Data Manipulation with R

This tutorial covers how to execute most frequently used data manipulation tasks with R. It includes various examples with datasets and code. This tutorial is designed for beginners who are very new to R programming language. It gives you a quick look at several functions used in R.

1. Replacing / Recoding values

By 'recoding', it means replacing existing value(s) with the new value(s).

Create Dummy Data
mydata = data.frame(State = ifelse(sign(rnorm(25))==-1,'Delhi','Goa'), Q1= sample(1:25))
In this example, we are replacing 1 with 6 in Q1 variable
mydata$Q1[mydata$Q1==1] <- 6
In this example, we are replacing "Delhi" with "Mumbai" in State variable. We need to convert the variable from factor to character.
mydata$State = as.character(mydata$State)
mydata$State[mydata$State=='Delhi'] <- 'Mumbai'
In this example, we are replacing 2 and 3 with NA values in whole dataset.
mydata[mydata == 2 | mydata == 3] <- NA
Another method

You have to first install the car package.
# Install the car package
install.packages("car")

# Load the car package
library("car")

# Recode 1 to 6
mydata$Q1 <- recode(mydata$Q1, "1=6")
Recoding a given range
# Recoding 1 through 4 to 0 and 5 and 6 to 1
mydata$Q1 <- recode(mydata$Q1, "1:4=0; 5:6=1")

You don't need to specify lowest and highest value of a range.
The lo keyword tells recode to start the range at the lowest value.
The hi keyword tells recode to end the range at the highest value.
# Recoding lowest value through 4 to 0 and 5 to highest value to 1
mydata$Q1 <- recode(mydata$Q1, "lo:4=0; 5:hi=1")

You can specify else condition in the recode statement. It means how to treat remaining values that was not already recoded.
# Recoding lowest value through 4 to 0, 5 and 6 to 1, remaining values to 3,
mydata$Q1 <- recode(mydata$Q1, "lo:4=0; 5:6=1;else = 3")

2. Recoding to a new column
# Create a new column called Ques1
mydata$Ques1<- recode(mydata$Q1, "1:4=0; 5:6=1")
Note : Make sure you have installed and loaded "car" package before running the above syntax.

How to use IF ELSE Statement

Sample Data
samples = data.frame(x =c(rep(1:10)), y=letters[1:10])
If a value of variable x is greater than 6, create a new variable called t1 and write 2 against the corresponding values else make it 1.
samples$t1 = ifelse(samples$x>6,2,1)
How to use AND Condition
samples$t3 = ifelse(samples$x>1 & samples$y=="b" ,2,1)
How to use NESTED IF ELSE Statement 
samples$t4 = ifelse(samples$x>=1 & samples$x<=4,1,ifelse(samples$x>=5 & samples$x<=7,2,3))

3. Renaming variables

To rename variables, you have to first install the dplyr package.

# Install the plyr package
install.packages("dplyr")

# Load the plyr package
library(dplyr)

# Rename Q1 variable to var1
mydata <- rename(mydata, var1 = Q1)


4. Keeping and Dropping Variables

In this example, we keep only first two variables .
mydata1 <- mydata[1:2]
In this example, we keep first and third through sixth variables .
mydata1 <- mydata[c(1,3:6)] 
In this example, we select variables using their names such as v1, v2, v3.

newdata <- mydata[c("v1", "v2", "v3")]


Deleting a particular column (Fifth column)
mydata [-5] 
Dropping Q3 variable
mydata$Q3 <- NULL

Deleting multiple columns
mydata [-(3:4) ] 
Dropping multiple variables by their names
df = subset(mydata, select = -c(x,z) )


5. Subset data (Selecting Observations)

By 'subsetting' data, it implies filtering rows (observations).

Create Sample Data
mydata = data.frame(Name = ifelse(sign(rnorm(25))==-1,'ABC','DEF'), age = sample(1:25))
Selecting first 10 observations
newdata <- mydata[1:10,]
Selecting values wherein age is equal to 3
mydata<-subset(mydata, age==3)
Copy data into a new data frame called 'newdata'
newdata<-subset(mydata, age==3)
Conditional Statement (AND) while selecting observations
newdata<-subset(mydata, Name=="ABC" & age==3)
Conditional Statement (OR) while selecting observations
newdata<-subset(mydata, Name=="ABC" | age==3)
Greater than or less than expression
newdata<-subset(mydata, age>=3)
Keeping only missing records
newdata<-subset(mydata, is.na(age))
Keeping only non-missing records
newdata<-subset(mydata, !is.na(age))

6. Sorting

Sorting is one of the most common data manipulation task. It is generally used when we want to see the top 5 highest / lowest values of a variable.

