- Introduction
- 1. Introduction to Quantitative Analysis
- 2. Descriptive Statistics
- 3. T-test for Difference in Means and Hypothesis Testing
- 4. Bivariate linear regression models
- 5. Multiple linear regression models
- 6. Assumptions and Violations of Assumptions
- 7. Interactions
- 8. Panel Data, Time-Series Cross-Section Models
- 9. Binary models: Logit
- 10. Frequently Asked Questions
- 11. Optional Material
- 12. Datasets
- 13. R Resources
- 14. References
- Published with GitBook
1. Introduction to Quantitative Analysis
1.1 Seminar
Setting a Working Directory
Before you begin, make sure to set your working directory to a folder where your course related files such as R scripts and datasets are kept. We recommend that you create a PUBLG100 folder for all your work. Create this folder on N: drive if you're using a UCL computer or somewhere on your local disk if you're using a personal laptop.
Once the folder is created, use the setwd() function in R to set your working directory.
| Recommended Folder Location | R Function | |
|---|---|---|
| UCL Computers | N: Drive | setwd("N:/PUBLG100") |
| Personal Laptop (Windows) | C: Drive | setwd("C:/PUBLG100") |
| Personal Laptop (Mac) | Home Folder | setwd("~/PUBLG100") |
After you've set the working directory, verify it by calling the getwd() function.
getwd()
Now download the R script for this seminar from the "Download .R Script" button above, and save it to your PUBLG100 folder.
If you're using a UCL computer, please make sure you're running R version 3.2.0. Some of the seminar tasks and exercises will not work with older versions of R. Click here for help on how to start the new version of R on UCL computers.
R Introduction
Let's get acquainted with R.
Take a look at R Studio. See the 4 windows:
- Upper-Left : R-script.
- Lower-Left : Console.
- Upper-Right : Environment (data, variables, ...)
- Lower-Right : Plots, help file, packages etc.
We begin by walking through the steps for creating and saving an R script.
- Create a new R script and save it as lab1.R to your
PUBLG100directory. - Now type the following commands in the new file you just created:
# Create a numeric and a character variable
a <- 5
typeof(a) # a is a numeric variable
[1] "double"
a
[1] 5
b <- "Yay stats class"
typeof(b) # b is a string variable
[1] "character"
b
[1] "Yay stats class"
Save your script, and re-open it to make sure your changes are still there.
# Create a vector
my.vector <- c(10,-7,99,34,0,-5) # a vector
my.vector
[1] 10 -7 99 34 0 -5
length(my.vector) # how many elements?
[1] 6
# subsetting
my.vector[1] # 1st vector element
[1] 10
my.vector[-1] # all elements but the 1st
[1] -7 99 34 0 -5
my.vector[2:4] # the 2nd to the 4th elements
[1] -7 99 34
my.vector[c(2,5)] # 2nd and 5th element
[1] -7 0
my.vector[length(my.vector)] # the last element
[1] -5
# delete variable 'a' from workspace
rm(a)
# delete everything from workspace
rm(list=ls())
# create a matrix
# type help("matrix") into the console and press ENTER
# read Description, Usage and Arguments
my.matrix1 <- matrix(data = c(1,2,30,40,500,600), nrow = 3, ncol = 2, byrow = TRUE,
dimnames = NULL)
my.matrix2 <- matrix(data = c(1,2,30,40,500,600), nrow = 2, ncol = 3, byrow = FALSE)
# How are the matrices different?
my.matrix1
[,1] [,2]
[1,] 1 2
[2,] 30 40
[3,] 500 600
my.matrix2
[,1] [,2] [,3]
[1,] 1 30 500
[2,] 2 40 600
# subsetting a matrix
my.matrix1[1,2] # element in row 1 and column 2
[1] 2
my.matrix1[2,1] # element in row 2 and column 1
[1] 30
my.matrix1[,1] # 1st column only
[1] 1 30 500
my.matrix1[1:2,] # rows 1 to 2
[,1] [,2]
[1,] 1 2
[2,] 30 40
my.matrix1[c(1,3),] # rows 1 and 3
[,1] [,2]
