This dataset includes information on food choices, nutrition, preferences, childhood favorites, and other information from college students. There are 126 responses from students. Data is raw and uncleaned. The dataset can be accessed here.
Functions used in this project include:
Import all necessary libraries
library(tidyverse)
library(ggplot2)
library(stringr)
library(rebus)
library(magrittr)Import the dataset and view the first 20 rows
food_choices <- read.csv("food_coded.csv", header = T)assess the imported dataframe
glimpse(food_choices)## Rows: 125
## Columns: 61
## $ GPA <chr> "2.4", "3.654", "3.3", "3.2", "3.5", "2.2…
## $ Gender <int> 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1,…
## $ breakfast <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ calories_chicken <int> 430, 610, 720, 430, 720, 610, 610, 720, 4…
## $ calories_day <dbl> NaN, 3, 4, 3, 2, 3, 3, 3, NaN, 3, 3, 4, 3…
## $ calories_scone <dbl> 315, 420, 420, 420, 420, 980, 420, 420, 4…
## $ coffee <int> 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,…
## $ comfort_food <chr> "none", "chocolate, chips, ice cream", "f…
## $ comfort_food_reasons <chr> "we dont have comfort ", "Stress, bored, …
## $ comfort_food_reasons_coded <int> 9, 1, 1, 2, 1, 4, 1, 1, 2, 1, 2, 3, 3, 1,…
## $ cook <dbl> 2, 3, 1, 2, 1, 3, 2, 3, 3, 3, 1, 3, 5, 2,…
## $ comfort_food_reasons_coded.1 <int> 9, 1, 1, 2, 1, 4, 1, 1, 2, 1, 2, 3, 3, 1,…
## $ cuisine <dbl> NaN, 1, 3, 2, 2, NaN, 1, 1, 1, 1, 1, 1, 1…
## $ diet_current <chr> "eat good and exercise", "I eat about thr…
## $ diet_current_coded <int> 1, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 2,…
## $ drink <dbl> 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2,…
## $ eating_changes <chr> "eat faster ", "I eat out more than usual…
## $ eating_changes_coded <int> 1, 1, 1, 1, 3, 1, 2, 2, 2, 1, 3, 4, 2, 1,…
## $ eating_changes_coded1 <int> 1, 2, 3, 3, 4, 3, 5, 5, 8, 3, 4, 5, 5, 3,…
## $ eating_out <int> 3, 2, 2, 2, 2, 1, 2, 2, 5, 3, 2, 1, 1, 4,…
## $ employment <dbl> 3, 2, 3, 3, 2, 3, 3, 2, 2, 3, 1, 2, 3, 2,…
## $ ethnic_food <int> 1, 4, 5, 5, 4, 4, 5, 2, 5, 5, 5, 5, 4, 5,…
## $ exercise <dbl> 1, 1, 2, 3, 1, 2, 1, 2, NaN, 1, 1, 1, 3, …
## $ father_education <dbl> 5, 2, 2, 2, 4, 1, 4, 3, 5, 5, 2, 3, 3, 2,…
## $ father_profession <chr> "profesor ", "Self employed ", "owns busi…
## $ fav_cuisine <chr> "Arabic cuisine", "Italian", "italian", "…
## $ fav_cuisine_coded <int> 3, 1, 1, 3, 1, 6, 4, 5, 1, 1, 4, 1, 4, 1,…
## $ fav_food <dbl> 1, 1, 3, 1, 3, 3, 1, 1, 3, 1, 1, 1, 3, 1,…
## $ food_childhood <chr> "rice and chicken ", "chicken and biscui…
## $ fries <int> 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ fruit_day <int> 5, 4, 5, 4, 4, 2, 4, 5, 4, 5, 5, 5, 4, 5,…
## $ grade_level <int> 2, 4, 3, 4, 4, 2, 4, 2, 1, 1, 3, 2, 1, 3,…
## $ greek_food <int> 5, 4, 5, 5, 4, 2, 5, 3, 5, 5, 1, 5, 3, 4,…
## $ healthy_feeling <int> 2, 5, 6, 7, 6, 4, 4, 3, 7, 3, 9, 1, 9, 8,…
## $ healthy_meal <chr> "looks not oily ", "Grains, Veggies, (mor…
## $ ideal_diet <chr> "being healthy ", "Try to eat 5-6 small m…
## $ ideal_diet_coded <int> 8, 3, 6, 2, 2, 2, 2, 2, 6, 2, 7, 2, 1, 2,…
## $ income <dbl> 5, 4, 6, 6, 6, 1, 4, 5, 5, 4, 3, 5, 5, 5,…
## $ indian_food <int> 5, 4, 5, 5, 2, 5, 5, 1, 5, 4, 1, 5, 3, 3,…
## $ italian_food <int> 5, 4, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 4, 5,…
## $ life_rewarding <dbl> 1, 1, 7, 2, 1, 4, 8, 3, 8, 