Lesson 4 Done
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---
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output:
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pdf_document: default
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html_document: default
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---
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Lesson 4
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========================================================
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@ -218,8 +223,12 @@ Highly corelated can mean that variables are dependent on the same thing or are
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Notes:
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```{r More Caution With Correlation}
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install.packages('alr3')
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#install.packages('alr3')
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library(alr3)
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library(ggplot2)
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data(Mitchell)
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ggplot(aes(x = Month, y = Temp), data = Mitchell) +
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geom_point()
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```
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Create your plot!
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@ -233,11 +242,13 @@ Create your plot!
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### Noisy Scatterplots
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a. Take a guess for the correlation coefficient for the scatterplot.
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0.9
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b. What is the actual correlation of the two variables?
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(Round to the thousandths place)
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```{r Noisy Scatterplots}
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with(Mitchell, cor.test(Month, Temp))
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```
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***
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@ -246,7 +257,9 @@ b. What is the actual correlation of the two variables?
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Notes:
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```{r Making Sense of Data}
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ggplot(aes(Month, Temp), data = Mitchell) +
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geom_point() +
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scale_x_continuous(breaks = seq(0, 204, 12))
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```
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***
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@ -256,16 +269,22 @@ Notes:
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What do you notice?
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Response:
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There is a cyclical pattern to the data going from low to high and back to low every 12 months. This is why I originally said there seems to be a 0.9 correlation coefficient to the data because I saw this pattern the first time I looked at the plot.
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Watch the solution video and check out the Instructor Notes!
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Notes:
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***
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```{r}
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ggplot(aes(x = (Month%%12), y = Temp), data = Mitchell) +
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geom_point()
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```
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### Understanding Noise: Age to Age Months
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Notes:
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```{r Understanding Noise: Age to Age Months}
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pf$age_with_months <- (pf$age) + (1 - (pf$dob_month/12))
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head(pf)
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```
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***
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@ -273,20 +292,27 @@ Notes:
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### Age with Months Means
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```{r Age with Months Means}
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library(dplyr)
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age_with_months <- group_by(pf, age_with_months)
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pf.fc_by_age_months <- summarize(
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age_with_months,
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friend_count_mean = mean(friend_count),
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friend_count_median = median(friend_count),
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n = n()
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)
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pf.fc_by_age_months <- arrange(pf.fc_by_age_months, age_with_months)
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head(pf.fc_by_age_months)
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```
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Programming Assignment
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```{r Programming Assignment}
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```
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***
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### Noise in Conditional Means
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```{r Noise in Conditional Means}
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ggplot(aes(x = age_with_months, y = friend_count_mean), data = subset(pf.fc_by_age_months, age_with_months<71)) +
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geom_line()
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```
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***
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@ -295,7 +321,16 @@ Programming Assignment
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Notes:
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```{r Smoothing Conditional Means}
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library(gridExtra)
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p1 <- ggplot(aes(x = age, y = friend_count_mean), data = subset(pf.fc_by_age, age < 71)) +
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geom_line() +
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geom_smooth()
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p2 <- ggplot(aes(x = age_with_months, y = friend_count_mean), data = subset(pf.fc_by_age_months, age_with_months < 71)) +
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geom_line() +
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geom_smooth()
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p3 <- ggplot(aes(x = round(age / 5) * 5, y = friend_count), data = subset(pf, age < 71)) +
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geom_line(stat = 'summary', fun.y = 'mean')
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grid.arrange(p1, p2, p3)
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```
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***
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@ -303,12 +338,12 @@ Notes:
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### Which Plot to Choose?
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Notes:
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***
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Make multiple plots during the exploritory phase and then refine them down into the best plots for distribution.
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### Analyzing Two Variables
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Reflection:
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***
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Making multiple plots can show different features of the data. Also while summaries and correlations are good for a lot of things they are not always the best at portraying the data.
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Click **KnitHTML** to see all of your hard work and to have an html
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page of this lesson, your answers, and your notes!
