Assignment for workshop 1

### Workshop 1 assignment
### Write your code underneath each of the instructions

## Basics and objects
# Use R as a calculator to calculate 0.7 divided by the square-root of 625 (hint: the function for square roots is sqrt())

# Save the results of the calculation above as an object with a, to you, intuitive name

# Create an object containing at least ten numbers of your choice

# Calculate the mean of this object and save it as an object.

# Calculate the standard deviation of this object
# save it as a new object, and print it to the console


## Help files

# Look at the help file for the mean function. What does the 'trim' argument in the function do, 
# and what is its default value?

# What happens if you run the mean function on your object with at least 10 numbers and set 
# trim to 0.1? Is this what you expected?



## Rectangular data structures and value access

# Load the tidyverse package. 
# Note, if you have not already installed tidyverse you need to do so first

# Below you have four vectors of values relating to fatality nubmers for 20 different conflicts. 
# Combine these four vectors into a tibble and name this something intuitive.

number_fatalities <- c(123, 5321, 9543, 120, 12, 
                       243, 123, 9877, 387, 921,
                       8981, 23, 541, 54, 644,
                       4185, 8, 15, 125, 352)

gpd_capita <- c(3411, 24212, 221, 55512, 9822,
                151, 5457, 2111, 35477, 1255,
                8787, 2154, 3578, 45777, 12,
                877, 4854, 1387, 15115, 56)
                
conflict_type <- c("insurgency", "war", "insurgecy", "insurgency", "war",
                   "war", "war", "war", "insurgency", "war",
                   "insurgency","insurgency","war","war","war",
                   "war","war","insurgency","war","war")
                   
region <- c("Europe", "Africa", "Asia" ,"Asia","Latin America",
            "Africa", "Africa", "Europe", "Latin America", "Latin America",
            "Europe", "Asia", "Asia", "Asia", "Asia",
            "Latin America", "Europe", "Europe", "Africa", "Asia")

# Calculate the correlation between the number of fatalities and gdp per capita. 
# Do this using both pearson's correlation and spearman's correlation.

# Calculate the mean number of fatalities and the standard deviation of the number of fatalities

# Make a table for how many conflicts there are in each region. Hint, use the table() function

# Access the conflict_type variable in your tibble using both the $ method and the hard brackets, [], method

# Access the entire third row of your tibble

# Access the value in the 8th row and 3rd column of your tibble

# BONUS: assume that the data you have are a random sample of conflicts. 
# Then create a 95% confidence interval for the true mean of the number of fatalities

# BONUS x2: What potential problems do you see with this confidence interval?