R is a widely used open-source statistical software environment used by over 2 million data scientists and analysts. It is based on the S programming language and is developed by the R Foundation. R provides a flexible and powerful environment for statistical analysis, modeling, and data visualization. Some key advantages include being free, having an extensive community for support, and allowing for automated replication through scripting. However, it also has some drawbacks like having a steep learning curve and scripts sometimes being difficult to understand.

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12. “R is a free software environment for
statistical computing and graphics.”
e GNU-Project: Open Source
e Based on “S” (programming language
developed by John Chambers at Bell-Labs)
e R Foundation: NPO for the development of R

13. §most widely used data analysis software - used by 2M +
data scientist, statisticians and analysts
§Most powerful statistical programming language
§flexible, extensible and comprehensive for productivity

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© 2021 Dynamic Communities 15

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© 2021 Dynamic Communities 16

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save file,
double-click
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22. • Mean – this is the average
• Median – splits the data in two halves
• Mode – the most popular value

23. • Variance – average squared difference between the data points
and the mean
• Standard Deviation – square root of the variance, more intuitive
• Percentiles – dataset is divided into 100 equal parts
• Quartiles – dataset is divided into four equal parts
• Interquartile range – middle 50% of data points

24. Advantages
• Free
• “Lingua franca” in methodological research: new statistical
procedures are often developed with R
• Large community: most problems are discussed on the internet
• No “point and click”: scripts make procedures transparent
and reproducible
• Flexible programming allows for automated replication with
new data

25. Drawbacks
• Not very intuitive
• No “Point and Click”: handling only through command line
and scripts
• Documentation is very technical at times
• Community-based: different developers (different, lacking
compatibility)
• Slow with very large data sets

27. Enter from command line
Ctrl + Enter from script
Assign variables:
x <- 2
Comments:
# Comment
Comment selection with Ctrl + Shift + c

28. FunctionName (arguments)
Function Effect
summary(x)
str(x)
head(x)
tail(x)
sum(x)
mean(x)
Summary information on x
Structure of x
Shows first 6 elements of x
Shows last 6 elements of x
Calculates the sum of a numeric vector
Calculates the arithmetic mean

30. Class Example
integer
numeric
character
logical
factor
date
complex
1, 2, 3
1.414, 3.14, 1.0
A, B, C TRUE,
FALSE
“A”, “1”, “rather correct”
date
complex numbers

31. sum(x) Sum of all elements
mean(x) Mean of all elements
prod(x) Product of all elements of x
diff(x) x2 − x1, x3 − x2, x4 − x3 etc.

32. x & y Logical AND
x | y Logical OR
!x Logical Negation
all(x) TRUE if all elements of x are TRUE
any(x) TRUE if at least one element of x is
TRUE

33. == Is equal to
!= Is not equal to
<,> Smaller than, Larger Than
<=, >= Smaller or equal, larger or equal
x in y Elements of x in y

36. c(1:4)
c("A","B","C")

37. ls <- list (1, 2, 3)
print (ls)

38. mx <- matrix (1:16, nrow = 4, ncol = 4)
print (mx)

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40. str(df)

41. names
dim

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44. Code Description
y ~ x
y ~ x1 + x2
y ~ x1 + x2 + 0 y ~
I(x1 + x2) y ~ . -
x1
y ~ x1 * x2
x has an effect on y
x1 and x2 have an effect on y
intercept set to zero
y is influenced by x1 plus x2
model of all variables except x1
interaction between x1 and x2

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46. Let’s see the data!

47. Diamonds
Data Frame

48. Let’s see the data!
Remember to press the
‘Run’ button or select
CTRL + ENTER to run
the command

49. Results!

50. Let’s see the data!
This creates a new table
called PerfectDiamonds.
SELECT statements
allow you to choose the
columns you want.

51. Let’s see the data!
This creates a new table
called PerfectDiamonds
using a Filter to select
only perfect diamonds

52. Power BI
Import Data

53. Let’s start simple!

54. Add in an R
component

55. Add in an R
component

56. Script
Component
Appears in the
Power BI
Canvas
R Script goes here

57. Script
Component
Appears in the
Power BI
Canvas
R Script goes here

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59. 10
20
30
2 3 4 5 6 7
displ
ct
y

60. 0.0
0.1
0.2
0.3
2 3 4 5 6 7
displ
densit
y
as.factor(year)
1999
2008
ggplot (data = mpg, aes (x = displ)) +
geom_density (aes (fill = as.factor(year)), alpha=0.5)

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65. With ggmap map graphics can be generated.

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67. https://shiny.rstudio.com/gallery/

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76. Tidy code is easier to
write, read, maintain
and frequently even
faster than the base R
counterparts.
It is also easier to learn.
So here we are!

77. ● Tidy Data is a standard approach to structure datasets
● Good for Data Analysis and Data Visualization
● Variables make up the columns
● Observations make up the rows
● Values go into cells

78. ● A Variable is a measurement
● Also known as:
● Independent or dependent variables
● Features – this is Microsoft’s terminology
● Predictors – (machine learning background)
● Outcomes – (social sciences background)
● The Response (if you have a statistics background)
● Attributes (if you have a dimensional modelling background)

79. ● A Variable can fall into three categories:
● Fixed Variables
● Known variables prior to the start of the investigation
● Measured Variables
● Data that’s captured during the investigative process
● Derived Variables
● Think of a calculated column in DAX or SQL

80. ● Ingests data from different sources
● There are lots of options to work with the file
● Headers
● Limiters
● https://cran.r-project.org/web/packages/readr/readr.pdf for more information

81. ● Easy data manipulation
● Built for data frames
● There are equivalents in SQL
● Written in C++ so it’s faster

82. ● 6 verbs for data manipulation
● Select
● Filter
● Mutate
● group_by
● Summarize
● Tally
● There are equivalents in SQL

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