Pittsburgh Bridges

What this code does

• Load Pittsburgh bridges data from UCI repository into an R data frame.
  
• Select columns related to rivers, lanes, and lengths.
  
• Change code from River = “A” to River = “Allegheny”. In the photo above, the Allegheny is the green colored river feeding into the Ohio; the rust-colored river is the Monongahela.

Very bare bones code, but a reasonable way to start. Other work that should be done: rename column headers; change codes for other rivers from “O” to “Ohio” and “M” to “Monongahela”, etc.

#install.packages("RCurl")

library(RCurl)

## Loading required package: bitops

url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/bridges/bridges.data.version2"

bridge_data <-getURL(url)
#getwd()
bridge_df <- read.csv(text=bridge_data,header=F,sep=",", stringsAsFactors = FALSE)
#bridge_df

head(bridge_df)

##   V1 V2 V3     V4       V5     V6 V7 V8      V9  V10    V11 V12  V13
## 1 E1  M  3 CRAFTS  HIGHWAY      ?  2  N THROUGH WOOD  SHORT   S WOOD
## 2 E2  A 25 CRAFTS  HIGHWAY MEDIUM  2  N THROUGH WOOD  SHORT   S WOOD
## 3 E3  A 39 CRAFTS AQUEDUCT      ?  1  N THROUGH WOOD      ?   S WOOD
## 4 E5  A 29 CRAFTS  HIGHWAY MEDIUM  2  N THROUGH WOOD  SHORT   S WOOD
## 5 E6  M 23 CRAFTS  HIGHWAY      ?  2  N THROUGH WOOD      ?   S WOOD
## 6 E7  A 27 CRAFTS  HIGHWAY  SHORT  2  N THROUGH WOOD MEDIUM   S WOOD

bridge_df <- bridge_df[,c(2,6,7)]

#View(bridge_df)

# Three rivers are: Allegheny, Monongahela, Ohio
# Note that to get this code to work, we needed to go back to our read.csv, 
# and add stringsAsFactors = FALSE

bridge_df$V2[bridge_df$V2=="A"] <- "Allegheny"

#View(bridge_df)

str(bridge_df)

## 'data.frame':    108 obs. of  3 variables:
##  $ V2: chr  "M" "Allegheny" "Allegheny" "Allegheny" ...
##  $ V6: chr  "?" "MEDIUM" "?" "MEDIUM" ...
##  $ V7: chr  "2" "2" "1" "2" ...