New Variables

We might want to create new variables because we want to

• add extra value for missing values, e.g. NA

• create quantitavive values, for example from numbers

• do transformation

Get some data to summarize

We will use data from the following page: https://data.baltimorecity.gov/Culture-Arts/Restaurants/k5ry-ef3g

Run this code in order to download sample data for this tutorial.

if (!file.exists("./data")) { dir.create("./data") }
url <- "https://data.baltimorecity.gov/api/views/k5ry-ef3g/rows.csv?accessType=DOWNLOAD"

download.file(url, destfile="./data/restaurants.csv", method="curl")

data <- read.csv("./data/restaurants.csv")

How to create a sequence

Sometimes, we might need to create a sequence.

> seq(1, 10, by=2)

[1] 1 3 5 7 9

> seq(1, 10, length=3)

[1]  1.0  5.5 10.0

Create new column by subsetting.

> data$nearMe = data$neighborhood %in% c("Roland Park", "Homeland")

> table(data$nearMe)

FALSE  TRUE
 1314    13

Create binary column

> data$wrongZip = ifelse(data$zipCode < 0, TRUE, FALSE)

> table(data$wrongZip)

FALSE  TRUE
 1326     1

Create categorical values

The following code will create categorize zipCode values into percentile 0-25, 25-50, 50-75 and 75-100.

data$zipGroups = cut(data$zipCode,breaks=quantile(data$zipCode))

table(data$zipGroups)
table(data$zipGroups, data$zipCode)

Easier way to do that using Hmisc package.

install.packages("Hmisc")
library(Hmisc)

data$zipGroups = cut2(data$zipCode, g=4)

table(data$zipGroups)

Create factor values

zipCode is integer and we might want to turn it into factor type.

> data$zipCodeFactor <- factor(data$zipCode)

> class(data$zipCodeFactor)
[1] "factor"

Levels of factor variables

> options <- sample(c("yes", "no"), size=10, replace=TRUE)
> options
 [1] "yes" "no"  "no"  "yes" "yes" "no"  "yes" "no"  "yes" "no"

> optionsFactor = factor(options, levels=c("yes", "no"))
> optionsFactor
 [1] yes no  no  yes yes no  yes no  yes no
Levels: yes no

relevel(optionsFactor, ref="yes")

as.numeric(optionsFactor)

Mutate function

Mutate function can be used to add variable to a new table.

install.packages("Hmisc")
library(Hmisc)
install.packages("plyr")
library(plyr)

newData = mutate(data, zipGroups=cut2(zipCode, g=4))

View(newData)

Transformations

# absolute value
abs(x)

# square root
sqrt(x)

# ceiling(3.4) is 4
ceiling(x)

# floor(3.4) is 3
floor(x)

# rounding
round(x, digits=n)

# signif(3.475, digits=2) is 3.5
signif(x, digits=n)

cos(x), sin(x) # etc.

# natural logarithm
log(x)

# other logaritmhs
log2(x), log10(x)

# exponentiating x
exp(x)

More here functions here: http://www.statmethods.net/management/functions.html