rhilfi

library(tidyverse)
Dieser_Alte_Hase=rnorm(10,2,2)
Dieses_Alte_Pferd=rnorm(10,4,2)
Dieses_Alte_Haus=rnorm(10,4,2)

data<-data.frame(Dieser_Alte_Hase, Dieses_Alte_Pferd, Dieses_Alte_Haus)
names(data)

data<-data %>% 

rhilfi

https://medium.com/@ValidScience/how-to-fix-rstudios-package-installation-on-windows-10-c1e602bf3a1f 

rhilfi

a<-rnorm(100, 30, 10)
hist(a, col="pink")

pdf("just_a_plain_histogram.pdf", width=8, height=5, useDingbats=FALSE)
hist(a, col="pink")
dev.off()

rhilfi

# remove NA in string variables

library(dplyr)
Names = c("Peter","NA", "Gabriel", "John", "Lennon","Stuart")
Country=c("Sweden", "Switzerland", "Cumberland", "Wonderland", "Nowhereland", "Land")
Numbers=c(1,2,3,4,NA,6)
id<- 1:6

data<-data.frame(id, Names, Numbers, Country)
head(data)

rhilfi

Name<-c("John", "Paul","George", "Ringo")
Age <-rnorm(4, 70, 2)
number_1<-c(2.34324,2.32145,5.432,5.3456)
number_2<-c(6.5432,8.32456,3.2346,9.3254245)

data<-data.frame(Name, Age, number_1, number_2)

data_rounded<-data %>% 
  mutate(across(where(is.numeric), round, 1))

rhilfi

# Calculate sum of different columns per row

id<-1:5
food<-c(1,4,3,2,1)
drink<-c(4,3,2,4,5)
names<-c("John", "Mick", "Paul","George", "Ringo")
books<-c(3,4,5,4,2)

data<-data.frame(id, food, drink,names, books )

rhilfi

# calculate with mutate_at 

# see also here: https://suzan.rbind.io/2018/02/dplyr-tutorial-2/ 

# create an example data frame ####
library(tidyverse)

id<-1:10
item1<-c(2,3,2,4,3,2,1,0,2,1)
item2<-c(4,3,2,1,2,3,1,0,1,2)

rhilfi

https://www.brianchildress.co/variables-in-markdown/#:~:text=The%20idea%20is%20pretty%20simple,later%20in%20the%20same%20document.

[variable]: 1 

rhilfi

# install.packages("questionr")
library(questionr)

Thats_a_cool_variable =rnorm(100, 50,12)
Thats_a_less_cool_variable =rnorm(100,50,12)
Thats_an_even_cooler_variable=rnorm(100,40,21)

Does_R_makes_you_angry=rep(0:1,each=50)
Does_R_makes_you_happy=rep(c(1,0),each=50)

rhilfi

library(summarytools)
library(dplyr)
first_name=c("Hans", "Peter", "", "Fritz", "Susi")

family_name=c("Meier", "Muller", "Zgraggen", "", "Zurcher")
age=c(43,43,23,55,40)

data<-data.frame(first_name, family_name, age)

summarytools::freq(data$first_name)