---
title: 'Laboratory Exercise Week 4'
author: "Your Name and Section, 10 pts"
date: "Todays Date"
output: word_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

*Directions*: 

* Write your R code inside the code chunks after each question.
* Write your answer comments after the `#` sign.
* To generate the word document output, click the button `Knit` and wait for the word document to appear.
* RStudio will prompt you (only once) to install the `knitr` package.
* Submit your completed laboratory exercise using Blackboard's Turnitin feature. Your Turnitin upload link is found on your Blackboard Course shell under the Laboratory folder.

***

For this exercise, you will need to use the package `mosaic` to find numerical and graphical summaries.

```{r warning=FALSE, message=FALSE}
# install packages if necessary
if (!require(mosaic)) install.packages(`mosaic`)
if (!require(dplyr)) install.packages(`dplyr`)
if (!require(gapminder)) install.packages(`gapminder`)
# load the package in R
library(mosaic) # load the package mosaic to use its functions
library(dplyr) # load the package dplyr to use its functions
library(gapminder)  # load the package gapminder for question 1
```


1. Using the gapminder data in the lesson, do the following:  
      i) use `filter` to select all countries with the following arguments:  
          a) life expectancy larger than 60 years.   
          b) United Kingdom and Vietnam and years greater than 1990.  
      ii) use `arrange` and `slice` to select the countries with the top 15 GDP per capital `gdpPercap`. Use the pipe `%>%` operator to string multiple functions.
      iii) use `mutate` to create a new variable called `gdpPercap_lifeExp` which is the quotient of `gdpPercap` and `lifeExp` and display the output.  
      iv) use `summarise` to find the average or mean value of the variable `gdpPercap_lifeExp` created in part (iii).     
      v) use `group_by` to group the countries by `continent`; and `summarise` to compute the average life expectancy `lifeExp` within each continent. Use the pipe `%>%` operator to string multiple functions.
      
      

### Code chunk
```{r} 
# load the necessary packages
library(mosaic)
library(dplyr)
library(gapminder)

# last R code line
```


2. The data set `MLB-TeamBatting-S16.csv` contains MLB Team Batting Data for selected variables. Load the data set from the given url using the code below. This data set was obtained from [Baseball Reference](https://www.baseball-reference.com/leagues/MLB/2016-standard-batting.shtml).
    * Tm - Team   
    * Lg - League: American League (AL), National League (NL)  
    * BatAge - Batters’ average age  
    * RPG - Runs Scored Per Game  
    * G - Games Played or Pitched  
    * AB - At Bats  
    * R - Runs Scored/Allowed  
    * H - Hits/Hits Allowed  
    * HR - Home Runs Hit/Allowed  
    * RBI - Runs Batted In  
    * SO - Strikeouts  
    * BA - Hits/At Bats  
    * SH - Sacrifice Hits (Sacrifice Bunts)  
    * SF - Sacrifice Flies  

    Using the `mlb16.data` data, do the following:      
      i) use `filter` to select teams with the following arguments:        
                 a) Cardinals team `STL`.    
                  b) teams with Hits `H` more than 1400 last 2016 season.   
                  c) team league `Lg` is National League `NL`.   
      ii) use `arrange` to select teams in decreasing number of home runs `HR`.      
      iii) use `arrange` to display the teams in decreasing number of `RBI`.       
      iv) use `group_by` to group the teams per league; and `summarise` to compute the average `RBI` within each league. Use the pipe `%>%` operator to string multiple functions.   
      


### Code chunk
```{r} 
# load the data set
mlb16.data <- read.csv("https://raw.githubusercontent.com/jpailden/rstatlab/master/data/MLB-TeamBatting-S16.csv")
str(mlb16.data) # check structure
head(mlb16.data)  # show first six rows



# last R code line
```