This tutorial explains how to convert data from wide to long format with R programming.
Note : Before running the code below, Install reshape2 package if not installed already.
R Code
The following program would create a sample data for demonstration.
Explanation :
Note : If you do not want to remove NA values, make na.rm = TRUE to na.rm = FALSE.
Tutorial : Convert data from long to wide format
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| R Code : Convert Data from Wide to Long |
R Code
The following program would create a sample data for demonstration.
df = read.table(text= "ID Product1 Product2 Product3 Product4The following code would turn our data from wide to long format.
1 1 NA 1 1
2 1 1 NA 1
3 1 1 NA NA
4 1 1 1 1", header=TRUE)
library(reshape2)
x = colnames(df[,-1])
t2 <- melt(df,id.vars = "ID",measure.vars = x , variable.name="Product", value.name="value",na.rm = TRUE)
t2 = t2[order(t2$ID),]
Explanation :
- id.vars - additional variables to keep in the output.
- measure.vars - variables to be reshaped
- variable.name - name of variable used to store measured variable names
- value.name - name of variable used to store values
Note : If you do not want to remove NA values, make na.rm = TRUE to na.rm = FALSE.
Tutorial : Convert data from long to wide format
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Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he has worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and Human Resource.
I would use tidyr for a nice clean data conversion
ReplyDelete#sample data
ReplyDeletehousehold <- data.frame(county = c("Nairobi",
"Kiambu",
"Laikipia",
"Murang'a"),
subCounty = c("Dagoretti",
"Lari",
"L.North",
"Kigumo"),
Name_p1 = c("Tiana",
"Joseph",
"Ochweri",
"Maina"),
Age_p1 = c(25, 16, 15, 45),
Name_p2 = c(NA,
"Mary",
NA,
"Susan"),
Age_p2 = c(NA, 34, NA, 16))
##How would you convert this to long format?