1. Introduction

In this part, I worked with a dataset showing the population and GDP for three countries (USA, China, and India) across three different years: 2000, 2005, and 2010. The dataset is untidy because it is presented in a wide format, where each year has its own set of columns for both population and GDP. The goal was to tidy the dataset, reshape it into a long format, and make it easier to analyze and visualize economic and population trends over time.

2. Load Libraries

To start, I loaded the libraries used for organizing, transforming, and analyzing the data.

library(tidyverse)

3. Create the Dataset

I recreated the dataset in wide format to match the structure of the original table. This table contains population and GDP data for the years 2000, 2005, and 2010, stored in separate columns for each year.

gdp_data <- data.frame(
  Country = c("USA", "China", "India"),
  `2000_Population` = c(282162411, 1262645000, 1053050912),
  `2000_GDP` = c(10285, 1198, 476),
  `2005_Population` = c(295516599, 1307560000, 1139964932),
  `2005_GDP` = c(13094, 2286, 834),
  `2010_Population` = c(309327143, 1340910000, 1224614327),
  `2010_GDP` = c(14964, 6087, 1708)
)

gdp_data
##   Country X2000_Population X2000_GDP X2005_Population X2005_GDP
## 1     USA        282162411     10285        295516599     13094
## 2   China       1262645000      1198       1307560000      2286
## 3   India       1053050912       476       1139964932       834
##   X2010_Population X2010_GDP
## 1        309327143     14964
## 2       1340910000      6087
## 3       1224614327      1708

4. Tidy the Data

The dataset was untidy because each year had separate columns for population and GDP. To tidy it, I converted the dataset into long format using pivot_longer(), creating columns for year, population, and GDP. This format makes it easier to perform comparisons and trend analyses across countries and time periods.

gdp_long <- gdp_data %>%
  pivot_longer(
    cols = -Country,
    names_to = c("Year", ".value"),
    names_sep = "_"
  )

gdp_long
## # A tibble: 9 × 4
##   Country Year  Population   GDP
##   <chr>   <chr>      <dbl> <dbl>
## 1 USA     X2000  282162411 10285
## 2 USA     X2005  295516599 13094
## 3 USA     X2010  309327143 14964
## 4 China   X2000 1262645000  1198
## 5 China   X2005 1307560000  2286
## 6 China   X2010 1340910000  6087
## 7 India   X2000 1053050912   476
## 8 India   X2005 1139964932   834
## 9 India   X2010 1224614327  1708

5. Transform and Summary

Next, I summarized population and GDP changes over time to highlight growth patterns for each country. This allowed me to observe how both variables evolved between 2000 and 2010.

summary_table <- gdp_long %>%
  group_by(Country) %>%
  summarise(
    Population_Growth = max(Population) - min(Population),
    GDP_Growth = max(GDP) - min(GDP)
  )

summary_table
## # A tibble: 3 × 3
##   Country Population_Growth GDP_Growth
##   <chr>               <dbl>      <dbl>
## 1 China            78265000       4889
## 2 India           171563415       1232
## 3 USA              27164732       4679

6. Visualization

To visualize the economic trends, I created a line chart showing GDP changes over time for each country. This chart illustrates how each nation’s economy developed during the decade, with China showing the fastest rate of GDP growth.

ggplot(gdp_long, aes(x = Year, y = GDP, color = Country, group = Country)) +
  geom_line(size = 1.2) +
  geom_point(size = 2) +
  labs(title = "GDP Growth (2000–2010)", y = "GDP per Capita ($)", x = "Year") +
  theme_minimal()

7. Conclusion

After tidying and transforming the dataset, I was able to clearly visualize population and GDP trends from 2000 to 2010. This part of the project demonstrated how reshaping wide-format data into long format provides flexibility for comparison, statistical summaries, and visual analysis of growth over time.