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lesson4/lesson4_student.tex
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\usepackage{graphicx,grffile}
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\makeatletter
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\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi}
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\def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi}
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\makeatother
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% Scale images if necessary, so that they will not overflow the page
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% margins by default, and it is still possible to overwrite the defaults
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\setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio}
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\IfFileExists{parskip.sty}{%
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\usepackage{parskip}
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}{% else
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\setlength{\parindent}{0pt}
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\setlength{\emergencystretch}{3em} % prevent overfull lines
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\providecommand{\tightlist}{%
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\setcounter{secnumdepth}{0}
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% Redefines (sub)paragraphs to behave more like sections
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\renewcommand{\subparagraph}[1]{\oldsubparagraph{#1}\mbox{}}
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%%% Use protect on footnotes to avoid problems with footnotes in titles
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\let\rmarkdownfootnote\footnote%
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\def\footnote{\protect\rmarkdownfootnote}
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%%% Change title format to be more compact
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\usepackage{titling}
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% Create subtitle command for use in maketitle
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\newcommand{\subtitle}[1]{
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\posttitle{
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\begin{center}\large#1\end{center}
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}
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}
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\setlength{\droptitle}{-2em}
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\title{}
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\pretitle{\vspace{\droptitle}}
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\posttitle{}
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\author{}
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\preauthor{}\postauthor{}
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\date{}
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\predate{}\postdate{}
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\begin{document}
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\section{Lesson 4}\label{lesson-4}
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\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
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\subsubsection{Scatterplots and Perceived Audience
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Size}\label{scatterplots-and-perceived-audience-size}
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Notes:
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\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
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\subsubsection{Scatterplots}\label{scatterplots}
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Notes:
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{library}\NormalTok{(ggplot2)}
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\NormalTok{pf <-}\StringTok{ }\KeywordTok{read.csv}\NormalTok{(}\StringTok{'pseudo_facebook.tsv'}\NormalTok{, }\DataTypeTok{sep =} \StringTok{'}\CharTok{\textbackslash{}t}\StringTok{'}\NormalTok{)}
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\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{geom_point}\NormalTok{()}
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\end{Highlighting}
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\end{Shaded}
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\includegraphics{lesson4_student_files/figure-latex/Scatterplots-1.pdf}
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\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
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\paragraph{What are some things that you notice right
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away?}\label{what-are-some-things-that-you-notice-right-away}
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Response:
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All of the data points are grouped into vertical lines and that the
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younger the age the more likely they are to have more friends.
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\subsubsection{ggplot Syntax}\label{ggplot-syntax}
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Notes:
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{geom_point}\NormalTok{() }\OperatorTok{+}
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\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{)}
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\end{Highlighting}
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\end{Shaded}
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\begin{verbatim}
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## Warning: Removed 4906 rows containing missing values (geom_point).
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\end{verbatim}
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\includegraphics{lesson4_student_files/figure-latex/ggplot Syntax-1.pdf}
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{summary}\NormalTok{(pf}\OperatorTok{$}\NormalTok{age)}
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\end{Highlighting}
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\end{Shaded}
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\begin{verbatim}
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## Min. 1st Qu. Median Mean 3rd Qu. Max.
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## 13.00 20.00 28.00 37.28 50.00 113.00
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\end{verbatim}
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Build one layer at a time to find errors easier
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\subsubsection{Overplotting}\label{overplotting}
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Notes:
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{geom_jitter}\NormalTok{(}\DataTypeTok{alpha =} \DecValTok{1}\OperatorTok{/}\DecValTok{20}\NormalTok{) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{)}
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\end{Highlighting}
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\end{Shaded}
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\begin{verbatim}
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## Warning: Removed 5190 rows containing missing values (geom_point).
