target="_blank" rel="nofollow" href="#fb3_img_img_68b40fa3-44c0-59a6-a390-b7d5aeb14660.jpg" alt="image"/> Note – Cold code in this section
While in the rest of the book, most code is “active” in this sense that the output that appears under a line or the plot that appears close to it are generated while the book was compiled, the code in this book is “cold”: the code is not executed. The reason is that the commands fromthis sectionwould produce long and irrelevant output. The listswould be long, because the author's computer has many packages installed, but also little relevant to you, because you have certainly a different configuration. Other commandswould even change packages as a side effect of compiling this book.
A first step in managing packages is knowing which packages can be updated.
# List all out-dated packages: old.packages()
Once we know which packages can be updated, we can execute this update:
# Update all available packages: update.packages()
If you are very certain that you want to update all packages at once, use the ask argument:
# Update all packages in batch mode: update.packages(ask = FALSE)
During an important project, you will want to update just one package to solve a bug and keep the rest what as they are in order to reduce the risk that code needs to rewritten and debugged while you are struggling to keep your deadline. Updating a package is done by the same function that is used to install packages.
# Update one package (example with the TTR package): install.packages(“TTR”)
4.8 Selected Data Interfaces
Most analysis will start with reading in data. This can be done from many types of electronic formats such as databases, spreadsheet, CSV files, fixed width text-files, etc.
Reading text from a file in a variable can be done by asking R to request the user to provide the file name as follows:
t <- readLines(file.choose())
file.choose()
or by providing the file name directly:
t <- readLines(“R.book.txt”)
readLines()
This will load the text of the file in one character string t. However, typically that is not exactly what we need. In order to manipulate data and numbers, it will be necessary to load data in a vector or data-frame for example.
In further sections – such as Chapter 15 “Connecting R to an SQL Database” on page 327 – we will provide more details about data-input. Below, we provide a short overview that certainly will come in handy.
4.8.1 CSV Files
For the example we have first downloaded the CSV file with currency exchange rates from http://www.ecb.europa.eu/stats/policy_and_exchange_rates/euro_reference:exchange_rates/html/index.en.html.4 This file is now on a local hard-drive and will be read in from there.5
CSV
import – csv
# To read a CSV-file it needs to be in the current directory # or we need to supply the full path. getwd() # show actual working directory setwd(“./data”) # change working directory data <- read.csv(“eurofxref-hist.csv”) is.data.frame(data) ncol(data) nrow(data) head(data) hist(data$CAD, col = ‘khaki3’) plot(data$USD, data$CAD, col = ‘red’)
In the aforementioned example, we have first copied the file to our local computer, but that is not necessary. The function read.csv() is able to read a file directly from the Internet.
Figure 4.4: The histogram of the CAD.
Figure 4.5: A scatter-plot of one variable with another.
Finding data
Once the data is loaded in R it is important to be able to make selections and further prepare the data.We will come back to this in much more detail in Part IV “DataWrangling” on page 335, but present here already some essentials.
# get the maximum exchange rate maxCAD <- max(data$CAD) # use SQL-like selection d0 <- subset(data, CAD == maxCAD) d1 <- subset(data, CAD > maxCAD - 0.1) d1[,1] ## [1] 2008-12-30 2008-12-29 2008-12-18 1999-02-03 ## [5] 1999-01-29 1999-01-28 1999-01-27 1999-01-26 ## [9] 1999-01-25 1999-01-22 1999-01-21 1999-01-20 ## [13] 1999-01-19 1999-01-18 1999-01-15 1999-01-14 ## [17] 1999-01-13 1999-01-12 1999-01-11 1999-01-08 ## [21] 1999-01-07 1999-01-06 1999-01-05 1999-01-04 ## 4718 Levels: 1999-01-04 1999-01-05 … 2017-06-05 d2<- data.frame(d1$Date,d1$CAD) d2 ## d1.Date d1.CAD ## 1 2008-12-30 1.7331 ## 2 2008-12-29 1.7408 ## 3 2008-12-18 1.7433 ## 4 1999-02-03 1.7151 ## 5 1999-01-29 1.7260 ## 6 1999-01-28 1.7374 ## 7 1999-01-27 1.7526 ## 8 1999-01-26 1.7609 ## 9 1999-01-25 1.7620 ## 10 1999-01-22 1.7515 ## 11 1999-01-21 1.7529 ## 12 1999-01-20 1.7626 ## 13 1999-01-19 1.7739 ## 14 1999-01-18 1.7717 ## 15 1999-01-15 1.7797 ## 16 1999-01-14 1.7707 ## 17 1999-01-13 1.8123 ## 18 1999-01-12 1.7392 ## 19 1999-01-11 1.7463 ## 20 1999-01-08 1.7643 ## 21 1999-01-07 1.7602 ## 22 1999-01-06 1.7711 ## 23 1999-01-05 1.7965 ## 24 1999-01-04 1.8004 hist(d2$d1.CAD, col = ‘khaki3’)
Writing to a CSV file
It is also possible to write data back into a file. Best is to use a structured format such as a CSV-file.
subset()
write.csv(d2, “output.csv”, row.names = FALSE) new.d2 <- read.csv(“output.csv”) print(new.d2) ## d1.Date d1.CAD ## 1 2008-12-30 1.7331
