This question is about tidy temporal data. Below are total daily pedestrian traffic counts for the month of March for 2020 and 2019.

walk_daily_counts %>% 
  arrange(day, month)

## # A tibble: 62 × 6
##    Date       Count  year month   day wday 
##    <date>     <int> <dbl> <ord> <int> <ord>
##  1 2019-03-01 34485  2019 Mar       1 Fri  
##  2 2020-03-01 26840  2020 Mar       1 Sun  
##  3 2019-03-02 33896  2019 Mar       2 Sat  
##  4 2020-03-02 27900  2020 Mar       2 Mon  
##  5 2019-03-03 27036  2019 Mar       3 Sun  
##  6 2020-03-03 28003  2020 Mar       3 Tue  
##  7 2019-03-04 33865  2019 Mar       4 Mon  
##  8 2020-03-04 27949  2020 Mar       4 Wed  
##  9 2019-03-05 34463  2019 Mar       5 Tue  
## 10 2020-03-05 24936  2020 Mar       5 Thu  
## # … with 52 more rows

To compare daily counts of pedestrians in March for 2019 compared to 2020, we could use a scatterplot. But first we would need to pivot the data to make daily counts for each year as column.

Fill in the blanks for the following code to get our desired output:

walk_daily_counts_wide <- walk_daily_counts_wide %>% 
  # (a) which pivot function
  # (b) which id_cols
  # (c) which column forms names_from
  # (d) which column forms values_from
  pivot_---(id_cols = ---, names_from = ---, values = ---)

## # A tibble: 31 × 3
##      day `2019` `2020`
##    <int>  <int>  <int>
##  1     1  34485  26840
##  2     2  33896  27900
##  3     3  27036  28003
##  4     4  33865  27949
##  5     5  34463  24936
##  6     6  33763  33456
##  7     7  35403  30580
##  8     8  43030  27444
##  9     9  40673  25149
## 10    10  36208  26425
## # … with 21 more rows

1. pivot_longer()

   pivot_wider()
2. day

   month

   wday
3. day

   year

   Date
4. wday

   Count

   year