---
tags: gps
---
# GPS Skills Course Series - R (2023Winter)

**Course Details**
Dates: January 30th, 2023 - February 17th, 2023
Days: Tuesday/Thursday
Time: 2-3:20pm

**Course Agenda:**
(Github page)

**Software Installation:**
*Refer to Canvas course software download files.

**Lesson Data (download)**
*Refer to Canvas course data download files.

## NOTES:
A copy of the instructor live session notes will be uploaded in canvas at the end of session lesson.

~~*(Include only for Python course)*
Jupyterlab will be used for the lessons
[m] Markdown cell = notes
[#]also works in code cell for notes
[b] = add cell below [a] is above
[r]Raw cells cannot have text edits~~

https://www.markdownguide.org/getting-started/
https://www.markdownguide.org/basic-syntax/
*End*

## Session 1
### Name (Last, First) / Email
1. Doe, John / [e-mail] (example)
2. Kang, Eastern / [e-mail] (instructor)
3. Romstadt, Lisa / [e-mail]
4. Kazden, Matthew/[e-mail]
5. Yamada, Sohei / [e-mail]
6. Cho, Harrison / [e-mail]
7. Weitong Li / [e-mail]
8. Paula Jaramillo/ [e-mail]
9. Nakamura, Masaki / [e-mail]
10. Phrasavath, Bonaly / [e-mail]
11. Ilaria, Simmen/ [e-mail]
12. Hernandez, I
13. Lascano, Dayra / [e-mail]
14. Orosz, Stephanie / [e-mail]
15. Faiaz, Muhtadi / [e-mail]
16. Pykurel,Himanshu/ [e-mail],
17. Zhuohan, Fang / [e-mail]
18. Kato, Hiroaki / h
19. Nogueda,Lesley/ [e-mail]
20. Edwards, Lauryn/ [e-mail]
21. Berman-Schneider, Ray/ [e-mail]
22. Ye Yuan/ [e-mail]
23. Junyi Hua/[e-mail]
24. Cuevas, Christopher/[e-mail]
25. Tianning, Lan / [e-mail]
26. Coyle, Jackson / [e-mail]
27. Yamakawa, Daisuke / [e-mail]
28. Groper, Danielle / [e-mail]
29. Tsuru, Yutaro / [e-mail]
30. Jordan Chu / [e-mail]
31. Bo,Yutong/[e-mail]
32. Yamashita, Daichi / [e-mail]
33. Yamashita, Aoi / [e-mail]
34. Tan,Jingyi/ [e-mail]
35. Artan, Warsan / [e-mail]
36. Xuran, Wang / [e-mail]
37. Ito, Yoshi / [e-mail]
38. Chen, Wenxin / [e-mail]
39. Zhiyuan,Chi /[e-mail]
40. Yuxin Guo / [e-mail]
41. Leung, Taysia/ [e-mail] (Asynchronous Video)
42. Ahluwalia, Gurkriti / [e-mail]
## Notes

#### You can use R as a calculator

#### Variables and assignments
use assignment operator `<-` to assign values to variables

x <- 1/40
x_2 <- x*2 # try to use unique variables names 

df <- x*2


rm(x) # delete variable
?rm() # use ? and function to get help in RStudio 

## Session 1 Questions:
Please enter any questions not answered during live session here:
1.

### End Session 1

## Session 2
### Name (Last, First) / Email
1. Doe, John / [e-mail] (example)
2. Phrasavath, Bonaly / [e-mail]
3. Hernandez, Isai / [e-mail]
4. Tianning, Lan / [e-mail]
5. Weitong Li / [e-mail]
6. Romstadt,Lisa / [e-mail]
7. Lascano, Dayra / [e-mail]
8. Yamada, Sohei / [e-mail]
9. Tsuru, Yutaro / [e-mail]
10. ilaria simmen / [e-mail]
11. Orosz, Stephanie/ [e-mail]
12. Lesley Nogueda / [e-mail]
13. Himanshu Pykurel/ [e-mail]
14. Xiangning Wu / [e-mail]
15. Nakamura, Masaki / [e-mail]
16. Cuevas, Christopher/[e-mail]
17. Yamashita, Daichi / [e-mail]
18. Tan,Jingyi /[e-mail]
19. Groper, Danielle / [e-mail]
20. Xuran, Wang / [e-mail]
21. Coyle, Jackson / [e-mail]
22. Artan, Warsan / [e-mail]
23. Yamakawa, Daisuke / [e-mail]
24. Ito, Yoshi / [e-mail]
25. Chen, Wenxin / [e-mail]
26. Tsuru, Yutaro / [e-mail]
27. Chu, Jordan / [e-mail]
28. Zhiyuan, Chi / [e-mail]
29. Yuxin Guo / [e-mail]
30. Leung, Taysia/ [e-mail] (Asynchronous Video)
31. Ahluwalia, Gurkriti / [e-mail]