3, 8, 1, 9, 10…
## $ marital_status <dbl> 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2,…
## $ meals_dinner_friend <chr> "rice, chicken, soup", "Pasta, steak, ch…
## $ mother_education <dbl> 1, 4, 2, 4, 5, 1, 4, 2, 5, 5, 4, 4, 4, 4,…
## $ mother_profession <chr> "unemployed", "Nurse RN ", "owns business…
## $ nutritional_check <int> 5, 4, 4, 2, 3, 1, 4, 4, 2, 5, 2, 5, 2, 2,…
## $ on_off_campus <dbl> 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 3, 1, 1, 2,…
## $ parents_cook <int> 1, 1, 1, 1, 1, 2, 2, 1, 2, 3, 1, 1, 2, 2,…
## $ pay_meal_out <int> 2, 4, 3, 2, 4, 5, 2, 5, 3, 3, 2, 3, 2, 3,…
## $ persian_food <dbl> 5, 4, 5, 5, 2, 5, 5, 1, 5, 4, 2, 5, 3, 3,…
## $ self_perception_weight <dbl> 3, 3, 6, 5, 4, 5, 4, 3, 4, 3, 1, 2, 5, 3,…
## $ soup <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1,…
## $ sports <dbl> 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1,…
## $ thai_food <int> 1, 2, 5, 5, 4, 4, 5, 1, 5, 4, 2, 5, 3, 5,…
## $ tortilla_calories <dbl> 1165, 725, 1165, 725, 940, 940, 940, 725,…
## $ turkey_calories <int> 345, 690, 500, 690, 500, 345, 690, 500, 3…
## $ type_sports <chr> "car racing", "Basketball ", "none", "nan…
## $ veggies_day <int> 5, 4, 5, 3, 4, 1, 4, 4, 3, 5, 5, 5, 3, 5,…
## $ vitamins <int> 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2,…
## $ waffle_calories <int> 1315, 900, 900, 1315, 760, 1315, 1315, 13…
## $ weight <chr> "187", "155", "I'm not answering this. ",…This dataframe consists of 125 rows and 61 columns.
To check for the number of null values in each column, I’ll use the
ColSums function, store it in a tibble then rename the
columns of the tibble
nulls <- tibble(names(food_choices), colSums(is.na(food_choices)))
names(nulls) <- c('column', 'n')
nulls %>%
filter(n>0) %>%
arrange(-n) # arrange in descending order of number of nulls ## # A tibble: 20 × 2
## column n
## <chr> <dbl>
## 1 calories_day 19
## 2 comfort_food_reasons_coded 19
## 3 cuisine 17
## 4 exercise 13
## 5 employment 9
## 6 cook 3
## 7 mother_education 3
## 8 drink 2
## 9 fav_food 2
## 10 sports 2
## 11 calories_scone 1
## 12 father_education 1
## 13 income 1
## 14 life_rewarding 1
## 15 marital_status 1
## 16 on_off_campus 1
## 17 persian_food 1
## 18 self_perception_weight 1
## 19 soup 1
## 20 tortilla_calories 1From the query above, it is evident that there are 20 columns with at
least one null value.
To handle null values, there are
different approaches, including deleting the rows which contain these
null values. This might not be the best choice here considering the
relatively small size of the dataset. Instead, I have chosen to replace
null values with the modal (mode) value for categorical values and with
the mean for other numeric columns.
mode_calories <- names(sort(table(food_choices$calories_day), decreasing = T))[1] #extract the modal value
food_choices$calories_day[is.na(food_choices$calories_day)] <- mode_calories # replace null values with the extracted modal valuesrepeat this step for the cuisine column
mode_cuisine <- names(sort(table(food_choices$cuisine), decreasing = T))
food_choices$cuisine[is.na(food_choices$cuisine)] <- mode_cuisineThere are other columns in the dataframe containing missing values, however, it won’t be efficient to repeat the same code for so many columns. Instead, I’ve utilized a “for” loop that checks for the modal value in all these columns and replaces the nulls with them.
