Data management
Instructions
- Homework assignment is here with all questions commented out.
- Complete all of the coding tasks in the
.qmdfile - Upload your individual exercise to your
GitHubrepo by Wed 11:59pm. - Remember to clean your github repo and sort hw submissions by weeks. Each week should have one folder.
1. Exploring a data set
We have learned several functions to explore a data set, including
library(ggplot2)
library(tidyverse)
myPath <- "/Users/howardliu/Dropbox/Essex/GV900/week5/lab-week5/"
world.data <- read.csv(paste0(myPath, "world.csv") )
table( world.data $ democ_regime )
##
## No Yes
## 75 114
dim(world.data)
## [1] 191 62
head(world.data)
## country colony confidence decentralization dem_other
## 1 Afghanistan UK NA NA 10.5
## 2 Albania Soviet Union 49.33593 0.74 63.0
## 3 Algeria France 52.05573 NA 40.8
## 4 Andorra Spain NA NA 100.0
## 5 Angola Portugal NA NA 40.8
## 6 Antigua & Barbuda UK NA NA 87.5
## dem_other5 democ_regime district_size3 durable effectiveness enpp_3
## 1 10% No single member 4 13.71158
## 2 Approx 60% Yes 3 35.46099 1-3 parties
## 3 Approx 40% No 6 or more members 5 32.62411
## 4 100% Yes NA 78.72340
## 5 Approx 40% No 3 19.14894
## 6 Approx 90% Yes single member NA 59.81088 1-3 parties
## eu fhrate04_rev fhrate08_rev frac_eth frac_eth3 free_business
## 1 Not member 2.5 3 0.7693 High NA
## 2 Not member 5 8 0.2204 Low 68.0
## 3 Not member 2.5 3 0.3394 Medium 71.2
## 4 Not member Most free 12 0.7139 High NA
## 5 Not member 2.5 3 0.7867 High 43.4
## 6 Not member 6 10 0.1643 Low NA
## free_corrupt free_finance free_fiscal free_govspend free_invest free_labor
## 1 NA NA NA NA NA NA
## 2 34 70 92.6 74.2 70 52.1
## 3 32 30 83.5 73.4 45 56.4
## 4 NA NA NA NA NA NA
## 5 19 40 85.1 62.8 35 45.2
## 6 NA NA NA NA NA NA
## free_monetary free_overall free_property free_trade gdp08 gdp_10_thou
## 1 NA NA NA NA 30.6 NA
## 2 78.7 66.0 35 85.8 24.3 0.1535
## 3 77.2 56.9 30 70.7 276.0 0.1785
## 4 NA NA NA NA NA NA
## 5 62.6 48.4 20 70.4 106.3 0.0857
## 6 NA NA NA NA NA 1.0449
## gdp_cap2 gdp_cap3 gdppcap08 gender_equal3 gini04 gini08 hi_gdp indy
## 1 NA NA NA 1919
## 2 Low Middle 7715 28.2 31.1 Low GDP 1991
## 3 Low Middle 8033 35.3 35.3 Low GDP 1962
## 4 NA NA NA 1278
## 5 Low Middle 5899 NA NA Low GDP 1975
## 6 High High NA NA NA High GDP 1981
## oecd old2006 old2003 pmat12_3 pop03 pop08 pop08_3
## 1 Not member NA NA NA 27.4 >=16.8 mil
## 2 Not member 8.479821 7.278363 Low post-mat 3169064 3.1 <=4.3 mil
## 3 Not member 4.578136 4.045199 31832610 34.4 >=16.8 mil
## 4 Not member NA NA 66000 NA
## 5 Not member 2.450295 2.930542 13522110 18.0 >=16.8 mil
## 6 Not member NA 8.186610 78580 