## Notes:

coat <- ("calico", "black", "tabby")
weight <- c(2.1,5.0, 3.2)
likes_string <- c(1,0,1)

creating a csv and saving in a data folder

dir.create("data") # creating a directory

# creating a .csv file
write.csv(cats,
          file = "data/feline_data.csv", 
          row.names = FALSE)

# assigning feline_data.csv  to variable cats
cats <- write.csv(cats,
          file = "data/feline_data.csv", 
          row.names = FALSE)

cats$weight
cats$coat
cats$weight + 2

cats$weight_plus_2 <- cats$weight + 2

paste("My cat is", cats$coat)
View(cats)

cats$weight + cats$coat

### 5 main data types in R
- doulbe
- integer
- complex
- logical
- character

# use typeof() to find data type
Typeof("cats$weight")

---

##############################
### Quick clarification for converting variable types.

Suppose you have a mixed variable type recorded as presented in the class:

weight<- c(2.1,5.0, 3.2, "a")

R is going to recognize that as `character type` because of "a".
If you import the csv file with this type of mixed variable types, and if you use the option `stringAsFactors = TRUE`, then your `character type` of variable will be recognized as `factor`, which is equivalent to `integer type` of variable.

We can convert the `weight` back to `numeric type` using the `as.numeric()` function, but because R recognizes the `weight` variable as factor, the converted output would be 1, 2, 3, in increasing order. For our example, 2.1 = 1, 3.2 = 2, 5.0 = 3, and "a" = 4.

However, if you selected `stringAsFactros = FALSE` option, then R recognizes the weight variable as `character type`, and converting that variable to numeric would produce `NA` for mismatching type.

Please see table for a brief summary:

| | Original data | Converted using `as.numeric()` |
| -------- | -------- | -------- |
| when string is factor | (2.1, 5.0, 3.2, "a") | (1, 3, 2, 4) |
| when string is character| (2.1, 5.0, 3.2, "a") | (2.1, 5.0, 3.2, NA)|

---

## Session 2 Questions:
Please enter any questions not answered during live session here:
1.

### End Session 2

---

## Session 3
### Name (Last, First) / Email
Doe, John / [e-mail] (example)
Nakamura, Masaki / [e-mail]
Yamada, Sohei / [e-mail]
Tianning, Lan / [e-mail]
Phrasavath, Bonaly / [e-mail]
Orosz, Stephanie / [e-mail]
Nogueda, Lesley/[e-mail]
Paula Jaramillo/[e-mail]
Pykurel, Himanshu/ [e-mail]
Cho, Harrison / [e-mail]
Artan, Warsan / [e-mail]
ilaria simmen
Lascano, Dayra / [e-mail]
Chen, Wenxin / [e-mail]
Groper, Danielle / [e-mail]
Coyle, Jackson / [e-mail]
Romstadt, Lisa / [e-mail]
Tsuru, Yutaro / [e-mail]
Cuevas, Christopher/[e-mail]
Xuran, Wang / [e-mail]
Chu, Jordan / [e-mail]
Yamakawa, Daisuke / [e-mail]
Zhiyuan, Chi / [e-mail]
Tan,Jingyi / [e-mail]
Yuxin Guo / [e-mail]
Leung, Taysia/ [e-mail] (Asynchronous Video)
Ahluwalia, Gurkriti / [e-mail]
****
# Notes

## importing data

# download data directly from a link.
# 
download.file("https://raw.githubusercontent.com/datacarpentry/r-intro-geospatial/master/_episodes_rmd/data/gapminder_data.csv",
              destfile = "data/gapminder_data.csv") #make sure to have the "data"  file in your Rstudio working directory 

# exploring data

str(gapminder)

class(gapminder$year)

nrow(gapminder)
ncol(gapminder)
dim(gapminder)

colnames(gapminder)
head(gapminder)

head(gapminder, n=10)

Challenge 1
answer:

# check the last few lines
tail(gapminder)
tail(gapminder, n=15)

#random rows
gapminder[sample(nrow(gapminder), 5),]

challenge 2
answer:

str(gapminder)

