Nothing
## ---- include=FALSE-----------------------------------------------------------
library(knitr)
opts_chunk$set(fig.width = 6, fig.height = 4)
## ---- message=FALSE-----------------------------------------------------------
library(macleish)
## -----------------------------------------------------------------------------
head(whately_2015)
tail(whately_2015)
## ---- eval=FALSE--------------------------------------------------------------
# head(orchard_2015)
# tail(orchard_2015)
## ---- eval=FALSE--------------------------------------------------------------
# help("etl_extract.etl_macleish")
## ----daily, message=FALSE, warning=FALSE--------------------------------------
library(ggplot2)
library(dplyr)
library(lubridate)
daily <- whately_2015 %>%
mutate(the_date = as.Date(when, tz = "EST")) %>%
group_by(the_date) %>%
summarize(
N = n(), avg_temp = mean(temperature),
max_temp = max(temperature),
min_temp = min(temperature)
)
## ----temp-plot, message=FALSE-------------------------------------------------
temp_plot <- ggplot(data = whately_2015, aes(x = when, y = temperature)) +
geom_line(color = "lightgray") +
geom_line(data = daily, aes(
x = as.POSIXct(the_date),
y = avg_temp
)) +
xlab(NULL) +
ylab("Temperature (Celsius)")
if (require(mgcv)) {
temp_plot + geom_smooth()
} else {
temp_plot
}
## ----temp-table---------------------------------------------------------------
monthly_w <- whately_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(avg_temp = mean(temperature))
monthly_o <- orchard_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(avg_temp = mean(temperature))
monthly_w %>%
inner_join(monthly_o, by = "the_month")
## ----temp-extremes------------------------------------------------------------
whately_2015 %>%
arrange(desc(temperature)) %>%
head(1)
whately_2015 %>%
arrange(temperature) %>%
head(1)
## -----------------------------------------------------------------------------
daily <- daily %>%
mutate(temp_range = max_temp - min_temp)
daily %>%
select(temp_range) %>%
summary()
## -----------------------------------------------------------------------------
daily %>%
arrange(desc(temp_range)) %>%
head(1)
daily %>%
arrange(temp_range) %>%
head()
## ----orchard-anolomies--------------------------------------------------------
orchard_2015 %>%
filter(month(when) == 11) %>%
ggplot(aes(x = when, y = temperature)) +
geom_line()
## ----humidity-table-----------------------------------------------------------
monthly_w <- whately_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(avg_humidity_w = mean(rel_humidity))
monthly_o <- orchard_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(avg_humidity_o = mean(rel_humidity))
monthly_w %>%
inner_join(monthly_o, by = "the_month")
## ---- message=FALSE, warning=FALSE--------------------------------------------
require(clifro)
orchard_2015 %>%
with(windrose(wind_speed, wind_dir))
## ---- message=FALSE-----------------------------------------------------------
whately_2015 %>%
with(windrose(wind_speed, wind_dir))
## -----------------------------------------------------------------------------
whately_2015 %>%
summarize(total_rainfall = sum(rainfall))
## ----rain-table---------------------------------------------------------------
monthly_w <- whately_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(total_precip_w = sum(rainfall))
monthly_o <- orchard_2015 %>%
mutate(the_month = month(when, label = TRUE, abbr = FALSE)) %>%
group_by(the_month) %>%
summarize(total_precip_o = sum(rainfall))
monthly_w %>%
inner_join(monthly_o, by = "the_month")
## ---- message=FALSE-----------------------------------------------------------
daily_precip <- whately_2015 %>%
mutate(the_date = as.Date(when, tz = "EST")) %>%
group_by(the_date) %>%
summarize(N = n(), total_precip = sum(rainfall)) %>%
mutate(
cum_precip = cumsum(total_precip),
cum_rescale = (cum_precip / max(cum_precip)) * max(total_precip)
)
## ----daily-precip, message=FALSE----------------------------------------------
ggplot(
data = daily_precip,
aes(x = the_date, y = total_precip)
) +
geom_bar(stat = "identity") +
geom_line(aes(y = cum_rescale), color = "blue") +
ylab("Daily Precipitation (mm)") +
xlab(NULL)
## -----------------------------------------------------------------------------
names(macleish_layers)
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# library(leaflet)
# leaflet() %>%
# addTiles() %>%
# addPolygons(
# data = macleish_layers[["boundary"]],
# weight = 1
# ) %>%
# addPolygons(
# data = macleish_layers[["buildings"]],
# weight = 1
# ) %>%
# addMarkers(
# data = filter(macleish_layers[["landmarks"]], grepl("Met", Label)),
# popup = ~Label
# )
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