Extracting Indexes and Dates of First and Last Non-Na Values in a Tibble with Summarise All

In this article, we will explore how to extract the indexes and corresponding dates of the first and last non-na values for each column in a tibble using the summarise_all function from the dplyr package. We will also discuss various approaches to achieve this and provide code examples to illustrate the concepts.

Introduction

A tibble is a type of data structure in R that provides a more efficient and convenient alternative to traditional data frames. Tibbles are ideal for working with structured data, such as time series data or datasets with multiple variables. In this article, we will focus on extracting indexes and corresponding dates of the first and last non-na values for each column in a tibble.

Problem Statement

Suppose you have a tibble df with various time series (columns) and observations (rows for different dates). You want to extract the indexes and corresponding dates of the first and last non-na values for each column. However, using the summarise_all function from the dplyr package, you get two columns for each time series and a function (first.idx or last.idx), resulting in only one row.

Solution Approach

To achieve this, we can use various approaches to manipulate the data in the tibble. In this article, we will explore different techniques and provide code examples to illustrate the concepts.

Approach 1: Using summarise_all with list Function

One way to extract indexes and corresponding dates of first and last non-na values is by using the summarise_all function from the dplyr package. This approach involves defining a list of functions that perform the desired operations.

library(dplyr)

df %>% 
  summarise_all(.funs = list(first.idx = ~min(which(!is.na(.))), 
                      last.idx = ~max(which(!is.na(.)))) %>% 
  print()

However, as mentioned in the problem statement, this approach results in two columns for each time series and function, resulting in only one row.

Approach 2: Using mutate and across Functions

Another way to achieve this is by using the mutate and across functions from the dplyr package. This approach involves applying the desired operations to each column separately.

library(dplyr)

df %>% 
  mutate(across(-Date, list(first.idx = ~as.character(min(which(!is.na(.)))),
                        last.idx = ~as.character(max(which(!is.na(.))))))) %>% 
  mutate(across(contains("_"), ~ifelse(. == row_number(), as.character(Date), NA), .names = "date_{.col}")) %>% 
  fill(starts_with("date"), .direction = "updown") %>% 
  slice(1) %>% 
  select(-c(Date, A, B)) %>% 
  pivot_longer(everything())

This approach results in a data frame with one column for each time series and the indexes and corresponding dates in four rows.

Approach 3: Using tidyr Package

The tidyr package provides various functions to manipulate and transform data. One way to achieve this is by using the pivot_longer function from the tidyr package.

library(dplyr)
library(tidyr)

df %>% 
  pivot_longer(everything(), names_to = "name", values_to = "value") %>% 
  group_by(name) %>% 
  summarise(first.idx = min(which(!is.na(value))), last.idx = max(which(!is.na(value)))) %>% 
  ungroup() %>% 
  select(-value, name, first.idx, last.idx)

This approach also results in a data frame with one column for each time series and the indexes and corresponding dates in four rows.

Explanation

Let’s break down the approaches step by step:

Approach 1: Using summarise_all Function

• The summarise_all function is used to apply a list of functions to each column in the data frame.
• The .funs = list(first.idx = ~min(which(!is.na(.))), last.idx = ~max(which(!is.na(.)))) argument specifies the list of functions to be applied. In this case, we are applying the min and max functions to find the first and last non-na values for each column.
• The resulting data frame has two columns for each time series and function, resulting in only one row.

Approach 2: Using mutate and across Functions

• The mutate function is used to add new columns to the data frame. In this case, we are adding two new columns first.idx and last.idx that contain the indexes of the first and last non-na values for each column.
• The across function is used to apply a function to each column in the data frame. In this case, we are applying the min and max functions to find the first and last non-na values for each column.
• The resulting data frame has one column for each time series and the indexes and corresponding dates in four rows.

Approach 3: Using tidyr Package

• The pivot_longer function is used to pivot the data from a long format to a wide format. In this case, we are pivoting the data by columns.
• The group_by function is used to group the data by column. In this case, we are grouping the data by column name.
• The summarise function is used to summarize the data. In this case, we are summarizing the first and last non-na values for each column.
• The resulting data frame has one column for each time series and the indexes and corresponding dates in four rows.

Conclusion

In conclusion, there are several approaches to extract the indexes and corresponding dates of the first and last non-na values for each column in a tibble using the summarise_all function from the dplyr package. By understanding the different techniques and providing code examples, we can effectively achieve our goals.

References

• Dplyr: A Grammar of Data Manipulation
• PivotLonger Function in tidyr Package

Last modified on 2024-11-28