Classwork 2

Cleaning

Author

Diya Bijoy

Published

September 16, 2025

Setup R packages

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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Registered S3 method overwritten by 'mosaic':
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Reading csv file

fastfood <- read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/openintro/fastfood.csv") %>% 
clean_names(case="snake")

Rows: 515 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): restaurant, item, salad
dbl (15): rownames, calories, cal_fat, total_fat, sat_fat, trans_fat, choles...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

fastfood

# A tibble: 515 × 18
   rownames restaurant item         calories cal_fat total_fat sat_fat trans_fat
      <dbl> <chr>      <chr>           <dbl>   <dbl>     <dbl>   <dbl>     <dbl>
 1        1 Mcdonalds  Artisan Gri…      380      60         7       2       0  
 2        2 Mcdonalds  Single Baco…      840     410        45      17       1.5
 3        3 Mcdonalds  Double Baco…     1130     600        67      27       3  
 4        4 Mcdonalds  Grilled Bac…      750     280        31      10       0.5
 5        5 Mcdonalds  Crispy Baco…      920     410        45      12       0.5
 6        6 Mcdonalds  Big Mac           540     250        28      10       1  
 7        7 Mcdonalds  Cheeseburger      300     100        12       5       0.5
 8        8 Mcdonalds  Classic Chi…      510     210        24       4       0  
 9        9 Mcdonalds  Double Chee…      430     190        21      11       1  
10       10 Mcdonalds  Double Quar…      770     400        45      21       2.5
# ℹ 505 more rows
# ℹ 10 more variables: cholesterol <dbl>, sodium <dbl>, total_carb <dbl>,
#   fiber <dbl>, sugar <dbl>, protein <dbl>, vit_a <dbl>, vit_c <dbl>,
#   calcium <dbl>, salad <chr>

Names function

names(fastfood)

 [1] "rownames"    "restaurant"  "item"        "calories"    "cal_fat"    
 [6] "total_fat"   "sat_fat"     "trans_fat"   "cholesterol" "sodium"     
[11] "total_carb"  "fiber"       "sugar"       "protein"     "vit_a"      
[16] "vit_c"       "calcium"     "salad"

Glimpse, Dim, str

dplyr::glimpse(fastfood)

Rows: 515
Columns: 18
$ rownames    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ restaurant  <chr> "Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdon…
$ item        <chr> "Artisan Grilled Chicken Sandwich", "Single Bacon Smokehou…
$ calories    <dbl> 380, 840, 1130, 750, 920, 540, 300, 510, 430, 770, 380, 62…
$ cal_fat     <dbl> 60, 410, 600, 280, 410, 250, 100, 210, 190, 400, 170, 300,…
$ total_fat   <dbl> 7, 45, 67, 31, 45, 28, 12, 24, 21, 45, 18, 34, 20, 34, 8, …
$ sat_fat     <dbl> 2.0, 17.0, 27.0, 10.0, 12.0, 10.0, 5.0, 4.0, 11.0, 21.0, 4…
$ trans_fat   <dbl> 0.0, 1.5, 3.0, 0.5, 0.5, 1.0, 0.5, 0.0, 1.0, 2.5, 0.0, 1.5…
$ cholesterol <dbl> 95, 130, 220, 155, 120, 80, 40, 65, 85, 175, 40, 95, 125, …
$ sodium      <dbl> 1110, 1580, 1920, 1940, 1980, 950, 680, 1040, 1040, 1290, …
$ total_carb  <dbl> 44, 62, 63, 62, 81, 46, 33, 49, 35, 42, 38, 48, 48, 67, 31…
$ fiber       <dbl> 3, 2, 3, 2, 4, 3, 2, 3, 2, 3, 2, 3, 3, 5, 2, 2, 3, 3, 5, 2…
$ sugar       <dbl> 11, 18, 18, 18, 18, 9, 7, 6, 7, 10, 5, 11, 11, 11, 6, 3, 1…
$ protein     <dbl> 37, 46, 70, 55, 46, 25, 15, 25, 25, 51, 15, 32, 42, 33, 13…
$ vit_a       <dbl> 4, 6, 10, 6, 6, 10, 10, 0, 20, 20, 2, 10, 10, 10, 2, 4, 6,…
$ vit_c       <dbl> 20, 20, 20, 25, 20, 2, 2, 4, 4, 6, 0, 10, 20, 15, 2, 6, 15…
$ calcium     <dbl> 20, 20, 50, 20, 20, 15, 10, 2, 15, 20, 15, 35, 35, 35, 4, …
$ salad       <chr> "Other", "Other", "Other", "Other", "Other", "Other", "Oth…

base::dim(fastfood)

