Editing text variables

https://data.baltimorecity.gov/Transportation/Baltimore-Fixed-Speed-Cameras/dz54-2aru

if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://data.baltimorecity.gov/api/views/dz54-2aru/rows.csv?accessType=DOWNLOAD"
download.file(fileUrl,destfile="./data/cameras.csv",method="curl")
cameraData <- read.csv("./data/cameras.csv")
names(cameraData)

[1] "address"      "direction"    "street"       "crossStreet"  "intersection" "Location.1"  

tolower(names(cameraData))

[1] "address"      "direction"    "street"       "crossstreet"  "intersection" "location.1"  

• Good for automatically splitting variable names
• Important parameters: x, split

splitNames = strsplit(names(cameraData),"\\.")
splitNames[[5]]

[1] "intersection"

splitNames[[6]]

[1] "Location" "1"       

mylist <- list(letters = c("A", "b", "c"), numbers = 1:3, matrix(1:25, ncol = 5))
head(mylist)

$letters
[1] "A" "b" "c"

$numbers
[1] 1 2 3

[[3]]
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

http://www.biostat.jhsph.edu/~ajaffe/lec_winterR/Lecture%203.pdf

mylist[1]

$letters
[1] "A" "b" "c"

mylist$letters

[1] "A" "b" "c"

mylist[[1]]

[1] "A" "b" "c"

http://www.biostat.jhsph.edu/~ajaffe/lec_winterR/Lecture%203.pdf

• Applies a function to each element in a vector or list
• Important parameters: X,FUN

splitNames[[6]][1]

[1] "Location"

firstElement <- function(x){x[1]}
sapply(splitNames,firstElement)

[1] "address"      "direction"    "street"       "crossStreet"  "intersection" "Location"    

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026895

fileUrl1 <- "https://dl.dropboxusercontent.com/u/7710864/data/reviews-apr29.csv"
fileUrl2 <- "https://dl.dropboxusercontent.com/u/7710864/data/solutions-apr29.csv"
download.file(fileUrl1,destfile="./data/reviews.csv",method="curl")
download.file(fileUrl2,destfile="./data/solutions.csv",method="curl")
reviews <- read.csv("./data/reviews.csv"); solutions <- read.csv("./data/solutions.csv")
head(reviews,2)

  id solution_id reviewer_id      start       stop time_left accept
1  1           3          27 1304095698 1304095758      1754      1
2  2           4          22 1304095188 1304095206      2306      1

head(solutions,2)

  id problem_id subject_id      start       stop time_left answer
1  1        156         29 1304095119 1304095169      2343      B
2  2        269         25 1304095119 1304095183      2329      C

• Important parameters: pattern, replacement, x

names(reviews)

[1] "id"          "solution_id" "reviewer_id" "start"       "stop"        "time_left"  
[7] "accept"     

sub("_","",names(reviews),)

[1] "id"         "solutionid" "reviewerid" "start"      "stop"       "timeleft"   "accept"    

testName <- "this_is_a_test"
sub("_","",testName)

[1] "thisis_a_test"

gsub("_","",testName)

[1] "thisisatest"

grep("Alameda",cameraData$intersection)

[1]  4  5 36

table(grepl("Alameda",cameraData$intersection))

FALSE  TRUE 
   77     3 

cameraData2 <- cameraData[!grepl("Alameda",cameraData$intersection),]

grep("Alameda",cameraData$intersection,value=TRUE)

[1] "The Alameda  & 33rd St"   "E 33rd  & The Alameda"    "Harford \n & The Alameda"

grep("JeffStreet",cameraData$intersection)

integer(0)

length(grep("JeffStreet",cameraData$intersection))

[1] 0

http://www.biostat.jhsph.edu/~ajaffe/lec_winterR/Lecture%203.pdf

library(stringr)
nchar("Jeffrey Leek")

[1] 12

substr("Jeffrey Leek",1,7)

[1] "Jeffrey"

paste("Jeffrey","Leek")

[1] "Jeffrey Leek"

paste0("Jeffrey","Leek")

[1] "JeffreyLeek"

str_trim("Jeff      ")

[1] "Jeff"

• Names of variables should be
  • All lower case when possible
  • Descriptive (Diagnosis versus Dx)
  • Not duplicated
  • Not have underscores or dots or white spaces
• Variables with character values
  • Should usually be made into factor variables (depends on application)
  • Should be descriptive (use TRUE/FALSE instead of 0/1 and Male/Female versus 0/1 or M/F)