hacksu/r-lesson

Taught 10/22.
Notes if taught again:
-Downloads and loading a data set took awhile. -Challenges were too challenging.

1. Download R Studio. https://rstudio.com/products/rstudio/download/

2. What is R?

• Open source language used a lot in stats and for graphing data
• Derived from S
• Data manipulation language

But what do I use it for?

• Stats
• Deep Learning
• connect APIs
• build and host interactive web apps
• big data processing with Apache Spark
• more, more, more

3. Clone this github to get the CSV files.

4. Connect repository to R Studio. File > New Project > Existing Directory

Moving on... Basic data types in R: char, numeric, integer, logical, complex

Smallest data set you can create is a vector.

c(1,2,3)

In R, you can do math with with vectors without needing a for loop

2 * c(1,2,3)

------------IMPORTING CUSTOM DATASETS & DOING BASIC VISUALIZATIONS--------- Let’s learn how to import our own dataset and create some basic visualizations with it. Grab “apps.csv” from GitHub. Import KHE CSV Go to files tab > apps.csv > import dataset Make sure the box contains a grid of the data "school", "shirt", "demo", etc.; if not, cancel and click 'import dataset' again Press import

Dataset should be visible for you to browse. Double-check environment tab on right for variable name.

Let’s see what our column names are.

colnames(apps)

Ask if this returned column names.

If not, it looks like we are not totally done importing the data. Let’s fix this.

read.csv(“apps.csv”) Call for the column names again and we’ll be able to see them. colnames(apps)

If you have a long filename, we could store the data source in our own variable. We’re using “<-” which is an assignment operator.

t<- read.csv(“apps.csv”)

There is some n/a data entries. We want to clean these out.

apps=na.omit(apps)

Next, let’s get the number of t-shirts by size.

table(apps$shirt)

Let’s put this into a bar-chart.

barplot(table(apps$shirt))

But I find blue boring so let’s change into a color.

barplot(table(apps$shirt), col="purple")

I want to find out the age of attendees and this would be best represented by a boxplot.

boxplot(apps$age)

CHALLENGE 1: FIGURE OUT HOW TO MAKE IT A BOXPLOT WHERE THE OUTLIERS ARE A TRIANGLE SHAPE AND MAKE THE MIDDLE LINE BLUE

boxplot(apps$age, medcol="blue",bg="red",outpch=6)

Let's make a line chart of when accounts were created: plot(table(apps$created), line="l")

It should show the dates out of order on the x-axis. We can convert these string values into a date by saving it as a new local global value.

Use this to convert into dates

dates <- as.Date(apps$created, "%m/%d/%Y")

IF THERE’S TIME, FIGURE OUT HOW TO MAKE THESE DATES NEW TO OLD

Now call to see the new graph:

plot(table(dates), type="l")

--------------------------R STUDIO DATA SETS & SOME STATS--------------------------------- Moving on to some other R capabilities...
Let's say you want to easily import a given data set. R Studio has many data sets sitting in a package that downloads with the software.

data() and press enter

A list of available datasets will show up. Enter the name inside that parenthesis. Call dataset by given name. Let's use:

mtcars

Name Descriptions: mpg: Miles/(US) gallon cyl: Number of cylinders disp: Displacement (cu.in.) hp: Gross horsepower drat: Rear axle ratio wt: Weight (1000 lbs) qsec: 1/4 mile time vs: V/S am: Transmission (0 = automatic, 1 = manual) gear: Number of forward gears carb: Number of carburetors http://www.sthda.com/english/wiki/r-built-in-data-sets

Let’s plot this.

plot(mtcars)

There's a lot of data here. Maybe we're only interested in a few columns.

plot(mtcars[c(1,3,8)])

I’d like to know the average mpg.

mean(mtcars$mpg)

CHALLENGE 2: FIND THE FIVE NUMBER SUMMARY USED IN STATS ON JUST MTCARS MPG

summary(mtcars$mpg)

CHALLENGE 3: CREATE A LINEAR REGRESSION LINE USING MPG AND CYL, THEN PLOT IT. Challenge to find linear relationship or something.

plot(lm(mtcars$mpg~mtcars$cyl, data=mtcars))

--------------------------------------------HEATMAP---------------------------------------------------------- Next, let’s create a heatmap using gradebook.csv file. Import gradebook dataset. Put the dataset in a var. I’m going to also save the dataset into something easier to work with.

grades <- gradebook

Let’s have it print out the student ID instead of the row number, but we’re going to save that in a new var

row.names(grades) <- grades$Student ID

CHALLENGE 4: FIGURE OUT HOW TO INPUT THE DATA IN NAMES INTO A MATRIX.

g_matrix <- data.matrix(grades)

Create the heatmap

heatmap(g_matrix, Rowv=NA, Colv=NA, col = cm.colors(256), scale="column", margins=c(5,10))

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Helpful shortcuts: clear - ctrl-l or Option + Command + L

Find installed libraries installed.packages(lib.loc = NULL, priority = NULL, noCache = FALSE, fields = NULL, subarch = .Platform$r_arch,)