NA
## popcat3 pr_sys protact3 regime_type3 region sources
## 1 Moderate (1-29m) No Dictatorship Middle East NA
## 2 Moderate (1-29m) No Moderate Parliamentary democ C&E Europe NA
## 3 Moderate (1-29m) Yes Dictatorship Africa NA
## 4 Small (under 1m) No Parliamentary democ W. Europe NA
## 5 Moderate (1-29m) Yes Dictatorship Africa NA
## 6 Small (under 1m) No Parliamentary democ S. America NA
## typerel unions urban03 urban06 vi_rel3 votevap00s women05 women09
## 1 Muslim NA NA 23.28 NA NA 27.7
## 2 Muslim NA 44.2390 46.14 20-50% 59.56 6.4 16.4
## 3 Muslim NA 58.8302 63.94 >50% NA NA 7.7
## 4 Roman Catholic NA 91.7404 90.28 20.95 14.3 35.7
## 5 Roman Catholic NA 36.1806 53.96 NA NA 37.3
## 6 Protestant NA 37.7566 39.60 76.34 10.5 10.5
## womyear womyear2 yng2003 young06
## 1 NA NA NA
## 2 1920 1944 or before 27.34834 26.35428
## 3 1962 After 1944 33.91887 28.94154
## 4 1973 After 1944 NA NA
## 5 1975 After 1944 47.62524 46.32196
## 6 1951 After 1944 20.66509 NA
tail(world.data)
## country colony confidence decentralization dem_other
## 186 Vietnam France 99.86241 NA 58.3
## 187 Western Samoa Other NA NA 58.3
## 188 Yemen UK NA NA 10.5
## 189 Serbia & Montenegro Soviet Union 31.64857 NA 63.0
## 190 Zambia UK NA NA 40.8
## 191 Zimbabwe UK 60.01903 0.87 40.8
## dem_other5 democ_regime district_size3 durable effectiveness enpp_3
## 186 Approx 60% No >1 to 5 members 46 40.18912
## 187 Approx 60% No single member NA 52.00946
## 188 10% No single member 7 26.00473
## 189 Approx 60% No >1 to 5 members 0 29.31442
## 190 Approx 40% Yes single member 4 24.58629 1-3 parties
## 191 Approx 40% No single member 13 27.65957
## eu fhrate04_rev fhrate08_rev frac_eth frac_eth3 free_business
## 186 Not member 1.5 2 0.2383 Low 60.7
## 187 Not member 6 10 0.1376 Low 73.2
## 188 Not member 3 4 NA 74.4
## 189 Not member 5.5 9 0.5736 Medium NA
## 190 Not member 4 8 0.7808 High 66.4
## 191 Not member 1.5 1 0.3874 Medium 30.0
## free_corrupt free_finance free_fiscal free_govspend free_invest free_labor
## 186 27 30 76.1 73.4 20 68.4
## 187 44 30 79.6 67.5 30 80.8
## 188 23 30 83.2 51.3 45 65.4
## 189 NA NA NA NA NA NA
## 190 28 50 72.4 82.6 50 57.0
## 191 18 10 58.4 NA NA 48.2
## free_monetary free_overall free_property free_trade gdp08 gdp_10_thou
## 186 58.1 49.8 15 68.9 240.1 0.0436
## 187 73.8 60.4 55 70.0 0.8 0.1484
## 188 65.1 54.4 30 76.1 55.3 0.0537
## 189 NA NA NA NA NA 0.1922
## 190 63.3 58.0 30 79.9 17.1 0.0361
## 191 NA 21.4 5 44.8 2.2 0.0639
## gdp_cap2 gdp_cap3 gdppcap08 gender_equal3 gini04 gini08 hi_gdp indy
## 186 Low Low 2785 36.1 34.4 Low GDP 1962
## 187 Low Middle 4485 NA NA Low GDP 1990
## 188 Low Low 2400 Low 33.4 