Data Types
Arvind Sharma
2024-09-05
1 Cross Section Data
1.1 Infidelity data
# install.packages("AER")
library("AER")## Loading required package: car## Loading required package: carData## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Loading required package: survivaldata("Affairs")
str(Affairs)## 'data.frame': 601 obs. of 9 variables:
## $ affairs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ gender : Factor w/ 2 levels "female","male": 2 1 1 2 2 1 1 2 1 2 ...
## $ age : num 37 27 32 57 22 32 22 57 32 22 ...
## $ yearsmarried : num 10 4 15 15 0.75 1.5 0.75 15 15 1.5 ...
## $ children : Factor w/ 2 levels "no","yes": 1 1 2 2 1 1 1 2 2 1 ...
## $ religiousness: int 3 4 1 5 2 2 2 2 4 4 ...
## $ education : num 18 14 12 18 17 17 12 14 16 14 ...
## $ occupation : int 7 6 1 6 6 5 1 4 1 4 ...
## $ rating : int 4 4 4 5 3 5 3 4 2 5 ...head(Affairs)## affairs gender age yearsmarried children religiousness education occupation
## 4 0 male 37 10.00 no 3 18 7
## 5 0 female 27 4.00 no 4 14 6
## 11 0 female 32 15.00 yes 1 12 1
## 16 0 male 57 15.00 yes 5 18 6
## 23 0 male 22 0.75 no 2 17 6
## 29 0 female 32 1.50 no 2 17 5
## rating
## 4 4
## 5 4
## 11 4
## 16 5
## 23 3
## 29 5tail(Affairs[,-c(5:9)])## affairs gender age yearsmarried
## 1935 7 male 47 15.0
## 1938 1 male 22 1.5
## 1941 7 female 32 10.0
## 1954 2 male 32 10.0
## 1959 2 male 22 7.0
## 9010 1 female 32 15.0## Greene (2003)
## Tab. 22.3 and 22.41.1.1 Scatterplot
This scatterplot will show each data point as a dot, with age on the x-axis and the number of affairs on the y-axis.
# Create a scatterplot
plot(Affairs$age, Affairs$affairs,
xlab = "Age",
ylab = "Number of Affairs",
main = "Scatterplot of Age vs. Number of Affairs"
)
1.1.2 Barplot
This bar plot will show the frequency of affairs for each gender category.
# Create a bar plot of the frequency of affairs by gender
barplot(table(Affairs$gender),
xlab = "Gender",
ylab = "Frequency",
main = "Bar Plot of Frequency of Affairs by Gender")
# install.packages("texreg")
library(texreg)## Version: 1.39.4
## Date: 2024-07-23
## Author: Philip Leifeld (University of Manchester)
##
## Consider submitting praise using the praise or praise_interactive functions.
## Please cite the JSS article in your publications -- see citation("texreg").# Simple Linear Regression models
age <- lm(affairs ~ age , data = Affairs)
children <- lm(affairs ~ children , data = Affairs)
religiousness <- lm(affairs ~ religiousness, data = Affairs)
yearsmarried <- lm(affairs ~ yearsmarried , data = Affairs)
rating <- lm(affairs ~ rating , data = Affairs)
# Multiple Linear Regression
m1 <- lm(affairs ~ age + children + religiousness + yearsmarried + rating, data = Affairs)
# Creating a table of all models using screenreg
screenreg(list(age, children, religiousness, yearsmarried, rating, m1))##
## ===================================================================================
## Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
## -----------------------------------------------------------------------------------
## (Intercept) 0.36 0.91 *** 2.73 *** 0.55 * 4.74 *** 5.99 ***
## (0.49) (0.25) (0.38) (0.24) (0.48) (0.79)
## age 0.03 * -0.04 *
## (0.01) (0.02)
## childrenyes 0.76 * -0.20
## (0.30) (0.34)
## religiousness -0.41 *** -0.49 ***
## (0.11) (0.11)
## yearsmarried 0.11 *** 0.17 ***
## (0.02) (0.04)
## rating -0.84 *** -0.71 ***
## (0.12) (0.12)
## -----------------------------------------------------------------------------------
## R^2 0.01 0.01 0.02 0.03 0.08 0.13
## Adj. R^2 0.01 0.01 0.02 0.03 0.08 0.12
## Num. obs. 601 601 601 601 601 601
## ===================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.052 Time Series
2.1 Souvenir Sales Data
Monthly sales for a souvenir shop at a beach resort town in Queensland, Australia, for January 1987-December 1993.
souvenir <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
?plot## Help on topic 'plot' was found in the following packages:
##
## Package Library
## base /Library/Frameworks/R.framework/Resources/library
## graphics /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
##
##
## Using the first match ...plot(souvenir,
xlab = "Time",
ylab = "Souvenir Sales",
type = "b"
)
We can do better and plat the year values instead of data index values.