[1,] 1 2
[2,] 500 600
## ------------------------------------------------------------------------
# let's look at countries after WW2
# were western countries more democratic than the rest?
# (we proxy the "West" by NATO members)
# load the Polity IV dataset
my.data <- read.csv("http://uclspp.github.io/PUBLG100/data/polity.csv")
# View(my.data) # opens a window with the data set
dim(my.data) # returns number of rows and columns
[1] 16894 7
my.data[1:10,] # look at the top 10 rows of the data set
id scode country year polity2 democ nato
1 1 AFG Afghanistan 1800 -6 1 0
2 2 AFG Afghanistan 1801 -6 1 0
3 3 AFG Afghanistan 1802 -6 1 0
4 4 AFG Afghanistan 1803 -6 1 0
5 5 AFG Afghanistan 1804 -6 1 0
6 6 AFG Afghanistan 1805 -6 1 0
7 7 AFG Afghanistan 1806 -6 1 0
8 8 AFG Afghanistan 1807 -6 1 0
9 9 AFG Afghanistan 1808 -6 1 0
10 10 AFG Afghanistan 1809 -6 1 0
names(my.data) # the variable names in the data set
[1] "id" "scode" "country" "year" "polity2" "democ" "nato"
# we saw the variable name country but we only saw Afghanistan in the
# first 10 rows. Are there other countries in the data set?
levels(my.data$country) # levels displays levels of a factor variable
[1] "Afghanistan " "Albania "
[3] "Algeria " "Angola "
[5] "Argentina " "Armenia "
[7] "Australia " "Austria "
[9] "Azerbaijan " "Baden "
[11] "Bahrain " "Bangladesh "
[13] "Bavaria " "Belarus "
[15] "Belgium " "Benin "
[17] "Bhutan " "Bolivia "
[19] "Bosnia " "Botswana "
[21] "Brazil " "Bulgaria "
[23] "Burkina Faso " "Burundi "
[25] "Cambodia " "Cameroon "
[27] "Canada " "Cape Verde "
[29] "Central African Republic " "Chad "
[31] "Chile " "China "
[33] "Colombia " "Comoros "
[35] "Congo Brazzaville " "Congo Kinshasa "
[37] "Costa Rica " "Croatia "
[39] "Cuba " "Cyprus "
[41] "Czech Republic " "Czechoslovakia "
[43] "Denmark " "Djibouti "
[45] "Dominican Republic " "East Timor "
[47] "Ecuador " "Egypt "
[49] "El Salvador " "Equatorial Guinea "
[51] "Eritrea " "Estonia "
[53] "Ethiopia " "Fiji "
[55] "Finland " "France "
[57] "Gabon " "Gambia "
[59] "Georgia " "Germany "
[61] "Germany East " "Germany West "
[63] "Ghana " "Gran Colombia "
[65] "Greece " "Guatemala "
[67] "Guinea " "Guinea-Bissau "
[69] "Guyana " "Haiti "
[71] "Honduras " "Hungary "
[73] "India " "Indonesia "
[75] "Iran " "Iraq "
[77] "Ireland " "Israel "
[79] "Italy " "Ivory Coast "
[81] "Jamaica " "Japan "
[83] "Jordan " "Kazakhstan "
[85] "Kenya " "Korea "
[87] "Korea North " "Korea South "
[89] "Kosovo " "Kuwait "
[91] "Kyrgyzstan " "Laos "
[93] "Latvia " "Lebanon "
[95] "Lesotho " "Liberia "
[97] "Libya " "Lithuania "
[99] "Luxembourg " "Macedonia "
[101] "Madagascar " "Malawi "
[103] "Malaysia " "Mali "
[105] "Mauritania " "Mauritius "
[107] "Mexico " "Modena "
[109] "Moldova " "Mongolia "
[111] "Montenegro " "Morocco "
[113] "Mozambique " "Myanmar (Burma) "
[115] "Namibia " "Nepal "
[117] "Netherlands " "New Zealand "
[119] "Nicaragua " "Niger "
[121] "Nigeria " "Norway "
[123] "Oman " "Orange Free State "
[125] "Pakistan " "Panama "
[127] "Papal States " "Papua New Guinea "
[129] "Paraguay " "Parma "
[131] "Peru " "Philippines "
[133] "Poland " "Portugal "
[135] "Prussia " "Qatar "
[137] "Romania " "Russia "
[139] "Rwanda " "Sardinia "
[141] "Saudi Arabia " "Saxony "
[143] "Senegal " "Serbia "
[145] "Serbia and Montenegro " "Sierra Leone "
[147] "Singapore " "Slovak Republic "
[149] "Slovenia " "Solomon Islands "
[151] "Somalia " "South Africa "
[153] "South Sudan " "Spain "
[155] "Sri Lanka " "Sudan "
[157] "Sudan-North " "Suriname "
[159] "Swaziland " "Sweden "
[161] "Switzerland " "Syria "
[163] "Taiwan " "Tajikistan "
