r语言-data-preprocessing.rar

library(xlsx) ## Loading required package: rJava ## Loading required package: xlsxjars setwd("/Users/chenfeiyang") cameraData <- read.xlsx("./data/cameras.xlsx", sheetIndex = 1, header = TRUE) cameraData <- read.xlsx("./data/cameras.xlsx", "Baltimore Fixed Speed Cameras", header = TRUE) head(cameraData) ## address direction street crossStreet ## 1 S CATON AVE & BENSON AVE N/B Caton Ave Benson Ave ## 2 S CATON AVE & BENSON AVE S/B Caton Ave Benson Ave ## 3 WILKENS AVE & PINE HEIGHTS AVE E/B Wilkens Ave Pine Heights ## 4 THE ALAMEDA & E 33RD ST S/B The Alameda 33rd St ## 5 E 33RD ST & THE ALAMEDA E/B E 33rd The Alameda ## 6 ERDMAN AVE & N MACON ST E/B Erdman Macon St ## intersection Location.1 ## 1 Caton Ave & Benson Ave (39.2693779962, -76.6688185297) ## 2 Caton Ave & Benson Ave (39.2693157898, -76.6689698176) ## 3 Wilkens Ave & Pine Heights (39.2720252302, -76.676960806) ## 4 The Alameda & 33rd St (39.3285013141, -76.5953545714) ## 5 E 33rd & The Alameda (39.3283410623, -76.5953594625) ## 6 Erdman & Macon St (39.3068045671, -76.5593167803) # Read specific rows and columns in Excel colIndex <- 2:3 rowIndex <- 1:4 cameraDataSubset <- read.xlsx("./data/cameras.xlsx", sheetIndex = 1, colIndex = colIndex, rowIndex = rowIndex) cameraDataSubset ## direction street ## 1 N/B Caton Ave ## 2 S/B Caton Ave ## 3 E/B Wilkens Ave # Subsetting - quick review set.seed(13435) X <- data.frame(var1 = sample(1:5), var2 = sample(6:10), var3 = sample(11:15)) X <- X[sample(1:5), ] X$var2[c(1, 3)] = NA X ## var1 var2 var3 ## 1 2 NA 15 ## 4 1 10 11 ## 2 3 NA 12 ## 3 5 6 14 ## 5 4 9 13 X[, 1] ## [1] 2 1 3 5 4 X[, "var1"] ## [1] 2 1 3 5 4 X[1:2, "var2"] ## [1] NA 10 # Logicals and: & , or: | X[(X$var1 <= 3 & X$var3 > 11), ] ## var1 var2 var3 ## 1 2 NA 15 ## 2 3 NA 12 X[(X$var1 <= 3 | X$var3 > 15), ] ## var1 var2 var3 ## 1 2 NA 15 ## 4 1 10 11 ## 2 3 NA 12 ## Dealing with missing values X[which(X$var2 > 8), ] ## var1 var2 var3 ## 4 1 10 11 ## 5 4 9 13 # Sorting sort(X$var1) ## [1] 1 2 3 4 5 sort(X$var1, decreasing = TRUE) ## [1] 5 4 3 2 1 sort(X$var2, na.last = TRUE) ## [1] 6 9 10 NA NA # Ordering X[order(X$var1), ] ## var1 var2 var3 ## 4 1 10 11 ## 1 2 NA 15 ## 2 3 NA 12 ## 5 4 9 13 ## 3 5 6 14 X[order(X$var1, X$var3), ] ## var1 var2 var3 ## 4 1 10 11 ## 1 2 NA 15 ## 2 3 NA 12 ## 5 4 9 13 ## 3 5 6 14 ## Sort using the arrange function of the plyr package library(plyr) arrange(X, var1) ## var1 var2 var3 ## 1 1 10 11 ## 2 2 NA 15 ## 3 3 NA 12 ## 4 4 9 13 ## 5 5 6 14 arrange(X, desc(var1)) ## var1 var2 var3 ## 1 5 6 14 ## 2 4 9 13 ## 3 3 NA 12 ## 4 2 NA 15 ## 5 1 10 11 # Add row and column X$var4 <- rnorm(5) X ## var1 var2 var3 var4 ## 1 2 NA 15 0.18760 ## 4 1 10 11 1.78698 ## 2 3 NA 12 0.49669 ## 3 5 6 14 0.06318 ## 5 4 9 13 -0.53613 Y <- cbind(X, rnorm(5)) Y ## var1 var2 var3 var4 rnorm(5) ## 1 2 NA 15 0.18760 0.62578 ## 4 1 10 11 1.78698 -2.45084 ## 2 3 NA 12 0.49669 0.08909 ## 3 5 6 14 0.06318 0.47839 ## 5 4 9 13 -0.53613 1.00053