Let me now declare the data as time series.
souvenirtimeseries <- ts(souvenir,
frequency = 12,
start = c(1987,1)
)
souvenirtimeseries## Jan Feb Mar Apr May Jun Jul
## 1987 1664.81 2397.53 2840.71 3547.29 3752.96 3714.74 4349.61
## 1988 2499.81 5198.24 7225.14 4806.03 5900.88 4951.34 6179.12
## 1989 4717.02 5702.63 9957.58 5304.78 6492.43 6630.80 7349.62
## 1990 5921.10 5814.58 12421.25 6369.77 7609.12 7224.75 8121.22
## 1991 4826.64 6470.23 9638.77 8821.17 8722.37 10209.48 11276.55
## 1992 7615.03 9849.69 14558.40 11587.33 9332.56 13082.09 16732.78
## 1993 10243.24 11266.88 21826.84 17357.33 15997.79 18601.53 26155.15
## Aug Sep Oct Nov Dec
## 1987 3566.34 5021.82 6423.48 7600.60 19756.21
## 1988 4752.15 5496.43 5835.10 12600.08 28541.72
## 1989 8176.62 8573.17 9690.50 15151.84 34061.01
## 1990 7979.25 8093.06 8476.70 17914.66 30114.41
## 1991 12552.22 11637.39 13606.89 21822.11 45060.69
## 1992 19888.61 23933.38 25391.35 36024.80 80721.71
## 1993 28586.52 30505.41 30821.33 46634.38 104660.67options(scipen = 200)
options(digits = 2)Lets plot the data with ?plot.ts
?plot.ts
plot.ts(souvenirtimeseries,
ylab = "Souvenir Sales",
xlab = " Time (monthly)"
) 
3 Pooled Cross Section
Pooled cross-sectional data typically involves data from multiple cross-sectional surveys conducted at different points in time, with the goal of combining them into a single dataset for analysis.
3.1 CPS78_85
Pooled Cross Section data for two years
N=1084 (550 and 534)
t=2 (1978 and 1985)
Note you have different people in CPS in year 1978 and 1985.
# install.packages("wooldridge")
library("wooldridge")
data("cps78_85")
# summary(cps78_85)
str(cps78_85)## 'data.frame': 1084 obs. of 15 variables:
## $ educ : int 12 12 6 12 12 8 11 15 16 15 ...
## $ south : int 0 0 0 0 0 0 0 0 0 0 ...
## $ nonwhite: int 0 0 0 0 0 0 0 0 0 0 ...
## $ female : int 0 1 0 0 0 0 0 1 1 0 ...
## $ married : int 0 1 1 1 1 1 0 0 0 1 ...
## $ exper : int 8 30 38 19 11 43 2 9 17 23 ...
## $ expersq : int 64 900 1444 361 121 1849 4 81 289 529 ...
## $ union : int 0 1 1 1 0 0 0 0 0 1 ...
## $ lwage : num 1.22 1.61 2.14 2.07 1.65 ...
## $ age : int 25 47 49 36 28 56 18 29 38 43 ...
## $ year : int 78 78 78 78 78 78 78 78 78 78 ...
## $ y85 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y85fem : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y85educ : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y85union: int 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"help(cps78_85)
?table
table(cps78_85$y85)##
## 0 1
## 550 534You need to inspect the structure and characteristics of the dataset itself. Will have to read up on CPS website to confirm this is pooled cross section data.
4 Panel Data
4.1 Countries
7 countries (A-G) tracked from 1990 to 2000 (time=10 years) each.
The two way table shows the panel data is balanced.
coplot seems to be useful here as well and gives us 7
charts - one for each country for the 10 years.