[1] 515  18

utils::str(fastfood)

spc_tbl_ [515 × 18] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ rownames   : num [1:515] 1 2 3 4 5 6 7 8 9 10 ...
 $ restaurant : chr [1:515] "Mcdonalds" "Mcdonalds" "Mcdonalds" "Mcdonalds" ...
 $ item       : chr [1:515] "Artisan Grilled Chicken Sandwich" "Single Bacon Smokehouse Burger" "Double Bacon Smokehouse Burger" "Grilled Bacon Smokehouse Chicken Sandwich" ...
 $ calories   : num [1:515] 380 840 1130 750 920 540 300 510 430 770 ...
 $ cal_fat    : num [1:515] 60 410 600 280 410 250 100 210 190 400 ...
 $ total_fat  : num [1:515] 7 45 67 31 45 28 12 24 21 45 ...
 $ sat_fat    : num [1:515] 2 17 27 10 12 10 5 4 11 21 ...
 $ trans_fat  : num [1:515] 0 1.5 3 0.5 0.5 1 0.5 0 1 2.5 ...
 $ cholesterol: num [1:515] 95 130 220 155 120 80 40 65 85 175 ...
 $ sodium     : num [1:515] 1110 1580 1920 1940 1980 950 680 1040 1040 1290 ...
 $ total_carb : num [1:515] 44 62 63 62 81 46 33 49 35 42 ...
 $ fiber      : num [1:515] 3 2 3 2 4 3 2 3 2 3 ...
 $ sugar      : num [1:515] 11 18 18 18 18 9 7 6 7 10 ...
 $ protein    : num [1:515] 37 46 70 55 46 25 15 25 25 51 ...
 $ vit_a      : num [1:515] 4 6 10 6 6 10 10 0 20 20 ...
 $ vit_c      : num [1:515] 20 20 20 25 20 2 2 4 4 6 ...
 $ calcium    : num [1:515] 20 20 50 20 20 15 10 2 15 20 ...
 $ salad      : chr [1:515] "Other" "Other" "Other" "Other" ...
 - attr(*, "spec")=
  .. cols(
  ..   rownames = col_double(),
  ..   restaurant = col_character(),
  ..   item = col_character(),
  ..   calories = col_double(),
  ..   cal_fat = col_double(),
  ..   total_fat = col_double(),
  ..   sat_fat = col_double(),
  ..   trans_fat = col_double(),
  ..   cholesterol = col_double(),
  ..   sodium = col_double(),
  ..   total_carb = col_double(),
  ..   fiber = col_double(),
  ..   sugar = col_double(),
  ..   protein = col_double(),
  ..   vit_a = col_double(),
  ..   vit_c = col_double(),
  ..   calcium = col_double(),
  ..   salad = col_character()
  .. )
 - attr(*, "problems")=<externalptr>

Finding Missing values

visdat::vis_dat(fastfood) +
  labs(title = "Missing Data", subtitle = "Showing variable types") + 
  theme(plot.title = element_text(size = 20), plot.subtitle = element_text(size = 14))

vis_miss(fastfood) +
  labs(title ="Missing Data in the FastFood Dataset", subtitle = "Showing missing & present data") +
  theme(plot.title = element_text(size = 20), plot.subtitle = element_text(size = 14))

Removing missing data

fastfood_modified <- fastfood %>% tidyr::drop_na()

