diff --git a/blogContent/posts/data-science/html/IntroToR.html b/blogContent/posts/data-science/html/IntroToR.html new file mode 100644 index 0000000..8509946 --- /dev/null +++ b/blogContent/posts/data-science/html/IntroToR.html @@ -0,0 +1,1293 @@ + + + + + + Introduction to R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Introduction to R

Jeffery Russell
9-30-19

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RIT Scientific Computing Group

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R

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History

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  • Back in the day (1976) Bell Laboratories created the S statistical programming language
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  • People were sad because it was exclusively licensed by AT&T
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  • During the 90's a group of people developed a S replacement called R and it was licensed under GNU
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Why use R

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  • Statistics and data analysis
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  • Machine learning
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  • Fast prototyping
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  • Creating graphs
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  • Writing research papers and reports
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R Markdown

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Using R markdown you can easily create reports and presentations by embedding your code in the report. +This has major advantages.

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  • Easier to reproduce other people's research
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  • Easy to re-run your analysis if the data changes – no more re-generating 10 charts and re-computing statistical tests
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  • Easy to export to other formats
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+

Embedding Code Output in a Document

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summary(cars)
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+ +
     speed           dist       
+ Min.   : 4.0   Min.   :  2.00  
+ 1st Qu.:12.0   1st Qu.: 26.00  
+ Median :15.0   Median : 36.00  
+ Mean   :15.4   Mean   : 42.98  
+ 3rd Qu.:19.0   3rd Qu.: 56.00  
+ Max.   :25.0   Max.   :120.00  
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+

Embedding Graphs in a Document

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plot(mtcars$wt, mtcars$mpg, main="Weight vs MPG", xlab = "weight", ylab="MPG")
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plot of chunk unnamed-chunk-2

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Syntax

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  • Syntax of R is C-esk with its use of curly braces
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  • Variables are similar to python since it infers your data type used
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  • Type system is rather weird, the base unit for everything is a vector– even integers
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+ +
x <- 0
+if (x < 0) 
+{
+  print("Negative number")
+} else if (x > 0) 
+{
+  print("Positive number")
+} else
+{
+  print("Zero")
+}
+
+ +
[1] "Zero"
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+

Syntax: For Loops

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for(i in 1:5)
+{
+  print(i)
+}
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+ +
[1] 1
+[1] 2
+[1] 3
+[1] 4
+[1] 5
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+ +
for(i in (1:5)*2)
+{
+  print(i)
+}
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[1] 2
+[1] 4
+[1] 6
+[1] 8
+[1] 10
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+

Syntax: While Loops

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x <- 2
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+while(x == 2)
+{
+  print("Stonks")
+  x = x + 1
+}
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+ +
[1] "Stonks"
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+

Syntax cont

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Syntax: Arrays

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Arrays are 1 indexed.

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for(i in c(1,4,5))
+{
+  print(i)
+}
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+ +
[1] 1
+[1] 4
+[1] 5
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ar <- c(1,3,9)
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+print(ar[2])
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[1] 3
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Getting Help

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Using the built in help command, you can view documentation for any function.

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help(plot)
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Making Graphs

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plot(x=1:10, y=(1:10)^2, xlab = "x", ylab = "y", main="Ez")
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plot of chunk unnamed-chunk-10

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ML Example pt: 1

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ggplot(data = iris) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Iris Flower Set") + 
+  geom_point(mapping = aes(x=Sepal.Length, y=Petal.Length, color = Species)) + 
+  labs(x = "Sepal Length", y = "Petal Length", color="Phase") +
+  theme_bw()
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plot of chunk unnamed-chunk-11

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ML Example pt: 2

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ggplot(data = iris) + theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Iris Flow Length") + 
+  geom_boxplot(mapping = aes(y=Petal.Length, x = Species), outlier.colour = "red", outlier.shape = 1) + 
+  labs(x = "Flower Type", y = "Petal Length") +
+  coord_flip() +
+  theme_bw()
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plot of chunk unnamed-chunk-12

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+

Super Cool ML Example pt: 3

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sc <- spark_connect(master = "local")
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+iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
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+partitions <- iris_tbl %>%
+  sdf_partition(training = 0.7, test = 0.3, seed = 1111)
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+iris_training <- partitions$training
+iris_test <- partitions$test
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+dt_model <- iris_training %>%
+  ml_decision_tree(Species ~ .)
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+pred <- ml_predict(dt_model, iris_test)
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+ml_multiclass_classification_evaluator(pred)
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[1] 0.9451737
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Resources

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Questions?

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Workshop

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Visualize the built in “mpg” data-set from the tidyverse library.

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Step 1: Install Tidyverse package in R and include it in your R Script

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# Install tidyverse
+install.packages("tidyverse")
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+# Include tidyverse in project
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+library(tidyverse)
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+

Engine Size Vs MPG Normal Plot

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plot(x = mpg$displ, y=mpg$hwy, main="Engine Size(Liters) vs MPG")
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plot of chunk unnamed-chunk-14

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Engine Size Vs MPG

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ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy))
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plot of chunk unnamed-chunk-15

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Engine Size Vs MPG with color Mappings

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ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy, color=class))
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plot of chunk unnamed-chunk-16

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Engine Size Vs MPG with shape Mappings

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ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy, shape=class))
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plot of chunk unnamed-chunk-17

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Engine Size Vs MPG For each Class

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ggplot(data = mpg) + geom_point(mapping=aes(x=displ, y = hwy)) + facet_wrap(~ class, nrow=3)
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plot of chunk unnamed-chunk-18

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Engine Size Vs MPG With Line of Best Fit

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ggplot(data = mpg) + 
+  geom_point(aes(x=displ, y = hwy, color=class))+
+  geom_smooth(aes(x=displ, y = hwy))
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plot of chunk unnamed-chunk-19

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Engine Size vs MPG With Line of Best Fit

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ggplot(data = mpg, aes(x=displ, y = hwy, color=class)) + 
+  geom_point()+
+  geom_smooth()
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plot of chunk unnamed-chunk-20

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