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\end{verbatim}
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\includegraphics{lesson4_student_files/figure-latex/Overplotting-1.pdf}
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\paragraph{What do you notice in the
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plot?}\label{what-do-you-notice-in-the-plot}
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Response:
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The bar for 69 is still clearly visible and it is more obvious that the
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number generally decreases as the age increases.
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\subsubsection{Coord\_trans()}\label{coord_trans}
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Notes:
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\paragraph{Look up the documentation for coord\_trans() and add a layer
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to the plot that transforms friend\_count using the square root
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function. Create your
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plot!}\label{look-up-the-documentation-for-coord_trans-and-add-a-layer-to-the-plot-that-transforms-friend_count-using-the-square-root-function.-create-your-plot}
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{geom_point}\NormalTok{(}\DataTypeTok{alpha =} \DecValTok{1}\OperatorTok{/}\DecValTok{20}\NormalTok{) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{coord_trans}\NormalTok{(}\DataTypeTok{y =} \StringTok{"sqrt"}\NormalTok{)}
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\end{Highlighting}
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\end{Shaded}
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\begin{verbatim}
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## Warning: Removed 4906 rows containing missing values (geom_point).
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\end{verbatim}
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\includegraphics{lesson4_student_files/figure-latex/unnamed-chunk-1-1.pdf}
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\paragraph{What do you notice?}\label{what-do-you-notice}
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First off coord\_trans does not work with geom\_jitter, second the
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datapoints near the bottom are more spread out vertically to present
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them as more of a focus.
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To use jitter you need more advanced syntax to only jitter the ages,
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also to prevent possible negatives if 0 is jittered. To do this in
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\texttt{geom\_point()} pass
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\texttt{position\ =\ position\_jitter(h\ =\ 0)}
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\begin{Shaded}
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\begin{Highlighting}[]
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\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{geom_point}\NormalTok{(}\DataTypeTok{alpha =} \DecValTok{1}\OperatorTok{/}\DecValTok{20}\NormalTok{, }\DataTypeTok{position =} \KeywordTok{position_jitter}\NormalTok{(}\DataTypeTok{h =} \DecValTok{0}\NormalTok{)) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{) }\OperatorTok{+}
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\StringTok{ }\KeywordTok{coord_trans}\NormalTok{(}\DataTypeTok{y =} \StringTok{"sqrt"}\NormalTok{)}
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\end{Highlighting}
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\end{Shaded}
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\begin{verbatim}
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## Warning: Removed 5185 rows containing missing values (geom_point).
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\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/coord_trans_advanced-1.pdf}
|
||||
|
||||
\subsubsection{Alpha and Jitter}\label{alpha-and-jitter}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friendships_initiated, }\DataTypeTok{color =}\NormalTok{ gender), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{(}\DataTypeTok{alpha =} \DecValTok{1}\OperatorTok{/}\DecValTok{10}\NormalTok{, }\DataTypeTok{position =} \KeywordTok{position_jitter}\NormalTok{(}\DataTypeTok{h =} \DecValTok{0}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{coord_trans}\NormalTok{(}\DataTypeTok{y =} \StringTok{"sqrt"}\NormalTok{)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 5170 rows containing missing values (geom_point).
|
||||
\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Alpha and Jitter-1.pdf}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Overplotting and Domain
|
||||
Knowledge}\label{overplotting-and-domain-knowledge}
|
||||
|
||||
Notes:
|
||||
|
||||
plotting as a percentage of the whole
|
||||
|
||||
\subsubsection{Conditional Means}\label{conditional-means}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{library}\NormalTok{(dplyr)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Attaching package: 'dplyr'
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## The following objects are masked from 'package:stats':
|
||||
##
|
||||
## filter, lag
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## The following objects are masked from 'package:base':
|
||||
##
|
||||
## intersect, setdiff, setequal, union
|
||||
\end{verbatim}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\NormalTok{age_groups <-}\StringTok{ }\KeywordTok{group_by}\NormalTok{(pf, age)}
|
||||
\NormalTok{pf.fc_by_age <-}\StringTok{ }\KeywordTok{summarise}\NormalTok{(age_groups,}
|
||||
\DataTypeTok{friend_count_mean =} \KeywordTok{mean}\NormalTok{(friend_count),}
|
||||
\DataTypeTok{friend_count_median =} \KeywordTok{median}\NormalTok{(friend_count),}
|
||||
\DataTypeTok{n =} \KeywordTok{n}\NormalTok{())}
|
||||
\NormalTok{pf.fc_by_age <-}\StringTok{ }\KeywordTok{arrange}\NormalTok{(pf.fc_by_age, age)}
|
||||
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count_mean), }\DataTypeTok{data =}\NormalTok{ pf.fc_by_age) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{() }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{,}\DecValTok{90}\NormalTok{)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 23 rows containing missing values (geom_path).