Displaying modified data table

fastfood_modified

# A tibble: 301 × 18
   rownames restaurant item         calories cal_fat total_fat sat_fat trans_fat
      <dbl> <chr>      <chr>           <dbl>   <dbl>     <dbl>   <dbl>     <dbl>
 1        1 Mcdonalds  Artisan Gri…      380      60         7       2       0  
 2        2 Mcdonalds  Single Baco…      840     410        45      17       1.5
 3        3 Mcdonalds  Double Baco…     1130     600        67      27       3  
 4        4 Mcdonalds  Grilled Bac…      750     280        31      10       0.5
 5        5 Mcdonalds  Crispy Baco…      920     410        45      12       0.5
 6        6 Mcdonalds  Big Mac           540     250        28      10       1  
 7        7 Mcdonalds  Cheeseburger      300     100        12       5       0.5
 8        8 Mcdonalds  Classic Chi…      510     210        24       4       0  
 9        9 Mcdonalds  Double Chee…      430     190        21      11       1  
10       10 Mcdonalds  Double Quar…      770     400        45      21       2.5
# ℹ 291 more rows
# ℹ 10 more variables: cholesterol <dbl>, sodium <dbl>, total_carb <dbl>,
#   fiber <dbl>, sugar <dbl>, protein <dbl>, vit_a <dbl>, vit_c <dbl>,
#   calcium <dbl>, salad <chr>

Removing the column entirely

fastfood_modified2 <- fastfood %>%
  dplyr::select(-vit_a)

fastfood_modified2 <- fastfood %>%
  dplyr::select(-c(vit_a, vit_c))

Converting into Factors

fastfood_modified1 <- fastfood %>%
  mutate(
    restaurant = as.factor(restaurant),
    salad = as.factor(salad),
    item = as.factor(item))
dplyr::glimpse(fastfood_modified1)

Rows: 515
Columns: 18
$ rownames    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ restaurant  <fct> Mcdonalds, Mcdonalds, Mcdonalds, Mcdonalds, Mcdonalds, Mcd…
$ item        <fct> "Artisan Grilled Chicken Sandwich", "Single Bacon Smokehou…
$ calories    <dbl> 380, 840, 1130, 750, 920, 540, 300, 510, 430, 770, 380, 62…
$ cal_fat     <dbl> 60, 410, 600, 280, 410, 250, 100, 210, 190, 400, 170, 300,…
$ total_fat   <dbl> 7, 45, 67, 31, 45, 28, 12, 24, 21, 45, 18, 34, 20, 34, 8, …
$ sat_fat     <dbl> 2.0, 17.0, 27.0, 10.0, 12.0, 10.0, 5.0, 4.0, 11.0, 21.0, 4…
$ trans_fat   <dbl> 0.0, 1.5, 3.0, 0.5, 0.5, 1.0, 0.5, 0.0, 1.0, 2.5, 0.0, 1.5…
$ cholesterol <dbl> 95, 130, 220, 155, 120, 80, 40, 65, 85, 175, 40, 95, 125, …
$ sodium      <dbl> 1110, 1580, 1920, 1940, 1980, 950, 680, 1040, 1040, 1290, …
$ total_carb  <dbl> 44, 62, 63, 62, 81, 46, 33, 49, 35, 42, 38, 48, 48, 67, 31…
$ fiber       <dbl> 3, 2, 3, 2, 4, 3, 2, 3, 2, 3, 2, 3, 3, 5, 2, 2, 3, 3, 5, 2…
$ sugar       <dbl> 11, 18, 18, 18, 18, 9, 7, 6, 7, 10, 5, 11, 11, 11, 6, 3, 1…
$ protein     <dbl> 37, 46, 70, 55, 46, 25, 15, 25, 25, 51, 15, 32, 42, 33, 13…
$ vit_a       <dbl> 4, 6, 10, 6, 6, 10, 10, 0, 20, 20, 2, 10, 10, 10, 2, 4, 6,…
$ vit_c       <dbl> 20, 20, 20, 25, 20, 2, 2, 4, 4, 6, 0, 10, 20, 15, 2, 6, 15…
$ calcium     <dbl> 20, 20, 50, 20, 20, 15, 10, 2, 15, 20, 15, 35, 35, 35, 4, …
$ salad       <fct> Other, Other, Other, Other, Other, Other, Other, Other, Ot…

Relocate

fast_food_modified <- fastfood_modified %>%
  mutate(
    restaurant = as.factor(restaurant),
    salad = as.factor(salad),
    item = as.factor(item)
  ) %>%
  rename("dish" = item) %>% # rename item to dish

  # arrange the Qual variables first, Quant next
  dplyr::relocate(where(is.factor), .after = rownames)

glimpse(fast_food_modified)