[165] "Tanzania " "Thailand "
[167] "Togo " "Trinidad and Tobago "
[169] "Tunisia " "Turkey "
[171] "Turkmenistan " "Tuscany "
[173] "Two Sicilies " "UAE "
[175] "Uganda " "Ukraine "
[177] "United Kingdom " "United Province CA "
[179] "United States " "Uruguay "
[181] "USSR " "Uzbekistan "
[183] "Venezuela " "Vietnam "
[185] "Vietnam North " "Vietnam South "
[187] "Wuerttemburg " "Yemen "
[189] "Yemen North " "Yemen South "
[191] "Yugoslavia " "Zambia "
[193] "Zimbabwe "
# we drop all oberservations which are not from 1946
my.data <- my.data[my.data$year==1946,]
my.data[1:10,]
id scode country year polity2 democ nato
147 147 AFG Afghanistan 1946 -10 0 0
248 248 ALB Albania 1946 -9 0 0
531 531 ARG Argentina 1946 -8 -88 0
669 669 AUL Australia 1946 10 10 0
884 884 AUS Austria 1946 10 10 0
1262 1262 BEL Belgium 1946 10 10 1
1480 1480 BHU Bhutan 1946 -10 0 0
1737 1737 BOL Bolivia 1946 -5 1 0
2000 2000 BRA Brazil 1946 7 7 0
2189 2189 BUL Bulgaria 1946 -6 0 0
summary(my.data$polity2) # descriptive statistics of polity variable
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-10.0000 -7.0000 -1.0000 0.2319 8.0000 10.0000 3
# now lets check if western countries were more democratic than the other countries in 1946
table(my.data$nato, my.data$polity2)
-10 -9 -8 -7 -6 -5 -4 -3 -1 0 1 2 3 4 5 7 8 10
0 4 7 3 3 4 3 2 5 3 1 1 5 1 1 3 2 2 8
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 9
# descriptive summary stats of polity variable by nato membership
summary(my.data$polity2[my.data$nato==0]) # not in nato
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-10.000 -7.000 -3.000 -1.207 4.750 10.000 3
summary(my.data$polity2[my.data$nato==1]) # nato member
Min. 1st Qu. Median Mean 3rd Qu. Max.
-9.000 10.000 10.000 7.818 10.000 10.000
## illustration
boxplot(my.data$polity2 ~ as.factor(my.data$nato),
frame = FALSE,
main = "Polity IV Scores of NATO founders vs others in 1946",
xlab = "NATO member",
ylab = "Polity Score")
# plots you can do with R
library(maps)
# ATTENTION: maps v3.0 has an updated 'world' map. #
# Many country borders and names have changed since 1990. #
# Type '?world' or 'news(package="maps")'. See README_v3. #
library(mapdata)
map('worldHires',
c('UK', 'Ireland', 'Isle of Man','Isle of Wight'),
xlim=c(-11,3), ylim=c(49,60.9))
# where are we?
# let's check google maps to find latitude and longitude of SPP
# it's: 51.525051, -0.130186
points(-0.130186,51.525051,col=2,pch=20,cex=1.8) # note longitude goes first
Additional Resources
- UK general election 2015
- Interactive maps of Crime data in Greater London
- R-bloggers posts on maps
- spatial.ly - Advanced Visualizations, Analysis and Resources from James Cheshire
Exercises
- Close and then re-open RStudio.
- If your workspace is not empty, clear it.
- Check your working directory in R.
- Change your working directory to your folder N:/PUBLG100.
- Create a vector with all even elements between 1 and 11 in ascending order.
- Delete the last element from your vector.
Create a square matrix with 3 rows and 3 columns.
- Row 1: smallest positive integers divisible by 3, in ascending order.
- Row 2: smallest prime numbers, in ascending order.
- Row 3: greatest mutiples of 6, smaller than 60, in descending order.
Change the elements on the main diagonal to 1's.
- Load "polity.csv" and call your data frame "df".
- Pick the year 1991 and drop all observations from the data set that are not from 1991.
- Summarize the variable democ (stands for democracy) and produce a frequency table.
- Delete all observations from the data where democ < 0.
- Print a histogram of the democracy -- type help("hist").