# food_choices' is your data frame
#
for(col in c('exercise', 'employment', 'cook', 'mother_education')) {
mode_val <- names(sort(table(food_choices[[col]]), decreasing = TRUE)[1]) # extract the modal value
food_choices[[col]][is.na(food_choices[[col]])] <- mode_val
}The above step has been repeated here.
for(col in c('drink', 'fav_food', 'sports')) {
mode_1 <- names(sort(table(food_choices[[col]]), decreasing = T)[1])
food_choices[[col]][is.na(food_choices[[col]])] <- mode_1
}Now, all null values have been treated and this can once again be
confirmed using the ColSums function.
colSums(is.na(food_choices))## GPA Gender
## 0 0
## breakfast calories_chicken
## 0 0
## calories_day calories_scone
## 0 1
## coffee comfort_food
## 0 0
## comfort_food_reasons comfort_food_reasons_coded
## 0 19
## cook comfort_food_reasons_coded.1
## 0 0
## cuisine diet_current
## 0 0
## diet_current_coded drink
## 0 0
## eating_changes eating_changes_coded
## 0 0
## eating_changes_coded1 eating_out
## 0 0
## employment ethnic_food
## 0 0
## exercise father_education
## 0 1
## father_profession fav_cuisine
## 0 0
## fav_cuisine_coded fav_food
## 0 0
## food_childhood fries
## 0 0
## fruit_day grade_level
## 0 0
## greek_food healthy_feeling
## 0 0
## healthy_meal ideal_diet
## 0 0
## ideal_diet_coded income
## 0 1
## indian_food italian_food
## 0 0
## life_rewarding marital_status
## 1 1
## meals_dinner_friend mother_education
## 0 0
## mother_profession nutritional_check
## 0 0
## on_off_campus parents_cook
## 1 0
## pay_meal_out persian_food
## 0 1
## self_perception_weight soup
## 1 1
## sports thai_food
## 0 0
## tortilla_calories turkey_calories
## 1 0
## type_sports veggies_day
## 0 0
## vitamins waffle_calories
## 0 0
## weight
## 0This column is a duplicate so it will be dropped.
food_choices <- food_choices %>%
select(-comfort_food_reasons_coded)Due to the large number of columns, I will clean only a select few that will be useful in answering the questions highlighted below.
Before that, the GPA column presents an exciting opportunity to flex
our data cleaning muscle. First, I’ll use the table
function to have a general overview of the values contained within this
column.
table(food_choices$GPA) # gpa distribution##
## 2.2 2.25 2.4 2.6 2.71 2.8 2.9
## 1 1 1 2 1 5 2
## 3 3.1 3.2 3.292 3.3 3.35 3.4
## 11 3 10 1 9 1 9
## 3.5 3.6 3.605 3.63 3.65 3.654 3.67
## 13 7 1 1 1 1 1
## 3.68 3.7 3.73 3.75 3.77 3.79 bitch 3.8
## 1 10 1 1 1 1 6
## 3.83 3.87 3.882 3.89 3.9 3.904 3.92
## 2 1 1 1 7 1 1
## 4 nan Personal Unknown
## 4 2 1 1pat <- "\\d*\\.\\d*\\s" # regex to check for decimal numbers followed by a space
matched_gpa <- str_subset(food_choices$GPA, pat)
cleaned_gpa<- sub(" bitch", "", str_subset(food_choices$GPA, pat)) # use the sub function to replace the unnecessary text with an empty string
food_choices$GPA[which(food_choices$GPA %in% matched_gpa)] <- cleaned_gpa
dgt <- "\\d*\\.?\\d+" # regex to check for all decimal numbers
char_gpa <- str_subset(food_choices$GPA, dgt)
round(mean(as.numeric(char_gpa)), 2)## [1] 3.42wrds <- "^[^0-9]"
unc <- str_subset(food_choices$GPA, wrds)
food_choices$GPA[which(food_choices$GPA %in% unc)] <- round(mean(as.numeric(char_gpa)), 2) # replace gpa's with unknown value with mean
food_choices$GPA <- as.numeric(food_choices$GPA) # convert to numeric
class(food_choices$GPA)## [1] "numeric"For the gpa column, there were no missing values, however some inconsistencies in formatting were noted and duly corrected. I replaced values that were “unknown” with the mean value of the column.
