lesson3_student.rmd

Lesson 3
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### What to Do First?
Notes:

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### Pseudo-Facebook User Data
Notes:

```{r Pseudo-Facebook User Data}
setwd("~/Repos/data-analysis-with-r/")
pf <- read.csv("./data/pseudo_facebook.tsv", sep="\t")
names(pf)
```

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### Histogram of Users' Birthdays
Notes:

```{r Histogram of Users\' Birthdays}
library(ggplot2)
library(ggthemes)
qplot(x = dob_day, data = pf ) +
    scale_x_discrete(breaks = 1:31)
```

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#### What are some things that you notice about this histogram?
Response:

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### Moira's Investigation
Notes:

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### Estimating Your Audience Size
Notes:

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#### Think about a time when you posted a specific message or shared a photo on Facebook. What was it?
Response:

#### How many of your friends do you think saw that post?
Response:
50

#### 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?
Response:
20%
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### Perceived Audience Size
Notes:

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### Faceting
Notes:

```{r Faceting}

```

#### Let’s take another look at our plot. What stands out to you here?
Response:

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### Be Skeptical - Outliers and Anomalies
Notes:

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### Moira's Outlier
Notes:
#### Which case do you think applies to Moira’s outlier?
Response:

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### Friend Count
Notes:

#### What code would you enter to create a histogram of friend counts?

```{r Friend Count}
names(pf)
qplot(x = friend_count, data = pf )
```

#### How is this plot similar to Moira's first plot?
Response:

***

### Limiting the Axes
Notes:

```{r Limiting the Axes}
qplot(x = friend_count, data = pf, xlim = c(0,1000))

qplot(x = friend_count, data = pf ) +
    scale_x_continuous(limits = c(0,1000))

```

### Exploring with Bin Width
Notes:

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### Adjusting the Bin Width
Notes:

### Faceting Friend Count
```{r Faceting Friend Count}
# What code would you add to create a facet the histogram by gender?
# Add it to the code below.
qplot(x = friend_count, data = pf, binwidth = 10) +
  scale_x_continuous(limits = c(0, 1000),
                     breaks = seq(0, 1000, 50))
```

***

### Omitting NA Values
Notes:

```{r Omitting NA Values}
qplot(x = friend_count, data = subset(pf, !is.na(gender)), binwidth = 10) +
  scale_x_continuous(limits = c(0, 1000),
                     breaks = seq(0, 1000, 50))
```

***

### Statistics 'by' Gender
Notes:

```{r Statistics \'by\' Gender}
qplot(x = friend_count, data = subset(pf,!is.na(gender)), binwidth = 25, fill = gender) + 
  scale_x_continuous(limits = c(0, 1000), breaks = seq(0, 1000, 50)) +
  facet_grid(. ~ gender)

by(pf$friend_count,pf$gender,summary)
```

#### Who on average has more friends: men or women?
Response:
Male
#### What's the difference between the median friend count for women and men?
Response:

#### Why would the median be a better measure than the mean?
Response:

***

### Tenure
Notes:

```{r Tenure}
str(pf)
qplot(x = tenure, data = pf, binwidth = 30,
      color=I('black'), fill=I("#099DD9"))
```

***

#### How would you create a histogram of tenure by year?

```{r Tenure Histogram by Year}
qplot(x = tenure/365, data = pf, binwidth = 0.25,
      color=I('black'), fill=I("#099DD9")) +
  scale_x_continuous(breaks = seq(1, 7, 1), limits = c(0, 7))
```

***

### Labeling Plots
Notes:

```{r Labeling Plots}
qplot(x = tenure/365, data = pf,
      xlab = "Number of years using Facebook",
      ylab = "Number of users in sample",
      color=I('black'), fill=I("#099DD9")) +
    scale_x_continuous(breaks = seq(1, 7, 1), limits = c(0, 7))

