homeclimate.rmd

```{r setup}
library(jsonlite)
library(tidyverse)

source("R/keys.r")
```


```{r data_scd30}
# parameters
value_days = 10

# query thingspeak channel
url_base = glue::glue("https://api.thingspeak.com/channels/{id_thingspeak}/feeds.json?")
url_query = glue::glue(url_base, "api_key={key_read}", "&days={value_days}")
data_sensor_raw <- read_json(url_query, simplifyVector = TRUE)

# shape raw data
data_sensor <- tibble(data_sensor_raw$feeds) %>% 
  mutate(time = lubridate::ymd_hms(created_at, tz = "Europe/Paris")) %>% 
  mutate(across(field1:field3, as.numeric)) %>% 
  select(time, co2 = field1, temp = field2, rh = field3) %>% 
  pivot_longer(cols = co2:rh) 


# write_rds(data_sensor, file = glue::glue("data/archive_", format(Sys.time(), "%Y%m%d"),".rds"))
```

```{r plot_scd30}

# style
list_variables = c("co2","rh") 
list_colors = c("#2CA02CFF","#1F77B4FF","#FF7F0EFF")
label_variables <- as_labeller(c("co2" = "co2 [ppm]", "rh" = "rh [%]", "temp" = "temp [°C]"))

table_threshold <- tibble(
  name = c("co2","rh","temp"),
  value = c(800, 50, 19))

# plot selected data
# data_sensor <- readr::read_rds("data/archive_20210206.rds")

plot_sensor <- data_sensor %>% 
  filter(name %in% list_variables) %>% 
  ggplot(aes(x = time, y = value, color = name)) +
  geom_line(alpha = 0.8) +
  stat_smooth (
    geom = "line", alpha = 0.3, size = 3,
    method = "gam", formula = y ~ s(x, k = 12)) +
  geom_hline(
    data = table_threshold %>% filter(name %in% list_variables),
    aes(yintercept = value), linetype = 2) + 
  facet_wrap(vars(name), scale = "free_y", labeller = label_variables) +
  scale_color_manual(values = list_colors) + labs(x=NULL, y=NULL) +
  theme_bw() + theme(legend.position = "none")

# export graphics and data
ggsave(
  plot_sensor, file = "R/figures/sensor_scd30.png",
  dpi=120, width=210, height=100, scale=1, units="mm")


```