ECON403 R lab01

2019-01-30

Why R?

• Free
• Best statistics packages
• Great visualization tools
• Strong numerical computional tools
• tidyvers

Intended Learning Outcome

After this lecture, you

• know basic R syntax
• plot functions
• solve equations

Pre-assessment

• How many of you use R before?
• How many of you use any programming language before?

1. Getting Help

1.1 Accessing the help files

• Get help of a particular function.

?mean

• Search the help files for a word or phrase.

help.search('weighted mean')

• Find help for a package.

help(package = 'dplyr')

1. 2 More about an object

• Get a summary of an object’s structure.

str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

• Find the class an object belongs to

class(iris)

'data.frame'

2. Using Libraries

• Download and install a package from CRAN.

install.packages("tidyverse")

Installing package into ‘/home/edubc2018/R/x86_64-pc-linux-gnu-library/3.4’
(as ‘lib’ is unspecified)

• Load the package into the session, making all its functions available to use.

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 2.2.1     ✔ purrr   0.2.5
✔ tibble  2.0.1     ✔ dplyr   0.7.8
✔ tidyr   0.8.2     ✔ stringr 1.2.0
✔ readr   1.1.1     ✔ forcats 0.2.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

3. Working Directory

• Find the current working directory (where inputs are found and outputs are sent).

getwd()

'/home/edubc2018/econ/econ403'

• Change the current working directory.

setwd('C://file//path')

4. Vectors

4.1 Creating Vectors

• Join elements into a vector

c(2, 4, 6)

1. 2
2. 4
3. 6

• An integer sequence

2:6

1. 2
2. 3
3. 4
4. 5
5. 6

• A complex sequence

seq(2, 3, by=0.5)

1. 2
2. 2.5
3. 3

• Repeat a vector

rep(1:2, times=3)

1. 1
2. 2
3. 1
4. 2
5. 1
6. 2

• Repeat elements of a vector

x=rep(1:2, each=3)
x

1. 1
2. 1
3. 1
4. 2
5. 2
6. 2

4.2 Selecting Vector Elements

x
x[4]# fourth element.

1. 1
2. 1
3. 1
4. 2
5. 2
6. 2

2

x[-4] # All but the fourth.

1. 1
2. 1
3. 1
4. 2
5. 2

x[2:4]# Elements two to four.

1. 1
2. 1
3. 2

x[-(2:4)]# All elements except two to four.

1. 1
2. 2
3. 2

x[c(1,5)] # Elements one and five.

1. 1
2. 2

x[x < 2] # All elements less than zero.

1. 1
2. 1
3. 1

5. Programming

5.1 For Loop

for (variable in sequence/vector){
Do something 
}

# Example
for (i in 1:4){
j <- i + 10 
print(j) 
}

[1] 11
[1] 12
[1] 13
[1] 14

5.2 If Statements

if (condition){ 
Do something 
} else { 
Do something different  
}

# Example
if (i > 3){ 
print('Yes') 
} else { 
print('No')  
}

[1] "Yes"

5.3 Functions

function_name <- function(var){ 
Do something 
return(new_variable) 
}

# Example
square <- function(x){ 
squared <- x*x 
return(squared) 
}
square(2)

4

6. Reading and Writing Data

6.1 Read and write a comma separated value file.

df <- read.csv('https://nb.vse.cz/~zouharj/econ/wage1.csv')
write.csv(df, 'file.csv')
head(df, 3)

wage	educ	exper	tenure	nonwhite	female	married	numdep	smsa	northcen	⋯	trcommpu	trade	services	profserv	profocc	clerocc	servocc	lwage	expersq	tenursq
3.10	11	2	0	0	1	0	2	1	0	⋯	0	0	0	0	0	0	0	1.131402	4	0
3.24	12	22	2	0	1	1	3	1	0	⋯	0	0	1	0	0	0	1	1.175573	484	4
3.00	11	2	0	0	0	0	2	0	0	⋯	0	1	0	0	0	0	0	1.098612	4	0

6.2 Tidyverse read_excel read_csv

library(readxl)
wage1 = read_csv('https://nb.vse.cz/~zouharj/econ/wage1.csv')
head(wage1,3)

Parsed with column specification:
cols(
  .default = col_integer(),
  wage = col_double(),
  lwage = col_double()
)
See spec(...) for full column specifications.

