Yan two Graphs

# load data
 nations <- read.csv("~/DATA 110/nations.csv")

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, union

# Adding a new variable
nations <- nations |>
  mutate(nations, GDP_trillions = (gdp_percap * population)/1e+12, na.rm = TRUE)
# View the added variable
View(nations)

library(dplyr)
# filter the data to 4 countries
filtered_nations <- nations %>%
  filter(country %in% c("Belgium", "Chile", "Colombia", "Cuba"))
# View the filtered data
View(filtered_nations)

library(ggplot2)
library(RColorBrewer)
# Set 1 from RColorBrewer
colors <- brewer.pal(4, "Set1")
# Create the dot line plot
ggplot(filtered_nations, aes(x = year, y = GDP_trillions, color = country)) +
   geom_line() +
  geom_point() +
  scale_color_manual(values = colors) +
  labs(title = "GDP Over Time for 4 Countries",
       x = "Year",
       y = "GDP (in Trillions of Dollars)") +
  theme_minimal()

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_line()`).

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).

library(dplyr)
# Adding a new variable
nations <- nations %>%
  mutate(GDP_trillions = (gdp_percap * population) / 1e12)
# View the added variable
View(nations)

library(dplyr)
# Group by region and year, then summarize the total GDP
summarized_data <- nations %>%
  group_by(region, year) %>%
  summarise(GDP = sum(GDP_trillions, na.rm = TRUE))

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

# View the summarized data
View(summarized_data)

library(ggplot2)
library(RColorBrewer)
# summary of the 7 regions
summarized_data

# A tibble: 175 × 3
# Groups:   region [7]
   region               year   GDP
   <chr>               <int> <dbl>
 1 East Asia & Pacific  1990  5.52
 2 East Asia & Pacific  1991  6.03
 3 East Asia & Pacific  1992  6.50
 4 East Asia & Pacific  1993  7.04
 5 East Asia & Pacific  1994  7.64
 6 East Asia & Pacific  1995  8.29
 7 East Asia & Pacific  1996  8.96
 8 East Asia & Pacific  1997  9.55
 9 East Asia & Pacific  1998  9.60
10 East Asia & Pacific  1999 10.1 
# ℹ 165 more rows

region <- c("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean", "Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa")
# Set color palette
colors <- brewer.pal(7, "Set2")
# Create an area plot with thin white lines
ggplot(summarized_data, aes(x = year, y = GDP, fill = region)) +
  geom_area(color = "white", linewidth = 0.2) +
    scale_fill_manual(values = colors) +
  labs(title = "GDP Over Time for 7 Regions",
       x = "Year",
       y = "GDP (Trillions)") +
  theme_minimal()