Lesson 3 part 1
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lesson2/Rplots.pdf
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lesson2/Rplots.pdf
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lesson3/lesson3.Rproj
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lesson3/lesson3.Rproj
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Version: 1.0
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RestoreWorkspace: Default
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SaveWorkspace: Default
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AlwaysSaveHistory: Default
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EnableCodeIndexing: Yes
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UseSpacesForTab: Yes
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NumSpacesForTab: 2
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Encoding: UTF-8
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RnwWeave: Sweave
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LaTeX: pdfLaTeX
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@ -6,13 +6,15 @@ Lesson 3
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### What to Do First?
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Notes:
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***
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Read in Pseudo Facebook data.
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### Pseudo-Facebook User Data
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Notes:
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```{r Pseudo-Facebook User Data}
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pf <- read.csv('pseudo_facebook.tsv', sep='\t')
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names(pf)
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```
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***
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@ -21,8 +23,11 @@ Notes:
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Notes:
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```{r Histogram of Users\' Birthdays}
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install.packages('ggplot2')
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# install.packages('ggplot2')
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library(ggplot2)
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qplot(x=dob_day, data=pf, binwidth = 1) +
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scale_x_continuous(breaks = 1:31)
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```
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***
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@ -30,17 +35,16 @@ library(ggplot2)
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#### What are some things that you notice about this histogram?
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Response:
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***
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Day 1 and day 31
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### Moira's Investigation
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Notes:
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***
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Moira is looking at how people estimate their audience size.
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### Estimating Your Audience Size
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Notes:
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***
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#### Think about a time when you posted a specific message or shared a photo on Facebook. What was it?
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Response:
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@ -51,7 +55,7 @@ Response:
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#### Think about what percent of your friends on Facebook see any posts or comments that you make in a month. What percent do you think that is?
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Response:
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***
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about 10%
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### Perceived Audience Size
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Notes:
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@ -61,25 +65,27 @@ Notes:
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Notes:
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```{r Faceting}
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qplot(x=dob_day, data=pf, binwidth = 1) +
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scale_x_continuous(breaks = 1:31) +
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facet_wrap(~dob_month, ncol=3)
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```
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#### Let’s take another look at our plot. What stands out to you here?
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Response:
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***
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Most of the 1st birthdays are in Janually, probably from FBs default or choosing the first option on the list.
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### Be Skeptical - Outliers and Anomalies
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Notes:
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***
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Some outliers are extreme examples, but other times they show bad data or errors in collection
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### Moira's Outlier
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Notes:
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#### Which case do you think applies to Moira’s outlier?
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Response:
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***
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Bad data about an extreme case.
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### Friend Count
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Notes:
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@ -87,25 +93,25 @@ Notes:
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#### What code would you enter to create a histogram of friend counts?
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```{r Friend Count}
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qplot(x=friend_count, data=pf, binwidth = 1)
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```
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#### How is this plot similar to Moira's first plot?
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Response:
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***
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Massive spike at low values and the scale on the axes is not very helpful.
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### Limiting the Axes
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Notes:
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```{r Limiting the Axes}
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qplot(x=friend_count, data=pf, binwidth = 1, xlim=c(0,1000))
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```
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### Exploring with Bin Width
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Notes:
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***
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Lower binwidth gives more precise info but can become cluttered.
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### Adjusting the Bin Width
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Notes:
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@ -116,7 +122,8 @@ Notes:
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# Add it to the code below.
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qplot(x = friend_count, data = pf, binwidth = 10) +
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scale_x_continuous(limits = c(0, 1000),
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breaks = seq(0, 1000, 50))
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breaks = seq(0, 1000, 50)) +
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facet_wrap(~gender)
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```
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***
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@ -125,34 +132,43 @@ qplot(x = friend_count, data = pf, binwidth = 10) +
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Notes:
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```{r Omitting NA Values}
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qplot(x = friend_count, data = pf[!is.na(pf$gender),], binwidth = 10) +
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scale_x_continuous(limits = c(0, 1000),
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breaks = seq(0, 1000, 50)) +
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facet_wrap(~gender)
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```
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***
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Can use na.omit but be careful because that will omit rows that have na in other values too.
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### Statistics 'by' Gender
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Notes:
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```{r Statistics \'by\' Gender}
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table(pf$gender)
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by(pf$friend_count, pf$gender, summary)
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```
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#### Who on average has more friends: men or women?
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Response:
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Women
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#### What's the difference between the median friend count for women and men?
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Response:
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22
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#### Why would the median be a better measure than the mean?
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Response:
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***
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Because it is the middle number in the dataset and is not as influenced by the extreme outliers.
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### Tenure
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Notes:
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```{r Tenure}
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qplot(x = tenure, data = pf, binwidth=30,
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color = I('black'), fill= I('#099DD9'))
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```
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***
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@ -160,7 +176,9 @@ Notes:
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#### How would you create a histogram of tenure by year?
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```{r Tenure Histogram by Year}
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qplot(x = tenure / 365, data = pf, binwidth=0.25,
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color = I('black'), fill= I('#099DD9')) +
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scale_x_continuous(breaks = c(0:8))
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```
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***
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@ -169,7 +187,11 @@ Notes:
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Notes:
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```{r Labeling Plots}
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qplot(x = tenure / 365, data = pf, binwidth=0.25,
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color = I('black'), fill= I('#099DD9')) +
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scale_x_continuous(breaks = c(0:8), lim = c(0,7)) +
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xlab('Number of yeas using Facebook') +
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ylab('Number of users in sample')
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```
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***
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@ -178,34 +200,40 @@ Notes:
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Notes:
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```{r User Ages}
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qplot(x = age, data = pf, binwidth=1,
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color = I('black'), fill= I('#099DD9')) +
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scale_x_continuous(breaks = seq(0,115,5), lim = c(12,115)) +
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xlab('Ages of Facebook Users') +
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ylab('Number of users in sample')
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```
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#### What do you notice?
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Response:
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***
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There are an abnormal amount of users over 100 years old...
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### The Spread of Memes
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Notes:
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***
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Get the min max from the data with summary(pf$age)
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### Lada's Money Bag Meme
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Notes:
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***
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Memes tend to reaccure.
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Log scale instead of linear can show low numbers
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### Transforming Data
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Notes:
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***
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Engagement variables are often long tailed (over dispersed)
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log10(variable) with show -Inf for undefined variables such as 0
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### Add a Scaling Layer
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Notes:
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```{r Add a Scaling Layer}
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qplot(x = log10(friend_count + 1), data = pf)
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```
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***
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