Finish statistics for 2018.

This commit is contained in:
daniel 2018-12-28 17:09:26 +01:00
parent 2fbfa7aa6e
commit 0e76645882
2 changed files with 85 additions and 6 deletions

38
columns.tex Normal file
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@ -0,0 +1,38 @@
% From: https://support.rstudio.com/hc/en-us/community/posts/202717656-Can-we-have-columns-in-rmarkdown-beamer-presentations-
%
% In the YAML header of your Rmd file, add a reference to a tex file to add to the tex file preamble ;
%
% output:
% beamer_presentation:
% includes:
% in_header: mypreamble.tex
%
% In mypreamble.tex, add:
%
% \def\begincols{\begin{columns}}
% \def\begincol{\begin{column}}
% \def\endcol{\end{column}}
% \def\endcols{\end{columns}}
%
% Finally, in the Rmd file, use this kind of syntax:
%
% ## Two Column Layout
%
% \begincols
% \begincol{.48\textwidth}
%
% This slide has two columns.
%
% \endcol
% \begincol{.48\textwidth}
%
% ```{r pressure}
% plot(pressure)
% ```
%
% \endcol
% \endcols
\def\begincols{\begin{columns}}
\def\begincol{\begin{column}}
\def\endcol{\end{column}}
\def\endcols{\end{columns}}

53
vhk.Rmd
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--- ---
title: "Vorhofkatheter-Statistik" title: "Vorhofkatheter-Statistik"
author: "Daniel Kraus" author: "Daniel Kraus"
date: '2018-12-30' date: '2018-12-29'
output: output:
ioslides_presentation: default ioslides_presentation: default
slidy_presentation: default
beamer_presentation: default beamer_presentation: default
--- ---
```{r setup, include=FALSE} ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE) knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
library(tidyverse) library(tidyverse)
raw_data = read_csv('vhk.csv') %>% raw_data = read_csv('vhk.csv') %>%
@ -28,7 +29,7 @@ cath_by_year %>%
geom_col() + geom_col() +
scale_y_continuous(breaks = seq(from = 0, to = max_y_break, by = 10)) + scale_y_continuous(breaks = seq(from = 0, to = max_y_break, by = 10)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) + scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
labs(x = NULL, y = NULL) labs(x = NULL, y = "Anzahl Katheter")
``` ```
## Alter der Patienten ## Alter der Patienten
@ -39,7 +40,7 @@ raw_data %>%
coord_cartesian(ylim = c(20, 100)) + coord_cartesian(ylim = c(20, 100)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) + scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
scale_y_continuous(breaks = seq(from = 20, to = 100, by = 10)) + scale_y_continuous(breaks = seq(from = 20, to = 100, by = 10)) +
labs(x = NULL, y = NULL) labs(x = NULL, y = "Jahre")
``` ```
## Geschlecht der Patienten ## Geschlecht der Patienten
@ -52,13 +53,53 @@ raw_data %>% group_by(Year) %>% summarise(PercentFemale = sum(Sex == "weiblich")
labs(x = NULL, y = "Anteil Frauen") labs(x = NULL, y = "Anteil Frauen")
``` ```
## Katheterlokalisation
Ist da ein Trend hin zu immer mehr Kathetern von links?!
```{r insertion_site}
raw_data %>% mutate(Side = factor(Side, levels = c("rechts", "links"))) %>%
ggplot(aes(x = Year)) +
facet_grid(InsertionSite ~ Side) +
geom_bar() +
labs(x = NULL, y = "Anzahl Katheter")
```
## Anteil der Arztrollen
Um 2014 herum haben einige die Facharztprüfung abgelegt, ist das der Grund für die Auffälligkeit 2015/2016?
```{r percent_residents}
raw_data %>% group_by(Year) %>%
summarize(Assistenzarzt = sum(SurgeonRole == "Assistenzarzt") / n(),
Facharzt = sum(SurgeonRole == "Facharzt") / n(),
Oberarzt = sum(SurgeonRole == "Oberarzt") / n()) %>%
gather(key = Role, value = Percent, Assistenzarzt, Facharzt, Oberarzt) %>%
ggplot(aes(x = Year, y = Percent)) +
scale_x_continuous(breaks = seq(from = first_year, to = last_year, by = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
facet_grid(Role ~ .) +
geom_col() +
labs(x = NULL, y = "Anteil in den gelegten Kathetern")
```
## Hitparade der Durchleuchtungsdauern
```{r greatest_fluoroscopy}
raw_data %>%
group_by(Surgeon) %>%
summarize(FluoroscopyIndex = median(InsertionFluoroscopyDuration, na.rm = TRUE)) %>%
arrange(desc(FluoroscopyIndex)) %>%
top_n(-10, FluoroscopyIndex) %>%
mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
ggplot(aes(x = Surgeon, y = FluoroscopyIndex)) +
geom_col() +
coord_flip() +
labs(x = NULL, y = "Median der Durchleuchtungsdauer [s]")
```
## Hitparade der Implanteure ## Hitparade der Implanteure
```{r greatest_surgeons} ```{r greatest_surgeons}
raw_data %>% count(Surgeon) %>% arrange(n) %>% top_n(10, n) %>% mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>% raw_data %>% count(Surgeon) %>% arrange(n) %>% top_n(10, n) %>% mutate(Surgeon = factor(Surgeon, levels = Surgeon)) %>%
ggplot(aes(x = Surgeon, y = n)) + ggplot(aes(x = Surgeon, y = n)) +
geom_col() + geom_col() +
coord_flip() + coord_flip() +
labs(x = NULL, y = NULL) labs(x = NULL, y = "Gesamtzahl Katheter")
``` ```
## Hitparade der Assistenten ## Hitparade der Assistenten
@ -67,6 +108,6 @@ raw_data %>% count(Assistant) %>% arrange(n) %>% top_n(10, n) %>% mutate(Assista
ggplot(aes(x = Assistant, y = n)) + ggplot(aes(x = Assistant, y = n)) +
geom_col() + geom_col() +
coord_flip() + coord_flip() +
labs(x = NULL, y = NULL) labs(x = NULL, y = "Gesamtzahl Katheter")
``` ```