|
||||
\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Conditional Means-1.pdf}
|
||||
|
||||
\subsubsection{Overlaying Summaries with Raw
|
||||
Data}\label{overlaying-summaries-with-raw-data}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friendships_initiated), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{(}\DataTypeTok{alpha =} \DecValTok{1}\OperatorTok{/}\DecValTok{10}\NormalTok{, }\DataTypeTok{position =} \KeywordTok{position_jitter}\NormalTok{(}\DataTypeTok{h =} \DecValTok{0}\NormalTok{), }\DataTypeTok{color =} \StringTok{'orange'}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{13}\NormalTok{, }\DecValTok{90}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{coord_trans}\NormalTok{(}\DataTypeTok{y =} \StringTok{"sqrt"}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{(}\DataTypeTok{stat =} \StringTok{'summary'}\NormalTok{, }\DataTypeTok{fun.y =}\NormalTok{ mean) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{(}\DataTypeTok{stat =} \StringTok{'summary'}\NormalTok{, }\DataTypeTok{fun.y =}\NormalTok{ median, }\DataTypeTok{color =} \StringTok{'blue'}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{(}\DataTypeTok{stat =} \StringTok{'summary'}\NormalTok{, }\DataTypeTok{fun.y =}\NormalTok{ quantile, }\DataTypeTok{fun.args =} \KeywordTok{list}\NormalTok{(}\DataTypeTok{probs =} \FloatTok{0.1}\NormalTok{), }\DataTypeTok{color =} \StringTok{'red'}\NormalTok{, }\DataTypeTok{linetype =} \DecValTok{2}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{(}\DataTypeTok{stat =} \StringTok{'summary'}\NormalTok{, }\DataTypeTok{fun.y =}\NormalTok{ quantile, }\DataTypeTok{fun.args =} \KeywordTok{list}\NormalTok{(}\DataTypeTok{probs =} \FloatTok{0.9}\NormalTok{), }\DataTypeTok{color =} \StringTok{'red'}\NormalTok{, }\DataTypeTok{linetype =} \DecValTok{2}\NormalTok{) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{coord_cartesian}\NormalTok{(}\DataTypeTok{xlim =} \KeywordTok{c}\NormalTok{(}\DecValTok{13}\NormalTok{,}\DecValTok{70}\NormalTok{), }\DataTypeTok{ylim =} \KeywordTok{c}\NormalTok{(}\DecValTok{0}\NormalTok{,}\DecValTok{1000}\NormalTok{))}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
|
||||
|
||||
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
|
||||
|
||||
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
|
||||
|
||||
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 5183 rows containing missing values (geom_point).
|
||||
\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Overlaying Summaries with Raw Data-1.pdf}
|
||||
|
||||
\paragraph{What are some of your observations of the
|
||||
plot?}\label{what-are-some-of-your-observations-of-the-plot}
|
||||
|
||||
Response:
|
||||
|
||||
I notice that the median is always lower than the mean and that the
|
||||
median is closer to the center of the main body of datapoints. It
|
||||
appears that the data is long tailed towards the high friend counts
|
||||
which pulls the mean upwards.
|
||||
|
||||
\subsubsection{Moira: Histogram Summary and
|
||||
Scatterplot}\label{moira-histogram-summary-and-scatterplot}
|
||||
|
||||
See the Instructor Notes of this video to download Moira's paper on
|
||||
perceived audience size and to see the final plot.
