Summary

This page is the hand in (final project) for the Coursera course Reproducible Templates for Analysis and Dissemination thought by Melinda Higgins. It countains a few basic R Markdown objects as requested for the assignment:

The layout chosen is the theme cayman from the package prettydoc.

As bonus there are a few elements I also implemented on my Corona-Info-Site for Austria.

Plot of pressure dataset

pressure %>% 
  ggplot(aes(x=temperature, y=pressure)) +
  geom_point() +
  labs(title = "Plot of the R-built-in pressure dataset",
       x = "Temperature [°C]",
       y = "Pressure [mmHg]",
       caption = "https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/pressure.html") +
  theme_clean()

Table of the cars dataset

head(cars) %>% 
  kbl(align = "cc", col.names = c("Speed [mph]", "Stopping Distance [feet]"),
      caption = "Speed and Stopping Distances of Cars (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/cars.html)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                position = "left")
Speed and Stopping Distances of Cars (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/cars.html)
Speed [mph] Stopping Distance [feet]
4 2
4 10
7 4
7 22
8 16
9 10

Some Corona Facts and Figures

The following items are parts of my Corona-Data-Infosite for friends and family on https://charmingquark.at/covid.html.

Timeline of dominant Covid variants in Austria

Data source: https://www.ecdc.europa.eu/en/publications-data/data-virus-variants-covid-19-eueea
GISAID Data Acknowledgement: https://www.ecdc.europa.eu/en/publications-data/gisaid-acknowledgements

A sample of positive Covid-tests is genetically sequenced. The following graph is showing the timeline of the percentage of specific variants over time for Austria.

# Last sucessful download on 2022-02-14
ecdc_varseq_weekly <- read.csv(file = "ecdc_varseq_weekly.csv") %>%
  mutate(dateRep = as.Date(dateRep)) %>%   
  mutate(year=str_sub(year_week,1,4), week=str_sub(year_week,6,7),
         year_week2=paste0(year,"-W",week,"-1"),
         dateRep=ISOweek::ISOweek2date(year_week2))

# Adding the "popular" names of the variants
ecdc_varseq_weekly$variant <- str_replace_all(ecdc_varseq_weekly$variant,
                c("B.1.617.2" = "B.1.617.2 (Delta)",
                  "B.1.1.529" = "B.1.1.529 (Omikron)",
                  "B.1.1.7" = "B.1.1.7 (Alpha)",
                  "B.1.1.7+E484K" = "B.1.1.7+E484K (Alpha)",
                  "B.1.351" = "B.1.351 (Beta)"))

ecdc_varseq_weekly %>% 
  filter(country_code == "AT", # filtering for Austria
         variant != "Other") %>% 
  group_by(variant) %>% 
  mutate(percent_variant_max = max(percent_variant, # calculating the maximum percentage of a specific variant over the whole period 
                                   na.rm = TRUE)) %>% 
  filter(percent_variant_max > 10) %>% # only keep variants that had a minimum share of 10% at some point
  ungroup() %>% 
  filter(percent_variant > 0.5) %>% # only plot variant when over 0.5%
  ggplot(aes(x = dateRep, 
             y = percent_variant, 
             color = factor(variant))) +
  geom_line(size = 1) +
  scale_x_date(date_labels = "%Y-%m") +
  labs(title = "Covid 19 Virus Variants detected in Austria", 
       subtitle = "   only variants that reached a minimum share of 10% at some point in time",
       x = "", y = "Share of variant de [%]",
       color = "Virus variant",
       caption = paste0("Data: gisaid.org via ECDC; ",
                        max(ecdc_varseq_weekly$dateRep,
                            na.rm = TRUE))) +
  theme_minimal()