---
title: "Lab01: Practice Code"
author: "Kenya Amano"
date: "10/2/2020"
output:
html_document:
df_print: paged
pdf_document: default
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Prerequiste
```{r, message=FALSE}
rm(list = ls()) # Clear memory
library(tidyverse) # Load package
```
# Vector Practice
1. `Vector1` : The numbers one through five and then the number six five times
2. `Vector2` : 10 randomly drawn numbers from a normal distribution with a mean 10 and a s.d. of 1
3. `Vector3` : Results of 10 single binomial trials with a probability of 0.4
4. `Vector4` : Sample 100 observations from a 5-trial binomial distribution with a probability of success of 0.4
5. `Vector5` : The numbers one through three and the word apple
```{r}
```
6. What type of data is Vector2?
7. Round up Vector2 to two decimal place
8. What happened in Vector5?
```{r}
```
# Matrices Practice
1. Matrix1: Create 5 by 5 matrix containing all NAs
2. Assign Matrix1 the row names (a,b,c,d,e) and the column names (1,2,3,4,5)
3. Replace the NAs in the first columne of Matrix1 with "Inf"
```{r}
```
# List Practice
1. Create a list that contains Vector1, Vector2, Vector3, and Matrix1
2. Name each list component as Vector1, Vector2, Vector3, and Matrix1 respectively
3. Locate Vector2 from the list
```{r}
```
# Data Frames Practice 1
# Working directory
Check if your working directory is correct (where you have saved `Lab01Data.csv` and `Lab01Survey.csv`)
```{r}
basedir <- getwd()
rowdata.folder <- paste(basedir, "Specify if you create a folder", sep = "/")
```
## 1. Load Lab01data.csv in R
```{r}
# Load data in simple way
Dta <- read.csv("Lab01Data.csv", header = TRUE, stringsAsFactors = FALSE)
# Specify the folder
Dta <- read.csv(paste(rowdata.folder, "Lab01Data.csv", sep="/"))
# Directly download from website
DataURL <- "http://staff.washington.edu/kamano/MLE/LabMaterials/Lab01/Lab01Data.csv"
Dta <- read.csv(DataURL)
```
## 2. What is the data structure? What does that tell us about type?
```{r}
# Check structure
```
## 3. Check the names and summary statistics of the data. Fix any names that are less than good.
```{r}
# Check and fix names
```
## 4. Remove observations with missing values
```{r}
```
## 5. Calculate the average GDP per capita for Brazil for the observed period. Repeat the calculation for all countries.
```{r}
```
## 6. Plot GDP per capita (on the x-axis) and Polity2 (on the y-axis)
```{r}
```
## 7. Create a new variable called "democracy". Assign 0 to countries with negative value or zero polity2 score, and assign 1 to countries with positive score.
```{r, results='hide'}
```
# Data Frames Practice 2
## 1. Read in the data "lab1_survey.csv"
```{r}
# Clear and load data
rm(list = ls())
SurveyData <- read.csv(file = "Lab01Survey.csv")
# or
DataURL2 <- "http://staff.washington.edu/kamano/MLE/LabMaterials/Lab01/Lab01Survey.csv"
SurveyData <- read.csv(DataURL2)
```
## 2. Inspect the data. What format are they in? What values do the data take, and how do those values correspond with the survey?
```{r}
```
## 3. Generate some summary statistics.
```{r}
```
## 4. How are these two variables related to each other (assuming equal intervals b/w categories)?
```{r}
```
The correlation b/w R knowledge and LaTeX knowledge is `r cor1`, or more nicely, `r round(cor1, 2)`.
## 5. Are there any problems with the way the data are coded? (Think about lecture yesterday.)
## 6. Recode the data
```{r}
```
## 7. Why is this coding method better?
## 8. Generate some plots of the data: bar charts are good here, scatterplots even better.
```{r, echo= FALSE}
```
# LaTex in R Markdown
$$
1 + 1 = 2
$$
$$
11 \times 11 = 121 \\
$$
$$
E = mc^2
$$
I think it's Einstein who proposed $E = mc^2$.
$$
x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
$$
$$
\begin{split}
X & = (x+a)(x-b) \\
& = x(x-b) + a(x-b) \\
& = x^2 + x(a-b) - ab
\end{split}
$$
# Install guide for Chris's packages and TinyTeX
```{r}
# Install tile
install.packages("https://faculty.washington.edu/cadolph/software/tile_0.4.15.tar.gz", repos = NULL, type="source")
# Install simcf
install.packages("https://faculty.washington.edu/cadolph/software/simcf_0.2.18.tar.gz", repos = NULL, type="source")
```
# Install tinytex
## Caution!!!! It takes sooooooooooooo long!! ##
```{r}
install.packages("tinytex")
tinytex::install_tinytex()
```