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Correlation}\label{correlation}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{cor.test}\NormalTok{(pf}\OperatorTok{$}\NormalTok{age, pf}\OperatorTok{$}\NormalTok{friend_count)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Pearson's product-moment correlation
|
||||
##
|
||||
## data: pf$age and pf$friend_count
|
||||
## t = -8.6268, df = 99001, p-value < 2.2e-16
|
||||
## alternative hypothesis: true correlation is not equal to 0
|
||||
## 95 percent confidence interval:
|
||||
## -0.03363072 -0.02118189
|
||||
## sample estimates:
|
||||
## cor
|
||||
## -0.02740737
|
||||
\end{verbatim}
|
||||
|
||||
Look up the documentation for the cor.test function.
|
||||
|
||||
What's the correlation between age and friend count? Round to three
|
||||
decimal places. Response:
|
||||
|
||||
-0.027
|
||||
|
||||
\subsubsection{Correlation on Subsets}\label{correlation-on-subsets}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{with}\NormalTok{(pf[pf}\OperatorTok{$}\NormalTok{age }\OperatorTok{<=}\StringTok{ }\DecValTok{70}\NormalTok{,], }\KeywordTok{cor.test}\NormalTok{(age, friend_count))}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Pearson's product-moment correlation
|
||||
##
|
||||
## data: age and friend_count
|
||||
## t = -52.592, df = 91029, p-value < 2.2e-16
|
||||
## alternative hypothesis: true correlation is not equal to 0
|
||||
## 95 percent confidence interval:
|
||||
## -0.1780220 -0.1654129
|
||||
## sample estimates:
|
||||
## cor
|
||||
## -0.1717245
|
||||
\end{verbatim}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Correlation Methods}\label{correlation-methods}
|
||||
|
||||
Notes:
|
||||
|
||||
\url{http://www.statisticssolutions.com/correlation-pearson-kendall-spearman/}
|
||||
|
||||
\subsection{Create Scatterplots}\label{create-scatterplots}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{library}\NormalTok{(ggplot2)}
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ www_likes_received, }\DataTypeTok{y =}\NormalTok{ likes_received), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{()}\CommentTok{#alpha = 1/20, position = position_jitter(h = 0)) +}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/unnamed-chunk-2-1.pdf}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\CommentTok{#xlim(13, 90) +}
|
||||
\CommentTok{#coord_trans(y = "sqrt")}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Strong Correlations}\label{strong-correlations}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ www_likes_received, }\DataTypeTok{y =}\NormalTok{ likes_received), }\DataTypeTok{data =}\NormalTok{ pf) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{() }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{xlim}\NormalTok{(}\DecValTok{0}\NormalTok{, }\KeywordTok{quantile}\NormalTok{(pf}\OperatorTok{$}\NormalTok{www_likes_received, }\FloatTok{0.95}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{ylim}\NormalTok{(}\DecValTok{0}\NormalTok{, }\KeywordTok{quantile}\NormalTok{(pf}\OperatorTok{$}\NormalTok{likes_received, }\FloatTok{0.95}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_smooth}\NormalTok{(}\DataTypeTok{method =} \StringTok{'lm'}\NormalTok{, }\DataTypeTok{color =} \StringTok{'red'}\NormalTok{)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 6075 rows containing non-finite values (stat_smooth).
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## Warning: Removed 6075 rows containing missing values (geom_point).
|
||||
\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Strong Correlations-1.pdf}
|
||||
|
||||
What's the correlation betwen the two variables? Include the top 5\% of
|
||||
values for the variable in the calculation and round to 3 decimal
|
||||
places.