33.4 Low GDP 1991
## 189 High Middle NA NA NA High GDP 1964
## 190 Low Low 1356 52.6 50.8 Low GDP 1980
## 191 Low Low 188 56.8 50.1 Low GDP 1980
## oecd old2006 old2003 pmat12_3 pop03 pop08 pop08_3
## 186 Not member 5.437487 5.261546 81314240 86.2 >=16.8 mil
## 187 Not member 4.595193 4.978562 178000 0.2 <=4.3 mil
## 188 Not member 2.284142 2.622207 19173160 23.1 >=16.8 mil
## 189 Not member 14.114708 14.008000 Low post-mat 8104000 NA
## 190 Not member 3.032360 2.693117 10402960 12.6 4.4-16.4 mil
## 191 Not member 3.711219 3.101562 13101750 11.7 4.4-16.4 mil
## popcat3 pr_sys protact3 regime_type3 region sources
## 186 Large (30m+) No Dictatorship Asia-Pacific NA
## 187 Small (under 1m) No Dictatorship Asia-Pacific NA
## 188 Moderate (1-29m) No Dictatorship Middle East NA
## 189 Moderate (1-29m) No Low Dictatorship C&E Europe NA
## 190 Moderate (1-29m) No Presidential democ Africa NA
## 191 Moderate (1-29m) No Dictatorship Africa NA
## typerel unions urban03 urban06 vi_rel3 votevap00s women05 women09
## 186 eastern NA 25.4076 26.88 <20% NA NA 25.8
## 187 Protestant NA 22.8014 22.60 76.62 NA NA
## 188 Muslim NA 25.6836 27.72 NA NA 0.3
## 189 Orthodox NA 52.0384 52.44 20-50% NA NA NA
## 190 other 12.5 40.3128 35.14 55.74 12.7 15.2
## 191 Protestant 13.9 37.4674 36.38 >50% NA NA 15.2
## womyear womyear2 yng2003 young06
## 186 1946 After 1944 30.62024 28.78953
## 187 1990 After 1944 35.46627 40.41566
## 188 1967 After 1944 45.24762 45.99981
## 189 1946 After 1944 19.59660 18.03825
## 190 1962 After 1944 46.82837 45.63621
## 191 1957 After 1944 43.43543 39.46990
There are some other functions we can use. For example, the names function tells us the names of all the variables included in a data frame object.
names(world.data)
## [1] "country" "colony" "confidence" "decentralization"
## [5] "dem_other" "dem_other5" "democ_regime" "district_size3"
## [9] "durable" "effectiveness" "enpp_3" "eu"
## [13] "fhrate04_rev" "fhrate08_rev" "frac_eth" "frac_eth3"
## [17] "free_business" "free_corrupt" "free_finance" "free_fiscal"
## [21] "free_govspend" "free_invest" "free_labor" "free_monetary"
## [25] "free_overall" "free_property" "free_trade" "gdp08"
## [29] "gdp_10_thou" "gdp_cap2" "gdp_cap3" "gdppcap08"
## [33] "gender_equal3" "gini04" "gini08" "hi_gdp"
## [37] "indy" "oecd" "old2006" "old2003"
## [41] "pmat12_3" "pop03" "pop08" "pop08_3"
## [45] "popcat3" "pr_sys" "protact3" "regime_type3"
## [49] "region" "sources" "typerel" "unions"
## [53] "urban03" "urban06" "vi_rel3" "votevap00s"
## [57] "women05" "women09" "womyear" "womyear2"
## [61] "yng2003" "young06"
The colnames function gives us the same results as well.
