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mattmacfa

Matt MacFarlane

Recently Published

First Keras Test!!
dplyr part 1
Final Presentation
still a draft
FRED Economic Data - Next Steps
HTML
Test Draft
Plot
Plot
Plot
Plot
Masters Race overview
agency law
HTML contract law
contract law
unintentional tort w
HTML unintentional
unintentional tort
unintentional torts
tort intentional
IP law
crim law and ethics
Agency agreement masters
Masters Agency Agreement
HTML masters exhib a
masters exhibit A
HTML masters pt 1
Masters part 1
masters bike shop case
USGA Twitter Retweet Network
Farnoosh retweet network
planetmoney retweet network
F1 Twitter Network
WDIP retweet network
wdippodcast retweet network v2
Plot
wdippodcast retweet network
WDIPpodcast
min freq = 2, statistically representative (size = frequency)
GM$GM.Open, 2020-01-01
pandemic & recovery
us capitol
f1 cmms
Dude solutions hashtag freq
Emaint hashtag freq
Hippo CMMS
Hippo CMMS
USGA Hashtag Freq
USGA Hashtag Freq
Fiix Software
Dude Solutions
Hippo CMMS Hashtag Freq
F1 hashtag freq
F1 hashtag freq
HTML f1 banner
HTML USGA
HippoCMMS Twitter
F1 Min freq = 2
F1 public tweets
F1 public tweets
MaintenanceCare ROI Ebook
Scrum Guide 2020
HTML
Agile Manifesto
Agile Manifesto
HTML usga
HTML USGA
CHI
Chi HTML
chi interview 2
chi
interview
Plot
tidycensus
library(tidycensus) library(tidyverse) library(sf) # Get dataset with geometry set to TRUE orange_value <- get_acs(geography = "tract", state = "CA", county = "Orange", variables = "B25077_001", geometry = TRUE) # Plot the estimate to view a map of the data plot(orange_value["estimate"])
Compare changes in county lines from different census years
# Get a historic Census tract shapefile from 1990 for Williamson County, Texas williamson90 <- tracts(state = "TX", county = "Williamson", cb = TRUE, year = 1990) # Compare with a current dataset for 2016 williamson16 <- tracts(state = "TX", county = "Williamson", cb = TRUE, year = 2016) # Plot the geometry to compare the results par(mfrow = c(1, 2)) plot(williamson90$geometry) plot(williamson16$geometry)
census data using tigris package. plot functions. note:
note - could be helpful in competitive intelligence
GM Tweets by source
Exploratory GM Timeline output by source
note: discretized by source of tweet. Notice "Lithium Tech"? HUGE surge in retweets from this twitter provider. Clearly, something is going on with ev battery tech
Plot
Tesla Twitter Timeline
n = 3232 obs. of 91 variables. used: rtweet ggplot2 applied a smoothing runction.
Twitter Analysis of General Motors "GE" User Timeline
rtweet package to get_timeline("GE") tidyverse. ggplot2
GM$GM.Open
Time Series using quantmod package
Plot
Analyzing Social Media Data in R
Sentiment Analysis of 6 Focus Groups
# Plot the sentiment scores ggplot(data = score_df2, aes(x = sentiment, y = score, fill = sentiment)) + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
top counts
top 300
ALL top words
top 30
top 75
Top 12
top 7
n = 15
n = 50
n = 35
n = 25
Plot wc
wc
wordclouddd
All words
Top 5 words
30 words
wordcloud 2
Victoria S. Wordcloud
2:21 am Wednesday March 17
Sentiment analysis in R
SixSigma package R
Inputs and Outputs
BRK-B
f1 twitter account info
just a visualization of activity
WDIP Twitter
Answers the same questions, in the same way, as previous
Dr. Carpenter Twitter Account
Which twitter publishing platform receives the most likes? Which receives the most retweets?
wdip time series plot
time series plot of what does it profit twitter account
dawn tweets
time series plot of dr. carpenter twitter account
Famoosh
Time Series plot of tweets (since inception).
Pie chart of retweets / total tweets
n = 31 simple random sample of 1% of sustainability
MPG in ggplot2
eh. kinda pretty.
Using PLOTLY! with R
Plotly & ggplotly HTML
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme_wsj() install.packages("plotly") library(plotly) ggplotly()
ggthemes
