sebastiansauer

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Related Setup: https://gist.github.com/hofmannsven/6814278

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sebastiansauer

library(ggplot2)
# library(cowplot)
library(dplyr)

logist <- function(x){
  y = exp(x) / (1 + exp(x))
}

p1 <- ggplot(data_frame())

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# Plot path diagram with {DiagrammeR}

data(Wage, package = "ISLR")  #load data


lm_wage <- lm(wage ~ health + age + health:age, data = Wage)  # run linear model
summary(lm_wage)

library(semPlot)  # plot path diagram with {semPlot}
semPaths(lm_wage,  whatLabel = "est", vsize.man = 16, edge.label.cex=0.6)

sebastiansauer

###############################################################################
### Beispiel-Analyse des Datensatzes "tips"
### von Sebastian Sauer, letztes Update: 2016-01-18
### Zugang zu den Daten: Der Datensatz findet sich im Paket "reshape2"
###############################################################################

# Hinweis: Es ist ganz normal, Syntax/Befehle nachzuschlagen :)
# Eine Möglichkeit dazu ist mit help(Befehl), z.B.
help(library)  # oder mit google :)

sebastiansauer

library(dplyr)
data(Wage, package = "ISLR")


Wage %>% 
  mutate(wage_f = ntile(wage, 2)) %>%  # bin it
  group_by(wage_f, health, race) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(x = factor(wage_f), y = count, fill = race)) + 
  geom_bar(stat = "identity") + 

sebastiansauer

#Barplots with exact numbers


data(tips, package = "reshape2")  # load some data

library(dplyr)
library(tidyr)
library(ggplot2)

tips %>% 

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SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

all : $(SLIDES) $(HANDOUTS)

%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@ 

sebastiansauer

# plot normal distribution with ggplot2, simply


library(cowplot)


p1 <- ggplot(data = data.frame(x = c(-3, 3)), aes(x)) +
  stat_function(fun = dnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") +
  scale_y_continuous(breaks = NULL)
p1

sebastiansauer

# Exam grading, as a convenience function for teachers

# Sebastian Sauer
# Stand: 2016-05-20


# need to be installed upfront with "install.packages()"
library(ggplot2)
library(car)
library(tidyr)

sebastiansauer

# normal distribution with serveral shaded areas

library(ggplot2)
library(dplyr)

mean.1 <-0
sd.1 <- 1
zstart <- 2
zend <- 3
zcritical <- 1.65