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{with}\NormalTok{(pf, }\KeywordTok{cor.test}\NormalTok{(www_likes_received, likes_received))}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Pearson's product-moment correlation
|
||||
##
|
||||
## data: www_likes_received and likes_received
|
||||
## t = 937.1, df = 99001, p-value < 2.2e-16
|
||||
## alternative hypothesis: true correlation is not equal to 0
|
||||
## 95 percent confidence interval:
|
||||
## 0.9473553 0.9486176
|
||||
## sample estimates:
|
||||
## cor
|
||||
## 0.9479902
|
||||
\end{verbatim}
|
||||
|
||||
Response:
|
||||
|
||||
0.948 Variable is a superset of another
|
||||
|
||||
\subsubsection{Moira on Correlation}\label{moira-on-correlation}
|
||||
|
||||
Notes:
|
||||
|
||||
Highly corelated can mean that variables are dependent on the same thing
|
||||
or are similar.
|
||||
|
||||
\subsubsection{More Caution with
|
||||
Correlation}\label{more-caution-with-correlation}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\CommentTok{#install.packages('alr3')}
|
||||
\KeywordTok{library}\NormalTok{(alr3)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## Loading required package: car
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## Loading required package: carData
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Attaching package: 'car'
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## The following object is masked from 'package:dplyr':
|
||||
##
|
||||
## recode
|
||||
\end{verbatim}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{library}\NormalTok{(ggplot2)}
|
||||
\KeywordTok{data}\NormalTok{(Mitchell)}
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ Month, }\DataTypeTok{y =}\NormalTok{ Temp), }\DataTypeTok{data =}\NormalTok{ Mitchell) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{()}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/More Caution With Correlation-1.pdf}
|
||||
|
||||
Create your plot!
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Noisy Scatterplots}\label{noisy-scatterplots}
|
||||
|
||||
\begin{enumerate}
|
||||
\def\labelenumi{\alph{enumi}.}
|
||||
\tightlist
|
||||
\item
|
||||
Take a guess for the correlation coefficient for the scatterplot.
|
||||
\end{enumerate}
|
||||
|
||||
0.9
|
||||
|
||||
\begin{enumerate}
|
||||
\def\labelenumi{\alph{enumi}.}
|
||||
\setcounter{enumi}{1}
|
||||
\tightlist
|
||||
\item
|
||||
What is the actual correlation of the two variables? (Round to the
|
||||
thousandths place)
|
||||
\end{enumerate}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{with}\NormalTok{(Mitchell, }\KeywordTok{cor.test}\NormalTok{(Month, Temp))}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Pearson's product-moment correlation
|
||||
##
|
||||
## data: Month and Temp
|
||||
## t = 0.81816, df = 202, p-value = 0.4142
|
||||
## alternative hypothesis: true correlation is not equal to 0
|
||||
## 95 percent confidence interval:
|
||||
## -0.08053637 0.19331562
|
||||
## sample estimates:
|
||||
## cor
|
||||
## 0.05747063
|
||||
\end{verbatim}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Making Sense of Data}\label{making-sense-of-data}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(Month, Temp), }\DataTypeTok{data =}\NormalTok{ Mitchell) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{() }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{scale_x_continuous}\NormalTok{(}\DataTypeTok{breaks =} \KeywordTok{seq}\NormalTok{(}\DecValTok{0}\NormalTok{, }\DecValTok{204}\NormalTok{, }\DecValTok{12}\NormalTok{))}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Making Sense of Data-1.pdf}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{A New Perspective}\label{a-new-perspective}
|
||||
|
||||
What do you notice? Response:
|
||||
|
||||
There is a cyclical pattern to the data going from low to high and back
|
||||
to low every 12 months. This is why I originally said there seems to be
|
||||
a 0.9 correlation coefficient to the data because I saw this pattern the
|
||||
first time I looked at the plot.
