---
tags: gps
---
# 2021-GPS-Data-Skills-Course-intro-r-notes
## Collaborative Notes:
first name last name
Belinda Panelo
Wooseok Choi
Yongun Ra
Bonnie Devenney
Shelby Sellers
Nikki Jazayeri
Daniela Hall-Lagunes
Amanda Lee-Low
Ama Debrah
Ada Tong
Halle Sousa
Harrison Tang
Hannah Kase Woods
Tyler Spencer
Chase Farrell
Swati Nair
Khang Do
Alejandra Guzman
Ayush Jain
Camila Urzúa
Isaac Wang
Chandler Katschke
Sam Yeung
Cate Pollock
Manabu Hiratsuka
William Gao
Lei Lei
Anthony Nguyen
Bowin Lee
Alexander Schiller
Elissa Bozhkov
Xin Meng
Rachel Lietzow
Claudia Fernández
Daniel Blaugher
Emily Carlton
Leo Do
Jennifer Escobar
Mario Heredia
Dizhi Wang
Morgan Furr
Lu Pang
Deepika Bagaria
Adrian R. Valdez
Eeshita Chib (Esha)
Ziqra Raza
Kenneth Cashin Brown
Jack Alegre
Colin Trobough
Jin Huang
Zahrah Zimmerer
Michael Hatch?
Jonathan Bazan
Laura Nora
Audrey Lau
Anran Shi
Steven Zhang
Ben Swearingen
###########Notes here#########
# 01/05/2020, Lecture 1, Intro to R and RStudio
## Basic Navigation
Writing your code in r file (file -> new file -> R Script)
Use # to make notes (not executed as code)
## Basic RStudio layout:
When you first open RStudio, you will be greeted by three panels:
The interactive R console/Terminal (entire left)
Environment/History/Connections (tabbed in upper right)
Files/Plots/Packages/Help/Viewer (tabbed in lower right)
* Key command to run code:
```
crtl + enter
cmd + enter
```
* assignment operator:
```
<-
* Quick trick, the hotkey for <- is "alt"+"-"
```
R can used for basic math
#hash tag is used for commenting
```
# Create a variable called "abc" with value 34
# Create a variable called "xyz" with value "abc" + 78
# What is the value of "xyz"
abc <- 34
xyz <- abc + 78
```
## Logical operator
```
< # less than
> # greater than
== # equal to
<= # less or equal
>= # greater or equal
```
Difference between "=" and "=="
* "=" is for variable assignment, and in R, it is preferred to use "<-" for assignment.
> Example: x = 5 is equivalent to x <- 5, which means create a number 5, and assign this value to your variable x
* "==" is a binary operator (i.e. a sign that requires two inputs) that is used to compare the equivalence of two objects.
> Example: 5 == 5 will return TRUE
> 5 == 4 will return FALSE
Question about parentahsis:
"()" is used when you call a function (i.e. a piece of program you or someone else wrote before)
ls() #shows list of variables
Variable name formatting is important
* do not put spaces in variable names or use `.`
* be consistent when creating variable names e.g.
* no spaces
* underscores
* camelcase
### looking for R packages use:
cran-project.org
### installing packages
use the packages tab in the bottom right RStudio pane.
* install icon
* or checkmark package you want to load
Rtools https://cran.r-project.org/bin/windows/Rtools/
```
#loading a library in code:
library(ggplot2)
```
### saving file:
save as
##################Lesson 2########################
# 01/07/2020, Lecture 2
## Vector
```
1:5 # same as a collection of 1,2,3,4,5
```
**Broadcasting**: simultaneously operate on every number of the vector
```
x <- c(1,2,3,4,5)
x ^ 2 # output 1,4,9,16,25
```
**Dataframe** is a 2D tabular data
```
# creation of dataframe from vectors
newdaataFrame <- data.frame(a, y, z)
# view dataframe
View(newDataFrame) # shows the infomration about the dataframe
```
### Loaing gapmidner data
data file gapminder.csv is available in canvas
telling R where to find the gapminder dataset
```
Session/set working directory/choose directory/
the location where you saved the data
```
loading data in to R
```
gapminder <- read.csv(file = "gapminder.csv")
```
looking at the gapminder dataset details
```
View(gapminder)
str(gapminder) #str = structure displays the rows and columns and the data types
```
gapminder data has:
1704 obs = observations / rows
6 variables = variables / columns
Data types: Factors, integers, numeric, factor,
```
#Exploring data types using class():
t <- 2
class(t)
u <- "2"
#changing data types
r <- as.integer(t) # converts data type to an integer
as.numeric # convert variable into numeric type
as.character # convert variable into character type
```
Check current working directory location
```
getwd() # displays the working directory you are working in for the R session
```
Usage of double quote: double quote is used to denote a variable of string type.