colnames(world.data)
## [1] "country" "colony" "confidence" "decentralization"
## [5] "dem_other" "dem_other5" "democ_regime" "district_size3"
## [9] "durable" "effectiveness" "enpp_3" "eu"
## [13] "fhrate04_rev" "fhrate08_rev" "frac_eth" "frac_eth3"
## [17] "free_business" "free_corrupt" "free_finance" "free_fiscal"
## [21] "free_govspend" "free_invest" "free_labor" "free_monetary"
## [25] "free_overall" "free_property" "free_trade" "gdp08"
## [29] "gdp_10_thou" "gdp_cap2" "gdp_cap3" "gdppcap08"
## [33] "gender_equal3" "gini04" "gini08" "hi_gdp"
## [37] "indy" "oecd" "old2006" "old2003"
## [41] "pmat12_3" "pop03" "pop08" "pop08_3"
## [45] "popcat3" "pr_sys" "protact3" "regime_type3"
## [49] "region" "sources" "typerel" "unions"
## [53] "urban03" "urban06" "vi_rel3" "votevap00s"
## [57] "women05" "women09" "womyear" "womyear2"
## [61] "yng2003" "young06"
We can also apply the summary function without specifying variable names. Then, R will provide the summary of ALL the variables included in a data frame object.
summary(world.data)
## country colony confidence decentralization
## Length:191 Length:191 Min. : 0.5167 Min. :0.380
## Class :character Class :character 1st Qu.:38.3669 1st Qu.:1.225
## Mode :character Mode :character Median :49.1978 Median :1.510
## Mean :47.9704 Mean :1.516
## 3rd Qu.:59.2929 3rd Qu.:1.800
## Max. :99.8624 Max. :2.450
## NA's :120 NA's :124
## dem_other dem_other5 democ_regime district_size3
## Min. : 10.50 Length:191 Length:191 Length:191
## 1st Qu.: 40.80 Class :character Class :character Class :character
## Median : 58.30 Mode :character Mode :character Mode :character
## Mean : 60.51
## 3rd Qu.: 87.50
## Max. :100.00
##
## durable effectiveness enpp_3 eu
## Min. : 0.00 Min. : 0.00 Length:191 Length:191
## 1st Qu.: 4.00 1st Qu.: 28.19 Class :character Class :character
## Median : 9.00 Median : 40.31 Mode :character Mode :character
## Mean : 22.49 Mean : 45.77
## 3rd Qu.: 31.25 3rd Qu.: 62.77
## Max. :191.00 Max. :100.00
## NA's :31 NA's :5
## fhrate04_rev fhrate08_rev frac_eth frac_eth3
## Length:191 Min. : 0.000 Min. :0.0000 Length:191
## Class :character 1st Qu.: 4.000 1st Qu.:0.1997 Class :character
## Mode :character Median : 8.000 Median :0.4343 Mode :character
## Mean : 7.553 Mean :0.4394
## 3rd Qu.:11.250 3rd Qu.:0.6611
## Max. :12.000 Max. :0.9302
## NA's :3 NA's :3
## free_business free_corrupt free_finance free_fiscal
## Min. :10.00 Min. : 5.00 Min. :10.00 Min. :35.90
## 1st Qu.:55.70 1st Qu.:26.00 1st Qu.:30.00 1st Qu.:68.20
## Median :65.80 Median :34.00 Median :50.00 Median :77.50
## Mean :64.92 Mean :40.42 Mean :48.61 Mean :75.62
## 3rd Qu.:76.60 3rd Qu.:51.75 3rd Qu.:60.00 3rd Qu.:84.00
## Max. :99.90 Max. :93.00 Max. :90.00 Max. :99.90
## NA's :18 NA's :17 NA's :18 NA's :18
## free_govspend free_invest free_labor free_monetary
## Min. : 6.90 Min. : 5.00 Min. :20.00 Min. :46.50
## 1st Qu.:54.95 1st Qu.:35.00 1st Qu.:50.10 1st Qu.:66.85
## Median :73.40 Median :50.00 Median :60.80 Median :71.90
## Mean :67.59 Mean :50.75 Mean :62.08 Mean :71.30
## 3rd Qu.:83.25 3rd Qu.:70.00 3rd Qu.:75.90 3rd Qu.:76.55
## Max. :98.40 Max. :95.00 Max. :98.90 Max. :88.80
## NA's :24 NA's :24 NA's :18 NA's :19
## free_overall free_property free_trade gdp08
