year <-  c(2007, 2008, 2009, 2010, 2011, 2012)
sqfeet <-  c(14214216, 14359041, 14752886, 15341886, 15573100, 15740742)
kwh <-  c(2141705, 2108088, 2150841, 2211414, 2187164, 2057364)
kwhpersqft <-  kwh/sqfeet
plot(year,kwhpersqft)

m <-  matrix(1:18, nrow=6,ncol=3)

library(measurement)
conv_unit(100,'F','C')
conv_unit(100,'m','ft')

# Create a new column with the temperature in Celsius
library(measurement)
url <- "http://people.terry.uga.edu/rwatson/data/centralparktemps.txt"
t <- read.table(url, header=T, sep=',')
# compute Celsius
t$Ctemp = round(conv_unit(t$temperature,'F','C'),1)

• Select all the data on the page and copy it.
• In RStudio
  • File > New File > Text File
  • Paste the data
  • Remove descriptive information
  • Edit headings so they occupy a single row (e.g., Cement_Production)
  • Remove any remaining blank rows
  • Save the file as CO2.txt
  • Import Dataset > From Local File > CO2.txt
  • plot(CO2$Year,CO2$Total)
  • Save the file for future use
    • write_csv(CO2,"CO2_yearly_emissions.csv")

The saved file is available.

library(dplyr)
library(lubridate)
library(DBI)
conn <- dbConnect(RMySQL::MySQL(), "richardtwatson.com", dbname="Weather", user="db2", password="student")
# Query the database and create file t for use with R
t <- dbGetQuery(conn,"select * from record;")
t$year <- year(t$timestamp)
t$month <- month(t$timestamp)
t$hour <- hour(t$timestamp)
head(t)

#  Compute the average temperature at 5pm in August
t %>% filter(hour==17 & month==8) %>% summarize(mean=mean(airTemp))

#  Compute the maximum temperature for each day in August 
t$day <- day(t$timestamp)
t %>% filter(month==8) %>% group_by(day) %>% summarize(max=max(airTemp))