This is a report showing the distribution of the square footage of enclosed vs. unenclosed sidewalk cafes. It is based on the latest release of data by NYC at the time of the report generation. This report was generated on Mon Apr 20 15:30:09 2026

library(readr)
sidewalk <- read_csv("mydata/Sidewalk_Cafes.csv")
## Rows: 1008 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Entity.Type, Sidewalk.Cafe.Type, Entity.Name, Camis.Trade.Name, Add...
## dbl (4): License.Number, Lic.Area.Sq.Ft, Address.Zip.Code, Camis.Phone.Number
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

The data was split into two data frames containing the enclosed and unenclosed data using the following code:

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
enclosed <- filter(sidewalk,sidewalk$Sidewalk.Cafe.Type == "Enclosed")
unenclosed <- filter(sidewalk,sidewalk$Sidewalk.Cafe.Type == "Unenclosed")

Note, the use of the filter function from the dplyr package. It works on tibbles and is identical to the subset function

boxplot(enclosed$Lic.Area.Sq.Ft,unenclosed$Lic.Area.Sq.Ft, names=c("Enclosed","Unenclosed"),main="NYC Sidewalk Cafe Size")