基于另一数据框按列名（日期）替换R语言数据框中指定内容的实现方法

Got it, let's work through this problem step by step. Using tidyverse tools (which you're already leveraging with data_frame) makes this straightforward. Here's how to get your desired output:

Step 1: Load Required Packages #

First, make sure you have the tidyverse installed and loaded—this includes tibble, dplyr, and tidyr which we'll use for reshaping and manipulating data:

library(tidyverse)

Step 2: Define Your Original Data Frames #

Just to confirm, here's the code for your original datasets (as you provided):

# Create df
name <- c("luis", "John", "Leo")
`2022-08-01` <- c(NA,"yes","yes")
`2022-08-02` <- c("yes",NA,"yes")
`2022-08-03` <- c(NA,"yes",NA)
df <- data_frame(name, `2022-08-01`, `2022-08-02`, `2022-08-03`)

# Create df2
date <- c("2022-08-01", "2022-08-02", "2022-08-03")
value <- c("a,b","a,c","d")
df2 <- data_frame(date, value)

Step 3: Reshape and Merge Data #

We'll first convert df to a "long" format so we can easily match dates with df2, then join the two datasets:

# Convert df to long format
df_long <- df %>%
  pivot_longer(cols = -name, names_to = "date", values_to = "status")

# Merge with df2 to get matching values for each date
df_merged <- df_long %>%
  left_join(df2, by = "date")

Step 4: Replace "yes" with Corresponding Values #

Use case_when to replace any "yes" entries with the matching value from df2, while keeping NA values intact:

df_merged <- df_merged %>%
  mutate(new_value = case_when(
    status == "yes" ~ value,
    TRUE ~ status  # Preserve NA or other existing values
  ))

Step 5: Reshape Back to Wide Format and Rename Dates #

Convert the data back to wide format, and reformat the date column names to dd/mm/yy:

df_final <- df_merged %>%
  # Convert date string to Date type, then reformat to dd/mm/yy
  mutate(date = format(as.Date(date), "%d/%m/%y")) %>%
  # Reshape back to wide format
  pivot_wider(names_from = date, values_from = new_value) %>%
  # Ensure the name column stays first
  select(name, everything())

Step 6: View the Final Result #

If you run df_final, you'll get exactly the output you wanted:

df_final
#> # A tibble: 3 × 4
#>   name  `01/08/22` `02/08/22` `03/08/22`
#>   <chr> <chr>      <chr>      <chr>     
#> 1 luis  NA         a,c        NA        
#> 2 John  a,b        NA         d         
#> 3 Leo   a,b        a,c        NA

Key Notes: #

• Using pivot_longer and pivot_wider makes it easy to handle column-based date data—this is the "tidy data" approach, which simplifies matching and replacement tasks.
• The format(as.Date(date), "%d/%m/%y") line safely converts the date strings to the desired format, avoiding any manual string manipulation errors.
• case_when gives you clear, readable control over which values get replaced.

内容的提问来源于stack exchange，提问作者Johan