|
||||
|
||||
Watch the solution video and check out the Instructor Notes! Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ (Month}\OperatorTok{%%}\DecValTok{12}\NormalTok{), }\DataTypeTok{y =}\NormalTok{ Temp), }\DataTypeTok{data =}\NormalTok{ Mitchell) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_point}\NormalTok{()}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/unnamed-chunk-3-1.pdf}
|
||||
|
||||
\subsubsection{Understanding Noise: Age to Age
|
||||
Months}\label{understanding-noise-age-to-age-months}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\NormalTok{pf}\OperatorTok{$}\NormalTok{age_with_months <-}\StringTok{ }\NormalTok{(pf}\OperatorTok{$}\NormalTok{age) }\OperatorTok{+}\StringTok{ }\NormalTok{(}\DecValTok{1} \OperatorTok{-}\StringTok{ }\NormalTok{(pf}\OperatorTok{$}\NormalTok{dob_month}\OperatorTok{/}\DecValTok{12}\NormalTok{))}
|
||||
\KeywordTok{head}\NormalTok{(pf)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## userid age dob_day dob_year dob_month gender tenure friend_count
|
||||
## 1 2094382 14 19 1999 11 male 266 0
|
||||
## 2 1192601 14 2 1999 11 female 6 0
|
||||
## 3 2083884 14 16 1999 11 male 13 0
|
||||
## 4 1203168 14 25 1999 12 female 93 0
|
||||
## 5 1733186 14 4 1999 12 male 82 0
|
||||
## 6 1524765 14 1 1999 12 male 15 0
|
||||
## friendships_initiated likes likes_received mobile_likes
|
||||
## 1 0 0 0 0
|
||||
## 2 0 0 0 0
|
||||
## 3 0 0 0 0
|
||||
## 4 0 0 0 0
|
||||
## 5 0 0 0 0
|
||||
## 6 0 0 0 0
|
||||
## mobile_likes_received www_likes www_likes_received age_with_months
|
||||
## 1 0 0 0 14.08333
|
||||
## 2 0 0 0 14.08333
|
||||
## 3 0 0 0 14.08333
|
||||
## 4 0 0 0 14.00000
|
||||
## 5 0 0 0 14.00000
|
||||
## 6 0 0 0 14.00000
|
||||
\end{verbatim}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Age with Months Means}\label{age-with-months-means}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{library}\NormalTok{(dplyr)}
|
||||
|
||||
\NormalTok{age_with_months <-}\StringTok{ }\KeywordTok{group_by}\NormalTok{(pf, age_with_months)}
|
||||
\NormalTok{pf.fc_by_age_months <-}\StringTok{ }\KeywordTok{summarize}\NormalTok{(}
|
||||
\NormalTok{ age_with_months,}
|
||||
\DataTypeTok{friend_count_mean =} \KeywordTok{mean}\NormalTok{(friend_count),}
|
||||
\DataTypeTok{friend_count_median =} \KeywordTok{median}\NormalTok{(friend_count),}
|
||||
\DataTypeTok{n =} \KeywordTok{n}\NormalTok{()}
|
||||
\NormalTok{)}
|
||||
|
||||
\NormalTok{pf.fc_by_age_months <-}\StringTok{ }\KeywordTok{arrange}\NormalTok{(pf.fc_by_age_months, age_with_months)}
|
||||
|
||||
\KeywordTok{head}\NormalTok{(pf.fc_by_age_months)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## # A tibble: 6 x 4
|
||||
## age_with_months friend_count_mean friend_count_median n
|
||||
## <dbl> <dbl> <dbl> <int>
|
||||
## 1 13.2 46.3 30.5 6
|
||||
## 2 13.2 115. 23.5 14
|
||||
## 3 13.3 136. 44.0 25
|
||||
## 4 13.4 164. 72.0 33
|
||||
## 5 13.5 131. 66.0 45
|
||||
## 6 13.6 157. 64.0 54
|
||||
\end{verbatim}
|
||||
|
||||
\subsubsection{Noise in Conditional
|
||||
Means}\label{noise-in-conditional-means}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age_with_months, }\DataTypeTok{y =}\NormalTok{ friend_count_mean), }\DataTypeTok{data =} \KeywordTok{subset}\NormalTok{(pf.fc_by_age_months, age_with_months}\OperatorTok{<}\DecValTok{71}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{()}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Noise in Conditional Means-1.pdf}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Smoothing Conditional
