2/meteo/master/data/stations.csv",
                            col_types = "cciddi")
```

Dictionary:

* **day**: date of data colletion
* **ta**: average temperature (°C)
* **tx**: maximum temperature (°C)
* **tn**: minimum temperature (°C)
* **slp**: atmospheric pressure at sea level (hPa)
* **h**: average relative humidity (%)
* **pp**: total rainfall and / or snowmelt (mm)
* **vv**: average visibility (km)
* **v**: average wind speed (km / h)
* **vm**: maximum sustained wind speed (km / h)
* **ra**: boolean indicating whether there was rain or drizzle
* **sn**: boolean indicating whether it snowed
* **ts**: boolean indicating whether there were storm
* **fg**: boolean indicating whether there was floo

Below is a visual representation of the data per station:

```{r echo = FALSE, message = FALSE, fig.retina = 2, fig.align = "center"}
library(visdat)
meteo <- readr::read_csv("data/meteo.csv", col_types = "iDddddiddddllll")
# for(i in unique(meteo$station)) vis_dat(meteo[meteo$station == i, ])
sta <- unique(meteo$station)
print(sta[1])
vis_dat(meteo[meteo$station == sta[1], ], FALSE)
print(sta[2])
vis_dat(meteo[meteo$station == sta[2], ], FALSE)
print(sta[3])
vis_dat(meteo[meteo$station == sta[3], ], FALSE)
print(sta[4])
vis_dat(meteo[meteo$station == sta[4], ], FALSE)
```