#selecting data by cutoff

below_average <- gapminder$lifeExp < 70.5
head(gapminder)

adding wrong length vector

below_average <- c(TRUE, TRUE, TRUE, TRUE)
head(cbind(gapminder, below_average))


below_average <- c(TRUE, FALSE, FALSE, TRUE)
head(cbind(gapminder, below_average))

#overwrite content with new data 

below_average <- as.logical(gapminder$lifeExp < 70.5)
below_average

gapminder <- cbind(gapminder, below_average)

# add new row

new_row <- list('Norway', 2016, 500000, 'nordic', 80.3, 49400.0, FALSE)
new_row

# factors

# makes continent a character 
gapminder$continent <- as.character(gapminder$continent)

str(gapminder)

# dplyr

#installing packages:
install.packages("dplyr")

library(dplyr)

year_country_gdp <- select(gapminder, year, country, gdpPercap)
head(year_country_gdp)

year_country_gdp <- gapminder %>% 
  select(year, country, gdpPercap)

year_country_gdp_euro <- gapminder %>%
  filter(continent == "Europe") %>%
  select(year, country, gdpPercap)

## Session 3 Questions:
Please enter any questions not answered during live session here:
1.

### End Session 3

## Session 4
### Name (Last, First) / Email
1. Doe, John / [e-mail] (example)
2. Pykurel, Himanshu/ [e-mail]
3. Ilaria Simmen/ [e-mail]
4. Phrasavath, Bonaly / [e-mail]
5. Lascano, Dayra / [e-mail]
6. Nogueda, Lesley / [e-mail]
8. Tsuru, Yutaro / [e-mail]
9. Nakamura, Masaki / [e-mail]
10. Cuevas, Christopher/[e-mail]
11. Groper, Danielle / [e-mail]
12. Artan, Warsan / [e-mail]
13. Coyle, Jackson / [e-mail]
14. Xuran, Wang / [e-mail]
15. Chu, Jordan / [e-mail]
16. Zhiyuan, Chi / [e-mail]
17. Chen, Wenxin / [e-mail]
18. Tan, Jingyi / [e-mail]
19. Yuxin Guo / [e-mail]
20. Yamakawa, Daisuke / [e-mail]
21. Leung, Taysia/ [e-mail] (Asynchronous Video)
22. Ahluwalia, Gurkriti / [e-mail]

### _NOTES_

# download data directly from a link.
# 
download.file("https://raw.githubusercontent.com/datacarpentry/r-intro-geospatial/master/_episodes_rmd/data/gapminder_data.csv",
              destfile = "data/gapminder_data.csv") 
#make sure to have the "data"  file in your Rstudio working directory 

# downlod `gapminder_wide` data from a link.
download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_wide.csv",
             destfile = "data/gapminder_wide.csv")

### loading libraries

install.packages("tidyverse", dependencies = TRUE)

library(tidyr)
library(dplyr)
library(tidyverse)

### loading data

#assign data to gapminder variable
gapminder <- read.csv("data/gapminder_data.csv",
                      stringsAsFactors = FALSE)

gap_wide <- read.csv("data/gapminder_wide.csv", stringsAsFactors = FALSE)

### converting wide to long format

gap_long <- gap_wide %>%
  pivot_longer(
    cols = c(starts_with('pop'),
             starts_with('lifeExp'),
             starts_with('gdpPercap')),
    names_to = "obstype_year", 
    values_to = "obs_values"
  )

View(gap_long) #to view the data

gap_long$year <- as.integer(gap_long$year)

gap_long <- gap_long %>% separate(obstype_year, into = c('obs_type', 'year'), sep = "_")
gap_long$year <- as.integer(gap_long$year)

View(gap_long)

## Session 4 Questions:
Please enter any questions not answered during live session here:
1.

### End Day 4

## Session 5
### Name (Last, First) / Email
Doe, John / [e-mail] (example)
Nakamura, Masaki / [e-mail]
Phrasavath, Bonaly / [e-mail]
Lascano, Dayra / [e-mail]
Ye, Yuan / [e-mail]
Tianning , Lan / [e-mail]
Tsuru, Yutaro / [e-mail]
Chen, Wenxin / [e-mail]
Coyle, Jackson / [e-mail]
Tan, Jingyi/ [e-mail]
Groper, Danielle / [e-mail]
Artan, Warsan / [e-mail]
Cuevas, Christopher/[e-mail]
Xuran, Wang / [e-mail]
Yuxin Guo / [e-mail]
Yamakawa, Daisuke / [e-mail]
Leung, Taysia/ [e-mail] (Asynchronous Video)
Chu, Jordan / [e-mail]
Ahluwalia, Gurkriti / [e-mail]