Change the numerical values in the gender column to align with the
data dictionary using the case_match function Gender:
1 - Female 2 - Male
food_choices %>% select(Gender) %>% unique() # inspect the values in this column## Gender
## 1 2
## 2 1food_choices$Gender <- food_choices %>% select(Gender) %>% mutate(Gender= case_match(Gender, 1 ~ 'Female', 2 ~ 'Male'))
food_choices$Gender <-as.character(food_choices$Gender$Gender)utilize case when to change the values in the exercise column exercise:
food_choices %>% select(exercise) %>% unique() #inspect for missing values## exercise
## 1 1
## 3 2
## 4 3food_choices$exercise <- food_choices %>% select(exercise) %>% mutate(exercise = case_when(exercise == 1 ~ 'Everyday', exercise == 2 ~ '2-3 times weekly',exercise== 3 ~ 'Once a week'))
food_choices$exercise <- as.character(food_choices$exercise$exercise)In the weight column, the following inconsistencies have been observed
I’ll use the str_replace function for data cleaning
here. Also, unknown values were replaced with the mean of the weight
column
table(food_choices$weight)##
## 100 105 110
## 1 1 1
## 112 113 115
## 1 2 1
## 116 118 120
## 1 1 3
## 123 125 127
## 1 5 1
## 128 129 130
## 2 2 4
## 135 137 138
## 8 1 1
## 140 144 lbs 145
## 8 1 4
## 150 155 156
## 7 6 1
## 160 165 167
## 3 5 2
## 168 169 170
## 1 1 7
## 175 180 184
## 6 6 1
## 185 187 190
## 6 1 5
## 192 195 200
## 1 1 4
## 205 210 230
## 1 2 1
## 260 264 265
## 1 1 1
## I'm not answering this. nan Not sure, 240
## 1 2 1# clean up values containing "lbs" and "not sure"
lbs <- str_subset(food_choices$weight, "lbs$")
ns <- str_subset(food_choices$weight, "Not")
food_choices$weight[which(food_choices$weight %in% lbs)] <- str_replace(lbs, "144 lbs", "144")
food_choices$weight[which(food_choices$weight %in% ns)] <- str_replace(ns, "Not sure, 240$", "240")
# to avoid data loss, replace unspecified values with the mean of the column
unsp <- str_subset(food_choices$weight, "^[^0-9].*") #regex to check for non-numeric entries
sp <- str_subset(food_choices$weight, "^[0-9].*")
food_choices$weight[which(food_choices$weight %in% unsp)] <- round(mean(as.numeric(sp)), 0) # replace them here with the meanq
food_choices$weight <- as.numeric(food_choices$weight)To answer the 2nd question, these variables will be of interest;
exercise, nutritional_checks,
veggie_day, fruit_day. The
exercise column has been cleaned already.
The values to be replaced in the nutritional check column:
table(food_choices$nutritional_check)##
## 1 2 3 4 5
## 10 36 20 43 16# the integer responses need to be changed to a more meaningful format
food_choices$nutritional_check <- food_choices %>% select(nutritional_check) %>% mutate(nutritional_check = case_match(nutritional_check, 1 ~ "Never", 2 ~ "Certain products only", 3 ~ "Very rarely", 4 ~ "On most products", 5 ~ "On everything"))
food_choices$nutritional_check <- as.character(food_choices$nutritional_check$nutritional_check)Same will be done using the veggies_day column. Importantly, the values were converted to factors, to aid the reintegration into the dataframe.
table(food_choices$veggies_day)##
## 1 2 3 4 5
## 3 11 21 37 53# same has to be done for the veggies_day column
food_choices$veggies_day <- food_choices %>% select(veggies_day) %>% mutate(veggies_day = case_match(veggies_day, 1 ~ "Very unlikely", 2 ~ "unlikely", 3 ~ "neutral", 4 ~ "likely", 5 ~ "very likely"))
food_choices$veggies_day <- factor(food_choices$veggies_day$veggies_day, levels = c("very unlikely", "unlikely", "neutral", "likely", "very likely"))Repeat this step for the fruit_day column also.
