week 7 HW chart 2

library(readr)
library(ggplot2)
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

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(tidyverse)

## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.2.1 --

## v tibble  2.1.3     v purrr   0.3.2
## v tidyr   1.0.0     v stringr 1.4.0
## v tibble  2.1.3     v forcats 0.4.0

## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x plotly::filter() masks dplyr::filter(), stats::filter()
## x dplyr::lag()     masks stats::lag()

As with the first chart, I didn’t really know where to begin with mutating in dplyr. However, this csv manipulation in Excel took a lot longer than I expected, so now I understand why R might be a more useful tool in many instances. (Unfortunately I don’t feel capable enough in R at the moment, so Excel it is…)

setwd("C:/Users/Don A/Documents/Don's files/MC")
week7hw2 <- read_csv("nations2b.csv")

## Parsed with column specification:
## cols(
##   year = col_double(),
##   region = col_character(),
##   gdpt = col_double()
## )

Lots of trial and error with this one – and this is as close as I could get…

ggplot(data = week7hw2) +
  geom_area(mapping = aes(x = year, y = gdpt, group = region, color = region )) +
  ggtitle("GDP by World Bank Region") +
  labs(x = "year", y = "GDP ($ trillions)") +
  scale_fill_brewer(palette = "Sets")

## Warning in pal_name(palette, type): Unknown palette Sets