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---
title: "Weekly chart data analysis"
author: "Gregor Aisch"
date: "9/22/2020"
output:
html_document:
df_print: tibble
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
Load some useful packages using `needs`
```{r}
needs(tidyverse, directlabels, ggrepel)
```
Let's load yearly, seasonal and monthly averages for air temperature
```{r temp, echo=F, message=FALSE, warning=FALSE, cache=TRUE}
temp <- read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/annual/air_temperature_mean/regional_averages_tm_year.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr, time='Year', value=as.numeric(Deutschland)) %>%
bind_rows(
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/air_temperature_mean/regional_averages_tm_autumn.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Autumn', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/air_temperature_mean/regional_averages_tm_spring.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Spring', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/air_temperature_mean/regional_averages_tm_summer.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Summer', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/air_temperature_mean/regional_averages_tm_winter.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Winter', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_01.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='January', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_02.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='February', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_03.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='March', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_04.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='April', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_05.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='May', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_06.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='June', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_07.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='July', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_08.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='August', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_09.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='September', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_10.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='October', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_11.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='November', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/air_temperature_mean/regional_averages_tm_12.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='December', value=as.numeric(Deutschland))
) %>%
mutate(measure='temperature')
temp
```
Load yearly, seasonal and monthly averages for precipitation
```{r precip, echo=F, message=FALSE, warning=FALSE, cache=TRUE}
precip <- read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/annual/precipitation/regional_averages_rr_year.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr, time='Year', value=as.numeric(Deutschland)) %>%
bind_rows(
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/precipitation/regional_averages_rr_autumn.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Autumn', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/precipitation/regional_averages_rr_spring.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Spring', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/precipitation/regional_averages_rr_summer.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Summer', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/seasonal/precipitation/regional_averages_rr_winter.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='Winter', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_01.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='January', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_02.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='February', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_03.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='March', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_04.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='April', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_05.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='May', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_06.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='June', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_07.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='July', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_08.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='August', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_09.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='September', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_10.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='October', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_11.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='November', value=as.numeric(Deutschland)),
read_delim('https://opendata.dwd.de/climate_environment/CDC/regional_averages_DE/monthly/precipitation/regional_averages_rr_12.txt',
delim=';', skip = 1) %>%
transmute(year=Jahr,time='December', value=as.numeric(Deutschland))
) %>%
mutate(measure='precipitation')
precip
```
compute climate baseline (1961-1990)
```{r}
temp.base <- temp %>%
filter(year>1960 & year <= 1990) %>%
group_by(time) %>%
summarise(base=mean(value))
temp.base
```
```{r}
precip.base <- precip %>%
filter(year>1960 & year <= 1990) %>%
group_by(time) %>%
summarise(base=mean(value))
precip.base
```
Join temperature and precipitation data, add baseline, compute anomalies.
```{r out, message=FALSE, cache=TRUE}
out <- temp %>%
left_join(temp.base) %>%
bind_rows(left_join(precip, precip.base)) %>%
mutate(anomaly=ifelse(measure=='temperature',
value-base, # difference anomaly for temp.
(value-base)/base*100)) %>% # pct. anomaly for rain
select(year, time, measure, anomaly) %>%
pivot_wider(names_from=measure, values_from=anomaly)
out
```
Save the 2019 & 2020 data for our first plot.
```{r}
out %>%
filter(year==2019 & !(time %in% c('Year','Spring','Summer','Winter','Autumn'))) %>%
write_csv('temp-precip-anomalies-2019.csv')
out %>%
filter(year==2020 & !(time %in% c('Year','Spring','Summer','Winter','Autumn'))) %>%
write_csv('temp-precip-anomalies-2020.csv')
```
For the scatterplot custom line annotations we need to generate some markup:
```{r}
colors <- tribble(#####1976b3
~time, ~color,
'Year', '#333333',
'Summer', '#308c00',
'June', '#308c00',
'July', '#308c00',
'August', '#308c00',
#~~~~~~~~~~~~~~~~~
'Winter', '#6ea2ff',
'December', '#6ea2ff',
'January', '#6ea2ff',
'February', '#6ea2ff',
#~~~~~~~~~~~~~~~~~
'Autumn', '#ac2125',
'September', '#ac2125',
'October', '#ac2125',
'November', '#ac2125',
#~~~~~~~~~~~~~~~~~
'Spring', '#fac10e',
'March', '#fac10e',
'April', '#fac10e',
'May', '#fac10e',
)
out %>%
filter(year==2020) %>%
filter(!(time %in% c('Year','Summer','Winter','Spring','Autumn'))) %>%
left_join(colors) %>%
transmute(x1=0,
y1=0,
x2=round(temperature,2),
y2=round(precipitation,2),
paste0('@color:',color),
'@width:2') %>%
format_csv(col_names = F) %>%
str_replace_all(',@', ' @')
```
### Time for some plotting
Plot all months for 2019
```{r plot-2019, fig.width=7, fig.height=7, cache=T}
out %>%
filter(year==2019) %>%
filter(!(time %in% c('Summer','Winter','Spring','Autumn', 'Year'))) %>%
ggplot(aes(xend=temperature, yend=precipitation,color=time)) +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
geom_segment(x=0,y=0, arrow = arrow(length = unit(7, 'points'))) +
geom_text(aes(temperature, precipitation, label=time)) +
theme_minimal()
```
Plot all months for 2020
```{r plot-2020, fig.width=7, fig.height=7, cache=T}
out %>%
filter(year==2020) %>%
filter(!(time %in% c('Summer','Winter','Spring','Autumn', 'Year'))) %>%
ggplot(aes(xend=temperature, yend=precipitation,color=time)) +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
geom_segment(x=0,y=0, arrow = arrow(length = unit(7, 'points'))) +
geom_text(aes(temperature, precipitation, label=time)) +
theme_minimal()
```
Plot all seasons for years since 2010
```{r seasons, fig.width=7, fig.height=7, cache=T}
d <- out %>%
filter(year>=2010) %>%
filter(time %in% c('Summer','Winter','Spring','Autumn'))
d %>% write_csv('temp-precip-anomalies-2010-2020.csv')
d %>%
ggplot(aes(xend=temperature, yend=precipitation,color=time)) +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
geom_segment(x=0,y=0, arrow = arrow(length = unit(7, 'points'))) +
geom_text(aes(temperature, precipitation, label=year)) +
theme_minimal()
```
Average seasons since 2000:
```{r avg-seasons, fig.width=7, fig.height=7, cache=T}
out %>%
filter(year>=2000) %>%
filter(time %in% c('Summer','Winter','Spring','Autumn')) %>%
group_by(time) %>%
summarise(temperature=mean(temperature), precipitation=mean(precipitation)) %>%
ggplot(aes(xend=temperature, yend=precipitation,color=time)) +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
geom_text_repel(aes(temperature, precipitation, label=time)) +
theme_minimal() +
geom_segment(x=0,y=0, arrow = arrow(length = unit(7, 'points')))
```
Let's look at where all the years since 1961 are in the plot (highlighting the most recent years):
```{r all_years, echo=FALSE, fig.width=7, fig.height=7}
out %>%
filter(time=='Year' & year > 1960) %>%
mutate(recent=year>=2010) %>%
ggplot(aes(temperature, precipitation, label=year, color=recent)) +
geom_point() +
geom_text_repel() +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
theme_minimal()
```