Given a column name, extracting last non-NA value

For the following data set df, I hope to give the column name and return the last non-NA value of that column:

         date cumul_val1 cumul_val2 month_val1 month_val2
1  2020-05-31   48702.97   45919.59         NA         NA
2  2020-06-30   69403.68   62780.21   20700.71   16860.62
3  2020-07-31   83631.36   75324.61   14227.68   12544.40
4  2020-08-31   98485.95   88454.14   14854.59   13129.53
5  2020-09-30  117072.67  103484.20   18586.72   15030.06
6  2020-10-31  133293.80  116555.76   16221.13   13071.56
7  2020-11-30  150834.45  129492.36   17540.65   12936.60
8  2020-12-31  176086.22  141442.95   25251.77   11950.59
9  2021-02-28         NA   13985.87         NA   13985.87
10 2021-03-31         NA         NA         NA   13589.95
11 2021-04-30         NA         NA         NA   12663.94
12 2021-05-31         NA         NA         NA   14078.32

This means we can implement something like this, but without passing specific date values:

> df[df$date == '2020-12-31', "cumul_val1"]
[1] 176086.2
> df[df$date == '2021-02-28', "cumul_val2"]
[1] 13985.87
> df[df$date == '2020-12-31', "month_val1"]
[1] 25251.77
> df[df$date == '2021-05-31', "month_val2"]
[1] 14078.32

May I ask how to achieve it? Thanks.

Data:

df <- structure(list(date = c("2020-05-31", "2020-06-30", "2020-07-31", 
"2020-08-31", "2020-09-30", "2020-10-31", "2020-11-30", "2020-12-31", 
"2021-02-28", "2021-03-31", "2021-04-30", "2021-05-31"), cumul_val1 = c(48702.97, 
69403.68, 83631.36, 98485.95, 117072.67, 133293.8, 150834.45, 
176086.22, NA, NA, NA, NA), cumul_val2 = c(45919.59, 62780.21, 
75324.61, 88454.14, 103484.2, 116555.76, 129492.36, 141442.95, 
13985.87, NA, NA, NA), month_val1 = c(NA, 20700.71, 14227.68, 
14854.59, 18586.72, 16221.13, 17540.65, 25251.77, NA, NA, NA, 
NA), month_val2 = c(NA, 16860.62, 12544.4, 13129.53, 15030.06, 
13071.56, 12936.6, 11950.59, 13985.87, 13589.95, 12663.94, 14078.32
)), class = "data.frame", row.names = c(NA, -12L))

library(tidyverse)

get_last <- function(df, column_name) {
  df %>% 
    pull(!!sym(column_name)) %>% 
    na.omit() %>% 
    last()
}

get_last(df, "cumul_val1")

[1] 176086.2

OR

df %>%  
  pivot_longer(-date) %>%  
  group_by(name) %>% 
  drop_na() %>% 
  slice_tail(n = 1)

# A tibble: 4 x 3
# Groups:   name [4]
  date       name         value
  <chr>      <chr>        <dbl>
1 2020-12-31 cumul_val1 176086.
2 2021-02-28 cumul_val2  13986.
3 2020-12-31 month_val1  25252.
4 2021-05-31 month_val2  14078.

A data.table approach

library(data.table)
# set to data.table
setDT(df)
# melt to long format, get max data/value by variable
melt(df, id.vars = "date")[!is.na(value), .(last_val = value[date == max(date)]), by = variable]
#      variable  last_val
# 1: cumul_val1 176086.22
# 2: cumul_val2  13985.87
# 3: month_val1  25251.77
# 4: month_val2  14078.32

In base R:

last_complete <- function(df, col) tail(df[[col]][!is.na(df[[col]])], 1)
last_complete(df, "cumul_val1")
#[1] 176086.2
last_complete(df, "month_val1")
#[1] 25251.77