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Pull global temperature data from Climate Reanalyzer and create a daily temperature anomaly chart
library(tidyverse)
library(jsonlite)
library(splitstackshape)
library(RColorBrewer)
# pull daily temperature from https://climatereanalyzer.org/clim/t2_daily/
temp_json <- fromJSON("https://climatereanalyzer.org/clim/t2_daily/json/cfsr_world_t2_day.json")
# create dataframe
temp <- as.data.frame(temp_json) %>%
cSplit('data', ',') %>%
mutate(data_001 = parse_number(data_001),
data_366 = parse_number(data_366)) %>%
head(-3) %>%
pivot_longer(2:ncol(.), names_to = "day") %>%
rename(year = name,
temp = value) %>%
mutate(year = parse_number(year),
day = parse_number(day)) %>%
filter(complete.cases(.)) %>%
group_by(day) %>%
mutate(baseline_1991_2020 = mean(temp[year >= 1991 & year <= 2020]),
temp_anom = temp-baseline_1991_2020) %>%
ungroup()
# draw plot
color_scale <- brewer.pal(9, "YlOrRd")
ggplot(temp) +
geom_line(aes(day, temp, group = year), colour = "grey") +
geom_rect(data = . %>% filter(year == 2023),
aes(xmin = lag(day, default = 1),
xmax = day,
ymin = baseline_1991_2020,
ymax = baseline_1991_2020 + temp_anom,
fill = temp_anom)) +
scale_fill_gradientn(colors = color_scale) +
geom_line(aes(day, baseline_1991_2020, group = year), colour = "black", linetype = "dashed") +
geom_line(data = . %>% filter(year == 2023), aes(day, temp, group = year), colour = "black") +
theme_minimal()
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