```

***

### User Ages
Notes:

```{r User Ages}
qplot(x=age, data=subset(pf,!is.na(gender)), binwidth = 1,
      color = I("black"), fill = I("#099DD9")) +
  scale_x_discrete(breaks = seq(0, 113, 10))
```

#### What do you notice?
Response:

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### The Spread of Memes
Notes:

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### Lada's Money Bag Meme
Notes:

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### Transforming Data
Notes:

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### Add a Scaling Layer
Notes:

```{r Add a Scaling Layer}
library(gridExtra)
p1 <- qplot(x = friend_count, data = pf)
p2 <- p1 + scale_x_log10()
p3 <- p1 + scale_x_sqrt()
grid.arrange(p1,p2,p3,ncol = 1)
h1 <- qplot(x = friend_count, data = pf)
h2 <- qplot(x = log10(friend_count), data = pf)
h3 <- qplot(x = sqrt(friend_count), data = pf)
grid.arrange(h1,h2,h3,ncol = 1)

```

***


### Frequency Polygons

```{r Frequency Polygons}
ggplot(data = subset(pf, !is.na(gender))) +
  geom_freqpoly(aes(x = friend_count, y = ..count../sum(..count..),color = gender)) + 
  scale_x_continuous(limits = c(0, 1000), breaks = seq(0, 1000, 50)) +
  xlab('Friend Count') + 
  ylab('Percentage of users with that friend count')
```

***

### Likes on the Web
Notes:

```{r Likes on the Web}
ggplot(data = subset(pf, !is.na(gender))) + 
  geom_freqpoly(aes(x = www_likes, color = gender)) +
  scale_x_continuous(limits = c(1, 1000), breaks = seq(1, 1000, 50)) +
  scale_x_log10()

by(pf$www_likes,pf$gender,sum)
```


***

### Box Plots
Notes:

```{r Box Plots}
qplot(x=gender,y=friend_count,
      data = subset(pf, !is.na(gender)), geom = "boxplot")
```

#### Adjust the code to focus on users who have friend counts between 0 and 1000.

```{r}
q1 <- qplot(x=gender,y=friend_count,
      data = subset(pf, !is.na(gender) & friend_count > 0 & friend_count < 1000 ),
      geom = "boxplot")

q2 <- qplot(x=gender,y=friend_count,
      data = subset(pf, !is.na(gender)), geom = "boxplot",
      ylim = c(0,1000))

q3 <- qplot(x=gender,y=friend_count,
      data = subset(pf, !is.na(gender)), geom = "boxplot") + 
  coord_cartesian(ylim = c(0,1000))

grid.arrange(q1,q2,q3,ncol = 3)

```

***

### Box Plots, Quartiles, and Friendships
Notes:

```{r Box Plots, Quartiles, and Friendships}
qplot(x = gender, y = friend_count,
      data = subset(pf, !is.na(gender)),
      geom = "boxplot") +
  coord_cartesian(ylim = c(0,250))

by(pf$friend_count,pf$gender,summary)
```

#### On average, who initiated more friendships in our sample: men or women?
Response:
#### Write about some ways that you can verify your answer.
Response:
```{r Friend Requests by Gender}
qplot(x = gender, y = friendships_initiated,
      data = subset(pf, !is.na(gender)),
      geom = "boxplot") +
  coord_cartesian(ylim = c(0,250))

by(pf$friendships_initiated,pf$gender,summary)
```

Response:

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### Getting Logical
Notes:

```{r Getting Logical}
summary(pf$mobile_likes)

summary(pf$mobile_likes > 0)

mobile_check_in <- NA
pf$mobile_check_in <- ifelse(pf$mobile_likes > 0, 1, 0)
pf$mobile_check_in <- factor(pf$mobile_check_in)
summary(pf$mobile_check_in)

sum(pf$mobile_check_in == 1) / length(pf$mobile_check_in)
table(pf$mobile_likes > 0) / length(pf$mobile_likes)

```

Response:

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### Analyzing One Variable
Reflection:

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