DATA 606 Data Project Proposal
Wilson Chau
Data Preparation
I downloaded the lastest dataset from 2021. I uploaded it into github and reading the RAW data from github.
# load data
# examine all available CES microdata files.
library(tidyverse)
happiness <- read.csv("https://raw.githubusercontent.com/Wilchau/Data606_Happiness_Project/main/world-happiness-report-2021.csv.xls")Research question
You should phrase your research question in a way that matches up with the scope of inference your dataset allows for.
I feel like happiness is a very important factor to living these days. This dataset was reported for 2021 on happiness. Goveronment study down and being isolated contributed to the happiness of everyone.
I wanted to see a correlation on how freedom to make life choices, and social support can contribute to the happiness of others?
Cases
What are the cases, and how many are there?
There’s a total of 149 observations with 20 variables to focus on.
head(happiness)## Country.name Regional.indicator Ladder.score Standard.error.of.ladder.score
## 1 Finland Western Europe 7.842 0.032
## 2 Denmark Western Europe 7.620 0.035
## 3 Switzerland Western Europe 7.571 0.036
## 4 Iceland Western Europe 7.554 0.059
## 5 Netherlands Western Europe 7.464 0.027
## 6 Norway Western Europe 7.392 0.035
## upperwhisker lowerwhisker Logged.GDP.per.capita Social.support
## 1 7.904 7.780 10.775 0.954
## 2 7.687 7.552 10.933 0.954
## 3 7.643 7.500 11.117 0.942
## 4 7.670 7.438 10.878 0.983
## 5 7.518 7.410 10.932 0.942
## 6 7.462 7.323 11.053 0.954
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 72.0 0.949 -0.098
## 2 72.7 0.946 0.030
## 3 74.4 0.919 0.025
## 4 73.0 0.955 0.160
## 5 72.4 0.913 0.175
## 6 73.3 0.960 0.093
## Perceptions.of.corruption Ladder.score.in.Dystopia
## 1 0.186 2.43
## 2 0.179 2.43
## 3 0.292 2.43
## 4 0.673 2.43
## 5 0.338 2.43
## 6 0.270 2.43
## Explained.by..Log.GDP.per.capita Explained.by..Social.support
## 1 1.446 1.106
## 2 1.502 1.108
## 3 1.566 1.079
## 4 1.482 1.172
## 5 1.501 1.079
## 6 1.543 1.108
## Explained.by..Healthy.life.expectancy
## 1 0.741
## 2 0.763
## 3 0.816
## 4 0.772
## 5 0.753
## 6 0.782
## Explained.by..Freedom.to.make.life.choices Explained.by..Generosity
## 1 0.691 0.124
## 2 0.686 0.208
## 3 0.653 0.204
## 4 0.698 0.293
## 5 0.647 0.302
## 6 0.703 0.249
## Explained.by..Perceptions.of.corruption Dystopia...residual
## 1 0.481 3.253
## 2 0.485 2.868
## 3 0.413 2.839
## 4 0.170 2.967
## 5 0.384 2.798
## 6 0.427 2.580Data collection
Describe the method of data collection.
This data was collected in 2021 by Sustainable Development Solutions Network.
Type of study
What type of study is this (observational/experiment)?
This type of study is obsersvational.
Data Source
If you collected the data, state self-collected. If not, provide a citation/link.
This is where I got the dataset: https://www.kaggle.com/datasets/ajaypalsinghlo/world-happiness-report-2021
This data is collected by the World Happiness Report where they do a landmark survey of the state of global happiness. This is supported by the government and public policy. There was no attempt made to affect the outcome of this dataset.
Dependent Variable
What is the response variable? Is it quantitative or qualitative?
The response variable is freedom to make life choices, and social support equantitative(numeric value)
Independent Variable(s)
The indepedent variables are Ladder score, Regional indciator, Explained by: Social Support, and Explained by: freedom to make life chocies.
Relevant summary statistics
Provide summary statistics for each the variables. Also include appropriate visualizations related to your research question (e.g. scatter plot, boxplots, etc). This step requires the use of R, hence a code chunk is provided below. Insert more code chunks as needed.
head(happiness)## Country.name Regional.indicator Ladder.score Standard.error.of.ladder.score
## 1 Finland Western Europe 7.842 0.032
## 2 Denmark Western Europe 7.620 0.035
## 3 Switzerland Western Europe 7.571 0.036
## 4 Iceland Western Europe 7.554 0.059
## 5 Netherlands Western Europe 7.464 0.027
## 6 Norway Western Europe 7.392 0.035
## upperwhisker lowerwhisker Logged.GDP.per.capita Social.support
## 1 7.904 7.780 10.775 0.954
## 2 7.687 7.552 10.933 0.954
## 3 7.643 7.500 11.117 0.942
## 4 7.670 7.438 10.878 0.983
## 5 7.518 7.410 10.932 0.942
## 6 7.462 7.323 11.053 0.954
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 72.0 0.949 -0.098
## 2 72.7 0.946 0.030
## 3 74.4 0.919 0.025
## 4 73.0 0.955 0.160
## 5 72.4 0.913 0.175
## 6 73.3 0.960 0.093
## Perceptions.of.corruption Ladder.score.in.Dystopia
## 1 0.186 2.43
## 2 0.179 2.43
## 3 0.292 2.43
## 4 0.673 2.43
## 5 0.338 2.43
## 6 0.270 2.43
## Explained.by..Log.GDP.per.capita Explained.by..Social.support
## 1 1.446 1.106
## 2 1.502 1.108
## 3 1.566 1.079
## 4 1.482 1.172
## 5 1.501 1.079
## 6 1.543 1.108
## Explained.by..Healthy.life.expectancy
## 1 0.741
## 2 0.763
## 3 0.816
## 4 0.772
## 5 0.753
## 6 0.782
## Explained.by..Freedom.to.make.life.choices Explained.by..Generosity
## 1 0.691 0.124
## 2 0.686 0.208
## 3 0.653 0.204
## 4 0.698 0.293
## 5 0.647 0.302
## 6 0.703 0.249
## Explained.by..Perceptions.of.corruption Dystopia...residual
## 1 0.481 3.253
## 2 0.485 2.868
## 3 0.413 2.839
## 4 0.170 2.967
## 5 0.384 2.798
## 6 0.427 2.580summary(happiness$Social.support)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4630 0.7500 0.8320 0.8147 0.9050 0.9830summary(happiness$Freedom.to.make.life.choices)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3820 0.7180 0.8040 0.7916 0.8770 0.9700Looking through social support and freedom to make life choices. I see that a range of 0 to 1 is used here. Closest to 1 meaning sigifncant results while going close to 0 means there is a huge decline.
happiness %>%
pivot_longer(cols = c(Social.support, Freedom.to.make.life.choices)) %>%
ggplot(mapping = aes(x = value)) +
geom_histogram() +
facet_wrap(~name, scales = "free_x")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
happiness %>%
ggplot(mapping = aes(x = Ladder.score, y = Social.support)) +
geom_point()
happiness %>%
ggplot(mapping = aes(x = Ladder.score, y = Freedom.to.make.life.choices)) +
geom_point()