Tag Archives: R Open Lab

R Open Lab Fall 2018 – Randomness and linear regression

In today’s open lab, we didn’t cover a lot. We first looked at how to generate random samples with certain conditions. Then we did an easy example of linear regression in R. The purpose of this lab is to let attendants understand how randomness works in R and how to use linear regression model for their own scenario.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

 

R Open Lab Fall 2018 – R Shiny

If you still haven’t got any idea of how amazing and powerful R can be, here is the time. In this open lab, I introduce my favourite part of R —- R shiny, which is a package to bulid interactive web app for data visualization, dashboard, map interaction and so on. I start from showing several fancy example from shiny official gallery, then ui.R and server.R are introduced separately and also how they connect. The example of control widgets of input and output are given and practiced by attendee. The topic of leaflet —- a package to build map in R are discussed. At the end of the lab, all attendee can build one simple app on Shiny which really satisfy them.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – Data manipulation

Today we covered the topic of data manipulation. We first reviewed the basic ways to subset data frames such as logical expression and subset function. Then, we looked at ways to combine, merge, and split data frames. Finally, we covered the usage of package plyr.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – Text data in R

R is also a powerful tool to deal with text data. This time we first clarified the concept between character and string by practicing some tricky examples and some basic ideas that we can play with our text data such as substring, combining and replacing. Then a txt file was introduced to let attendee play with it. The result really interested them a lot.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – More visualization

Today we will explore more about the advanced data visualization in R. First, we will review the basic graphical functions covered in the last open lab and learn how to use additional parameters to achieve different goals. Then, we will focus on the powerful package ggplot2.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – Dataframe and basic visualization

This week we stepped into the most basic but important data structure called dataframe, several ways of constructing dataframes and importing dataframes are introduced. At the mean time, we reviewed the basic idea of extracting data by index/condition by giving some exercises to practice. Then, we focused on how to show the general picture of a dataset in numeric and graphic way at first glance.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – Functions, environment, and apply

The topic of this week is functions, environment, and apply family in R. We first cover the method of defining your own function in R, then we bring in the concept of environment since they are relevant. At last, we go over the apply family. Recall that we learned loops as one of the basic concepts at the very beginning; you can review it from the Starter Kit and the Lab featuring More Fundamentals. Although loop is conceptually simple and intuitive, it is inefficient. The apply family comes in handy in this case.

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018/blob/master/function%2C%20environment%2C%20apply.R

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – More Fundamentals

We reviewed a little bit about what is R and R Studio, how they work together and then continued the starter kit of R. First we talked about how to do calculation, commenting and assignment value to variables in R, then we also gave some tricky example to clarify those coding standards such as case sensitivity. The most important part of this open lab was to introduce different classes in R, which would be really useful after understanding this concept. Filtering or slicing is shown by some demonstrated examples and it can be deal with in R in multiple ways. We also started the beginning part of function and loop, and they would be interpreted and practiced in the next session. Hope all of you will get more interests in R and know the fact that how powerful it is in the World of Data

Here is the link to our open lab’s GitHub repository: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further questions regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

R Open Lab Fall 2018 – Getting Started

This is the first R open lab of this semester. We focus on introducing basic concepts to the new users of R language. The file we used is called Starter Kit.

Here is the link of the GitHub repository with all the scripts: https://github.com/wbh0912/R-Open-Lab-Fall-2018

If you have further question regarding topics covered in the material, please feel free to drop in during consultation hours or leave a comment.

Spring 2018 R Open Lab: Advanced Visualization

Apr 18

Today we will explore the advanced data visualization in R. First, we will review the basic graphic functions in R and learn how to use additional parameters to achieve different goals. Then, we will introduce the powerful package ggplot2. Here are the codes:

# Quick review of basic visualization
library(ggplot2)
plot(diamonds$carat, diamonds$price, main = “Price vs Carat”, xlab = “Carat”, ylab = “Price”)
pairs(~carat+depth+table+price, data = diamonds)
barplot(table(diamonds$cut))
hist(diamonds$price, breaks = 100)
boxplot(diamonds$price~diamonds$cut)
pie(c(10, 2, 4, 7), c(“A”, “B”, “C”, “D”))

d <- diamonds[sample(1:nrow(diamonds), 1000), ]

# Plot by factor
plot(d$carat, d$price, col = d$cut)
# Add legend
legend(“bottomright”,
legend = levels(diamonds$cut),
fill = 1:5, cex = 0.4)
# Add line
ols <- lm(price~carat, data = d)
abline(ols, lty = 2, lwd = 2)
# Add point
points(2, 2500, pch = 3)
# Add text
text(2, 2000, “new point”)
# Useful parameters
pch
main
xlab
ylab
lty # line type
lwd # line width
cex # character expand
col

 

# ggplot2 package
p <- ggplot(data = d)
p + geom_point(mapping = aes(x = carat, y = price,
col = d$cut))

# facet
p+geom_point(mapping = aes(x = carat, y = price))+
facet_wrap(~cut, nrow = 2)
p+geom_point(mapping = aes(x = carat, y = price))+
facet_grid(~cut)

# regression line
p+geom_point(mapping = aes(x = carat, y = price))+
geom_smooth(mapping = aes(x = carat, y = price), method = “auto”)

# other functions to explore
ggplot(data = )+
geom_histogram(mapping = aes())+
geom_bar(mapping = aes())+
stat_function(mapping = , fun = )+
labs(title = , x = , y = )+
geom_text()+
geom_abline()+
geom_boxplot()


Thank you all for showing up. If you have further questions regarding topics covered in the material, please feel free to drop by during next week’s lab or email me or leave a comment.

See you all next week!