|
||||
Means}\label{smoothing-conditional-means}
|
||||
|
||||
Notes:
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\KeywordTok{library}\NormalTok{(gridExtra)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
##
|
||||
## Attaching package: 'gridExtra'
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## The following object is masked from 'package:dplyr':
|
||||
##
|
||||
## combine
|
||||
\end{verbatim}
|
||||
|
||||
\begin{Shaded}
|
||||
\begin{Highlighting}[]
|
||||
\NormalTok{p1 <-}\StringTok{ }\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =}\NormalTok{ friend_count_mean), }\DataTypeTok{data =} \KeywordTok{subset}\NormalTok{(pf.fc_by_age, age }\OperatorTok{<}\StringTok{ }\DecValTok{71}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{() }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_smooth}\NormalTok{()}
|
||||
\NormalTok{p2 <-}\StringTok{ }\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age_with_months, }\DataTypeTok{y =}\NormalTok{ friend_count_mean), }\DataTypeTok{data =} \KeywordTok{subset}\NormalTok{(pf.fc_by_age_months, age_with_months }\OperatorTok{<}\StringTok{ }\DecValTok{71}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{() }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_smooth}\NormalTok{()}
|
||||
\NormalTok{p3 <-}\StringTok{ }\KeywordTok{ggplot}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =} \KeywordTok{round}\NormalTok{(age }\OperatorTok{/}\StringTok{ }\DecValTok{5}\NormalTok{) }\OperatorTok{*}\StringTok{ }\DecValTok{5}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ friend_count), }\DataTypeTok{data =} \KeywordTok{subset}\NormalTok{(pf, age }\OperatorTok{<}\StringTok{ }\DecValTok{71}\NormalTok{)) }\OperatorTok{+}
|
||||
\StringTok{ }\KeywordTok{geom_line}\NormalTok{(}\DataTypeTok{stat =} \StringTok{'summary'}\NormalTok{, }\DataTypeTok{fun.y =} \StringTok{'mean'}\NormalTok{)}
|
||||
\KeywordTok{grid.arrange}\NormalTok{(p1, p2, p3)}
|
||||
\end{Highlighting}
|
||||
\end{Shaded}
|
||||
|
||||
\begin{verbatim}
|
||||
## `geom_smooth()` using method = 'loess'
|
||||
\end{verbatim}
|
||||
|
||||
\begin{verbatim}
|
||||
## `geom_smooth()` using method = 'loess'
|
||||
\end{verbatim}
|
||||
|
||||
\includegraphics{lesson4_student_files/figure-latex/Smoothing Conditional Means-1.pdf}
|
||||
|
||||
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
|
||||
|
||||
\subsubsection{Which Plot to Choose?}\label{which-plot-to-choose}
|
||||
|
||||
Notes:
|
||||
|
||||
Make multiple plots during the exploritory phase and then refine them
|
||||
down into the best plots for distribution.
|
||||
|
||||
\subsubsection{Analyzing Two Variables}\label{analyzing-two-variables}
|
||||
|
||||
Reflection:
|
||||
|
||||
Making multiple plots can show different features of the data. Also
|
||||
while summaries and correlations are good for a lot of things they are
|
||||
not always the best at portraying the data.
|
||||
|
||||
Click \textbf{KnitHTML} to see all of your hard work and to have an html
|
||||
page of this lesson, your answers, and your notes!
|
||||
|
||||
|
||||
\end{document}
|
||||
Loading…
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Reference in New Issue
Block a user