Summarizing

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")

View data to make sure there is at least something.

Check size in bytes.

object.size(data)

print(object.size(data), units="Mb")

View data in table.

View(data)

View first few rows.

head(data, n=3)

View last few rows.

tail(data, n=3)

Get summary for each attribute.

summary(data)

Return types and more info about data.

str(data)

See quantile of values in specific column.

quantile(data$councilDistrict, na.rm=TRUE)

Get quantile for different percentiles.

quantile(data$councilDistrict, probs = c(0.5, 0.75, 0.9))

Create table of values from a column. Option ifany will enable the table to show missing values.

table(data$zipCode, useNA = "ifany")

Make two dimensional table.

table(data$councilDistrict, data$zipCode)

Check for missing values. The following command returns number of NA values.

sum(is.na(data$zipCode))

The same as above but this returns true or false.

any(is.na(data$zipCode))

Take all values and check if all fulfil a condition.

all(data$zipCode < 0)

Check all columns and get count of NA values for each column.

colSums(is.na(data))

Covert the command above to single command that returns true or false instead of counts.

all(colSums(is.na(data)) == 0)

table(data$zipCode %in% c("21212"))

Find all values that full fill a condition.

table(data$zipCode %in% c("21212"))

The same as above but with multiple values. There is OR condition between the values in the condition.

table(data$zipCode %in% c("21212", "21213"))

Filter out specific rows based on a condition and return values (not just numbers/counts).

data[data$zipCode %in% c("21212", "21213"),]

Cross tabs. The following example is nonsense. But it will break down the data by policeDistrict and counsilDistrict and create sums from zipCodes values.

xtabs = xtabs(zipCode ~ policeDistrict + councilDistrict, data=data)

-- to make data compact and easier to view... if possible.
ftable(xtabs)