wage	educ	exper	tenure	nonwhite	female	married	numdep	smsa	northcen	⋯	trcommpu	trade	services	profserv	profocc	clerocc	servocc	lwage	expersq	tenursq
3.10	11	2	0	0	1	0	2	1	0	⋯	0	0	0	0	0	0	0	1.131402	4	0
3.24	12	22	2	0	1	1	3	1	0	⋯	0	0	1	0	0	0	1	1.175573	484	4
3.00	11	2	0	0	0	0	2	0	0	⋯	0	1	0	0	0	0	0	1.098612	4	0

# Note that the Excel spreadsheet must be local (a URL does not work).
wage2 = read_excel('wage2.xls', sheet = 1)
head(wage2,3)

wage	hours	IQ	KWW	educ	exper	tenure	age	married	black	south	urban	sibs	brthord	meduc	feduc
769	40	93	35	12	11	2	31	1	0	0	1	1	2	8	8
808	50	119	41	18	11	16	37	1	0	0	1	1	.	14	14
825	40	108	46	14	11	9	33	1	0	0	1	1	2	14	14

7. Maths Functions

x = wage1$wage[1:10]
t(x)

3.1

3.24

3

6

5.3

8.75

11.25

5

3.6

18.18

t(round(x, 1)) # Round to n decimal places.

3.1

3.2

3

6

5.3

8.8

11.2

5

3.6

18.2

max(x) # Largest element.
sum(x)#Sum.
mean(x)#Mean.
median(x)#Median. 
min(x) # Smallest element.

18.1800003051758

67.4200003147125

6.74200003147125

5.15000009536743

3

var(x) # The variance.
cor(x, x) # Correlation.
sd(x) # The standard deviation

23.3610408509191

1

4.83332606503215

t(log(x))#Natural log.

1.131402

1.175573

1.098612

1.791759

1.667707

2.169054

2.420368

1.609438

1.280934

2.900322

t(exp(x))# Exponential.

22.19795

25.53372

20.08554

403.4288

200.3368

6310.688

76879.92

148.4132

36.59823

78609279

8. Plotting

8.1 Values of x in order.

plot(x)

8.2 Values of x against y.

plot(wage1$educ,
     wage1$wage,
    xlab= 'educ',
    ylab = 'wage',
    col = 'blue')

8.3 Curve for Functions

curve(15+6*x -3*x^2,
      xlab= 'experience',
      ylab = 'income'      
     )

8.3 add 'abline' and 'text' function

curve(15+6*x -3*x^2,
      xlab= 'experience',
      ylab = 'income',
      col = 'purple'
     )
abline(v=0.15, col="blue") # add vertical  lines # change line colors
abline(h=0.40, col="red") # add  horizontal lines # change line colors
abline(a = 15, b = 2, col = 'black') # add a: intercept; b: slope lines # change line colors
abline(a = 18, b = -2, col = 'gray') # add a: intercept; b: slope lines # change line colors
points(x = 0.8, y = 16.5, type = 'p'   ,col="red") # x,y coordinate vectors of points to plot.
text(x=0.8,y=16.2, labels = "solution: 15, 2", col = 'green') # x,y are coordinates where the text labels should be written

9. Solve equations

# install.packages("rootSolve")
library(rootSolve)

## =======================================================================
##  simultaneous equations
## =======================================================================
model <- function(x){
    c(F1 = 500-0.1*x[1] - x[2], 
      F2 = 0.05*x[1]- x[2])
} 

(ss <- multiroot(f = model, start = c(1, 1)))

$root

1. 3333.33333333347
2. 166.66666666667

$f.root

F1

-1.66551217262167e-11

F2

3.69482222595252e-12

$iter

3

$estim.precis

1.01749719760846e-11

10. Post-assessment

1. what is the standard deviation function in R?

   • a. std()
   • b. sd()

2. What is the way to get help in R?

   • a.help(myfunction)
   • b. ?myfunction

3. Subset first element in vector x

   • a. x[1]
   • b. x(1)
4. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210040301
   • download csv file, and load to R

11. Summary

• R syntax
  • R cheat sheet
• Plot
  • plot(x, y)
• solve equations
  • rootSolve

Reference

• R-cheat-sheet