## Min. : 1.00 Min. : 5.0 Min. :31.90 Min. : 0.2
## 1st Qu.:51.35 1st Qu.:30.0 1st Qu.:67.20 1st Qu.: 11.9
## Median :59.30 Median :40.0 Median :75.90 Median : 41.7
## Mean :59.18 Mean :43.9 Mean :74.37 Mean : 390.4
## 3rd Qu.:67.30 3rd Qu.:60.0 3rd Qu.:85.00 3rd Qu.: 242.4
## Max. :86.10 Max. :95.0 Max. :90.00 Max. :14200.0
## NA's :17 NA's :18 NA's :18 NA's :14
## gdp_10_thou gdp_cap2 gdp_cap3 gdppcap08
## Min. :0.0090 Length:191 Length:191 Min. : 188
## 1st Qu.:0.0503 Class :character Class :character 1st Qu.: 2308
## Median :0.1897 Mode :character Mode :character Median : 7703
## Mean :0.6018 Mean : 13828
## 3rd Qu.:0.6320 3rd Qu.: 19996
## Max. :4.7354 Max. :118040
## NA's :14 NA's :16
## gender_equal3 gini04 gini08 hi_gdp
## Length:191 Min. :24.40 Min. :24.70 Length:191
## Class :character 1st Qu.:32.42 1st Qu.:33.55 Class :character
## Mode :character Median :37.95 Median :39.20 Mode :character
## Mean :40.14 Mean :40.74
## 3rd Qu.:46.88 3rd Qu.:47.10
## Max. :70.70 Max. :74.30
## NA's :65 NA's :64
## indy oecd old2006 old2003
## Min. : 301 Length:191 Min. : 1.076 Min. : 1.846
## 1st Qu.:1915 Class :character 1st Qu.: 3.375 1st Qu.: 3.173
## Median :1960 Mode :character Median : 4.924 Median : 4.865
## Mean :1891 Mean : 7.300 Mean : 6.979
## 3rd Qu.:1977 3rd Qu.:11.210 3rd Qu.:10.656
## Max. :1994 Max. :20.232 Max. :18.997
## NA's :3 NA's :17 NA's :10
## pmat12_3 pop03 pop08 pop08_3
## Length:191 Min. :2.000e+04 Min. : 0.00 Length:191
## Class :character 1st Qu.:1.758e+06 1st Qu.: 2.70 Class :character
## Mode :character Median :6.720e+06 Median : 8.30 Mode :character
## Mean :3.318e+07 Mean : 36.95
## 3rd Qu.:2.121e+07 3rd Qu.: 24.60
## Max. :1.288e+09 Max. :1300.00
## NA's :4 NA's :14
## popcat3 pr_sys protact3 regime_type3
## Length:191 Length:191 Length:191 Length:191
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## region sources typerel unions
## Length:191 Mode:logical Length:191 Min. : 2.00
## Class :character NA's:191 Class :character 1st Qu.:11.45
## Mode :character Mode :character Median :19.10
## Mean :24.74
## 3rd Qu.:30.80
## Max. :96.10
## NA's :100
## urban03 urban06 vi_rel3 votevap00s
## Min. : 6.556 Min. : 10.32 Length:191 Min. :18.29
## 1st Qu.: 36.413 1st Qu.: 35.49 Class :character 1st Qu.:54.58
## Median : 57.491 Median : 56.76 Mode :character Median :65.12
## Mean : 55.620 Mean : 54.55 Mean :65.08
## 3rd Qu.: 73.830 3rd Qu.: 72.75 3rd Qu.:77.66
## Max. :100.000 Max. :100.00 Max. :98.39
## NA's :5 NA's :4 NA's :92
## women05 women09 womyear womyear2
## Min. : 0.00 Min. : 0.00 Min. :1893 Length:191
## 1st Qu.: 8.25 1st Qu.: 9.70 1st Qu.:1931 Class :character
## Median :13.00 Median :15.55 Median :1949 Mode :character
## Mean :15.38 Mean :17.18 Mean :1947
## 3rd Qu.:20.45 3rd Qu.:22.95 3rd Qu.:1960
## Max. :45.30 Max. :56.30 Max. :1990
## NA's :80 NA's :11 NA's :16
## yng2003 young06
## Min. :14.02 Min. :13.50
## 1st Qu.:21.31 1st Qu.:19.53
## Median :31.95 Median :30.65
## Mean :31.41 Mean :30.45
## 3rd Qu.:41.30 3rd Qu.:39.72
## Max. :49.77 Max. :50.50
## NA's :10 NA's :17
The str() function tells us the structure of a data frame object, meaning that it tells us which variables are factor, which ones are numerical, which ones are logical, etc.
