Tag Archives: R Open Lab

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!

Spring 2018 R Open Lab: Apply Family

Apr 11

The topic of this week is the apply family in R. 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. In this lab, we will cover apply,lapply, sapply,mapply, tapply, and sweep. Here are the codes for this lab:

# apply: better than loops!
m <- matrix(1:9, 3, 3, byrow = TRUE)
for (i in 1:3) {
print(mean(m[i, ]))
}

rowMeans(m)
apply(m, 1, mean)
apply(m, 2, mean)

sos <- function(x, y) {
return(x^2+y^2)
}
apply(m, 1, sos, y = 3)

apply(diamonds[, 2:4], 2, table) # data frame `diamonds` is defined in package `ggplot2`

sweep(m, 2, mu, “*”)
mu <- apply(m, 2, mean)
sweep(m, 2, mu, FUN = “-“)

 

# lapply and sapply
lapply(m, sos, y = 3)
l <- list(c(1, 2, 3), 4, 5, m)
lapply(l, sos, 3)
sapply(l, sos, 3)

lapply(1:10, function(x) x^2)
sapply(1:10, function(x) x^2, simplify = F)
unlist(lapply(1:10, function(x) x^2))
sapply(1:10, function(x) x^2)

 

# mapply
mapply(rep, 1:4, 4:1)
mapply(rep, 2:9, 4)

 

# tapply
s <- c(10:19, 2:5, 3:15)
i <- factor(c(rep(1, 10), rep(2, 4), rep(3, 13)))
tapply(s, i, sum)


Here are a few practice problems you can try by yourself (All of them require the data frame diamonds defined in the package ggplot2) :

Task 1: Find the color and clarity of largest 5 entries of price using apply family.

Task 2: Compute leave-one-out mean for carat and find which observation has the greatest leave-one-out mean.

Task 3: Compute mean and standard deviation for different groups of cut.


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!

Spring 2018 R Open Lab: Exploratory Data Analysis

Mar 28

This week ‘s topic is Exploratory Data Analysis in R. The goal of the lab is to give attendants some ideas about what they can learn when they first have a data set in their hands and the corresponding approaches. The lab started with introduction of data frame’s concept and how to create one in R. Then, we talked about different ways to import data into R. After that, we learned ways to explore features of a data frame using the data set “diamonds.csv”. With the information we learned, we started to manipulate the data frame into our desired form by reordering and subsetting. We ended this lab with 2 simple practices of what we learned so far.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/12ejSGZspc5_rBjDfvbu61H3lGdmYjAyf/view?usp=sharing

Here is the data set we used for this lab:

https://drive.google.com/file/d/19crfzpYAS3T0ZXaVxkWQVgk8dFbboFdf/view?usp=sharing

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!

Spring 2018 R Open Lab: Character Strings

Feb 28

For this week, the topic we discussed is character strings in R. This lab’s content is a steppingstone for text  analysis.  We started with introducing the concept of characters, character strings, and character string vectors in R. Then, we talked about operations on strings such as getting substring and combining different character strings.  Finally, we learned about extracting and replacing certain patterns within a text-form data set.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/12Etf6qQtpJPymIYrGRqLiaxt8LchNBUB/view?usp=sharing

Here is a reference for regular expressions in R:

https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf

Thank you all for showing up. If you have further question 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!

Spring 2018 R Open Lab: More Fundamentals

Last week, we walked through the R starter kit which introduced most of the useful basic concepts in R such as vectors, matrices, and loops. This week, we continued to talk about more basics in R and demonstrated examples. The goal of this lab is to get attendants a better understanding of how R language works so that they can transform their specific real-life problem into R algorithms smoothly.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/1SePSSVF980EJfCxv4eZ4AQlCP7vlTbe7/view?usp=sharing

The script also has comments and explanations. You can open it with R studio and run it step by step.

Thank you all for showing up. If you have further question 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!

R Open Lab – Merge and Filter Data

During the first 20-30 minutes of yesterday’s open lab, we talked about how to merge datasets and filter data using base R and dplyr package. The rest of the open lab were free discussions between participants and instructors.

Thank you to all who showed up!

Welcome to explore the materials I used for the open lab:

https://drive.google.com/open?id=0B4uO8w_uGtsCWjRId0p6QllKZlE

Enjoy the spring!

R Open Lab – ggplot

Data visualization is an integral part of data exploration and presentation. Yesterday, we talked about ggplot2, a package which provides a mature and consistent system for plotting in R.

We explored the advantage and disadvantages of ggplot2, the syntax and usage of the package.

As always, thank you to everyone who showed up.

Materials I used for the open lab can be found here.

https://drive.google.com/open?id=0B4uO8w_uGtsCMUVoLXl1Z0JsSlU

Enjoy spring! ❤️

R Open Labs – Apply Family

This Wednesday we talked about apply function family in base R. We covered apply(), tapply(), lapply(), sapply() and vapply(). We also briefly introduced the concept of factors in R.

As always, thank you to all who showed up! Next two weeks’ R Open Lab will be cancelled due to midterm week and spring break.

Good luck on your midterms and have a wonderful spring break! 😄

R Open Labs – readr Package

Today we introduced readr package. It is a package used for reading csv/xls/txt etc. data. It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes. 

We covered the functionality of the package and the difference between this package and base R.

Next week we will talk about apply family.

See you next Wednesday from 10 am – 12 pm at DSSC (Lehman Social Science Library Room 215)!