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)
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
2) Examine the dimensionality, structure and statistical summary of your data frame df!
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
3) Index the second column with simple square brackets [] and save the output as df.subset! Which class does the output belong to?
df.subset <- df[2] df.subset ## age ## 1 66 ## 2 53 ## 3 22 ## 4 36 class(df.subset) ## [1] "data.frame"
4) Index the variable age with double square brackets [[]] and save the output as age.persons. Which class does the output belong to?
age.persons <- df[["alter"]] # oder age.persons <- df[[2]] age.persons ## [1] 66 53 22 36 class(age.persons) ## [1] "numeric"
5) Add the variable weight in kg to the data frame df!
df$weight <- c(115, 110.2, 95, 87) df ## name age size city weight ## 1 Anna 66 170 Hamburg 115.0 ## 2 Otto 53 174 Berlin 110.2 ## 3 Natan 22 182 Berlin 95.0 ## 4 Ede 36 180 Cologne 87.0
6) Add another observation (person) to your df!
new.person <- data.frame( "name" = "Anna", "age" = 32, "size" = 174, "weight" = 63, "city" = "Hamburg" ) df <- rbind(df, new.person) df ## name age size city weight ## 1 Anna 66 170 Hamburg 115.0 ## 2 Otto 53 174 Berlin 110.2 ## 3 Natan 22 182 Berlin 95.0 ## 4 Ede 36 180 Cologne 87.0 ## 5 Anna 32 174 Hamburg 63.0
7) Calculate the mean value of the variable age and save the result as ages.mean!
ages.mean <- mean(df$age)
8) Index all observations (persons) that are older than the average ages.mean!
df[df$age > ages.mean, ]
9) Index all persons, which are lighter than 100 kg AND at least 180 cm tall!
df[df$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