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