Manipulating Data using base R package
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This document discusses various methods for subsetting and manipulating data in R, including: - Using the subset function to extract rows from a data frame based on logical expressions involving column names. - Logical subsetting using comparison operators like ==, | (OR), & (AND) to extract rows meeting certain criteria. - Selecting and removing specific columns from a data frame. - Using the which() function to identify row positions meeting a condition and subset based on the indices. - Sorting data using the order() function. - Calculating aggregates like means using the aggregate() function across groups defined by other variables. - Creating contingency tables using the table() and xt
![ Sub-setting of data is a very important component of data management
where the subset function helps us to pull out rows from the data frame
based on a logical expression using the column names.
#load the pre-set R sample data
>data(mtcars)
#sub-setting 4first rows and columns in the format [rows, columns]
>mtcars[1:4,1:3]
#sub-setting specific list of rows and columns in the format [rows, columns]
>mtcars[c(2,4,6), c(1,3,6)]
Sub-setting: rows
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![#using == operator: ( == )
>mtcars_93<-mtcars[mtcars$hp == 93, ] #with comma i.e. [row,column] format
>mtcars_equal93<-mtcars[mtcars$hp == 93] #without comma it will give the
results of columns ==93 which is not accurate
#using OR operator: ( | )
>mtcars_or<-mtcars[mtcars$hp==93 | mtcars$carb==1,]
>View(mtcars_or)
#using AND operator: (&)
> mtcars_and<-mtcars[ mtcars$mpg >= 20 & mtcars$hp <= 110,]
>View(mtcars_and)
Logical sub-setting
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![#selecting columns
>mtcars_col<-mtcars[ , c("mpg","cyl","hp")]
>View(mtcars_col)
#logical row sub-setting + sub-setting of columns
>mtcars_rc<-mtcars[mtcars$mpg >= 20 & mtcars$hp <= 110, c("wt","disp")]
>View(mtcars_rc)
#adding new columns where “add” is the new column name
>mtcars$add<-ifelse(mtcars$mpg<=15,"luxury",ifelse(mtcars$mpg<=
20,"sports","economy"))
>View(mtcars)
#remove a column
>mtcars$add<-NULL
Sub-setting: columns
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![#load the data
>sampledata<-read.csv(file.choose(),na.strings = c("NA",""),header = TRUE)
#identify the positions using “which” operator
>index_position<-which(sampledata$number.of.doors==“two”)
“Which” operator returns only the indices i.e. the positions of the logical
condition if true.
>head(index_position)
#Sub-setting the data using the indices
>newdata<-mtcars[index_position, ]
>View(newdata)
Manipulating Data: which()
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![#sort the data using order operator
>descending_order<-order(-sampledata$Sl.No)
>View(order)
#sort the dataset using descending_order R object values
>ordered<-sampledata[descending_order, ]
Or directly
>ordered<-sampledata[order(-sampledata$Sl.No), ]
>View(ordered)
Where “-” represents descending order
Manipulating Data: order()
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Manipulating Data using base R package
- 1. Manipulating Data Using base R package Rupak Roy
- 2. Sub-setting of data is a very important component of data management where the subset function helps us to pull out rows from the data frame based on a logical expression using the column names. #load the pre-set R sample data >data(mtcars) #sub-setting 4first rows and columns in the format [rows, columns] >mtcars[1:4,1:3] #sub-setting specific list of rows and columns in the format [rows, columns] >mtcars[c(2,4,6), c(1,3,6)] Sub-setting: rows Rupak Roy
- 3. #using == operator: ( == ) >mtcars_93<-mtcars[mtcars$hp == 93, ] #with comma i.e. [row,column] format >mtcars_equal93<-mtcars[mtcars$hp == 93] #without comma it will give the results of columns ==93 which is not accurate #using OR operator: ( | ) >mtcars_or<-mtcars[mtcars$hp==93 | mtcars$carb==1,] >View(mtcars_or) #using AND operator: (&) > mtcars_and<-mtcars[ mtcars$mpg >= 20 & mtcars$hp <= 110,] >View(mtcars_and) Logical sub-setting Rupak Roy
- 4. #selecting columns >mtcars_col<-mtcars[ , c("mpg","cyl","hp")] >View(mtcars_col) #logical row sub-setting + sub-setting of columns >mtcars_rc<-mtcars[mtcars$mpg >= 20 & mtcars$hp <= 110, c("wt","disp")] >View(mtcars_rc) #adding new columns where “add” is the new column name >mtcars$add<-ifelse(mtcars$mpg<=15,"luxury",ifelse(mtcars$mpg<= 20,"sports","economy")) >View(mtcars) #remove a column >mtcars$add<-NULL Sub-setting: columns Rupak Roy
- 5. #load the data >sampledata<-read.csv(file.choose(),na.strings = c("NA",""),header = TRUE) #identify the positions using “which” operator >index_position<-which(sampledata$number.of.doors==“two”) “Which” operator returns only the indices i.e. the positions of the logical condition if true. >head(index_position) #Sub-setting the data using the indices >newdata<-mtcars[index_position, ] >View(newdata) Manipulating Data: which() Rupak Roy
- 6. #sort the data using order operator >descending_order<-order(-sampledata$Sl.No) >View(order) #sort the dataset using descending_order R object values >ordered<-sampledata[descending_order, ] Or directly >ordered<-sampledata[order(-sampledata$Sl.No), ] >View(ordered) Where “-” represents descending order Manipulating Data: order() Rupak Roy
- 7. #average/mean mileage(mpg) based on cylinder(cyl) >aggregate(mtcars$mpg, by=list(mtcars$cyl), mean) OR >tapply (mtcars$mpg, mtcars$cyl, mean) Else >aggregate(mtcars$mpg ~ mtcars$cyl, FUN = mean) >aggregate(mpg ~ cyl, data = mtcars, FUN = mean) #average/mean mileage based on cyl and carb >aggregate(mpg~ cyl+carb, data = mtcars, FUN = mean) #total average/mean based on cylinder >average_mtcars<-aggregate(mtcars, by=list(mtcars$cyl), mean) >class(average_mtcars) >View(average_mtcars) Manipulating Data: aggregate() Rupak Roy
- 8. ?table: table uses the cross-classifying factors to build a contingency table of the counts at each combination of factor levels. #number of mtcars based on Transmission (am) and Cylinder(cyl) >table(mtcars$am,mtcars$cyl) where 4,6,8 are type of cylinders and 0 for automatic, 1 for manual total: 32cars Alternatively we can use xtabs() ?xtabs: Creates a contingency table (optionally a sparse matrix) from cross- classifying factors, usually contained in a data frame, using a formula interface. >xtabs(expenditure~category) in the format (numeric ~ factor1+factor2) Cross tabulation 4 6 8 0 3 4 12 1 8 3 2
- 9. Next: We will learn more easy way to manipulate data using a special package call DPLYR() Manipulating Data Rupak Roy