Introduction to R and RStudio

Part 2: tidyverse (follow along in RStudio)

Learning Outcomes

What you will learn in this session:

  • The Structure of R commands
  • About the tidyverse package for data frames
    • select() and rename columns (variables)
    • filter() rows (observations)
    • mutate() (define columns (variables); overwrite old or create new)
    • piping (connecting commands) with |>.»

Basics of R Commands

R commands consists of the command’s name followed by a pair of parentheses: command()

Inside the () we can define one or more arguments for the command.

VecTest=c(1,2,3)

sum(x=VecTest)
[1] 6
mean(VecTest)
[1] 2

  • Arguments in a command usually have names such as x= or data=

  • R does not require to use the argument’s name, but order matters

  • R commands have many arguments. Most have default values

  • We can nest commands. However, nesting too deeply makes code difficult to read.»

:::

Structure of R Commands

Most R commands have the following structure: \[\begin{equation} \underbrace{DataNew}_{\text{R object storing the result}}= \underbrace{Command}_{\text{Name of the command}} \underbrace{(\overbrace{Data}^{\text{1. Argument: Data to process}}, \overbrace{Arg2, Arg3, \dots, ArgN}^{\text{More Arguments}})}_{\text{Arguments inside () and separated by komma}} \end{equation}\]

Often the data argument is the first argument in a command. Usually named data= or x=.»

Use a Command with and without Argument Names 🤓

VecTest=c(1,2,3)


Result=mean(x=VecTest, trim=0, na.rm=FALSE)
cat("The mean of the values in vector VecTest is:", Result)
The mean of the values in vector VecTest is: 2

Result=mean(VecTest, 0, FALSE)
cat("The mean of the values in vector VecTest is:", Result)
The mean of the values in vector VecTest is: 2

Result=mean(VecTest)
cat("The mean of the values in vector VecTest is:", Result)
The mean of the values in vector VecTest is: 2

All three examples are equivalent

Try ? mean in the Rstudio console to see the default values.»

See: RandRStudioScriptPart2.R script100

Important Commands from tidyverse/dplyr Package

  • dplyr package is part of the tidyverse (meta) package
  • library(tidyverse) (loads the tidyverse and its packages)
  • select() selects columns (variables) from a data frame
  • filter() filters rows (observations) for specific criteria
  • mutate() calculates new or overwrites existing columns (variables) based on other columns (just like Excel).»

Titanic Dataset

library(rio)
DataTitanic=import("https://ai.lange-analytics.com/data/Titanic.csv")
head(DataTitanic)
  Survived Pclass                        Name    Sex Age SiblingsSpousesAboard
1        0      3      Mr. Owen Harris Braund   male  22                     1
2        1      1   Mrs. John Bradley Cumings female  38                     1
3        1      3       Miss. Laina Heikkinen female  26                     0
4        1      1 Mrs. Jacques Heath Futrelle female  35                     1
5        0      3     Mr. William Henry Allen   male  35                     0
6        0      3             Mr. James Moran   male  27                     0
  ParentsChildrenAboard FareInPounds
1                     0       7.2500
2                     0      71.2833
3                     0       7.9250
4                     0      53.1000
5                     0       8.0500
6                     0       8.4583

»

The select() Command

  • select(DataMine, Var1, Var2) selects columns (variables) Var1 and Var2 from a data frame DataMine. The first argument is the data= argument followed by the names of the selected variables.

  • select(Data, -Var1, -Var2) selects all columns (variables) except Var1 and Var2 from a data frame DataMine.

Here is an example using the DataTitanic data frame from the previous slide:

library(tidyverse)
DataTitanicSelVar=select(DataTitanic,Survived, Name, Sex, Age)
head(DataTitanicSelVar)
  Survived                        Name    Sex Age
1        0      Mr. Owen Harris Braund   male  22
2        1   Mrs. John Bradley Cumings female  38
3        1       Miss. Laina Heikkinen female  26
4        1 Mrs. Jacques Heath Futrelle female  35
5        0     Mr. William Henry Allen   male  35
6        0             Mr. James Moran   male  27

»

The filter() Command

The filter() command filters rows (observations) of a data frame for specific criteria. The first argument is the data= argument followed by the filter criteria.