Sorting a vector
x= sample(1:50)
x = sort(x, decreasing = TRUE)
The function sort() is used for sorting a 1 dimensional vector. It cannot be used for more than 1 dimensional vector.
Sorting a data frame
mydata = data.frame(Gender = ifelse(sign(rnorm(25))==-1,'F','M'), SAT= sample(1:25))
Sort gender variable in ascending order
mydata.sorted <- mydata[order(mydata$Gender),]
Sort gender variable in ascending order and then SAT in descending order
mydata.sorted1 <- mydata[order(mydata$Gender, -mydata$SAT),]
Note : "-" sign before mydata$SAT tells R to sort SAT variable in descending order.


7. Value labeling

Use factor() for nominal data
mydata$Gender <- factor(mydata$Gender, levels = c(1,2), labels = c("male", "female"))
Use ordered() for ordinal data
mydata$var2 <- ordered(mydata$var2, levels = c(1,2,3,4), labels = c("Strongly agree", "Somewhat agree", "Somewhat disagree", "Strongly disagree"))

8. Dealing with missing data

Number of missing values in a variable
colSums(is.na(mydata))
Number of missing values in a row
rowSums(is.na(mydata))
List rows of data that have missing values
mydata[!complete.cases(mydata),]
Creating a new dataset without missing data
mydata1 <- na.omit(mydata)
Convert a value to missing
mydata[mydata$Q1==999,"Q1"] <- NA 

9. Aggregate by groups

The following code calculates mean for variable "x" by grouped variable "y".
samples = data.frame(x =c(rep(1:10)), y=round((rnorm(10))))
mydata <- aggregate(x~y, samples, mean, na.rm = TRUE)

10. Frequency for a vector

To calculate frequency for State vector, you can use table() function.



11. Merging (Matching)

It merges only common cases to both datasets.
mydata <- merge(mydata1, mydata2, by=c("ID"))
Detailed Tutorial : Joining and Merging


12. Removing Duplicates
data = read.table(text="
X Y Z
6 5 0
6 5 0
6 1 5
8 5 3
1 NA 1
8 7 2
2 0 2", header=TRUE)

In the example below, we are removing duplicates in a whole data set. [Equivalent to NODUP in SAS]
mydata1 <- unique(data)
In the example below, we are removing duplicates by "Y" column. [Equivalent to NODUPKEY in SAS]
mydata2 <- subset(data, !duplicated(data[,"Y"]))

13. Combining Columns and Rows 

If the columns of two matrices have the same number of rows, they can be combined into a larger matrix using cbind function. In the example below, A and B are matrices.
newdata<- cbind(A, B)
Similarly, we can combine the rows of two matrices if they have the same number of columns with the rbind function. In the example below, A and B are matrices.
newdata<- rbind(A, B)

14. Combining Rows when different set of columns

The function rbind() does not work when the column names do not match in the two datasets. For example, dataframe1 has 3 column A B and C . dataframe2 also has 3 columns A D E. The function rbind() throws an error. The function smartbind() from gtools would combine column A and returns NAs where column names do not match.
install.packages("gtools") #If not installed
library(gtools)
mydata <- smartbind(mydata1, mydata2)

Next Step :
Learn Data Manipulation with dplyr Package


R Tutorials : 75 Free R Tutorials

About Author:

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has close to 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like retail and commercial banking, Telecom, HR and Automotive.


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7 Responses to "Data Manipulation with R"

  1. Hi Folk,
    I tried to replace one of the attribute of some variable by using the below code
    D22[D22$Q1==1]=6.
    Unfortunately it ddnt work. I dont have access to install car package.
    Could you please help in this..

    ReplyDelete
    Replies
    1. D22$Q1[D22$Q1==1]=6 should work. Thanks!

      Delete
  2. Hi Folk,

    I am working on some project on R,
    I need to identify outliers, I tried to use the quantile colde,
    But it didnt work,
    Could you please help me on the same

    ReplyDelete
    Replies
    1. If you want to see outliers for a particular column in the dataset, you can plot boxplot for that column. There are some standard ways to identify outliers based on Inter Quartile Range (IQR)

      Delete

  3. # how to work on percentiles
    duration=mydata_cluster_1$Q4_GPRS_VOLUME

    #how to get the quantile forspecific variable
    quant=quantile(mydata_cluster_1,c(.05,.1,.15,.2,.25,.30,.35,.40, .9, .95, .98)) )

    The above code I wrote on one single variable, what if I have to run it on all variables

    ReplyDelete
  4. Amazing article.. Please publish much more articles for beginners. And specially articles like knit and shiny (GUI BASED).

    ReplyDelete

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