1. Scatterplot
#SCATTERPLOT: USEFUL FOR PLOTTING RELATIONSHIPS BETWEEN TWO NUMERIC VARIABLES
library(ggvis)
library(DBI)
require(RMySQL)
# set a driver
m<- dbDriver("MySQL")
# connect to the database
conn <- dbConnect(m,user='student',password='student',host='wallaby.terry.uga.edu',dbname='ClassicModels')
# if error "in .local(drv, ...): cannot allocate a new connection: 16 connections already opened" appears loop through the connections and delete them. If there is no problem move on to query the database.
cons<- dbListConnections(MySQL())
for(con in cons)
dbDisconnect(con)
# Get the monthly total revenue of orders (or total revenue by month)
e <- dbGetQuery(conn,"SELECT MONTH(orderDate) AS orderMonth, SUM(quantityOrdered*priceEach) AS totalOrderRevenue FROM Orders, OrderDetails WHERE Orders.orderNumber = OrderDetails.orderNumber GROUP BY orderMonth;")
head(e)
# Plot data orders by month# Show the points and the line
e %>% ggvis(~orderMonth, ~totalOrderRevenue/ ) %>% layer_lines(stroke:='blue') %>%layer_points(fill:='red') %>%add_axis('x', title = 'Month') %>%add_axis('y',title='Total Revenue (Millions)', title_offset=30)

2. Scatterplot

3. Scatterplot # Get total revenue of orders by month and year.
f <- dbGetQuery(conn,"SELECT YEAR(orderDate) AS orderYear, MONTH(orderDate) AS orderMonth, SUM((quantityOrdered*priceEach)) AS totalRevenue FROM Orders, OrderDetails
WHERE Orders.orderNumber = OrderDetails.orderNumber GROUP BY orderYear, orderMonth;")
head(f)
# ggvis expects grouping variables to be a factor, so convert
f$orderYear <- as.factor(f$orderYear)
# Plot total order revenue by month and display by year
f %>% group_by(orderYear) %>% ggvis(~orderMonth,~totalRevenue/1000, stroke = ~orderYear) %>% layer_lines() %>% add_axis('x', title = 'Month') %>% add_axis('y',title=‘Total Revenue (Thousands)', title_offset=50)

4. Scatterplot

5. Exercise
Using the Text database, write a query to calculate total revenue for sold items by sale number. Plot the results of your query using ggvis as a scatterplot.

6. Multiple files library(sqldf)
options(sqldf.driver = "SQLite") # to avoid conflict with RMySQL
# Get total revenue of order by month for year 2004
orders <- dbGetQuery(conn,"SELECT 'Orders' as category, MONTH(orderDate) AS month, SUM((quantityOrdered*priceEach)) AS totalValue FROM Orders, OrderDetails WHERE Orders.orderNumber = OrderDetails.orderNumber and YEAR(orderDate) = 2004 GROUP BY month;”)
head(orders)
# Get total payment amount by month for year 2004
payments <- dbGetQuery(conn,"SELECT 'Payments' as category, MONTH(paymentDate) AS month, SUM(amount) AS value FROM Payments WHERE YEAR(paymentDate) = 2004 GROUP BY month;”)
head(payments)
# Concatenate the two files
g <- sqldf(”SELECT month, category, totalValue FROM orders UNION SELECT month, category, totalValue FROM payments”)
head(g)
# Plot total value of payment and total order revenue by month for year 2004
g %>% group_by(category) %>% ggvis(~month, ~totalValue, stroke = ~ category) %>%
layer_lines() %>%
add_axis('x',title='Month') %>%
add_axis('y',title=’Total Value',title_offset=70)

7. Multiple files

8. Smoothing # SMOOTHING: HELPS DETECT A TREND IN A LINE PLOT
url <- "
t <- read.table(url, header=T, sep=',')
head(t)
t11 <- sqldf(’SELECT * FROM t where month = 11')
t11 %>% ggvis(~year,~temperature) %>% layer_lines(stroke:='red') %>% layer_smooths(se=T, stroke:='blue') %>% add_axis('x',title='Year', format = '####') %>% add_axis('y',title='Temperature (F)', title_offset=50)

9. Smoothing

10. Box plot
# BOXPLOTS: EFFECTIVE MEANS OF DISPLAYING INFORMATION ABOUT ONE OR MORE VARIABLES
i <- dbGetQuery(conn,"SELECT amount FROM Payments;")
# Boxplot of payment amount
i %>% ggvis(~factor(0),~amount) %>% layer_boxplots() %>% add_axis('x',title='Checks') %>% add_axis('y',title='')

11. Box plot
k <- dbGetQuery(conn,"SELECT month(paymentDate) as month, amount FROM Payments WHERE year(paymentDate) = 2004;")
head(k)
# Boxplot of amounts paid per month in 2004
k %>% ggvis(~factor(month),~amount) %>% layer_boxplots() %>% add_axis('x',title='Month in 2004') %>% add_axis('y',title='Check Amount', title_offset=60)

12. Exercise
Using the ClassicModels database, write a query to report order revenue and month order date for year Plot the results of your query using ggvis as a boxplot.

13. Geographic data
ggmap supports multiple mapping systems, including Google maps
library(ggplot2)
library(ggmap)
library(mapproj)
library(DBI)
# Google maps requires lon and lat, in that order, to create markers
d <- dbGetQuery(conn,"SELECT y(officeLocation) AS lon, x(officeLocation) AS lat FROM Offices;")
# show offices in the United States
# vary zoom to change the size of the map
map <- get_googlemap('united states',marker=d,zoom=4)
ggmap(map) + labs(x = 'Longitude', y = 'Latitude') + ggtitle('US offices')

14. Map

15. John Snow 1854 Broad Street cholera map
Water pump

16. Cholera map (now Broadwick Street)
library(ggplot2)
library(ggmap)
library(mapproj)
library(readr)
url <- '
pumps <- read_delim(url, delim=',')
url <- '
deaths <- read_delim(url, delim=',')
map <- get_googlemap('broadwick street, london, united kingdom',markers=pumps,zoom=15)
ggmap(map) + labs(x = 'Longitude', y = 'Latitude') + ggtitle('Pumps and deaths') +
geom_point(aes(x=longitude,y=latitude,size=count),color='blue',data=deaths) +
xlim(-.14,-.13) + ylim(51.51,51.516)

17. Key points ggvis is based on a grammar of graphics
Very powerful and logical
Supports interactive graphics
You can visualize the results of SQL queries using R
The combination of MySQL and R provides a strong platform for data reporting