Analysis Happinees World Data
Done with chatGPT in Python and R
Hans U Deck
2024-03-26
0.Question to chatGPT: “Write a program in python to analyse this data”
1.Code in Python
I asked the question and chatGPT will do the whole job:
I will first load the data using pandas to understand its structure and contents.
import pandas as pd
# Load the data
data = pd.read_csv('Datasets/worldwide_happiness_report.csv')
# Display the first few rows of the dataframe
data.head()## Overall rank Country or region ... Generosity Perceptions of corruption
## 0 1 Finland ... 0.153 0.393
## 1 2 Denmark ... 0.252 0.410
## 2 3 Norway ... 0.271 0.341
## 3 4 Iceland ... 0.354 0.118
## 4 5 Netherlands ... 0.322 0.298
##
## [5 rows x 9 columns]The data contains the following columns: ‘Overall rank’, ‘Country or region’, ‘Score’, ‘GDP per capita’, ‘Social support’, ‘Healthy life expectancy’, ‘Freedom to make life choices’, ‘Generosity’, and ‘Perceptions of corruption’. Now, I will perform some basic statistical analysis on this data.
# Perform basic statistical analysis
data.describe()## Overall rank Score ... Generosity Perceptions of corruption
## count 156.000000 156.000000 ... 156.000000 156.000000
## mean 78.500000 5.407096 ... 0.184846 0.110603
## std 45.177428 1.113120 ... 0.095254 0.094538
## min 1.000000 2.853000 ... 0.000000 0.000000
## 25% 39.750000 4.544500 ... 0.108750 0.047000
## 50% 78.500000 5.379500 ... 0.177500 0.085500
## 75% 117.250000 6.184500 ... 0.248250 0.141250
## max 156.000000 7.769000 ... 0.566000 0.453000
##
## [8 rows x 8 columns]The data has been analyzed and the basic statistics have been provided. The next step would be to visualize this data to better understand the relationships between different variables. I will use matplotlib and seaborn for this purpose.
import matplotlib.pyplot as plt
import seaborn as sns
# Pairplot to visualize the relationships between variables
sns.pairplot(data)
import matplotlib.pyplot as plt
# List of columns for which we want to plot
columns = ['Score', 'GDP per capita', 'Social support', 'Healthy life expectancy', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption']
# Create scatter plots for each pair of variables
fig, axs = plt.subplots(len(columns), len(columns), figsize=(15,15))
for i in range(len(columns)):
for j in range(len(columns)):
axs[i, j].scatter(data[columns[i]], data[columns[j]])
axs[i, j].set(xlabel=columns[j], ylabel=columns[i])
plt.tight_layout()
plt.show()
# Create histograms for each variable
data.hist(bins=50, figsize=(20,15))## array([[<Axes: title={'center': 'Overall rank'}>,
## <Axes: title={'center': 'Score'}>,
## <Axes: title={'center': 'GDP per capita'}>],
## [<Axes: title={'center': 'Social support'}>,
## <Axes: title={'center': 'Healthy life expectancy'}>,
## <Axes: title={'center': 'Freedom to make life choices'}>],
## [<Axes: title={'center': 'Generosity'}>,
## <Axes: title={'center': 'Perceptions of corruption'}>, <Axes: >]],
## dtype=object)plt.show()
The histograms provide a visual representation of the distribution of each variable in the dataset. This is a basic analysis of the data. For a more detailed analysis, specific questions or hypotheses about the data would be helpful.
The histograms provide a visual representation of the distribution of each variable in the dataset. This is a basic analysis of the data. For a more detailed analysis, specific questions or hypotheses about the data would be helpful.
The data from the ‘worldwide_happiness_report.csv’ file has been loaded and analyzed. The dataset contains the following columns: ‘Overall rank’, ‘Country or region’, ‘Score’, ‘GDP per capita’, ‘Social support’, ‘Healthy life expectancy’, ‘Freedom to make life choices’, ‘Generosity’, and ‘Perceptions of corruption’.
1.1 Result from Python
A basic statistical analysis of the data reveals the following:
The dataset contains 156 entries.
The average happiness score is approximately 5.41, with a standard deviation of 1.11.
The minimum happiness score is 2.85, and the maximum is 7.77.
The average GDP per capita is 0.91, with a standard deviation of 0.40.
The average social support score is 1.21, with a standard deviation of 0.30.
The average healthy life expectancy score is 0.73, with a standard deviation of 0.24.
The average freedom to make life choices score is 0.39, with a standard deviation of 0.14.
The average generosity score is 0.18, with a standard deviation of 0.09.
The average perceptions of corruption score is 0.11, with a standard deviation of 0.09.
Histograms were also created for each variable to visualize their distributions. You can view the histograms at the following links:
Histogram 1
Histogram 2
This is a basic analysis of the data. For a more detailed analysis, specific questions or hypotheses about the data would be helpful. Please feel free to ask any further questions you may have.
