Creating a New Column in a Data Frame Based on Conditions and Values

In this article, we will explore how to create a new column in a data frame based on the condition of one column and values from another column. This problem can be solved using various techniques such as manipulating the existing columns or creating a new column based on conditional statements.

Introduction

When working with data frames, it’s often necessary to perform complex operations that involve multiple conditions and calculations. One common scenario is when you want to create a new column that combines values from another column based on specific conditions. In this article, we will delve into how to achieve this using R programming language and the dplyr library.

Problem Statement

The problem at hand involves creating a new column in the df1 data frame that takes values from the Covid.cases column and adds the current and last two values in the Stock_ret column when two consecutive NA are found. This is done as follows:

c.covid.case.trading <- c(20, 200, 34, 10, 43, 68, 11, 3, 7, 55)

df2 <- data.frame(ID, Stock_ret, Covid.cases, c.covid.case.trading)

Solution Using lag() + ifelse()

One approach to solve this problem is by using the lag() and ifelse() functions in combination with conditional statements.

# Create the initial data frame
df1 <- data.frame(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
                  Stock_ret = c(-0.40, 0.50, 0.60, NA, NA, -0.80, -0.10, NA, -0.15, 0.28),
                  Covid.cases = c(20, 200, 34, 10, 43, 15, 11, 3, 4, 55))

# Create a new column that combines values from Covid.cases and Stock_ret
df1 <- df1 %>% 
  mutate(new_col = ifelse(is.na(lag(Stock_ret, default = 0)) & is.na(lag(Stock_ret, n = 2)),
                           Covid.cases + lag(Covid.cases) + lag(Covid.cases, n = 2), Covid.cases))

In this solution:

• The lag() function is used to access the previous values in the sequence. In this case, we use lag(Stock_ret, default = 0) and lag(Stock_ret, n = 2) to access the current value and the last two consecutive NA values respectively.
• The ifelse() function is then used to apply a conditional statement that checks if both the current and previous values are NA. If this condition is true, it adds the current value of Covid.cases with the values from the previous two rows of Covid.cases. Otherwise, it uses the value of Covid.cases.

Example Output

The resulting data frame after applying this solution would be:

#    ID Stock_ret Covid.cases new_col
# 1   1     -0.40          20      20
# 2   2      0.50         200     200
# 3   3      0.60          34      34
# 4   4        NA          10      10
# 5   5        NA          43      43
# 6   6     -0.80          15      68
# 7   7     -0.10          11      11
# 8   8        NA           3       3
# 9   9     -0.15           4       4
# 10 10      0.28          55      55

Conclusion

In this article, we demonstrated how to create a new column in a data frame based on the condition of one column and values from another column using the lag() + ifelse() functions in combination with conditional statements.

While this solution might be straightforward for simple cases, real-world scenarios can involve more complex conditions and calculations. In such cases, it’s essential to consider alternative approaches that cater to specific needs.

Last modified on 2024-11-14