Data Visualization in R

Temperature and Rainfall Over the Years

Necessary Libraries

library(ggplot2)
library(plotly)

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library(tidyverse)

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library(gapminder)
library(gganimate)
library(dplyr)

Temp_and_rain -Data

Temp_and_rain <- read.csv("Temp_and_rain.CSV")

##Temp_and_rain -Scatter plot

ggplot(Temp_and_rain, aes(x = tem, y = rain)) +
  geom_point(color = 'blue') +
  labs(title = 'Scatter Plot of Temperature and Rainfall', x = 'Temperature (°C)', y = 'Rainfall (mm)') +
  theme_minimal()

###***Interpretation

The plot visually represents the relationship between temperature and rainfall using blue points on a scatter plot. The x-axis represents temperature in degrees Celsius; the y-axis represents rainfall in millimetres. Each point on the plot corresponds to a specific combination of temperature and rain from the dataset “Temp_and_rain.” The minimal theme enhances the simplicity in the plot’s appearance.

##Add a linear Regression line

ggplot(Temp_and_rain, aes(x = tem, y = rain)) +
  geom_point(color = 'blue') +
  geom_smooth(method = 'lm', se = FALSE, color = 'red') +
  labs(title = 'Scatter Plot of Temperature and Rainfall', x = 'Temperature (°C)', y = 'Rainfall (mm)') +
  theme_minimal()

## `geom_smooth()` using formula = 'y ~ x'

###***Interpretation

The plot illustrates the correlation between temperature and rainfall using blue points on a scatter plot. Additionally, a red linear regression line has been added through the data points, providing a visual representation of the overall trend.The linear regression line helps to assess the general direction and strength of the relationship between temperature and rainfall in the dataset.

##Base plot with points and regression line

plot(Temp_and_rain$tem, Temp_and_rain$rain, col = "skyblue", pch = 16,
     xlab = 'Temperature (°C)', ylab = 'Rainfall (mm)',
     main = 'Scatter Plot of Temperature and Rainfall Across Years')
lm_model <- lm(rain ~ tem, data = Temp_and_rain)
abline(lm_model, col = 'blue')

###***Interpretation

The provided R code generates a scatter plot depicting the relationship between temperature and rainfall across multiple years. The points on the plot, shown in sky blue, represent individual temperature and rainfall data pairs. Additionally, a linear regression line in blue is superimposed on the plot using a model created with the lm function. This line serves as a visual representation of the overall trend in the data. ##Add an Interactive Scatter Plot

plot_ly(data = Temp_and_rain, x = ~tem, y = ~rain, type = 'scatter', mode = 'markers',
        marker = list(color = ~Year, colorscale = 'Viridis'),
        text = ~paste('Year: ', Year)) %>%
  add_trace(x = ~tem, y = ~fitted(lm(rain ~ tem)), type = 'scatter', mode = 'lines',
            line = list(color = 'green')) %>%
  layout(title = 'Interactive Scatter Plot of Temperature and Rainfall Across Years',
         xaxis = list(title = 'Temperature (°C)'),
         yaxis = list(title = 'Rainfall (mm)'),
         showlegend = FALSE)

## A marker object has been specified, but markers is not in the mode
## Adding markers to the mode...

###***Interpretation

The plot displays temperature on the x-axis and rainfall on the y-axis from the dataset Temp_and_rain. Each point in the scatter plot is colored based on the corresponding year, using the Viridis color scale. Text labels provide information about the year for each data point. Additionally, a green line represents the fitted linear regression line for the relationship between temperature and rainfall. #Create an animated bubble chart

animated_bubble_chart <- ggplot(Temp_and_rain, aes(x = tem, y = rain, size = tem, color = as.factor(Year))) +
  geom_point(alpha = 0.7) +
  labs(
    title = "Animated Bubble Chart of Temperature and Rainfall",
    x = "Temperature",
    y = "Rainfall",
    size = "Temperature",
    color = "Year"
  ) +
  transition_states(Year, transition_length = 2, state_length = 1) +
  ease_aes('linear') +
  shadow_mark(past = TRUE, future = FALSE)
animate(animated_bubble_chart, nframes = length(unique(Temp_and_rain$Year)) * 3, fps = 5)

anim_save("Temp_and_rain.gif", animated_bubble_chart)

###***Interpretation

Each bubble on the chart represents a specific year, with its size corresponding to temperature and color indicating the year. The chart transitions smoothly between different years, providing a dynamic visual representation of how temperature and rainfall patterns evolve over time. The animation is designed to enhance insights into the temporal trends within the dataset, with a linear easing effect and added shadows to highlight past states.