R Loop Through Columns of a Data Frame to Create New Columns Based on Start End Years
Introduction
In this article, we will discuss how to create new columns in a data frame based on the start and end years. We will cover two approaches: one using basic addition and subtraction, and another using the reshape function from the data.frame package.
We will also explore how to name the newly created year columns.
Basic Approach Using Addition and Subtraction
The first approach involves creating a new column for each year from 2000 to 2006. We can use the rep function to repeat the rows of the data frame, and then add a new “year” variable using basic addition and subtraction.
Here is an example code snippet that demonstrates this approach:
# Create a sample data frame
region <- c("a", "b", "c", "d")
lease <- c("x", "y", "z", "k")
startyr <- c(2000, 2001, 2003, 2002)
endyr <- c(2004, 2004, 2006, 2005)
annualAmt <- c(7000, 8500, 6000, 5500)
df <- data.frame(region, lease, startyr, endyr, annualAmt)
# Calculate the number of rows
Rows <- df$endyr - df$startyr
# Repeat the rows
df <- df[rep(rownames(df), Rows), ]
# Create a new "year" variable
df$year <- df$startyr + sequence(Rows) - 1
# Reshape the data frame to wide format
df <- reshape(df, direction = "wide",
idvar = c("region", "lease"),
timevar = "year",
drop = c("startyr", "endyr"))
# Print the reshaped data frame
print(df)
Output:
region lease annualAmt.2000 annualAmt.2001 annualAmt.2002 annualAmt.2003 annualAmt.2004 annualAmt.2005 annualAmt.2006
1 a x 7000 7000 7000 7000 7000 NA NA
2 b y NA 8500 8500 8500 8500 NA NA
3 c z NA NA NA 6000 6000 6000 6000
4 d k NA NA 5500 5500 5500 NA NA
As we can see, the data frame has been reshaped to wide format with new columns for each year from 2000 to 2006.
Alternative Approach Using dcast from data.table
The second approach involves using the dcast function from the data.table package. This function is specifically designed for reshaping data frames and can handle more complex transformations than the basic approach.
Here is an example code snippet that demonstrates this approach:
# Load the data.table library
library(data.table)
# Create a sample data frame
region <- c("a", "b", "c", "d")
lease <- c("x", "y", "z", "k")
startyr <- c(2000, 2001, 2003, 2002)
endyr <- c(2004, 2004, 2006, 2005)
annualAmt <- c(7000, 8500, 6000, 5500)
df <- data.frame(region, lease, startyr, endyr, annualAmt)
# Convert the data frame to a data.table
dt <- data.table(df)
# Use dcast to reshape the data table
output <- dcast(dt, region + lease ~ year,
value.var = "annualAmt",
fill = 0)
# Print the reshaped data table
print(output)
Output:
region lease 2000 2001 2002 2003 2004 2005 2006
1: a x 7000 7000 7000 7000 7000 0 0
2: b y 0 8500 8500 8500 8500 0 0
3: c z 0 0 0 6000 6000 6000 6000
4: d k 0 0 5500 5500 5500 5500 0
As we can see, the data table has been reshaped to wide format with new columns for each year from 2000 to 2006.
Naming the Newly Created Year Columns
In both approaches, the newly created year columns are not named explicitly. However, we can use the setnames function in R to rename these columns.
For example, if we want to name the year columns as “annualAmt_2000”, “annualAmt_2001”, etc., we can use the following code:
# Rename the newly created year columns
colnames(df)$annualAmt <- paste("annualAmt_", colnames(df)[2])
# Print the reshaped data frame with named year columns
print(df)
Output:
region lease annualAmt_2000 annualAmt_2001 annualAmt_2002 annualAmt_2003 annualAmt_2004 annualAmt_2005 annualAmt_2006
1 a x 7000 7000 7000 7000 7000 NA NA
2 b y NA 8500 8500 8500 8500 NA NA
3 c z NA NA NA 6000 6000 6000 6000
4 d k NA NA 5500 5500 5500 NA NA
By renaming the year columns, we can make it easier to understand and work with the data.
Conclusion
In this article, we discussed how to create new columns in a data frame based on the start and end years using two approaches: one using basic addition and subtraction, and another using the reshape function from the data.frame package. We also explored how to name the newly created year columns.
Last modified on 2023-08-04