1. Reading a file R can read a wide variety of input formats Text,
Statistical package formats (e.g., SAS)
DBMS

2. Reading a text file Delimited text file, such as CSV
Creates a data frame
Specify as required
Presence of header
Separator
Row names
It will not find this local file on your computer.
Mac
require(readr)
t <- read.csv("~/Dropbox/Carolina/Paper2/Fixed Encoding Data/changeBrasil.txt", stringsAsFactors=FALSE)
t <- read.csv("C:\\Dropbox\Carolina\\Paper2\\Fixed Encoding Data\\changeBrasil.txt", stringsAsFactors=FALSE) PC

3. Reading a text file Can read a file using a URL
t <- read.table(url, header=T, sep=',')

4. Learning about an object
Click on the name of the file in the top-right window to see its content
url <- "
t <- read.table(url, header=T, sep=',')
head(t) # first six rows
tail(t) # last six rows
dim(t) # dimension
str(t) # structure of a dataset
class(t) #type of object
Click on the blue icon of the file in the top-right window to see its structure

5. Referencing data datasetName$columName Column Data set
# Referencing your data
# Qualify with tablename to reference fields
mean(t$temperature)
sd(t$temperature)
max(t$year)
range(t$month)
Column
Data set

6. Creating a new column Formula to transform Fahrenheit to Celsius
# Creating a new column t$Ctemp <- round((t$temperature-32)*5/9,1) head(t)

7. Renaming a column and writing a file
# Renaming a column colnames(t)[3] <- 'Ftemp' # rename third column to indicate Fahrenheit head(t) # Save a file write.table(t,"centralparktempsCF.txt")
The file is stored in your default location (maybe documents or the folder where you save the script)

8. sqldf A R package for using SQL with data frames Returns a data frame
Supports MySQL

9. Subset and Sort Selecting rows Selecting columns
Selecting rows and columns
Sorting on column name
library(sqldf)
options(sqldf.driver = "SQLite") # to avoid a conflict with RMySQL
trowSQL <- sqldf("select * from t where year = 1999")
tcol <- t[,c(1:2,4)]
tcolSQL <- sqldf("select year, month, Ctemp from t”)
trowcolSQL <- sqldf("select year, month, Ctemp from t where year > 1989 and year < 2000")
sSQL <- sqldf("select * from t order by year desc, month")

10. Recoding Some analyses might be facilitated by the recoding of data
Split a continuous measure into two categories
t$Category <- 'Other’
head(t)
t$Category[t$Ftemp >= 30] <- 'Hot’

11. Deleting information on a column
Assign NULL
t$Category <- NA

12. Aggregate data Summarize data using a specified function
Compute the mean monthly temperature for each year
# Average F temperate for each year
a <- aggregate(t$Ftemp, by=list(t$year), FUN=mean)
# Name columns
colnames(a) = c('year', 'mean') a
sqldf("select year, avg(Ftemp) as mean from t group by year")

13. Exercise
Using sqldf
Compute the maximum temperature for year 2000

14. Compile a notebook A notebook is a report of an analysis
Interweaves R code and output
File > Compile Notebook …
Select html, pdf, or Word output
Install knitr before use
Install suggested packages

15. HTML

16. Resources R books Reference card Quick-R DataCamp
If you ever use R and get an error, DO NOT PANIC. Google your error and search for answers in StackOverFlow—they are usually very good!

17. Key points R is a platform for a wide variety of data analytics
Statistical analysis
Data visualization
HDFS and MapReduce
Text mining
Energy Informatics
R is a programming language
Much to learn