![Running Calculations
• You can run/calculate
expressions directly in the R
console
• Results are printed in the
console
• This approach is good for
testing
1 / 200 * 30
#> [1] 0.15
(59 + 73 + 2) / 3
#> [1] 44.66667
sin(pi / 2)
#> [1] 1](https://image.slidesharecdn.com/03-introtoprogramminginr-250305025712-0754a944/85/Introduction-To-Programming-In-R-for-data-analyst-9-320.jpg)
![Objects
• In the console, you can also
store results in the object (some
call it a variable, it is a named
space in the memory)
• It allows you to reuse the value
• Assignment does not print value
• Expression can be simply a value
or some calculation
name <- expression
x <- 3 * 4
(x <- 3 * 4)
#> [1] 12](https://image.slidesharecdn.com/03-introtoprogramminginr-250305025712-0754a944/85/Introduction-To-Programming-In-R-for-data-analyst-10-320.jpg)
![Functions
• R has a large collection of built-
in functions
• You can call them using name
of the function and parenthesis
( and )
• In parenthesis you can provide
parameters for the function (if
it requires them)
function_name(arg1 = val1, ...)
seq(1, 10)
#> [1] 1 2 3 4 5 6 7 8 9 10](https://image.slidesharecdn.com/03-introtoprogramminginr-250305025712-0754a944/85/Introduction-To-Programming-In-R-for-data-analyst-14-320.jpg)
Introduction To Programming In R for data analyst
- 1. Introduction to Programming in R Przemyslaw (Pshemek) Pawluk MSc. Eng.
- 2. Introduction • Basic programming concepts in R • Tools - RStudio
- 3. Data Science / Data Analysis
- 4. Why do we use programming tools if we can do so much in Excel?
- 5. About R • R is an interpreted language, not a compiled one, meaning that all commands typed on the keyboard are directly executed without requiring to build a complete program like in most computer languages (C, Java, C#, . . .). • R’s syntax is very simple and intuitive.
- 6. About R • When R is running, variables, data, functions, results, etc., are stored in the active memory of the computer in the form of objects which have a names. • The user can do actions on these objects with operators (arithmetic, logical, comparison, . . .) and functions (which are themselves objects).
- 7. Data frame • A data frame is the most common way of storing data in R and, generally, is the data structure most often used for data analyses. • Under the hood, a data frame is a list of equal-length vectors. • Each element of the list can be thought of as a column and the length of each element of the list is the number of rows.
- 8. Basic commands • help(): prints documentation for a given R command • example(): view some examples on the use of a command • c() or scan(): enter data manually to a vector
- 9. Running Calculations • You can run/calculate expressions directly in the R console • Results are printed in the console • This approach is good for testing 1 / 200 * 30 #> [1] 0.15 (59 + 73 + 2) / 3 #> [1] 44.66667 sin(pi / 2) #> [1] 1
- 10. Objects • In the console, you can also store results in the object (some call it a variable, it is a named space in the memory) • It allows you to reuse the value • Assignment does not print value • Expression can be simply a value or some calculation name <- expression x <- 3 * 4 (x <- 3 * 4) #> [1] 12
- 11. Objects’ Names • Object name must start with a letter • It can only contain letters, numbers, _ and .. • You want your object names to be descriptive, so you’ll need a convention for multiple words. We recommend snake_case where you separate lowercase words with _. • Alternative is camelCase where each word starts with a capital letter use_snake_case orUseCamelCase some.people.use.periods And_aFew.People_RENOUNCEconvention
- 12. Objects’ Names • Object name must start with a letter • It can only contain letters, numbers, _ and .. • You want your object names to be descriptive, so you’ll need a convention for multiple words. We recommend snake_case where you separate lowercase words with _. • Alternative is camelCase where each word starts with a capital letter use_snake_case orUseCamelCase some.people.use.periods And_aFew.People_RENOUNCEconvention
- 13. R is case sensitive • R is a case sensitive language • Be careful with naming as in programming there is no guessing • If you want to see the value assigned to the variable surround the whole line with () r_rocks <- 2 ^ 3 r_rock #> Error: object 'r_rock' not found R_rocks #> Error: object 'R_rocks' not found
- 14. Functions • R has a large collection of built- in functions • You can call them using name of the function and parenthesis ( and ) • In parenthesis you can provide parameters for the function (if it requires them) function_name(arg1 = val1, ...) seq(1, 10) #> [1] 1 2 3 4 5 6 7 8 9 10
- 15. • Demos • Console • Help etc. • Results & Graphs
- 16. • Demos • Console • Help etc. • Results & Graphs
- 17. • Demos • Console • Help etc. • Results & Graphs
- 18. • Demos • Console • Help etc. • Results & Graphs
- 19. Working with data • data(): load built-in dataset • View(): view loaded datasets • read.csv() – requires a path or url to the csv file • read.table()
- 20. Data Types • Basic data types • int stands for integers. • dbl stands for doubles, or real numbers. • chr stands for character vectors, or strings. • dttm stands for date-times (a date + a time). • lgl stands for logical, vectors that contain only TRUE or FALSE. • fctr stands for factors, which R uses to represent categorical variables with fixed possible values. • date stands for dates.
- 21. Data Transformation in R • Pick observations by their values (filter()). • Reorder the rows (arrange()). • Pick variables by their names (select()). • Create new variables with functions of existing variables (mutate()). • Collapse many values down to a single summary (summarise()).
- 22. Filter • filter() allows you to subset observations based on their values. The first argument is the name of the data frame. • The second and subsequent arguments are the expressions that filter the data frame. jan1 <- filter(flights, month == 1, day == 1) filter(flights, month == 11 | month == 12) filter(flights, !(arr_delay > 120 | dep_delay > 120)) filter(flights, arr_delay <= 120, dep_delay <= 120)
- 23. Arrange • arrange() works similarly to filter() except that instead of selecting rows, it changes their order. It takes a data frame and a set of column names (or more complicated expressions) to order by. • If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns arrange(flights, year, month, day)
- 24. Select • select() allows you to rapidly zoom in on a useful subset using operations based on the names of the variables. select(flights, year, month, day)
- 25. Mutate • mutate() always adds new columns at the end of your dataset so we’ll start by creating a narrower dataset so we can see the new variables. • You can use the columns added in definitions of other columns that you add (be careful with cycles) mutate(flights, gain = dep_delay - arr_delay, speed = distance / air_time * 60 )
- 26. Summarize • summarise() is not terribly useful unless we pair it with group_by(). • This changes the unit of analysis from the complete dataset to individual groups. Then, when you use the dplyr verbs on a grouped data frame they’ll be automatically applied “by group”. by_day <- group_by(flights, year, month, day) summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
- 27. Hands-on
- 28. Task 1 – Code analysis • Why does this code not work? my_variable <- 10 my_varıable #> Error in eval(expr, envir, enclos): object 'my_varıable' not found
- 29. Task 2 – Code analysis • What is wrong with this two function calls? fliter(mpg, cyl = 8) filter(diamond, carat > 3)
- 30. Task 3 – Filter data • Find all diamonds in the diamonds data set that are smaller than 3 carats and cost more than 15,000 filter(diamonds,…)