R is a language and environment for statistical computing and graphics. R is free, this slide is for beginner. start from the basic first. variables, data structure, reading data, chart, function, conditional statement, iteration, grouping, reshape, string operations.

1. R for You
Andreas Chandra
linkedin.com/in/chandraandreas

2. Contents
- Basic R
- Data Structures
- Reading Data
- Charts
- Function
- Conditional Statements
- Iteration
- Grouping
- Reshape
- String Operations

3. BasicR

4. Basic R
- Math Operation
- Variable
- Data type
- Vectors
- Calling Function
- Missing Data

5. Basic R - Math
Basic Operation:
2 + 2
2 - 1
5 / 2
10 * 2
10 % 2
2 + (5 - 3)

6. Variable
X <- 10
X
10 <- Z
Z
A <- B <- 10
A
B
assign(“i”, 99)
# Untuk menghapus variabel
rm(B)

7. Data Types
● Numeric
● Chars
● Dates
● Boolean

8. Vectors
X <- c(1,2,3,4,5)
Operations
X * 3
X + 2

9. Factor
X <- c(“Januari”, “Februari”, “Maret”, “April”, “Januari”)
X_factor <- as.factor(X)
>X_factor
[1] Januari Februari Maret April Januari
Levels: April Februari Januari Maret

10. Calling Function
function(<var>)
Ex:
mean(var_name)
sd(var_name)
sqrt(varn_name)

11. Data Structures

12. Data Frames
# create
df <- data.frame()
Function in df:
nrow tail
ncol class
dim df[row, col]
rownames df[, c(“pertama”, “kedua”)]
head Df[2:3, ]

13. Lists
X <- list(1:3,4:6,7:9)
names(X)
None
> names(X) <- c("pertama", "kedua", "ketiga")
> X
$pertama
[1] 1 2 3
$kedua
[1] 4 5 6
$ketiga
[1] 7 8 9

14. Matrices
> X <- matrix(1:10, nrow = 5)
> Y <- matrix(11:20, nrow = 5)
>
> X
[,1] [,2]
[1,] 1 6
[2,] 2 7
[3,] 3 8
[4,] 4 9
[5,] 5 10
> Y
[,1] [,2]
[1,] 11 16
[2,] 12 17
[3,] 13 18
[4,] 14 19
[5,] 15 20
>
> Z <- X * Y
> Z
[,1] [,2]
[1,] 11 96
[2,] 24 119
[3,] 39 144
[4,] 56 171
[5,] 75 200

15. Arrays
> arr <- array(1:12, dim = c(2, 3, 2))
> arr
, , 1
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
, , 2
[,1] [,2] [,3]
[1,] 7 9 11
[2,] 8 10 12

16. Reading Data

17. read.table
> read.table(file, header = TRUE, sep = ",")
#full detail
> read.table(file, header = FALSE, sep = "", quote = ""'",
+ dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
+ row.names, col.names, as.is = !stringsAsFactors,
+ na.strings = "NA", colClasses = NA, nrows = -1,
+ skip = 0, check.names = TRUE, fill = !blank.lines.skip,
+ strip.white = FALSE, blank.lines.skip = TRUE,
+ comment.char = "#",
+ allowEscapes = FALSE, flush = FALSE,
+ stringsAsFactors = default.stringsAsFactors(),
+ fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

18. read.csv
> read.csv(file, header = TRUE, sep = ",", quote = """,
+ dec = ".", fill = TRUE, comment.char = "")

19. Excel (xlsx)
read.xlsx(file, sheetIndex, sheetName=NULL, rowIndex=NULL,
startRow=NULL, endRow=NULL, colIndex=NULL,
as.data.frame=TRUE, header=TRUE, colClasses=NA,
keepFormulas=FALSE, encoding="unknown")

20. Database (RODBC)
odbcConnect()

21. Charts

22. Histogram

23. Scatterplot
> library(ggplot2)
> library(gapminder)
>
> data(gapminder)
>
> #create plot
> plot(lifeExp ~ gdpPercap, data =
gapminder)
> #change scientific notation
> options(scipen = 999)

24. Boxplots
boxplot(gapminder$gdpPercap)

25. ggplot
ggplot(gapminder,
aes(x = lifeExp, y =
gdpPercap)) +
geom_point(aes(color =
factor(continent)))

26. Function

27. Basic Function
> hello_fun <- function(){
+ print("hello world")
+ }
>
> hello_fun()
[1] "hello world"

28. Function with args
> hello_fun <- function(name){
+ print(sprintf("hello world %s", name))
+ }
> hello_fun(name = "Jakarta")
[1] "hello world Jakarta"

29. Default args
> hello_fun <- function(name = "Bali"){
+ print(sprintf("hello world %s", name))
+ }
> hello_fun()
[1] "hello world Bali"
> hello_fun(name = "Bandung")
[1] "hello world Bandung"

30. Return Values
> hello_fun <- function(name = "Bali"){
+ return(sprintf("hello world %s", name))
+ }
> greetings <- hello_fun(name = "Bandung")
> greetings
[1] "hello world Bandung"

31. Conditional

32. IF ElSE
> grade_checker <- function(grade){
+ if(grade > 7){
+ return("Good")
+ }else if(grade > 4){
+ return("Enough")
+ }else{
+ return("Bad")
+ }
+ }
> grade_checker(9)
[1] "Good"

