The Task
This take-home exercise aims to reveal the demographic of the city of Engagement, Ohio USA by using visualization techniques in R.
The provided data can be used to get more insights into the mindset of the people in Ohio, USA. The report aims to answering questions such as the reason for unhappiness in the city of Ohio, the average household size in Ohio, the relation between age, education level and joviality and the impact of children living in household at a person’s happiness.
Links to the dataset:
Step-by-step Data Visualisation
Installing and launching R packages
Packages, namely tidyverse and ggplot2 are
required for this makeover exercise. This code chunk installs the
required packages and loads them onto RStudio environment.
packages = c('tidyverse','ggplot2')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
Data Preparation
Data Source
The dataset used in this exercise is Participants.csv, published by the IEEE for [VAST challenge 2022] (https://vast-challenge.github.io/2022/)
Importing the dataset
The code chunk below imports Participants.scv from the data
folder into R by using read_csv()
function of readr and saves it as Tibble data frame
called participants_data
participants_data <- read_csv("data/Participants.csv")
str(participants_data)
spec_tbl_df [1,011 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ participantId : num [1:1011] 0 1 2 3 4 5 6 7 8 9 ...
$ householdSize : num [1:1011] 3 3 3 3 3 3 3 3 3 3 ...
$ haveKids : logi [1:1011] TRUE TRUE TRUE TRUE TRUE TRUE ...
$ age : num [1:1011] 36 25 35 21 43 32 26 27 20 35 ...
$ educationLevel: chr [1:1011] "HighSchoolOrCollege" "HighSchoolOrCollege" "HighSchoolOrCollege" "HighSchoolOrCollege" ...
$ interestGroup : chr [1:1011] "H" "B" "A" "I" ...
$ joviality : num [1:1011] 0.00163 0.32809 0.39347 0.13806 0.8574 ...
- attr(*, "spec")=
.. cols(
.. participantId = col_double(),
.. householdSize = col_double(),
.. haveKids = col_logical(),
.. age = col_double(),
.. educationLevel = col_character(),
.. interestGroup = col_character(),
.. joviality = col_double()
.. )
- attr(*, "problems")=<externalptr> Data Description
Contains information about the residents Ohio, USA that have agreed to participate in this study.
- participantId (integer): unique ID assigned to each participant
- householdSize (integer): the number of people in the participant’s household
- haveKids (boolean): whether there are children living in the participant’s household
- age (integer): participant’s age in years at the start of the study
- educationLevel (string factor): the participant’s education level, one of: {“Low”, “HighSchoolOrCollege”, “Bachelors”, “Graduate”}
- interestGroup (char): a char representing the participant’s stated primary interest group, one of {“A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”, “I”, “J”}
- joviality (float): a value ranging from [0,1] indicating the participant’s overall happiness level at the start of the study.
Missing value check
Before we proceed with data visualization we check if the dataset has any null values using the code below.
participantId householdSize haveKids age
FALSE FALSE FALSE FALSE
educationLevel interestGroup joviality
FALSE FALSE FALSE Since there are no null values observed we proceed with our next step.
Data Wrangling
Display the various age groups present in the dataset using the code below to get an overview of the target age groups in the dataset
[1] "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[14] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43"
[27] "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56"
[40] "57" "58" "59" "60"Grouping age into categories
Since the number of age groups are too many to derive a generalized conclusion we group the ages into five categories - less than or equal to 20, in 20’s, 30’s, 40’s and the ones greater than 50 .
