Lesson 3
What to Do First?
Notes: Change directories and load the dataset
Pseudo-Facebook User Data
Notes: read in the TAB separated values from the dataset
pf <- read.csv('pseudo_facebook.tsv', sep = '\t')
names(pf)## [1] "userid" "age"
## [3] "dob_day" "dob_year"
## [5] "dob_month" "gender"
## [7] "tenure" "friend_count"
## [9] "friendships_initiated" "likes"
## [11] "likes_received" "mobile_likes"
## [13] "mobile_likes_received" "www_likes"
## [15] "www_likes_received"Histogram of Users’ Birthdays
Notes:
install.packages('ggplot2', repos = 'https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/ggplot2_2.1.0.tgz')## Warning: unable to access index for repository https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/ggplot2_2.1.0.tgz/src/contrib:
## cannot download all files## Warning: package 'ggplot2' is not available (for R version 3.3.1)## Warning: unable to access index for repository https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/ggplot2_2.1.0.tgz/bin/macosx/mavericks/contrib/3.3:
## cannot download all fileslibrary(ggplot2)
qplot(data = pf, x = dob_day) +
scale_x_continuous(breaks=1:31) +
facet_wrap(~dob_month, ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
What are some things that you notice about this histogram?
Response: A lot people with birthday of Jan. 1. Likely because of people not changing the default or not entering their birthday because of privacy concerns. It is hard to read because of the Jan. 1 spike.
Moira’s Investigation
Notes: Moira investigated the number of views one’s post got vs. how many views they thought they got.
Estimating Your Audience Size
Notes: On average, people estimated they got a fourth of the views they actually got.
Faceting
Notes:
qplot(data = pf, x = dob_day) +
scale_x_continuous(breaks=1:31) +
facet_wrap(~dob_month, ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Let’s take another look at our plot. What stands out to you here?
Response: Jan. 1 is an outlier
Moira’s Outlier
Notes: Someone registered that they thought 1,000,000 friends saw their post when there is a friend max at 5,000
What code would you enter to create a histogram of friend counts?
qplot(data = subset(pf, !is.na(gender)), x = friend_count, binwidth = 25) +
facet_wrap(~gender, ncol = 2)
***
How is this plot similar to Moira’s first plot?
Response: It is hard to read
Limiting the Axes
Notes:
qplot(data = subset(pf, !is.na(gender)), x = friend_count, binwidth = 25) +
scale_x_continuous(limits = c(0,1000), breaks = seq(0, 1000, 50)) +
facet_wrap(~gender, ncol = 2)## Warning: Removed 2949 rows containing non-finite values (stat_bin).
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))## Warning: Removed 2951 rows containing non-finite values (stat_bin).
Omitting NA Values
Notes:
qplot(x = friend_count, data = subset(pf, !is.na(gender)), binwidth = 10) +
scale_x_continuous(limits = c(0, 1000),
breaks = seq(0, 1000, 50)) +
facet_wrap(~gender)## Warning: Removed 2949 rows containing non-finite values (stat_bin).
Statistics ‘by’ Gender
Notes:
by(pf$friend_count, pf$gender, summary)## pf$gender: female
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 37 96 242 244 4923
## --------------------------------------------------------
## pf$gender: male
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 27 74 165 182 4917Who on average has more friends: men or women?
Response: Women
What’s the difference between the median friend count for women and men?
Response: 22
Why would the median be a better measure than the mean?
Response: Because it is not affected by outliers
Tenure
Notes:
qplot(x = tenure/365, data = pf, binwidth = .5)## Warning: Removed 2 rows containing non-finite values (stat_bin).
How would you create a histogram of tenure by year?
qplot(x = tenure/365, data = pf, binwidth = .5,
color = I('black'), fill = I('#099DD9')) +
scale_x_continuous(breaks = seq(1, 7, 1), limits = c(0,7))## Warning: Removed 26 rows containing non-finite values (stat_bin).
Labeling Plots
Notes:
qplot(x = tenure/365, data = pf, binwidth = .5,
xlab = 'Number of years using Facebook',
ylab = 'Number of users',
color = I('black'), fill = I('#099DD9')) +
scale_x_continuous(breaks = seq(1, 7, 1), limits = c(0,7))## Warning: Removed 26 rows containing non-finite values (stat_bin).