```
key # this is a variable
"key" # this is a string
```
Exercise:
1. Create a vector c numbers with every number bewteen 1- and 20
2. what type is it?
3. create a vector called name with your first and last name
4. what type is it?
Solution:
```
numbers <- 10:20
numbers2 <- c(10,11,12,13,14,15,16,17,18,19,20)
name <- c("Rick","McCosh")
```
working with gapminder datafram and continent column
gapminder$continent
$ indicw atates a column in the dataframe e.g. dataframe$columnname
**arguments** are the objects you put into your function when you invoke the function.
> example: summary(x), x would be called an argument
```
unique(x) # Gives you unique values, strip away the repeated values in vector x
length(x) # tell you the number of elements inside of x
summary(gapminder$lifeExp #summar
mean(gapinder$pop) #calculates mean
sd(gapminder$pop) #standard deviation
min(gapinder$pop) #minimum
max(gapminder$pop) #maximum
```
```
names(gapminder) #tells you the name of each column
head(gapminder) #displays first 6 rows of the dataset
head(gapminder, 10) # displays the first top 10 rows of the data frame
tail(gapminder)
tail(gapminder, 20) #displays the last bottom 20 rows
```
### Using the package Knitr
you will have to istall the Knitr
```
install.packages("knitr") #download and installs the Knitr package
library(knitr) #loads the knitr package in R for use
```
create an Rmarkdown document goto:
```
File menu/new file/R Markdown...
```
# #####Week 2 R Data Wrangling########
## Tidy data format
Rows = observations
columns = variables
## using dplyr package
start new rscript file
save to desktop
```
# use hash tag to make comments
```
set working directory
from Session menu in RStudio
```
setwd() #R function to set the working directory location
```
loading a package
```
library(dplyr) #load package
```
Read in data:
```
gapminder <- read.csv("gapminder.csv")
```
display the structure of the data
```
str(gapminder) #displays the structure of the data file
head(gapminder) #displays the first several rows in the data
summary(gapminder) #displays summary of the data
```
### working with data ####
```
continent_data <- select(gapminder, continent) #subset data into a new data frame
year_country_gdp_data <- select(gapminder, year, country, gdpPercap) #create another subset of data
head(year_country_gdp_data) # displays the first several lines
```
Filter rows based on condition
```
canada_data <- filter(gapminder, country == "canada") #filter only canada
head(canada_data)
unique(canada_data$country) #display only unique
```
```
portugal_data <- filter(gapminder, country =="Portugal") #filter portugal
```
combining functions using `%>%`
`control+shift+m` (windwos) `command+shift+m` (Mac)
```
#filtering dataset
country_year_pop_recent_only <- gapminder %>%
filter(year >= 1977) %>%
select(country, year, pop)
```
```
canugal_recent <- gapminder %>%
filter(country == "Canada" | country == "Portugal" ) %>%
filter(year >= 1992)
```
```
canugal_recent_redo <- gapminder %>%
filter(country == "Canada" | country == "Portugal" & year >= 1992) %>%
select(country, year, pop)
```
when writing fitlers make sure to:
run `filter` statement first then `select`
tab for auto completion
> example: when type read.csv("gapmin[tab]"), R autocomplete to read.csv("gapminder.csv")
command (control) + enter for executing a line of code
bri
```
gap_africa <- gapminder %>%
filter(continent == "Africa" ) %>%
select(lifeExp, country, year)
str(gap_africa)
```
select() #is for columns
filter() #is for rows
$ #represents column e.g. gapminder$lifExp
#### object not found errors or other errors
check for typos first
forgetting commas or closign parenthesis
observations #dataframe rows
variaables #dataframe columns
how to make a new column
```
#left of = is name of new column
#right of = is data in new column
gap_extra <- gapminder %>%
mutate(ratio = lifeExp/gdpPercap)
```
is there a command that selects everything but one specific column that you want to get rid of?