E.g., filter for female passengers from the dataset: Use DataTitanicSelVar that we created in the previous slide (note that we have to use == instead of = for the criteria):

DataTitanicSelVarFem=filter(DataTitanicSelVar, Sex=="female")
head(DataTitanicSelVarFem)
  Survived                               Name    Sex Age
1        1          Mrs. John Bradley Cumings female  38
2        1              Miss. Laina Heikkinen female  26
3        1        Mrs. Jacques Heath Futrelle female  35
4        1               Mrs. Oscar W Johnson female  27
5        1 Mrs. Nicholas (Adele Achem) Nasser female  14
6        1     Miss. Marguerite Rut Sandstrom female   4

»

The mutate() Command 🤓

mutate() creates or overwrites columns (variables) based on other columns (just like Excel). The first argument is the data= argument followed by the instructions on how to create the new variable.

E.g., mutate calculates new column Born based on Age during Titanic disaster (1912). Uses DataTitanicSelVarFem from previous slide:

DataTitatincSelVarFemBirthYear=mutate(DataTitanicSelVarFem, Born=1912-Age)
head(DataTitatincSelVarFemBirthYear)
  Survived                               Name    Sex Age Born
1        1          Mrs. John Bradley Cumings female  38 1874
2        1              Miss. Laina Heikkinen female  26 1886
3        1        Mrs. Jacques Heath Futrelle female  35 1877
4        1               Mrs. Oscar W Johnson female  27 1885
5        1 Mrs. Nicholas (Adele Achem) Nasser female  14 1898
6        1     Miss. Marguerite Rut Sandstrom female   4 1908

See: RandRStudioScriptPart2.R script200

»

Summary

  1. We selected variables \(Survived\), \(Name\), \(Sex\), \(Age\) and saved in DataTitanicSelVar
  2. We filtered for females and saved in DataTitanicSelVarFem
  3. We mutated to calculate new variable and saved finally in DataTitanicSelVarFemBirthYear

Could this be done easier?

Note, overwriting data frames such as DataTitanic is usually a bad idea!»

Alternative: Nesting

(I am not serious)

library(tidyverse)
DataTitanicFinal= mutate(
        filter(select(DataTitanic,Survived, Name, Sex, Age),
               Sex=="female"), 
                        Born=1912-Age)
head(DataTitanicFinal)
  Survived                               Name    Sex Age Born
1        1          Mrs. John Bradley Cumings female  38 1874
2        1              Miss. Laina Heikkinen female  26 1886
3        1        Mrs. Jacques Heath Futrelle female  35 1877
4        1               Mrs. Oscar W Johnson female  27 1885
5        1 Mrs. Nicholas (Adele Achem) Nasser female  14 1898
6        1     Miss. Marguerite Rut Sandstrom female   4 1908

»

Piping Schema

Piping Schema

Piping Schema

Alternative: Piping

(will be used throughout the course/book) 🤓

library(tidyverse)
DataTitanicFinal= DataTitanic |> 
                  select(Survived, Name, Sex, Age) |> 
                  filter(Sex=="female") |> 
                  mutate(Born=1912-Age)
head(DataTitanicFinal)
  Survived                               Name    Sex Age Born
1        1          Mrs. John Bradley Cumings female  38 1874
2        1              Miss. Laina Heikkinen female  26 1886
3        1        Mrs. Jacques Heath Futrelle female  35 1877
4        1               Mrs. Oscar W Johnson female  27 1885
5        1 Mrs. Nicholas (Adele Achem) Nasser female  14 1898
6        1     Miss. Marguerite Rut Sandstrom female   4 1908

»

See: RandRStudioScriptPart2.R script300

Questions

https://ai.lange-analytics.com/book/