This is the second homework assignment for Intro to R. You must submit both the RMD and “knitted” HTML files are due before class on Friday (Day 5)

Use this dataset on infant mortality for the following questions: http://johnmuschelli.com/intro_to_r/data/indicatordeadkids35.csv

## you can add more, or change...these are suggestions
library(tidyverse)
library(dplyr)
library(ggplot2)
library(tidyr)

Questions

1. Read the data using read_csv() and name it mort. Rename the first column to country using the rename() command in dplyr. Create an object year variable by extracting column names (using colnames()) and make it to an integer as.integer()), excluding the first column either with string manipulations or bracket subsetting or subsetting with is.na.

2. Reshape the data so that there is a variable named year corresponding to year (key) and a column of the mortalities named mortality (value). Hint: use the tidyr package and its gather() function. Name the output long and make year a numeric variable. Hint: remember that -COLUMN_NAME removes that column, gather all the columns but country.

3. Read in this the tab-delim file: http://johnmuschelli.com/intro_to_r/data/country_pop.txt and call it pop, which contains population information on each country (hint: use read_tsv()). Rename the second column to "Country" and the column "% of world population", to percent

4. Determine the population of each country in pop using arrange(). Get the order of the countries based on this (first is the highest population), and extract that column and call it pop_levels. Make a variable in the long data set named sorted that is the country variable coded as a factor based on pop_levels.

5. Subset long based on years 1975-2010, including 1975 and 2010 and call this long_sub using & or the between() function. Subset the following countries using dplyr::filter() and the %in% operator on the sorted country factor (sorted) for long_sub with c("Venezuela", "Bahrain", "Estonia", "Iran", "Thailand", "Chile", "Western Sahara", "Azerbaijan", "Argentina", "Haiti") and reassign to long_sub. Lastly, remove missing rows for mortality using filter() and is.na().

6. Plotting: create “spaghetti”/line plots for the countries in long_sub, using different colors for different countries, using sorted. The x-axis should be year, and the y-axis should be mortality. Make the plot using qplot and also ggplot.

7. Bonus, load the plotly package (library(plotly)) and assign the plot from 6) to g and run ggplotly(g).