> install.packages("ggthemes") WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding: https://cran.rstudio.com/bin/windows/Rtools/ Installing package into ‘C:/Users/Owner/Documents/R/win-library/4.0’ (as ‘lib’ is unspecified) trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.0/ggthemes_4.2.0.zip' Content type 'application/zip' length 440134 bytes (429 KB) downloaded 429 KB package ‘ggthemes’ successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\Owner\AppData\Local\Temp\RtmpopyjfL\downloaded_packages > library(ggthemes) > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_solarized() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_excel() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_excel_new() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_wsj() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_economist() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_fivethirtyeight() > ggplot(data=college) + + geom_bar(mapping=aes(x=region, fill=control)) + + theme_wsj() >
Plot - Adding Titles and subtitles
# bar chart again ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme(panel.background=element_blank()) + theme(plot.background=element_blank()) + scale_x_discrete(name="Region") + scale_y_continuous(name="Number of Schools", limits=c(0,500)) + scale_fill_manual(values=c("orange","blue"), guide=guide_legend(title="Institution Type")) + theme(legend.position="bottom") + ggtitle("More colleges are in the Southen US than any other region.", subtitle = "Source: U.S. Dept. of Education")
Plot annotations!!
college <- college %>% mutate(state=as.factor(state), region=as.factor(region), highest_degree=as.factor(highest_degree), control=as.factor(control), gender=as.factor(gender), loan_default_rate=as.numeric(loan_default_rate)) ggplot(data=college) + geom_point(mapping=aes(x=tuition, y=sat_avg, color=control, size=undergrads), alpha=1/2) + annotate("text", label="Elite Privates", x=45000, y=1450) + geom_hline(yintercept =mean(college$sat_avg)) + annotate("text", label="Mean SAT", x=47500, y=mean(college$sat_avg)-15) + geom_vline(xintercept=mean(college$tuition)) + annotate("text", label="Mean Tuition", y=700, x=mean(college$tuition)+7500) + theme(panel.background = element_blank(), legend.key = element_blank()) + scale_color_discrete(name="Institution Type") + scale_size_continuous(name="Undergraduates") + scale_x_continuous(name="Tuition") + scale_y_continuous(name="SAT Score")
Scatterplot with Avg
college <- college %>% mutate(state=as.factor(state), region=as.factor(region), highest_degree=as.factor(highest_degree), control=as.factor(control), gender=as.factor(gender), loan_default_rate=as.numeric(loan_default_rate)) ggplot(data=college) + geom_point(mapping=aes(x=tuition, y=sat_avg, color=control, size=undergrads), alpha=1/2) + annotate("text", label="Elite Privates", x=45000, y=1450) + geom_hline(yintercept =mean(college$sat_avg))
Plot
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme(panel.background=element_blank()) + theme(plot.background=element_blank()) + scale_x_discrete(name="Region") + scale_y_continuous(name="Number of Schools", limits=c(0,500)) + scale_fill_manual(values=c("orange", "blue"), guide=guide_legend(title="Institution Type", nrow=1, label.position = "bottom", keywidth=2.5)) + theme(legend.position="top")
Plot
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme(panel.background=element_blank()) + theme(plot.background=element_blank()) + scale_x_discrete(name="Region") + scale_y_continuous(name="Number of Schools", limits=c(0,500)) + scale_fill_manual(values=c("orange", "blue"))
Plot
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme(panel.background=element_blank()) + theme(plot.background=element_blank())
Plot
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control)) + theme(panel.background=element_rect(fill='purple'))
boxplot!
ggplot(data=college) + geom_boxplot(mapping=aes(x=control, y=tuition))
Plot
ggplot(data=college) + geom_histogram(mapping=aes(x=undergrads))
Stacked bar chart!
ggplot(data=college) + geom_bar(mapping=aes(x=region, fill=control))
Plot
# Create the scatterplot ggplot(data=college) + geom_line(mapping=aes(x=tuition, y=sat_avg, color=control)) + geom_point(mapping=aes(x=tuition, y=sat_avg, color=control)) ggplot(data=college, mapping=aes(x=tuition, y=sat_avg, color=control)) + geom_smooth(se=FALSE) + geom_point(alpha=1/25)
Plot
transparency! alpha=3/10
Plot
Plot
Plot