```
select(-lifeExp) #minus in select will remove columns
```
# #####Week 2 Day 4 R notes########
## Recap
Load dplyr
```
library(dplyr)
```
set working directory
```
setwd("~/Desktop")
```
or use the command under the `session` menu in RStudio
read in the data:
```
gapminder <- read.csv("gapminder.csv")
```
#data set informatin - structure, head, summary
```
str(gapminder)
head(gapminder)
summary(gapminder)
```
Recap
3 dplyr fucntions
select() #select columns
filter() #select rows
mutate() #create new column
```
gap_gdp <- gapminder %>%
mutate(gdp = gdpPercap *pop) %>% # this will create a new column called gdp
filter(continent == "Europe") %>% #select Europe as the continent data
select(country, year, pop) #display the columns country, year, pop
head(gap_gdp) #display first several rows from the new subset gap_gdp
```
creating a variable you use `<-` "assignment symbol"
in dplyr
mutate uses single `=` to create the column
pipe `%>%` chains functions together. It "pipes" the output from the last function into the input of the next function
Also, just to show how miserable life is without pipe, writing gap_gdp without pipe looks like this:
```
select(filter(mutate(gapminder, gdp=gdpPercap * pop) , continen=="Europe"), year, pop, gdp)
```
Does R change your underlying data automatically?
> No. All modification is done on the object inside R. Unless you explicitly export your object, the underlying data would not change.
Example 2: I want to find the gdp to life expenctancy ratio for all countries in Oceania since 1992 and I only want the columns of the ratio, country name, and year
```
#adding gdp column to the main gapminder data frame
gapminder <- gapminder %>%
mutate(gdp = gdpPercap * pop)
```
```
gapminder_oceania <- gapminder %>%
mutate(gdp_lifeexp_ration = gdp / lifeExp) %>%
filter(continent == "Oceania" & year >= 1992) %>%
select(country, year, gdp_lifeexp_ratio)
gapminder_oceania
```
```
#dataframe with a columnb indicating if life expectancy is at least 50 years for all countries in Asia & africa and I only want the columns with country, year, and if life expectancy is over 50 years
gap_century <- gapminder %>%
mutate(half_century = ifelse(lifeExp >= 50, "over 50", "under 50")) %>% #adding `ifelse` statement and new column `half_centruy`
filter(continent == "Asia" | continent == "Africa") %>%
select(country, year, half_centruy)
head(gap_century, 20)
```
##### missing values
you might see ".", " ", NA, -999, 0
R handles missing data as NA
```
gap_missing <- gapminder %>%
mutate(missing = ifelse(lifeExp < 30, NA, "not missing")) %>%
filter(is.na(missing))
gap_missing <- gapminder %>%
mutate(missing = ifelse(lifeExp < 30, NA, "not missing")) %>%
filter(!is.na(missing)) #add `!` to !is.na() to specify missing values
```
```
gap_oceania_missing <- gapminder %>%
mutate(new_column = ifelse(continent == "Oceania" & year != 1992, NA, "this is a test"))
gap_oceania_missing %>% filter (is.na(new_column))
```
group_by() and summarize() dplyr functions
```
gap_max_life <- gapminder %>%
group_by(continent) %>%
sumarize(max_life = max(lifeExp))
gap_max_life
```
```
gap_max_life_country <- gapminder %>%
group_by(continent, country) %>%
sumarize(max_life = max(lifeExp))
gap_max_life_country
```
Exercsie
i want to find the mean gdp per continent in 2007 for Europe and Asia
```
gap_mean_gdp <- gapminder %>%
filter(year == 2007 & continent == "Europe" | continent == "Asia") %>%
group_by(continent) %>%
summarize(mean_gdp = mean(gdp))
gap_mean_gdp
```
```
gap_new <- gapminder %>%
group_by(country) %>%
mutate(max_lifeExp = max(lifeExp))
gap_new
```
```
gap_new2 <- gapminder %>%
group_by(country) %>%
mutate(max_lifeExp = max(lifeExp))
filter(gdpPercap < 1000)
gap_new2
```
extra dplyr
```
#using %in% to simplify
gapminder %>%
filter(country == "Canada" | country == "Mexico" | country == "Spain")
gpminder %>%
filter(country %in% c("Canada", "Mexico", "Spain")) #using the %in%
```
arrange() - orders the rows of a data frame by the values of selected columns.
