1. Create a data frame df that contains the following variables for at least four observations:
  • name: name of at least four friends or acquaintances
  • age: the age of those persons
  • size: the height of those persons in cm
  • city: Place of residence of those persons (city)
Click here to see the answer 
df <- data.frame(
  name = c("Anna", "Otto", "Natan", "Ede"), 
  age  = c(66, 53, 22, 36),
  size = c(170, 174, 182, 180),
  city = c("Hamburg", "Berlin", "Berlin", "Cologne")
  )

df
##    name age size    city
## 1  Anna  66  170 Hamburg
## 2  Otto  53  174  Berlin
## 3 Natan  22  182  Berlin
## 4   Ede  36  180 Cologne
  1. Examine the dimensionality, structure and statistical summary of your data frame df!
Click here to see the answer 
dim(df)
## [1] 4 4

str(df)
## 'data.frame':    4 obs. of  4 variables:
##  $ name: Factor w/ 4 levels "Anna","Ede","Natan",..: 1 4 3 2
##  $ age : num  66 53 22 36
##  $ size: num  170 174 182 180
##  $ city: Factor w/ 3 levels "Berlin","Cologne",..: 3 1 1 2

summary(df)
##     name        age             size            city  
##  Anna :1   Min.   :22.00   Min.   :170.0   Berlin :2  
##  Ede  :1   1st Qu.:32.50   1st Qu.:173.0   Cologne:1  
##  Natan:1   Median :44.50   Median :177.0   Hamburg:1  
##  Otto :1   Mean   :44.25   Mean   :176.5              
##            3rd Qu.:56.25   3rd Qu.:180.5              
##            Max.   :66.00   Max.   :182.0
  1. Index the second column with simple square brackets [] and save the output as df.subset! Which class does the output belong to?
Click here to see the answer 
df.subset <- df[2]

df.subset
##   age
## 1  66
## 2  53
## 3  22
## 4  36

class(df.subset)
## [1] "data.frame"
  1. Index the variable age with double square brackets [] and save the output as age.persons. Which class does the output belong to?
Click here to see the answer 
age.persons <- df[["alter"]]

# oder

age.persons <- df[[2]]

age.persons
## [1] 66 53 22 36

class(age.persons)
## [1] "numeric"
  1. Add the variable weight in kg to the data frame df!
Click here to see the answer 
df$weight <- c(115, 110.2, 95, 87)

df
##    name age size    city weight
## 1  Anna  66  170 Hamburg  115.0
## 2  Otto  53  174  Berlin  110.2
## 3 Natan  22  182  Berlin   95.0
## 4   Ede  36  180 Cologne   87.0
  1. Add another observation (person) to your df!
Click here to see the answer 
new.person <- data.frame(
  "name"   = "Anna",
  "age"    = 32,
  "size"   = 174,
  "weight" = 63,
  "city"   = "Hamburg"
)

df <- rbind(df, new.person)

df
##    name age size    city weight
## 1  Anna  66  170 Hamburg  115.0
## 2  Otto  53  174  Berlin  110.2
## 3 Natan  22  182  Berlin   95.0
## 4   Ede  36  180 Cologne   87.0
## 5  Anna  32  174 Hamburg   63.0
  1. Calculate the mean value of the variable age and save the result as ages.mean!
Click here to see the answer 
ages.mean <- mean(df$age)
  1. Index all observations (persons) that are older than the average ages.mean!
Click here to see the answer 
df[df$age > ages.mean, ]
  1. Index all persons, which are lighter than 100 kg AND at least 180 cm tall!
Click here to see the answer 
df[df$weight = 180, ]
##    name age size    city weight
## 3 Natan  22  182  Berlin     95
## 4   Ede  36  180 Cologne     87

# or

subset(df, df$weight = 180)
##    name age size    city weight
## 3 Natan  22  182  Berlin     95
## 4   Ede  36  180 Cologne     87