# notes

## using ggplot

library("ggplot2")

ggplot(data= gapminder, aes(x= gdpPercap, y=lifeExp)) + 
  geom_point()

### challenge answer
ggplot(data= gapminder, aes(x= gdpPercap, y=lifeExp, color=continent)) + 
  geom_point()

## layers

ggplot(gapminder, aes(x = year, y=lifeExp, color=continent)) +
  geom_line()

ggplot(gapminder, aes(x = year, y=lifeExp, color=continent, group = country)) +
  geom_line()

ggplot(gapminder, aes(x = year, y=lifeExp, color=continent, group = country)) +
  geom_line() + geom_point()

ggplot(gapminder, aes(x = year, y=lifeExp, group = country)) + 
  geom_line(aes(color=continent)) +
  geom_point()

# challenge:  switch geom_line() and geom_point()
# challenge answer 

ggplot(gapminder, aes(x = year, y=lifeExp, group = country)) +  geom_point() +  geom_line(aes(color=continent)) 

## transformation and stats

ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) +
  geom_point(alpha=0.5) + 
  scale_x_log10()

### changing alpha

ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) +
  geom_point(aes(alpha=continent)) + 
  scale_x_log10()
# you will see a warning, but the plot still works 

ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) +
  geom_point(aes(alpha=0.5)) + 
  scale_x_log10() +
  geom_smooth(method = "lm", size = 1.5)

#challenge answer 
ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp)) +
  geom_point(aes(alpha=0.5, color="orange")) + 
  scale_x_log10() +
  geom_smooth(method = "lm", size = 1.5) 

# challenge answer 


ggplot(data = gapminder, aes(x= gdpPercap, y=lifeExp, color=continent)) +
  geom_point(aes(shape=continent)) + 
  scale_x_log10() +
  geom_smooth(method = "lm", size = 1.5)

#multi-pannel figures using face-wrap

americas <- gapminder[gapminder$continent == "Americas",]
  
ggplot(americas, aes(x=year, y=lifeExp)) +
  geom_line() +
  facet_wrap(~country) + theme(axis.text.x = element_text(angle = 45, hjust =1 )) +
  labs(x="year", y="life Expectancy", title="figure 1", color= "continent")

useful ggplot cheatsheet:

https://statsandr.com/blog/files/ggplot2-cheatsheet.pdf

## challenge 
ggplot(gapminder, aes(x=continent, y=lifeExp, fill=continent)) + geom_boxplot() + facet_wrap(~year) +
  ylab("life Expentancy") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank())

## Session 5 Questions:
Please enter any questions not answered during live session here:
1.

### End Day 5

## Session 6
### Name (Last, First) / Email
Doe, John / [e-mail] (example)
Pykurel, Himanshu / [e-mail]
Lascano, Dayra / [e-mail]
Nogueda, Lesley /
[e-mail]
Nakamura, Masaki / [e-mail]
Phrasavath, Bonaly / [e-mail]
Tsuru, Yutaro / [e-mail]
Artan, Warsan / [e-mail]
Groper, Danielle / [e-mail]
Cuevas, Christopher/[e-mail]
Xuran, Wang / [e-mail]
Yuxin Guo / [e-mail]
Coyle, Jackson / [e-mail]
Chen, Wenxin / [e-mail]
Yamakawa, Daisuke / [e-mail]
Leung, Taysia/ [e-mail] (Asynchronous Video)
Chu, Jordan / [e-mail]
Ahluwalia, Gurkriti / [e-mail]

### download data directly from a link.

download.file("https://raw.githubusercontent.com/datacarpentry/r-intro-geospatial/master/_episodes_rmd/data/gapminder_data.csv",
              destfile = "data/gapminder_data.csv") 
#make sure to have the "data"  file in your Rstudio working directory 

### use this google drive link to download the 'est.csv' file

https://drive.google.com/file/d/15ei1LmUURIvbJIjS9J-Y6qlyRx1jrpFB/view?usp=share_link

### Notes

library(tidyverse)

# read in gapminder data 


#source("filename.R")

# running a group of functions stored in another .r file
source("programming-with-r.R")

# useful for importing data sets and creating a R pipeline

### using .rmd rnotebook
Create and rnotebook file:
file menu/new file/r notebook

#filter the country to plot

gap-to-plot <- gapminder 

est <- read_csv("data/countries_estimated.csv/")

## Session 6 Questions:
Please enter any questions not answered during live session here:
1.

### End Day 6