writing out data to to csv
write.csv(gap_new, "gap_new.csv", row.names = FALSE)
visualization
# ###Week 3 R - lesson 5#######
#### ggplot2 CHEATSHEET https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
we will be using the GGPLOT2 package
set working directory to Desktop
```
setwd("~/Desktop")
```
or you can set working directory under the `sessions` menu.
```
# load libraries
library(ggplot2)
library(dplyr)
```
#making your first plot
```
# load gapminder dataset
gapminder <- read.csv("gapminder.csv")
# first ggplot function
ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) + geom_point()
# specify data
# specify asthetic axis aes(x, y)
# choose geom_[style]()
```
aes() = asthetic
##commenting tip:
selelct multiple lines
crtl+shift+c will set comment # for multiple lines
Saving plots
1. exprot button in interface
2. ggsave() #function
plots will save in the working directory
3. pdf(), or png(), jpeg() #save as pdf, png, or jpeg functions
```
# save to PDF example:
pdf("lifeExp_time.pdf", width=8, height=8)
ggplot(data = gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point()
dev.off() #stops outputting code to PDF
```
```
ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) + geom_point()+theme_classic() #added theme function
```
```
#line graph
ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country)) + geom_line()
#by = country tells ggplot to show a line for each country
```
```
#assign plot to a variable `p` (lowercase p used)
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
#adding geom and theme functions to the plot:
p <- p + geom_line() + theme_classic()
p #displays plot
```
```
#assign plot to a variable `p`
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
#adding geom and theme functions to the plot:
p <- p + geom_line() + geom_point() = theme_classic() #added geom_point() to code
p #display plot
```
```
#adding colors
#assign plot to a variable `p`
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country, color = continent)) # added `color` here
#adding geom and theme functions to the plot:
p <- p + geom_line() + theme_classic()
p
```
```
#assign plot to a variable `p`
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country, color = continent)) # added `color` here
#adding geom and theme functions to the plot:
p <- p + geom_line() + geom_point(color = "black") + theme_classic() #changed point color to black
p
```
```
#assign plot to a variable `p`
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country, color = continent))
p <- p geom_point(color = "black") + geom_line() + theme_classic() #changed order of points and lines
p
```
```
#changing geom_line() color
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country, color = continent))
p <- p geom_point(color = "black") + geom_line(color = "red") + theme_classic() #changed geom_line color to red
p
```
```
#using aes() in geom functions
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
p <- p geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic() #moved aes color for continent from main aes() to geom_line()
p
```
```
#create colors varialbe to select colors
colors <- c('red','black','green','pink,'yellow')
#manual assignment of colors
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
p <- p geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
p <- + scale_color_manual(values = colors) # order of the colors is continets in alphabetical order
p
```
### labels for plots
```
#manual assignment of colors
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
p <- p geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
p <- + scale_color_manual(values = colors) # order of the colors is continets in alphabetical order
