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Intro to R - Ch 07.R
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Intro to R - Ch 07.R
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############################################################
# R script to accompany Intro to R for Business, Chapter 07#
# Written by Troy Adair #
############################################################
# Load the previously referenced data frame in "Yellow_Sample.RData"
load("Yellow_Sample.RData")
# Load "validate" package into active memory
if (!require("validate")) install.packages("validate")
library("validate")
# Let's examine Yellow_Sample to remind us what's in it...
# Trick to list column names, numbered and vertically
as.data.frame(colnames(Yellow_Sample))
str(Yellow_Sample)
summary(Yellow_Sample)
# Let's add a new variable with sequential IDs for each row
Yellow_Sample$id<-seq.int(nrow(Yellow_Sample))
# Now we'll check to see that the new "id" variable has been added...
str(Yellow_Sample$id)
# attach() will place the data frame name into the search path, meaning that
# for many functions we can dispense with using the whole data frame name.
attach(Yellow_Sample)
# Now we can actually start using the validate package.
# validator() will take desired rules as inputs...
v <- validator( trip_distance<100,
fare_amount>=0,
fare_amount<100000,
total_amount>0)
v
# What class/type is v?
class(v)
typeof(v)
# Now, we "confront()" our data with that set of rules, specifying our unique key
cf <- confront(Yellow_Sample,v,key="id")
cf
# Let's put the cf into a data frame object to see if we can see more about it...
out <- as.data.frame(cf)
View(out)
# If we merge this with the original data, will get >4M rows...why?
rm(out)
# So, alternate way to wind up with a tractable data set...
# (Note: we'll see a much better way to handle this with the dplyr() package later)
v1<-validator(trip_distance<100)
v1
cf1<-confront(Yellow_Sample,v1,key="id")
out1<-as.data.frame(cf1)
View(out1)
v2<-validator(fare_amount>=0)
v2
cf2<-confront(Yellow_Sample,v2,key="id")
out2<-as.data.frame(cf2)
# View(out2)
v3<-validator(fare_amount<100000)
v3
cf3<-confront(Yellow_Sample,v3,key="id")
out3<-as.data.frame(cf3)
# View(out3)
v4<-validator(total_amount>0)
v4
cf4<-confront(Yellow_Sample,v4,key="id")
out4<-as.data.frame(cf4)
# View(out4)
# Now, to keep each validation rule and result clear...
names(out1)
names(out1)[3]<-"TD.LT.100"
names(out1)
names(out2)[3]<-"FARE.GE.0"
names(out3)[3]<-"FARE.LT.100000"
names(out4)[3]<-"AMT.GT.0"
# Now to merge the results of the confrontation with Yellow_Sample
# We'll start with "out1"....
YT_Sample_Validated <- merge(out1,Yellow_Sample, by.x="id",by.y="id")
head(YT_Sample_Validated,1)
# " ... and we'll use "subset" to drop "name" and "expression"...
YT_Sample_Validated <- subset(YT_Sample_Validated,select=-c(name,expression))
head(YT_Sample_Validated,1)
# Next, merge "out2" into our (working) merged data frame, YT_Sample_Validated....
YT_Sample_Validated <- merge(out2,YT_Sample_Validated, by.x="id",by.y="id")
YT_Sample_Validated <- subset(YT_Sample_Validated,select=-c(name,expression))
# Next, merge "out3" into our (working) merged data frame, YT_Sample_Validated....
YT_Sample_Validated <- merge(out3,YT_Sample_Validated, by.x="id",by.y="id")
YT_Sample_Validated <- subset(YT_Sample_Validated,select=-c(name,expression))
