Quiz 2

###### Quiz 2 #######

# For this quiz you will analyze UN Peacekeeping data. 

# At any given time, the UN is involved with many peacekeeping missions around the world. 

# Almost all member-states contribute personnel to those missions. There are five types of personnel: 
# experts on mission, troops, staff officers, individual police, and formed police units.
# In its efforts to involve more women in its global operations, the UN reports, monthly, the
# gender of each person sent on a mission. You can read more about it here:

#     https://peacekeeping.un.org/en/women-peacekeeping

# You can access the dataset here: https://tinyurl.com/UNpckpdata

# For this assignment, you will have to load, process, analyze, and visualize the data. In the
# process you’ll also learn more about UN peacekeeping, and how a little data science applied
# to publicly-available data sets can illuminate interesting contemporary issues like global
# conflict and gender inequality.

### PRELIMINARIES

### load the data off the Internet
peacekeeping_data <- read.csv("https://tinyurl.com/UNpckpdata")

### see the first 6 rows of the data
head(peacekeeping_data)

### set all blank or missing entries to NA
peacekeeping_data[peacekeeping_data == ""] <- NA

### omit every row in the data set that contains an NA
peacekeeping_data = na.omit(peacekeeping_data)

### Change the object class of the "Last_Reporting_Date" column from character string to Date
### (you have to let R know that it's currently in "%d/%m/%Y" format)
peacekeeping_data$Last_Reporting_Date <-
  as.Date(peacekeeping_data$Last_Reporting_Date, format = "%d/%m/%Y")

###### Questions

# (1) What are the dimensions of this data set?



# (2) What is the earliest date in the Last_Reporting_Date column? What is the latest date?



# (3) How many different unique entries are there in the ISOCode3 column?



# (4) How many different unique personnel types are associated with Argentina's missions?



# (5) Create a scatterplot with Male Personnel on the x axis and Female Personnel on the y axis. 
#     Label the axes and title the scatterplot. Write a few sentences interpreting your scatterplot. 
#     Use correct grammar, check spelling, etc.



### Run the following 2 lines of code:
aa <- data.frame(1:31, unique(peacekeeping_data$Contributing_Country)[1:31])
names(aa) <- c("list_of_birthdays", "list of countries")

### Now show aa, and observe it's a data frame with 2 columns.
### The first column is all the numbers from 1 to 31, and the second column are country names

aa


# (6) Identify the country associated with the date you were born (your birth month doesn't matter, only the day matters).
#     For example, if you were born on Sept 5, then the date you were born is 5, and the country is Austria.
#     Or, if you were born on April 1, then the date you were born is 1, and the country is Algeria.

#     Examine the data associated with the country you've identified. Write a paragraph of 5-10 sentences describing this data.
#     Feel free to describe it in any way you would consider useful. You are encouraged to analyze by calculating an average,
#     or a median, or an interquartile range, or making comparisons of sets of numbers, etc. You are encouraged to be creative.
#     Check spelling and ensure proper grammar.

# (7) Include a data visualization to accompany your paragraph, taking care to properly label it and the axes.