#creates labels for x and y
p <- labs(title = "Life Expectancy over time" , x = "Year", y = "life Expectancy")
p
```
```
# adding scales
p <- ggplot(data = gapminder, aes(x=year, y=lifeExp, by = country))
p <- p geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
p <- + scale_color_manual(values = colors) # order of the colors is continets in alphabetical order
#creates labels for x and y
p <- labs(title = "Life Expectancy over time" , x = "Year", y = "life Expectancy")
p <- p + scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))
p
```
```
# insrtuctors code:
colors <- c('red','black','green','pink,'yellow') #set variable for scale colors
P <- ggplot(data = gapminder, aes(x = year, y = lifeExp, by = country))
P <- P + geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
P <- P + scale_color_manual(values = colors)
P <- P + labs(title = "Life Expectancy over Time", x = "Year", y = "Life Expectancy")
P <- P + scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))
P
```
```
P <- ggplot(data = gapminder, aes(x = year, y = lifeExp, by = country))
P <- P + geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
P <- P + scale_color_manual(values = colors)
P <- P + labs(title = "Life Expectancy over Time", x = "Year", y = "Life Expectancy")
P <- P + scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))
P
```
```
# adding scale to the plot
P <- ggplot(data = gapminder, aes(x = year, y = lifeExp, by = country))
P <- P + geom_point(color = "black") + geom_line(aes(color = continent)) + theme_classic()
P <- P + scale_color_manual(values = colors)
P <- P + labs(title = "Life Expectancy over Time", x = "Year", y = "Life Expectancy")
P <- P + scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))
P <- P + scale_x_continuous(breaks = seq(1952,2007,10))
P
```
#### subsetting plots
```
# use dplyr command to subset data
dataOceania <- gapminder %>% filter(continent == "Oceania")
# create inital plot and use data subset
P <- ggplot(data = gapminder)
P <- P + geom_line(aes(x = year, y = lifeExp, by country), color ="red")
P <- P + geom_line(data = dataOceania), aes(x = year, y = lifeExp, by = country), color = "black) #plots `dataOceania` data subset
P
```
## challenge 1
1. Make a plot to highlight life expectancy over time for the country of your choice
2. Make the axis titles nice
```
#solution
dataIreland <- gapminder %>% filter(country == "Ireland")
P <- ggplot(data = gapminder)
P <- P + geom_line(aes(x = year, y = lifeExp, by = country), color = "red")
P <- P + geom_line(data = dataIreland, aes(x = year, y = lifeExp, by = country). color = "green")
P
```
## challenge 2
1. create a scatterplot of populations vs. year
```
#solution
P <- ggplot(data = gapminder, aes(x = year, y = pop)) + geom_point()
P
```
# #### R- Week 3 - last lessson
ggplot cheatsheet: https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Or alternatively, in Rstudio Help -> Cheatsheet -> ggplot2 Cheatsheet
load packages and data
```
library(ggplot2)
library(knitr)
library(dplyr)
gapminder <- read.csv("gapminder.csv")
```
```
## Review
# ggplot()
# aes()
# geom_line()
# color
# plots as layers
# everything is customizable
```
`aes()` stands for **aesthetic mapping**, which maps features of the data to visual characteristics of your plot
`geom_*` specify the type of graph you want to use
#### log scale
```
P <- ggplot(data = gapminder, aes(x= year, y=pop))
P <- + geom_point()
P <- P + scale_y_log10()
P <- labs(y = "log(pop_)")
P
```
#### transparency of points
```
P <- ggplot(data = gapminder, aes(x= year, y=pop))
P <- + geom_point(alpha = 0.5) #change transparency of points
P <- P + scale_y_log10()
P <- labs(y = "log(pop_)")
P
```
#### jitter of points
```
