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Exploring data through visualizations is a crucial aspect of data analysis. In this article, we’ll delve into Rstudio and utilize its powerful libraries, such as ggplot2 and dplyr, to create insightful data visualizations. We’ll focus on the Gapminder dataset, providing step-by-step explanations and code snippets to enhance your understanding.

Before We start, Make sure you Have:

Understanding ggplot2

Exploring the mtcars Dataset

Let’s start by visualizing the relationship between miles per gallon (mpg), horsepower (hp), and the number of gears in the mtcars dataset.

Analyzing the Gapminder Dataset

Loading and Summarizing GapminderData

Before we dive into visualizations, let’s load the Gapminder dataset and gain some insights into its structure.

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.
##         country        continent        year         lifeExp     
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
##  Australia  :  12                  Max.   :2007   Max.   :82.60  
##  (Other)    :1632                                                
##       pop              gdpPercap       
##  Min.   :6.001e+04   Min.   :   241.2  
##  1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Median :7.024e+06   Median :  3531.8  
##  Mean   :2.960e+07   Mean   :  7215.3  
##  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Max.   :1.319e+09   Max.   :113523.1  
## 
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

Visualizing Life Expectancy Across Countries

Creating a Heatmap

Let’s visualize life expectancy across countries and years using a heatmap.

Exploring Top 100 Countries by Life Expectancy

Creating a Bar Plot

Let’s narrow down our focus to the top 100 countries with the highest life expectancy and create a bar plot.

Adding Color to the Bar Plot

Enhancing the previous plot by adding vibrant colors using the viridis colorpalette.

Bonus: Histogram of Life Expectancy

Examining Continent-wise Life Expectancy Distribution

Let’s create a histogram to visualize the distribution of life expectancy in 2007 across continents.

Conclusion

In this journey through Rstudio and its data analysis capabilities, we’ve explored visualizations ranging from simple heatmaps to intricate bar plots. Data storytelling comes alive when we harness the power of code to uncover patterns and insights.