# Finally, merge "out4" into our (working) merged data frame, YT_Sample_Validated....
YT_Sample_Validated <- merge(out4,YT_Sample_Validated, by.x="id",by.y="id")
YT_Sample_Validated <- subset(YT_Sample_Validated,select=-c(name,expression))
# And let's take a look at what it looks like:
str(YT_Sample_Validated)
View(YT_Sample_Validated)
# Finally, save our validated data set for the next module...
save(YT_Sample_Validated,file="YT_Sample_Validated.RData")
## Dealing with data that needs to be changed and deleted
# Clear out Console and Enviroment
rm(list=ls(all=TRUE))
cat("\014")
DF <- read.csv("PakistanSuicideAttacks Ver 11 (30-November-2017).csv")
class(DF)
typeof(DF)
View(DF)
# Put "DF" in path
attach(DF)
# Install and load tidyverse
if (!require("tidyverse")) install.packages("tidyverse")
library(tidyverse)
# Using pipes
head(select(DF,Date,Time))
DF %>% select(Date, Time) %>% head
# dplyr function distinct
DF_deduped <- distinct(DF,Date,Time,Latitude,Longitude,.keep_all=TRUE)
View(DF_deduped)
# distinct() using pipes...
DF_deduped <- DF %>% distinct(Date,Time,Latitude,Longitude,.keep_all=TRUE)
View(DF_deduped)
# Using mutate() to do find and replace "N/A" with NA
DF_replaced <- mutate(DF_deduped,Time=replace(Time,Time=="N/A",NA))
View(DF_replaced)
# Replacing multiple variables/instances
DF_replaced <- mutate(DF_deduped,Time=replace(Time,Time=="N/A",NA),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="shiite","Shiite"),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="sunni","Sunni"),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="None",NA),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="",NA))
View(DF_replaced)
# So now pipes start to make sense
DF_replaced <- DF %>% distinct(Date,Time,Latitude,Longitude,.keep_all=TRUE) %>%
mutate(Time=replace(Time,Time=="N/A",NA),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="shiite","Shiite"),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="sunni","Sunni"),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="None",NA),
Targeted.Sect.if.any=replace(Targeted.Sect.if.any,Targeted.Sect.if.any=="",NA))
View(DF_replaced)
# One way to capitalize is override regular expression usage and pass a Perl expression...
DF_capped <- DF_replaced %>%
mutate(City = sub("(.)", "\\U\\1", City, perl=TRUE))
View(DF_capped)
# But we are using a program that allows us to cherry-pick functions from different packages
if (!require("R.utils")) install.packages("R.utils")
library(R.utils)
DF_capped <- DF_replaced %>% mutate(City = capitalize(City))
View(DF_capped)
# Cleaning up Date
# Will use separate() function from tidyr
# Split the Day of the Week and the Date
DF1 <- DF_capped %>% separate(col=Date,into=c("Day of Week", "Date"),"-",extra="merge")
View(DF1)
#Extract the Year
DF2 <- DF1 %>% mutate(Year=substr(Date, nchar(Date)-3, nchar(Date)),Date=substr(Date,1,nchar(Date)-5))
View(DF2)
# Standardize the "Month-Day" portion by substituting blanks for dashes
DF2$Date <- gsub("-", " ", DF2$Date)
View(DF2)
# Split the Month and the Day
DF3 <- DF2 %>% separate(col=Date,into=c("Month", "Day")," ",extra="merge")
View(DF3)
# Replace alpha Month with Month Number
DF4 <- DF3 %>% mutate(Month=replace(Month,Month=="Jan",1),
Month=replace(Month,Month=="Feb",2),
Month=replace(Month,Month=="Mar",3),
Month=replace(Month,Month=="Apr",4),
Month=replace(Month,Month=="May",5),
Month=replace(Month,Month=="Jun",6),
Month=replace(Month,Month=="Jul",7),
Month=replace(Month,Month=="Aug",8),
Month=replace(Month,Month=="Sep",9),
Month=replace(Month,Month=="Oct",10),
Month=replace(Month,Month=="Nov",11),
Month=replace(Month,Month=="Dec",12),
Month=replace(Month,Month=="January",1),
Month=replace(Month,Month=="February",2),
Month=replace(Month,Month=="March",3),
Month=replace(Month,Month=="April",4),
Month=replace(Month,Month=="May",5),
Month=replace(Month,Month=="June",6),
Month=replace(Month,Month=="July",7),
Month=replace(Month,Month=="August",8),
Month=replace(Month,Month=="September",9),
Month=replace(Month,Month=="October",10),
Month=replace(Month,Month=="November",11),
Month=replace(Month,Month=="December",12))
View(DF4)
# Re-order the columns to get the Date fields back together and to re-title some of the titles
DF5 = DF4 %>% select(S., "Day of Week", Day, Month, Year, Islamic.Date, Blast.Day.Type, Holiday.Type,
Time, City, Latitude, Longitude, Province, Location, Location.Category,
Location.Sensitivity,Open.Closed.Space, Influencing.Event.Event,
Target.Type, "Target.Sect.If.Any"=Targeted.Sect.if.any, Killed.Min, Killed.Max,
Injured.Min, Injured.Max, "No.of.Suicide.Blasts"=No..of.Suicide.Blasts,
"Explosive.Weight.Max"=Explosive.Weight..max.,Hospital.Names,
"Temperature.C"=Temperature.C., "Temperature.F"=Temperature.F.)