P <- ggplot(data = gapminder, aes(x= year, y=pop))
P <- P + geom_jitter(alpha = 0.5) #alpha changes transparency of points and add jitter to each point
P <- P + scale_y_log10()
P <- P + labs(y = "Log(Pop_)")
P
```
For readability in a plot, when there is a bunch of points stack on top of each other
# accessing help information
```
?geom_jitter()
```
#### modifying jitter height and width
```
P <- ggplot(data = gapminder, aes(x= year, y=pop))
P <- + geom_jitter(alpha = 0.5, height = 0, width = 0.5) #alpha changes transparency of points, add jitter to each point, adding height and width to the points
P <- P + scale_y_log10()
P <- labs(y = "log(pop_)")
P
```
#### Trendline
```
P <- ggplot(data = gapminder, aes(x= year, y=pop))
P <- + geom_jitter(alpha = 0.5, height = 0, width = 0.5)
P <- P + geom_smooth(method = "lm", size = 2) #draws a linear model line, size changes width of the line
P <- P + scale_y_log10()
P <- labs(y = "log(pop_)")
P
```
#### Box plots
## population per continent in year 2007
```
# create subset of data
gapminder2007 <- gapminder %>% filter(year == 2007)
View(gapminder2007)
# create plot using the data
P <- ggplot(data = gapminder2007, aes(x = continent, y = pop))
P <- P + geom_boxplot()
P <- P + scale_y_log10()
P <- P + labs(y = "Log(Pop)")
P
```
```
P <- ggplot(data = gapminder2007, aes(x = continent, y = pop))
P <- P + geom_boxplot()
P <- P + geom_jitter(height = 0, width = 0.1) #adding geom_jitter on top of the boxplot
P <- P + scale_y_log10()
P <- P + labs(y = "Log(Pop)")
P
```
## barplots
```
#create subset of data to for mean life expectancy
meanLifeExpContinent <- gaominder %>%
filter(year == 2007) %>%
group_by(continent) %>%
summarize(meanLifeExp + mean(lifeExp), SD(LifeExp))
View(meanLifeExpContinent)
#create the barplot
P <- ggplot(data = meanLifeExpContinent, aes(x = continent, y = MeanLifeExp))
P <- P + geom_col()
P
```
#### Add errorbars
```
P <- ggplot(data = meanLifeContinent, aes(x = continentm y = MeanLifeExp))
P <- P + geom_col()
P <- P + errorbar(aes(ymin = MeanLIfeExp - SDLifeExp, ymax = MeanLifeExp + SDLifeExp, width = 0.2))
P
```
#instructors code example up to this point
```
P <- ggplot(data = meanLifeExpContinent, aes(x = continent, y = MeanLifeExp))
P <- P + geom_col()
P <- P + geom_errorbar(aes(ymin= MeanLifeExp - SDLifeExp, ymax = MeanLifeExp + SDLifeExp), width = 0.2)
P
```
### multipanel plots
```
europe <- gapminder %>% filter(continent == "Europe")
P <- ggplot(data = europe, aes(x = year, y = pop))
P <- P + geom_line()
P <- P + facet_wrap(~country) # create multiple panels
P
```
```
europe <- gapminder %>% filter(continent == "Europe")
P <- ggplot(data = europe, aes(x = year, y = pop))
P <- P + geom_line()
P <- P + facet_wrap(~country) # create multiple panels
P <- P + theme(axis.text.x = element_text(angle = 45)) # adding a theme to the facet
P
```
#### instructors code:
```
europe <- gapminder %>% filter(continent == "Europe")
P <- ggplot(data = europe, aes(x= year, y = pop))
P <- P + geom_line()
P <- P + facet_wrap(~country)
P <- P + theme(axis.text.x = element_text(angle = 45))
P
```
#### Challenge
Create a plot of life expectancy over year for each country in Africa
```
africas <- gapminder %>% filter(continent == "Africa")
P <- ggplot(data = africas, aes(x = year, y = lifeExp))
P <- geom_line()
P <- P + facet_wrap(~country)
P
```
```
SomeCountries <- c("Tunisia", "Uganda", "Zambia", "Togo")
SomeCountriesData <- gapminder %>% filter(country %in% SomeCountries)
```
# Rmarkdown
file menu -> new file -> rmarkdown
#### cheat sheet
https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf
additional resoruce for Rmarkdown: https://rmarkdown.rstudio.com/lesson-1.html
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