table(food_choices$fruit_day)##
## 1 2 3 4 5
## 1 4 24 33 63food_choices$fruit_day <- food_choices %>% select(fruit_day) %>% mutate(fruit_day = case_match(fruit_day, 1 ~ "Very unlikely", 2 ~ "unlikely", 3 ~ "neutral", 4 ~ "likely", 5 ~ "very likely"))
food_choices$fruit_day <- factor(food_choices$fruit_day$fruit_day, levels = c("very unlikely", "unlikely", "neutral", "likely", "very likely"))convert the values in these columns to lowercase to ensure consistent formatting
food_choices$comfort_food <- str_to_lower(food_choices$comfort_food)
food_choices$comfort_food_reasons<- str_to_lower(food_choices$comfort_food_reasons)Now, to dive into deeper waters, I want to clean the father_profession column. There are 2 steps in this phase; - Trim words that contain extra spaces - There are some misspelled words in this column that need to be corrected.
table(str_to_lower(food_choices$father_profession))##
## accountant architect
## 2 1
## assembler banker
## 1 1
## beacon light beverage and food sales
## 1 1
## biohemical waste elimination business guy
## 1 1
## business man business owner
## 1 4
## car salesman ceo of company
## 1 1
## cfo clinical researcher
## 1 1
## commercial real estate commidity trader
## 1 1
## commissioner of erie county construction
## 1 2
## construction management contract negotiations
## 1 1
## corporate manager cross-guard
## 1 1
## dairy farmer dairy farmer
## 1 1
## dead beat deceased
## 1 1
## delivery man for fritolay dentist
## 1 1
## dentist design engineer
## 2 1
## doctor electrical engineer
## 1 2
## engineer engineer
## 1 1
## european logistics director fireman
## 1 1
## ford plant employee ge salesman
## 1 1
## handyman high school principal
## 1 1
## his own business hockey coach
## 1 1
## home marker house appraiser
## 1 1
## hvac professional hvac technician
## 1 1
## idk information systems architect
## 1 1
## insurance it
## 1 2
## journalist landscaping
## 1 1
## lawyer manager at pepsi
## 2 1
## mechanic mechanical engineer
## 2 1
## mechanical engineer nan
## 1 3
## not sure optometrist
## 1 1
## owner of new york lunch owns business
## 1 1
## owns his business owns his own promotional company
## 1 1
## pharmaceutical physical therapist
## 1 1
## police force police officer
## 1 1
## police officer politician
## 1 1
## president of automotive company profesor
## 1 1
## project manager radio telecommunications manager
## 1 1
## realtor retire
## 1 1
## retired retired
## 1 1
## retired - bus driver risk manager
## 1 1
## sales sales manager
## 1 1
## salesman school library media specialist
## 2 1
## self employed self employed
## 1 1
## self employed construction senior manager
## 1 1
## sergeant correctional officer service technition
## 1 1
## shirt designer small business owner
## 1 2
## solar engineering store manager at giant eagle
## 1 1
## subcontractor supervisor
## 1 1
## taxi driver teacher
## 1 2
## transportation truck driver
## 1 1
## truck driver union worker
## 1 1
## united nations unknown
## 1 1
## ups driver vice president of a company
## 1 1
## vp of vp of gnc
## 1 1
## welder works for kirila fire
## 1 1reg <- "\\s$"
# some words end with spaces which have to be cleaned
string_space <- str_subset(food_choices$father_profession, reg) # extract words that end with spaces
food_choices$father_profession <- ifelse(food_choices$father_profession %in% string_space, sub(reg, "", string_space), # substitute these words with the cleaned strings
food_choices$father_profession)To handle the misspelled words, I’ll use the hunspell library. The hunspell library will act as a spellchecker here. The code, albeit bulky, has been well labelled to make the logic here easy to follow.
