Untitled

# install.packages("tidyverse")
# install.packages("dslabs")
# install.packages("dplyr")
# install.packages("ggplot2")
library(tidyverse)

## ── Attaching packages ───────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.1     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dslabs)
library(dplyr)
library(ggplot2)

library(readxl)
nations <- read.csv("~/Downloads/nations.csv")

head(nations)

##   iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
## 1    AD   AND Andorra 1996         NA      64291       10.9                2.8
## 2    AD   AND Andorra 1994         NA      62707       10.9                3.2
## 3    AD   AND Andorra 2003         NA      74783       10.3                2.0
## 4    AD   AND Andorra 1990         NA      54511       11.9                4.3
## 5    AD   AND Andorra 2009         NA      85474        9.9                1.7
## 6    AD   AND Andorra 2011         NA      82326         NA                1.6
##                  region      income
## 1 Europe & Central Asia High income
## 2 Europe & Central Asia High income
## 3 Europe & Central Asia High income
## 4 Europe & Central Asia High income
## 5 Europe & Central Asia High income
## 6 Europe & Central Asia High income

nations2 <- nations %>%
   mutate(nations,gdp=(gdp_percap*population)/1000000000000) %>%
  filter(country == "China" | country == "Japan" | country == "United States"| country == "Germany")

ggplot((nations2), aes(x = year, y = gdp, color = country))+
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1")+
  ylab("GDP ($Trillions)")+
  ggtitle("China's Rise to become the Largest Economy")

head(nations, n=20)

##    iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
## 1     AD   AND Andorra 1996         NA      64291       10.9                2.8
## 2     AD   AND Andorra 1994         NA      62707       10.9                3.2
## 3     AD   AND Andorra 2003         NA      74783       10.3                2.0
## 4     AD   AND Andorra 1990         NA      54511       11.9                4.3
## 5     AD   AND Andorra 2009         NA      85474        9.9                1.7
## 6     AD   AND Andorra 2011         NA      82326         NA                1.6
## 7     AD   AND Andorra 2004         NA      78337       10.9                2.0
## 8     AD   AND Andorra 2010         NA      84419        9.8                1.7
## 9     AD   AND Andorra 2001         NA      67770       11.8                2.1
## 10    AD   AND Andorra 2002         NA      71046       11.2                2.1
## 11    AD   AND Andorra 1997         NA      64147       11.2                2.6
## 12    AD   AND Andorra 1993         NA      61003       11.4                3.4
## 13    AD   AND Andorra 2008         NA      85616       10.4                1.8
## 14    AD   AND Andorra 1999         NA      64161       12.6                2.3
## 15    AD   AND Andorra 2014         NA      72786         NA                1.5
## 16    AD   AND Andorra 2005         NA      81223       10.7                1.9
## 17    AD   AND Andorra 2012         NA      79316        9.5                1.6
## 18    AD   AND Andorra 2013         NA      75902         NA                1.5
## 19    AD   AND Andorra 1992         NA      58904       12.1                3.7
## 20    AD   AND Andorra 1995         NA      63854       11.0                3.0
##                   region      income
## 1  Europe & Central Asia High income
## 2  Europe & Central Asia High income
## 3  Europe & Central Asia High income
## 4  Europe & Central Asia High income
## 5  Europe & Central Asia High income
## 6  Europe & Central Asia High income
## 7  Europe & Central Asia High income
## 8  Europe & Central Asia High income
## 9  Europe & Central Asia High income
## 10 Europe & Central Asia High income
## 11 Europe & Central Asia High income
## 12 Europe & Central Asia High income
## 13 Europe & Central Asia High income
## 14 Europe & Central Asia High income
## 15 Europe & Central Asia High income
## 16 Europe & Central Asia High income
## 17 Europe & Central Asia High income
## 18 Europe & Central Asia High income
## 19 Europe & Central Asia High income
## 20 Europe & Central Asia High income

nations3 <- nations %>%
   mutate(nations,gdp=(gdp_percap*population)/1000000000000) %>%
  group_by(region) %>%
  group_by(year)
  view(nations3)
summarise(nations3, sum = sum(gdp_percap, na.rm = TRUE)) 

## `summarise()` ungrouping output (override with `.groups` argument)

## # A tibble: 25 x 2
##     year      sum
##    <int>    <dbl>
##  1  1990 1225550.
##  2  1991 1280913.
##  3  1992 1319715.
##  4  1993 1356759.
##  5  1994 1410427.
##  6  1995 1574917.
##  7  1996 1639416.
##  8  1997 1714568.
##  9  1998 1763558.
## 10  1999 1837916.
## # … with 15 more rows

ggplot(nations3, aes(x = year, y = gdp, fill = region))+
  geom_area()+
  geom_line()+
  ggtitle("GDP by World Bank Region")+
  scale_fill_brewer(palette = "Set2", aesthetics = "fill")

## Warning: Removed 766 rows containing missing values (position_stack).

## Warning: Removed 6 row(s) containing missing values (geom_path).