User Ages
Notes:
qplot(x = age, data = pf, binwidth = 1,
xlab = 'Years old',
ylab = 'Number of Users',
color = I('black'), fill = I('orange')) +
scale_x_continuous(breaks = seq(1, 113, 5), limits = c(0,113))
What do you notice?
Response: There are some spikes at ages over 100 which is unlikely
Transforming Data
Notes:
install.packages('gridExtra', repos = 'https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/gridExtra_2.2.1.tgz') ## Warning: unable to access index for repository https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/gridExtra_2.2.1.tgz/src/contrib:
## cannot download all files## Warning: package 'gridExtra' is not available (for R version 3.3.1)## Warning: unable to access index for repository https://cran.rstudio.com/bin/macosx/mavericks/contrib/3.3/gridExtra_2.2.1.tgz/bin/macosx/mavericks/contrib/3.3:
## cannot download all fileslibrary(gridExtra)Add a Scaling Layer
p1 <- ggplot(aes(x = friend_count), data = pf) + geom_histogram()
p2 <- p1 + scale_x_log10()
p3 <- p1 + scale_x_sqrt()
grid.arrange(p1, p2, p3, ncol = 1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1962 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Notes: (Another way to code it)
p1 <- qplot(x = friend_count, data = pf)
p2 <- qplot(x = friend_count, data = pf,
xlab = 'friend_count log10') +
scale_x_log10()
p3 <- qplot(x = friend_count, data = pf,
xlab = 'friend_count sqrt') +
scale_x_sqrt()
grid.arrange(p1, p2, p3, ncol = 1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1962 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Frequency Polygons
qplot(x = friend_count,
y = ..count../sum(..count..),
data = subset(pf, !is.na(gender)),
xlab = 'Friend Count',
ylab = 'Proportion of Users with that Friend Count',
binwidth = 10,
geom = 'freqpoly',
color = gender) +
scale_x_continuous(limits = c(0, 1000),
breaks = seq(0, 1000, 50))## Warning: Removed 2949 rows containing non-finite values (stat_bin).## Warning: Removed 4 rows containing missing values (geom_path).
Likes on the Web
Notes:
qplot(x = www_likes,
data = subset(pf, !is.na(gender)),
xlab = 'Likes',
ylab = 'Number',
geom = 'freqpoly',
color = gender) +
scale_x_continuous() +
scale_x_log10()## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 60935 rows containing non-finite values (stat_bin).
by(pf$www_likes, pf$gender, sum)## pf$gender: female
## [1] 3507665
## --------------------------------------------------------
## pf$gender: male
## [1] 1430175Box Plots
Notes:
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.
qplot(x = gender, y = friend_count,
data = subset(pf, !is.na(gender)),
geom = 'boxplot', ylim = c(1, 1000))## Warning: Removed 4911 rows containing non-finite values (stat_boxplot).
#qplot(x = gender, y = friend_count,
#data = subset(pf, !is.na(gender)),
#geom = 'boxplot') +
#scale_y_continuous(limits = c(0, 1000))
qplot(x = gender, y = friend_count,
data = subset(pf, !is.na(gender)),
geom = 'boxplot') +
coord_cartesian(ylim = c(0, 250))
Box Plots, Quartiles, and Friendships
Notes:
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)## pf$gender: female
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 37 96 242 244 4923
## --------------------------------------------------------
## pf$gender: male
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 27 74 165 182 4917On average, who initiated more friendships in our sample: men or women?
Response: Women
Write about some ways that you can verify your answer.
Response: Using the by function
by(pf$friendships_initiated, pf$gender, summary)## pf$gender: female
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 19.0 49.0 113.9 124.8 3654.0
## --------------------------------------------------------
## pf$gender: male
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 15.0 44.0 103.1 111.0 4144.0qplot(x= gender, y = friendships_initiated,
data = subset(pf, !is.na(gender)), geom = 'boxplot') +
coord_cartesian(ylim = c(0, 125))
Getting Logical
summary(pf$mobile_likes > 0)## Mode FALSE TRUE NA's
## logical 35056 63947 0pf$mobile_check_in <- ifelse(pf$mobile_likes > 0, 'Yes', 'No')
pf$mobile_check_in <- factor(pf$mobile_check_in)
summary(pf$mobile_check_in)## No Yes
## 35056 63947sum(pf$mobile_check_in == 'Yes')/length(pf$mobile_check_in) ## [1] 0.6459097Click KnitHTML to see all of your hard work and to have an html page of this lesson, your answers, and your notes!