# install.packages("foriegn")
library(foreign)
Panel <- read.dta(file = "http://dss.princeton.edu/training/Panel101.dta")
help(Panel)## No documentation for 'Panel' in specified packages and libraries:
## you could try '??Panel' head(x = Panel, n = 11L)## country year y y_bin x1 x2 x3 opinion op
## 1 A 1990 1342787840 1 0.278 -1.11 0.283 Str agree 1
## 2 A 1991 -1899660544 0 0.321 -0.95 0.493 Disag 0
## 3 A 1992 -11234363 0 0.363 -0.79 0.703 Disag 0
## 4 A 1993 2645775360 1 0.246 -0.89 -0.094 Disag 0
## 5 A 1994 3008334848 1 0.425 -0.73 0.946 Disag 0
## 6 A 1995 3229574144 1 0.477 -0.72 1.030 Str agree 1
## 7 A 1996 2756754176 1 0.500 -0.78 1.092 Disag 0
## 8 A 1997 2771810560 1 0.052 -0.70 1.416 Str agree 1
## 9 A 1998 3397338880 1 0.366 -0.70 1.549 Disag 0
## 10 A 1999 39770336 1 0.396 -0.64 1.794 Str disag 0
## 11 B 1990 -5934699520 0 -0.082 1.43 0.023 Agree 1 tail(x = Panel, n = 11L)## country year y y_bin x1 x2 x3 opinion op
## 60 F 1999 6349319168 1 0.28 -0.46 1.2 Disag 0
## 61 G 1990 1342787840 1 0.94 -1.52 1.5 Str disag 0
## 62 G 1991 -1518985728 0 1.10 -1.46 1.4 Agree 1
## 63 G 1992 1912769920 1 1.25 -1.41 1.4 Str agree 1
## 64 G 1993 1345690240 1 0.76 -1.35 1.9 Str disag 0
## 65 G 1994 2793515008 1 1.21 -1.33 2.2 Str disag 0
## 66 G 1995 1323696384 1 1.09 -1.41 2.8 Str disag 0
## 67 G 1996 254524176 1 0.78 -1.33 4.3 Str agree 1
## 68 G 1997 3297033216 1 1.26 -1.58 4.6 Disag 0
## 69 G 1998 3011820800 1 1.24 -1.60 6.1 Disag 0
## 70 G 1999 3296283392 1 1.23 -1.62 7.2 Disag 0 # simplest way to
table(Panel$year, Panel$country) ##
## A B C D E F G
## 1990 1 1 1 1 1 1 1
## 1991 1 1 1 1 1 1 1
## 1992 1 1 1 1 1 1 1
## 1993 1 1 1 1 1 1 1
## 1994 1 1 1 1 1 1 1
## 1995 1 1 1 1 1 1 1
## 1996 1 1 1 1 1 1 1
## 1997 1 1 1 1 1 1 1
## 1998 1 1 1 1 1 1 1
## 1999 1 1 1 1 1 1 1?coplot # conditioning plots
coplot(data = Panel,
formula = y ~ year | country,
type = "b" # Points and lines
) 
scatterplot(y ~ year | country,
boxplots = FALSE,
smooth = TRUE,
reg.line = FALSE,
data = Panel
)## Warning in plot.window(...): "reg.line" is not a graphical parameter## Warning in plot.xy(xy, type, ...): "reg.line" is not a graphical parameter## Warning in axis(side = side, at = at, labels = labels, ...): "reg.line" is not
## a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "reg.line" is not
## a graphical parameter## Warning in box(...): "reg.line" is not a graphical parameter## Warning in title(...): "reg.line" is not a graphical parameter
# fixed.dum <-lm(formula = y ~ x1 + factor(country) - 1, # no intercept but all dummies
# data = Panel
# )
#
# summary(fixed.dum)5 Session Info
https://www.rostrum.blog/2018/10/13/sessioninfo/
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] foreign_0.8-87 wooldridge_1.4-3 texreg_1.39.4 AER_1.2-12
## [5] survival_3.7-0 sandwich_3.1-0 lmtest_0.9-40 zoo_1.8-12
## [9] car_3.1-2 carData_3.0-5
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.7 cli_3.6.3 knitr_1.48 rlang_1.1.4
## [5] xfun_0.46 Formula_1.2-5 highr_0.11 jsonlite_1.8.8
## [9] htmltools_0.5.8.1 sass_0.4.9 rmarkdown_2.27 grid_4.4.1
## [13] evaluate_0.24.0 jquerylib_0.1.4 abind_1.4-5 fastmap_1.2.0
## [17] yaml_2.3.10 lifecycle_1.0.4 compiler_4.4.1 rstudioapi_0.16.0
## [21] lattice_0.22-6 digest_0.6.36 R6_2.5.1 splines_4.4.1
## [25] bslib_0.8.0 Matrix_1.7-0 tools_4.4.1 cachem_1.1.0# devtools::session_info()