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**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] <- 6In 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)In this example, we are replacing 2 and 3 with NA values in whole dataset.

mydata$State[mydata$State=='Delhi'] <- 'Mumbai'

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.**

Thelokeyword tells recode to start the range at the lowest value.

Thehikeyword 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")

install.packages("dplyr")

# Load the plyr package

library(dplyr)

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)The function

x = sort(x, decreasing = TRUE)

**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**

**table()**function.

**11. Merging (Matching)**

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**

install.packages("gtools") #If not installed

library(gtools)

mydata <- smartbind(mydata1, mydata2)

**Next Step**

**:**

**Learn Data Manipulation with dplyr Package**

Hi Folk,

ReplyDeleteI 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..

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

DeleteHi Folk,

ReplyDeleteI 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

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

ReplyDelete# 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

good

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

ReplyDeleteWhat function should we use To convert HTML file into data frame

ReplyDelete(Note: once you get the HTML file in a list format , how will you convert to a data frame )