Function for writing an automated report in R - r

Question

So I am trying to write an automated report in R with Functions. One of the questions I am trying to answer is this " During the first week of the month, what were the 10 most viewed products? Show the results in a table with the product's identifier, category, and count of the number of views.". To to this I wrote the following function
most_viewed_products_per_week <- function (month,first_seven_days, views){
month <- views....February.2020.2
first_seven_days <- function( month, date_1, date_2){
date_1 <-2020-02-01
date_2 <- 2020-02-07
return (first_seven_days)}
views <-function(views, desc){
return (views.head(10))}
}
print(most_viewed_products_per_week)
However the output I get is this:
function (month,first_seven_days, views){
month <- views....February.2020.2
first_seven_days <- function( month, date_1, date_2){
date_1 <-2020-02-01
date_2 <- 2020-02-07
return (first_seven_days)}
views <-function(views, desc){
return (views.head(10))}
How do I fix that?
This report has more questions like this, so I am trying to get my function writing as correct as possible from the start.
Thanks in advance,
Edo

Answer 1

It is a good practice to code in functions. Still I recommend you get your code doing what you want and then think about what parts you want to wrap in a function (for future re-use). This is to get you going.
In general: to support your analysis, make sure that your data is in the right class. I.e. dates are formatted as dates, numbers as double or integers, etc. This will give you access to many helper functions and packages.
For the case at hand, read up on {tidyverse}, in particular {dplyr} which can help you with coding pipes.
simulate data
As mentioned - you will find many friends on Stackoverflow, if you provide a reproducible example.
Your questions suggests your data look a bit like the following simulated data.
Adapt as appropriate (or provide example)
library(tibble) # tibble are modern data frames
library(dplyr) # for crunching tibbles/data frames
library(lubridate) # tidyverse package for date (and time) handling
df <- tribble( # create row-tibble
~date, ~identifier, ~category, ~views
,"2020-02-01", 1, "TV", 27
,"2020-02-02", 2, "PC", 40
,"2020-02-03", 1, "TV", 12
,"2020-02-03", 2, "PC", 2
,"2020-02-08", 3, "UV", 200
) %>%
mutate(date = ymd(date)) # date is read in a character - lubridate::ymd() for date
This yields
> df
# A tibble: 5 x 4
date identifier category views
<date> <dbl> <chr> <dbl>
1 2020-02-01 1 TV 27
2 2020-02-02 2 PC 40
3 2020-02-03 1 TV 12
4 2020-02-03 2 PC 2
5 2020-02-08 3 UV 200
Notice: date-column is in date-format.
work your algorithm
From your attempt it follows you want to extract the first 7 days.
Since we have a "date"-column, we can use a date-function to help us here.
{lubridate}'s day() extracts the "day-number".
> df %>% filter(day(date) <= 7)
# A tibble: 4 x 4
date identifier category views
<date> <dbl> <chr> <dbl>
1 2020-02-01 1 TV 27
2 2020-02-02 2 PC 40
3 2020-02-03 1 TV 12
4 2020-02-03 2 PC 2
Anything outside the first 7 days is gone.
Next you want to summarise to get your product views total.
df %>%
## ---------- c.f. above ------------
filter(day(date) <= 7) %>%
## ---------- summarise in bins that you need := groups -------
group_by(identifier, category) %>%
summarise(total_views = sum(views)
, .groups = "drop" ) # if grouping is not needed "drop" it
This gives you:
# A tibble: 2 x 3
identifier category total_views
<dbl> <chr> <dbl>
1 1 TV 39
2 2 PC 42
Now pick the top-10 and sort the order:
df %>%
## ---------- c.f. above ------------
filter(day(date) <= 7) %>%
group_by(identifier, category) %>%
summarise(total_views = sum(views), .groups = "drop" ) %>%
## ---------- make use of another helper function of dplyr
top_n(n = 10, total_views) %>% # note top-10 makes here no "real" sense :), try top_n(1, total_views)
arrange(desc(total_views)) # arrange in descending order on total_views
wrap in function
Now that the workflow is in place, think about breaking your code into the blocks you think are useful.
I leave this to you. You can assign interim results to new data frames and wrap the preparation of the data into a function and then the top_n() %>% arrange() in another function, ...
This yields:
# A tibble: 2 x 3
identifier category total_views
<dbl> <chr> <dbl>
1 2 PC 42
2 1 TV 39