Module 6: Apply 5

Import data

# csv file
data <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-07-01/weekly_gas_prices.csv')

Apply the following dplyr verbs to your data

Filter rows

filter(data, grade == "regular", fuel == "gasoline")

## # A tibble: 5,222 × 5
##    date       fuel     grade   formulation  price
##    <date>     <chr>    <chr>   <chr>        <dbl>
##  1 1990-08-20 gasoline regular all           1.19
##  2 1990-08-20 gasoline regular conventional  1.19
##  3 1990-08-27 gasoline regular all           1.25
##  4 1990-08-27 gasoline regular conventional  1.25
##  5 1990-09-03 gasoline regular all           1.24
##  6 1990-09-03 gasoline regular conventional  1.24
##  7 1990-09-10 gasoline regular all           1.25
##  8 1990-09-10 gasoline regular conventional  1.25
##  9 1990-09-17 gasoline regular all           1.27
## 10 1990-09-17 gasoline regular conventional  1.27
## # ℹ 5,212 more rows

Arrange rows

arrange(data, desc(price))

## # A tibble: 22,360 × 5
##    date       fuel     grade            formulation  price
##    <date>     <chr>    <chr>            <chr>        <dbl>
##  1 2022-06-13 gasoline premium          reformulated  6.06
##  2 2022-06-20 gasoline premium          reformulated  6.03
##  3 2022-06-06 gasoline premium          reformulated  5.96
##  4 2022-06-27 gasoline premium          reformulated  5.96
##  5 2022-06-13 gasoline midgrade         reformulated  5.86
##  6 2022-07-04 gasoline premium          reformulated  5.85
##  7 2022-06-20 gasoline midgrade         reformulated  5.83
##  8 2022-06-20 diesel   all              <NA>          5.81
##  9 2022-06-20 diesel   ultra_low_sulfur <NA>          5.81
## 10 2022-06-27 diesel   all              <NA>          5.78
## # ℹ 22,350 more rows

Select columns

select(data, date:price)

## # A tibble: 22,360 × 5
##    date       fuel     grade   formulation  price
##    <date>     <chr>    <chr>   <chr>        <dbl>
##  1 1990-08-20 gasoline regular all           1.19
##  2 1990-08-20 gasoline regular conventional  1.19
##  3 1990-08-27 gasoline regular all           1.25
##  4 1990-08-27 gasoline regular conventional  1.25
##  5 1990-09-03 gasoline regular all           1.24
##  6 1990-09-03 gasoline regular conventional  1.24
##  7 1990-09-10 gasoline regular all           1.25
##  8 1990-09-10 gasoline regular conventional  1.25
##  9 1990-09-17 gasoline regular all           1.27
## 10 1990-09-17 gasoline regular conventional  1.27
## # ℹ 22,350 more rows

Add columns

data %>%
  select(fuel, price) %>%
  mutate(price_cumsum = cumsum(price))

## # A tibble: 22,360 × 3
##    fuel     price price_cumsum
##    <chr>    <dbl>        <dbl>
##  1 gasoline  1.19         1.19
##  2 gasoline  1.19         2.38
##  3 gasoline  1.25         3.63
##  4 gasoline  1.25         4.87
##  5 gasoline  1.24         6.11
##  6 gasoline  1.24         7.36
##  7 gasoline  1.25         8.61
##  8 gasoline  1.25         9.86
##  9 gasoline  1.27        11.1 
## 10 gasoline  1.27        12.4 
## # ℹ 22,350 more rows

Summarize by groups

data %>%
    
    #group by grade
  group_by(grade) %>%   
    
    #Amount of entries per grade
  summarise(count = n(), .groups = "drop") %>%  
    
    #arrange in descending order
  arrange(desc(count))

## # A tibble: 6 × 2
##   grade            count
##   <chr>            <int>
## 1 all               6506
## 2 regular           5222
## 3 midgrade          4788
## 4 premium           4788
## 5 ultra_low_sulfur   960
## 6 low_sulfur          96