33. Switch
> class_switch <- function(class){
+ switch(class,
+ "H" = "High",
+ "M" = "Medium",
+ "L" = "Low",
+ NA)
+ }
> class_switch("H")
[1] "High"

34. Iter-ate

35. For
> names <- c("Ada", "Abu", "Abi", "Abe", "Abo")
> for(name in names){
+ print(name)
+ }
[1] "Ada"
[1] "Abu"
[1] "Abi"
[1] "Abe"
[1] "Abo"

36. While
> names <- c("Ada", "Abu", "Abi", "Abe", "Abo")
> index <- 1
> while (index <= length(names)){
+ print(names[index])
+ index = index + 1
+ }
[1] "Ada"
[1] "Abu"
[1] "Abi"
[1] "Abe"
[1] "Abo"

37. Grouping

38. apply
> a_matrix <- matrix(1:9, nrow = 3)
> a_matrix
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
>
> apply(a_matrix, 1, sum)
[1] 12 15 18

39. sapply, lapply
> a_list <- list(A = matrix(1:9, 3), B = 1:9, C = matrix(1:6, 3))
> lapply(a_list, mean)
$A
[1] 5
$B
[1] 5
$C
[1] 3.5
> sapply(a_list, mean)
A B C
5.0 5.0 3.5

40. aggregate
> data(diamonds)
> head(diamonds)
# A tibble: 6 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.230 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
2 0.210 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
3 0.230 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
4 0.290 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
5 0.310 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
6 0.240 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
>
> aggregate(price ~ cut + color, diamonds, mean)
cut color price
1 Fair D 4291.061
2 Good D 3405.382
3 Very Good D 3470.467
4 Premium D 3631.293
5 Ideal D 2629.095
6 Fair E 3682.312
7 Good E 3423.644

41. data.table
> require(data.table)
> head(diamonds)
# A tibble: 6 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.230 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
2 0.210 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
3 0.230 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
4 0.290 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
5 0.310 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
6 0.240 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
> diamonds_dt <- data.table(diamonds)
> diamonds_dt[, sum(price), by = cut]
cut V1
1: Ideal 74513487
2: Premium 63221498
3: Good 19275009
4: Very Good 48107623
5: Fair 7017600

42. Reshaping the
Data

43. cbind & rbind
> sport = c("Hoot", "Base", "Foot")
> league = c("Hoot1", "Base2", "Foot3")
> trophy = c("Hoot4", "Base5", "Foot6")
>
> trop1 = cbind(sport, league, trophy)
>
> trop2 <- data.frame(sport = c("Basket", "Golf"),
+ league = c("Laptop", "Notebook"),
+ trophy = c("rank1", "rank2"))
>
> trop3 = rbind(trop1, trop2)
>
> trop3
sport league trophy
1 Hoot Hoot1 Hoot4
2 Base Base2 Base5
3 Foot Foot3 Foot6
4 Basket Laptop rank1
5 Golf Notebook rank2

44. merge
> customer = data.frame(id = c(1,2,3),
+ name = c("Ada", "Adi", "Ade"),
+ address = c("Jkt", "Bdg", "Jog"))
> order = data.frame(id = c(1,2,3),
+ order = c("Gayuung", "Sabun", "Pipa"))
> trans = merge(customer, order, by.customer = c("id", "id"))
> trans
id name address order
1 1 Ada Jkt Gayuung
2 2 Adi Bdg Sabun
3 3 Ade Jog Pipa

45. Others
Plyr - merge
Data.table - merge

46. Reshape2 - melt
> provinsi <- c("DKI Jakarta", "Jawa Barat", "Jawa Tengah")
> SD <- c(100,200,300)
> SMP <- c(120,210,310)
> SMA <- c(130,220,320)
> df <- data.frame(provinsi, SD, SMP, SMA)
> melt(df, id = c("provinsi"))
provinsi variable value
1 DKI Jakarta SD 100
2 Jawa Barat SD 200
3 Jawa Tengah SD 300
4 DKI Jakarta SMP 120
5 Jawa Barat SMP 210
6 Jawa Tengah SMP 310
7 DKI Jakarta SMA 130
8 Jawa Barat SMA 220
9 Jawa Tengah SMA 320

47. Reshape - dcast

48. Strings

49. paste
Join 2 strings
> paste("Andreas", "Chandra")
> paste("DC", "2017", "01", "31", sep = "-")
[1] "DC-2017-01-31"
[1] "Hallo Andre" "Hey Chan" "Hallo Dra"

50. sprintf
> Name <- "Rio"
> Age <- 20
> City <- "NYC"
>
> sprintf("I am %s, %d years old, Live in %s", Name, Age,
City)
[1] "I am Rio, 20 years old, Live in NYC"
Printf format https://en.wikipedia.org/wiki/Printf_format_string

51. Regular expression
> library(stringr)
> bad_john <- str_detect(string = names_list, "John")
> good_john <- str_detect(string = names_list, ignore.case("John"))
Please use (fixed|coll|regex)(x, ignore_case = TRUE) instead of
ignore.case(x)
> names_list[good_john]
[1] "John Adams" "egi john" "John Ad" "john abs"
> sum(bad_john)
[1] 2
> sum(good_john)
[1] 4

52. Regular expression
str_detect()
str_split()
str_trim()
str_extract()

53. Jared P. Lander, 2014, R for Everyone