2.Code in R
The user wants the previous analysis to be written in R. However, I can only execute Python code. I can provide the equivalent R code, but I won’t be able to run it.
# Load the necessary libraries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(reshape2)
# Read the data
data <- read.csv('Datasets/worldwide_happiness_report.csv')
# Select only numeric columns
numeric_data <- select_if(data, is.numeric)
# Calculate the correlation matrix
correlation_matrix <- cor(numeric_data)
# Print the correlation matrix
print(correlation_matrix)## Overall.rank Score GDP.per.capita
## Overall.rank 1.00000000 -0.98909622 -0.80194654
## Score -0.98909622 1.00000000 0.79388287
## GDP.per.capita -0.80194654 0.79388287 1.00000000
## Social.support -0.76746531 0.77705779 0.75490573
## Healthy.life.expectancy -0.78741066 0.77988315 0.83546212
## Freedom.to.make.life.choices -0.54660640 0.56674183 0.37907907
## Generosity -0.04799261 0.07582369 -0.07966231
## Perceptions.of.corruption -0.35195851 0.38561307 0.29891985
## Social.support Healthy.life.expectancy
## Overall.rank -0.76746531 -0.78741066
## Score 0.77705779 0.77988315
## GDP.per.capita 0.75490573 0.83546212
## Social.support 1.00000000 0.71900946
## Healthy.life.expectancy 0.71900946 1.00000000
## Freedom.to.make.life.choices 0.44733316 0.39039478
## Generosity -0.04812645 -0.02951086
## Perceptions.of.corruption 0.18189946 0.29528281
## Freedom.to.make.life.choices Generosity
## Overall.rank -0.5466064 -0.04799261
## Score 0.5667418 0.07582369
## GDP.per.capita 0.3790791 -0.07966231
## Social.support 0.4473332 -0.04812645
## Healthy.life.expectancy 0.3903948 -0.02951086
## Freedom.to.make.life.choices 1.0000000 0.26974181
## Generosity 0.2697418 1.00000000
## Perceptions.of.corruption 0.4388433 0.32653754
## Perceptions.of.corruption
## Overall.rank -0.3519585
## Score 0.3856131
## GDP.per.capita 0.2989198
## Social.support 0.1818995
## Healthy.life.expectancy 0.2952828
## Freedom.to.make.life.choices 0.4388433
## Generosity 0.3265375
## Perceptions.of.corruption 1.0000000# Melt the correlation matrix for visualization
melted_cormat <- melt(correlation_matrix)
# Create a heatmap
ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1),
axis.text.y = element_text(size = 12)) +
coord_fixed()
# Deeper analysis: Outlier detection using Cook's distance
model <- lm(numeric_data$Social.support ~ ., data = numeric_data)
cooksd <- cooks.distance(model)
# Plot Cook's distance
plot(cooksd, pch="*", cex=2, main="Influential Obs by Cooks distance") # plot cook's distance
abline(h = 4*mean(cooksd, na.rm=T), col="red") # add cutoff line
# Deeper analysis: Hypothesis testing
# Perform a t-test (change variables as needed)
t_test_result <- t.test(numeric_data$GDP.per.capita, numeric_data$Social.support)
print(t_test_result)##
## Welch Two Sample t-test
##
## data: numeric_data$GDP.per.capita and numeric_data$Social.support
## t = -7.6126, df = 287.65, p-value = 3.875e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3821801 -0.2251533
## sample estimates:
## mean of x mean of y
## 0.9051474 1.2088141# Deeper analysis: Regression analysis
# Perform a linear regression (change variables as needed)
regression_result <- lm(numeric_data$GDP.per.capita ~ numeric_data$Social.support, data = numeric_data)
summary(regression_result)##
## Call:
## lm(formula = numeric_data$GDP.per.capita ~ numeric_data$Social.support,
## data = numeric_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.72690 -0.16476 0.02471 0.17799 0.67413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.30995 0.08762 -3.538 0.000534 ***
## numeric_data$Social.support 1.00520 0.07037 14.284 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2621 on 154 degrees of freedom
## Multiple R-squared: 0.5699, Adjusted R-squared: 0.5671
## F-statistic: 204 on 1 and 154 DF, p-value: < 2.2e-162.1 Result from R
This code should be run in an R environment. It begins by loading the necessary libraries, then reads the data from a CSV file. It selects only the numeric columns from the data, then calculates and prints a correlation matrix. The correlation matrix is then melted for visualization, and a heatmap is created using ggplot2.
Please note that the heatmap’s color gradient is set to range from blue (low correlation) to red (high correlation), with white indicating no correlation. The x and y-axis labels are set to the variable names, and the fill color represents the Pearson correlation coefficient.
If you have any further questions or need additional assistance, please don’t hesitate to ask.