basics.Rmd

---
title: "R Basics for Business Analysis"
output:
  html_notebook: default
  word_document: default
  pdf_document: default
  html_document:
    df_print: paged
---

In this script the basic operations are described and explained. The basic syntax of R is developed around "," (comma) separations and "()" (bracket) grouping. In general the syntax (grammar of programming language) is similar to 
```{r}
object <- function(parameter1, vector2, dataframe3)
```

In which is an object is the "thing" in which results can be stored, the function is something by which the information is processed (usually relates to a library), a parameter which is this case is a value, a vector which is the same as a variable (a sequence of values) and a data frame which is identical to a flat-table (just like a sheet in excel is). Good to know is that R has simplified data structures that are all comparable with each other. 

In the next part we intend do a simple linear regression on a dataset and retrieve the basic results from object that was created. The object is a list, which is different from values, vectors and data frames. A list is an unstructured, hierarchical object that can store anything. The advantage is that it accepts any and all information. The disadvantage is that it can therefore contain non-nonsensical data more easily. E.g. a list can store 2 values in one entry. A value, vector or data frame cannot do this. 

The syntax for the linear regression is:
```{r}
object <- (dependent_variable~ beta1 + beta2 + beta3, data=relevant_dataframe)
```
In order to read the list, make sure you use the print function as denoted below.
```{r}
print(object)
```
Note that a list is hierarchical and often contains many layers in which sublists are stored. Sub-setting these lists is conducted with square brackets ("[]"). If you would subset the third sublist, and then the first sublist of that sublist, this would be denoted as follows:
```{r}
print(object[3][1])
```
Use the command head(object) to read the first 10 lines of the iris and identify the relevant columns. Please create a model that uses Sepal.Length as depedent variable and Sepal.width, Petal.Length, Petal.Width as independent variables. Note that that R is CaSe SeNsItIvE. 

ANSWER: object1 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data=iris)

summary provides nice output, maybe they should also try a list method.