View(DF5)
# Let's convert the Month, Day, and Year into numeric variables...
DF6 <- DF5 %>% mutate_at(c(3,4,5), as.numeric)
# Using pipes to do most of the last bit at once...
DF_cleaned <- DF2 %>% separate(col=Date,into=c("Month", "Day")," ",extra="merge") %>%
mutate(Month=replace(Month,Month=="Jan",1),
Month=replace(Month,Month=="Feb",2),
Month=replace(Month,Month=="Mar",3),
Month=replace(Month,Month=="Apr",4),
Month=replace(Month,Month=="May",5),
Month=replace(Month,Month=="Jun",6),
Month=replace(Month,Month=="Jul",7),
Month=replace(Month,Month=="Aug",8),
Month=replace(Month,Month=="Sep",9),
Month=replace(Month,Month=="Oct",10),
Month=replace(Month,Month=="Nov",11),
Month=replace(Month,Month=="Dec",12),
Month=replace(Month,Month=="January",1),
Month=replace(Month,Month=="February",2),
Month=replace(Month,Month=="March",3),
Month=replace(Month,Month=="April",4),
Month=replace(Month,Month=="May",5),
Month=replace(Month,Month=="June",6),
Month=replace(Month,Month=="July",7),
Month=replace(Month,Month=="August",8),
Month=replace(Month,Month=="September",9),
Month=replace(Month,Month=="October",10),
Month=replace(Month,Month=="November",11),
Month=replace(Month,Month=="December",12)) %>%
select(S., "Day of Week", Day, Month, Year, Islamic.Date, Blast.Day.Type, Holiday.Type,
Time, City, Latitude, Longitude, Province, Location, Location.Category,
Location.Sensitivity,Open.Closed.Space, Influencing.Event.Event,
Target.Type, "Target.Sect.If.Any"=Targeted.Sect.if.any, Killed.Min, Killed.Max,
Injured.Min, Injured.Max, "No.of.Suicide.Blasts"=No..of.Suicide.Blasts,
"Explosive.Weight.Max"=Explosive.Weight..max.,Hospital.Names,
"Temperature.C"=Temperature.C., "Temperature.F"=Temperature.F.) %>%
mutate_at(c(3,4,5), as.numeric)
View(DF_cleaned)
# Using filter()to subset
DF_holidays <- DF_cleaned %>% filter(Blast.Day.Type=="Holiday")
View(DF_holidays)
# Using arrange()to sort
DF_sorted <- DF_cleaned %>% arrange(Target.Type,desc(Injured.Max))
View(DF_sorted)
# To convert Injured.Max to numeric, use mutate_at()...
str(DF_cleaned$Injured.Max)
DF_cleaned <- DF_cleaned %>% mutate_at(vars(Injured.Max), as.integer)
str(DF_cleaned$Injured.Max)
# Rerunning the sort...
DF_sorted <- DF_cleaned %>% arrange(Target.Type,desc(Injured.Max))
View(DF_sorted)
# Using summarise()
DF_cleaned %>% group_by(Target.Type) %>%
summarise(mean=mean(Injured.Max), n=n())
# Using sample_n() and sample_frac()
SampleN <- sample_n(DF_cleaned, 20)
View(SampleN)
SampleP <- sample_frac(DF_cleaned, .1)
View(SampleP)