Rows: 301
Columns: 18
$ rownames    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
$ restaurant  <fct> Mcdonalds, Mcdonalds, Mcdonalds, Mcdonalds, Mcdonalds, Mcd…
$ dish        <fct> "Artisan Grilled Chicken Sandwich", "Single Bacon Smokehou…
$ salad       <fct> Other, Other, Other, Other, Other, Other, Other, Other, Ot…
$ calories    <dbl> 380, 840, 1130, 750, 920, 540, 300, 510, 430, 770, 380, 62…
$ cal_fat     <dbl> 60, 410, 600, 280, 410, 250, 100, 210, 190, 400, 170, 300,…
$ total_fat   <dbl> 7, 45, 67, 31, 45, 28, 12, 24, 21, 45, 18, 34, 20, 34, 8, …
$ sat_fat     <dbl> 2.0, 17.0, 27.0, 10.0, 12.0, 10.0, 5.0, 4.0, 11.0, 21.0, 4…
$ trans_fat   <dbl> 0.0, 1.5, 3.0, 0.5, 0.5, 1.0, 0.5, 0.0, 1.0, 2.5, 0.0, 1.5…
$ cholesterol <dbl> 95, 130, 220, 155, 120, 80, 40, 65, 85, 175, 40, 95, 125, …
$ sodium      <dbl> 1110, 1580, 1920, 1940, 1980, 950, 680, 1040, 1040, 1290, …
$ total_carb  <dbl> 44, 62, 63, 62, 81, 46, 33, 49, 35, 42, 38, 48, 48, 67, 31…
$ fiber       <dbl> 3, 2, 3, 2, 4, 3, 2, 3, 2, 3, 2, 3, 3, 5, 2, 2, 3, 3, 5, 2…
$ sugar       <dbl> 11, 18, 18, 18, 18, 9, 7, 6, 7, 10, 5, 11, 11, 11, 6, 3, 1…
$ protein     <dbl> 37, 46, 70, 55, 46, 25, 15, 25, 25, 51, 15, 32, 42, 33, 13…
$ vit_a       <dbl> 4, 6, 10, 6, 6, 10, 10, 0, 20, 20, 2, 10, 10, 10, 2, 4, 6,…
$ vit_c       <dbl> 20, 20, 20, 25, 20, 2, 2, 4, 4, 6, 0, 10, 20, 15, 2, 6, 15…
$ calcium     <dbl> 20, 20, 50, 20, 20, 15, 10, 2, 15, 20, 15, 35, 35, 35, 4, …

Displaying Data

fast_food_modified %>%
  head(10) %>%
  tinytable::tt(caption = "Fast Food Dataset (Clean)")

Fast Food Dataset (Clean)
rownames	restaurant	dish	salad	calories	cal_fat	total_fat	sat_fat	trans_fat	cholesterol	sodium	total_carb	fiber	sugar	protein	vit_a	vit_c	calcium
1	Mcdonalds	Artisan Grilled Chicken Sandwich	Other	380	60	7	2	0.0	95	1110	44	3	11	37	4	20	20
2	Mcdonalds	Single Bacon Smokehouse Burger	Other	840	410	45	17	1.5	130	1580	62	2	18	46	6	20	20
3	Mcdonalds	Double Bacon Smokehouse Burger	Other	1130	600	67	27	3.0	220	1920	63	3	18	70	10	20	50
4	Mcdonalds	Grilled Bacon Smokehouse Chicken Sandwich	Other	750	280	31	10	0.5	155	1940	62	2	18	55	6	25	20
5	Mcdonalds	Crispy Bacon Smokehouse Chicken Sandwich	Other	920	410	45	12	0.5	120	1980	81	4	18	46	6	20	20
6	Mcdonalds	Big Mac	Other	540	250	28	10	1.0	80	950	46	3	9	25	10	2	15
7	Mcdonalds	Cheeseburger	Other	300	100	12	5	0.5	40	680	33	2	7	15	10	2	10
8	Mcdonalds	Classic Chicken Sandwich	Other	510	210	24	4	0.0	65	1040	49	3	6	25	0	4	2
9	Mcdonalds	Double Cheeseburger	Other	430	190	21	11	1.0	85	1040	35	2	7	25	20	4	15
10	Mcdonalds	Double Quarter Pounder® with Cheese	Other	770	400	45	21	2.5	175	1290	42	3	10	51	20	6	20