one <- c("18", "19", "20")
two <- c("21", "22", "23", "24", "25", "26", "27", "28", "29", "30" )
three <- c("31", "32", "33", "34", "35", "36", "37", "38", "39", "40")
four <- c( "41", "42", "43", "44", "45", "46", "47", "48", "49", "50")
five <- c( "51", "52", "53", "54", "55", "56", "57", "58", "59", "60")
participants_data_mod <- participants_data %>%
mutate(age_modified = case_when(
age %in% one ~ "<=20",
age %in% two ~ "20's",
age %in% three ~ "30's",
age %in% four ~ "40's",
age %in% five ~ "50+")) %>%
select(-age)
head(participants_data_mod)
# A tibble: 6 × 7
participantId householdSize haveKids educationLevel interestGroup
<dbl> <dbl> <lgl> <chr> <chr>
1 0 3 TRUE HighSchoolOrColl… H
2 1 3 TRUE HighSchoolOrColl… B
3 2 3 TRUE HighSchoolOrColl… A
4 3 3 TRUE HighSchoolOrColl… I
5 4 3 TRUE Bachelors H
6 5 3 TRUE HighSchoolOrColl… D
# … with 2 more variables: joviality <dbl>, age_modified <chr>Data Summary
summary(participants_data_mod)
participantId householdSize haveKids educationLevel
Min. : 0.0 Min. :1.000 Mode :logical Length:1011
1st Qu.: 252.5 1st Qu.:1.000 FALSE:710 Class :character
Median : 505.0 Median :2.000 TRUE :301 Mode :character
Mean : 505.0 Mean :1.964
3rd Qu.: 757.5 3rd Qu.:3.000
Max. :1010.0 Max. :3.000
interestGroup joviality age_modified
Length:1011 Min. :0.000204 Length:1011
Class :character 1st Qu.:0.240074 Class :character
Mode :character Median :0.477539 Mode :character
Mean :0.493794
3rd Qu.:0.746819
Max. :0.999234 participants_data_new <- summarise_at(group_by(participants_data_mod,age_modified,educationLevel),vars(joviality),funs(mean(.,na.rm=TRUE)))
head(participants_data_new)
# A tibble: 6 × 3
# Groups: age_modified [2]
age_modified educationLevel joviality
<chr> <chr> <dbl>
1 <=20 Bachelors 0.444
2 <=20 Graduate 0.583
3 <=20 HighSchoolOrCollege 0.483
4 <=20 Low 0.466
5 20's Bachelors 0.536
6 20's Graduate 0.565Sorting Data
As shown above, the values of the tibble data frame is sorted according to the age.
participants_data_mod[order(participants_data_mod$age_modified), ]
# A tibble: 1,011 × 7
participantId householdSize haveKids educationLevel interestGroup
<dbl> <dbl> <lgl> <chr> <chr>
1 8 3 TRUE Bachelors G
2 13 3 TRUE Bachelors J
3 18 3 TRUE Graduate I
4 29 3 TRUE Low C
5 35 3 TRUE Low J
6 53 3 TRUE Low H
7 67 3 TRUE HighSchoolOrCol… C
8 77 2 FALSE HighSchoolOrCol… C
9 88 2 FALSE HighSchoolOrCol… A
10 90 2 FALSE HighSchoolOrCol… E
# … with 1,001 more rows, and 2 more variables: joviality <dbl>,
# age_modified <chr>Data Visualization and Insights
A histogram is used to check the shape of the data distribution. The below graph reveals that the range of distribution of Joviality is from 0 to 1. We also observe that in our test data of participants there are very few participants having value of joviality closer to 1. Here the function geom_histogram is used to plot the distribution of joviality since joviality is a continuous variable.
ggplot(data=participants_data,
aes(x = joviality)) +
geom_histogram(bins=20,
boundary = 50,
color="black",
fill="light blue") +
coord_cartesian(xlim=c(0.01,1)) +
labs(
title = "Distribution of Joviality",
caption = "demographic information, Ohio USA"
) +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 1),
plot.caption = element_text(hjust = 0)
)
ggplot(data=participants_data_mod,
aes(x=age_modified, fill = educationLevel)) +
geom_bar()+
labs(
title = "Distribution of Age for different household types",
caption = "demographic information, Ohio USA"
) +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 1),
plot.caption = element_text(hjust = 0)
)
- from the above graph we can infer that the population consists of the lowest number of participants from the age group less than 20. It is also observed that from the group of participants the ones in their 20’s are the most jovial.
ggplot(data=participants_data_mod,
aes(x= joviality,
y= educationLevel, fill = haveKids)) +
geom_col() +
theme_classic()+
labs(
title = "Joviality Measure",
caption = "demographic information, Ohio USA"
) +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 1),
plot.caption = element_text(hjust = 0)
)+
facet_wrap(~age_modified)
ggplot(data=participants_data_mod,
aes(x= interestGroup,
y= joviality)) +
geom_col() +
theme_classic()+
labs(
title = "Joviality Measure",
caption = "demographic information, Ohio USA"
) +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 1),
plot.caption = element_text(hjust = 0)
)+
facet_wrap(~age_modified)
Conclusion
We can observe that on an average the people who are in high school or college and graduates are the ones who are the happiest of all in all age groups.
the joviality value changes across different age groups over different interests.