library(hunspell)## Warning: package 'hunspell' was built under R version 4.2.3exempt_words <- c("Idk", "nan", "HVAC", "GNC", "Kirila") #words the spellchecker should ignore
# write a function that checks for misspelled words that are not part of the exempt words and corrects them accordingly
correct_spelling <- function(sentence, exempt_words) {
words <- unlist(strsplit(food_choices$father_profession, "\\s+")) #split all sentences into individual words
wrong_words <- unlist(hunspell(words, dict = dictionary("en_US")))
wrong_words <- setdiff(wrong_words, exempt_words) # remove words found in exempt words
suggestions <- hunspell_suggest(wrong_words, dict = dictionary("en_US")) # spelling corrections for potentially misspelled words
corrected_words <- vector("list", length = length(wrong_words)) # store the corrected words in a vector
for (i in seq_along(wrong_words)) { # iterate over the wrong words to check if corrections exist and then select the first suggestion
if(length(suggestions[[i]])>0) {
corrected_words[[i]] <- suggestions[[i]][1]
} else {
corrected_words[[i]] <- wrong_words[i]
}
}
corrected_sentence <- sentence
for (i in seq_along(wrong_words)) { # use gsub to replace occurrences of wrong spellings with the right one
corrected_sentence <- gsub(wrong_words[i], corrected_words[[i]], corrected_sentence)
}
return(corrected_sentence)
}
# iterate through each observation in the column and then apply the correction function
food_choices$father_profession <-sapply(food_choices$father_profession, function(x) correct_spelling(x, exempt_words))
# replace some unhelpful values with unknown
food_choices %>%
select(father_profession) %>%
mutate(father_profession= ifelse(father_profession %in% c("idk", "nan", "not sure"), "unknown", father_profession))## father_profession
## 1 professor
## 2 Self employed
## 3 owns business
## 4 Assembler
## 5 IT
## 6 Taxi Driver
## 7 Shirt designer
## 8 Business guy
## 9 High School Principal
## 10 self employed construction
## 11 Idk
## 12 accountant
## 13 VP of
## 14 business owner
## 15 landscaping
## 16 Hockey Coach
## 17 Optometrist
## 18 Construction
## 19 Engineer
## 20 architect
## 21 CFO
## 22 subcontractor
## 23 small business owner
## 24 Commercial Real Estate
## 25 Manager at Pepsi
## 26 Insurance
## 27 Beverage and Food Sales
## 28 Dead beat
## 29 Electrical Engineer
## 30 Radio Telecommunications Manager
## 31 unknown
## 32 deceased
## 33 Lawyer
## 34 Dairy Farmer
## 35 Vice President of a company
## 36 Solar Engineering
## 37 engineer
## 38 cross-guard
## 39 Biochemical Waste Elimination
## 40 Retired
## 41 School Library Media Specialist
## 42 Welder
## 43 Design Engineer
## 44 Accountant
## 45 Electrical Engineer
## 46 Banker
## 47 Mechanic
## 48 Assembler
## 49 House Appraiser
## 50 unknown
## 51 Fireman
## 52 Commodity trader
## 53 Construction
## 54 HVAC Professional
## 55 Sergeant correctional officer
## 56 union worker
## 57 Salesman
## 58 Owns his business
## 59 Physical Therapist
## 60 Owns his own promotional company
## 61 Optometrist
## 62 Construction
## 63 police force
## 64 VP of GNC
## 65 Owner of New York Lunch
## 66 Dentist
## 67 small business owner
## 68 President of Automotive company
## 69 UPS driver
## 70 Insurance
## 71 Retired - Bus Driver
## 72 Dead beat
## 73 Police Officer
## 74 Risk Manager
## 75 retire
## 76 car salesman
## 77 dairy farmer
## 78 Dairy Farmer
## 79 self employed
## 80 Contract negotiations
## 81 engineer
## 82 IT
## 83 Works for Kirila Fire
## 84 Realtor
## 85 School Library Media Specialist
## 86 Lawyer
## 87 Service Technician
## 88 Accountant
## 89 handyman
## 90 Self employed
## 91 Project manager
## 92 Teacher
## 93 Truck Driver
## 94 Senior Manager
## 95 information systems architect
## 96 Supervisor
## 97 Delivery Man For Frito lay
## 98 unknown
## 99 Business Owner
## 100 business owner
## 101 VP of
## 102 salesman
## 103 Mechanical Engineer
## 104 GE Salesman
## 105 Business Owner
## 106 Ford Plant employee
## 107 Clinical Researcher
## 108 Small business owner
## 109 Sales
## 110 subcontractor
## 111 Retired
## 112 unknown
## 113 UPS driver
## 114 Teacher
## 115 Politician
## 116 Pharmaceutical
## 117 Business Man
## 118 His own business
## 119 Dentist
## 120 United Nations
## 121 Transportation
## 122 Doctor
## 123 CEO of company
## 124 Store manager at Giant Eagle
## 125 JournalistOverall, this project has helped to solidify my understanding of some functions essential for data cleaning in R. I hope anyone reading this has also found it useful!
AJANAKU AYOMIDE