str(world.data)
## 'data.frame': 191 obs. of 62 variables:
## $ country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ colony : chr "UK" "Soviet Union" "France" "Spain" ...
## $ confidence : num NA 49.3 52.1 NA NA ...
## $ decentralization: num NA 0.74 NA NA NA NA 2.4 NA 1.74 1.81 ...
## $ dem_other : num 10.5 63 40.8 100 40.8 87.5 87.5 63 58.3 100 ...
## $ dem_other5 : chr "10%" "Approx 60%" "Approx 40%" "100%" ...
## $ democ_regime : chr "No" "Yes" "No" "Yes" ...
## $ district_size3 : chr "single member" "" "6 or more members" "" ...
## $ durable : int 4 3 5 NA 3 NA 17 2 99 54 ...
## $ effectiveness : num 13.7 35.5 32.6 78.7 19.1 ...
## $ enpp_3 : chr "" "1-3 parties" "" "" ...
## $ eu : chr "Not member" "Not member" "Not member" "Not member" ...
## $ fhrate04_rev : chr "2.5" "5" "2.5" "Most free" ...
## $ fhrate08_rev : int 3 8 3 12 3 10 10 4 12 12 ...
## $ frac_eth : num 0.769 0.22 0.339 0.714 0.787 ...
## $ frac_eth3 : chr "High" "Low" "Medium" "High" ...
## $ free_business : num NA 68 71.2 NA 43.4 NA 62.1 83.4 90.3 73.6 ...
## $ free_corrupt : int NA 34 32 NA 19 NA 29 29 87 81 ...
## $ free_finance : int NA 70 30 NA 40 NA 30 70 90 70 ...
## $ free_fiscal : num NA 92.6 83.5 NA 85.1 NA 69.5 89.3 61.4 51.2 ...
## $ free_govspend : num NA 74.2 73.4 NA 62.8 NA 75.6 90.9 64.9 28.8 ...
## $ free_invest : int NA 70 45 NA 35 NA 45 75 80 75 ...
## $ free_labor : num NA 52.1 56.4 NA 45.2 NA 50.1 70.6 94.9 79.1 ...
## $ free_monetary : num NA 78.7 77.2 NA 62.6 NA 61.2 72.9 82.7 79.3 ...
## $ free_overall : num NA 66 56.9 NA 48.4 NA 51.2 69.2 82.6 71.6 ...
## $ free_property : int NA 35 30 NA 20 NA 20 30 90 90 ...
## $ free_trade : num NA 85.8 70.7 NA 70.4 NA 69.5 80.5 85.1 87.5 ...
## $ gdp08 : num 30.6 24.3 276 NA 106.3 ...
## $ gdp_10_thou : num NA 0.1535 0.1785 NA 0.0857 ...
## $ gdp_cap2 : chr "" "Low" "Low" "" ...
## $ gdp_cap3 : chr "" "Middle" "Middle" "" ...
## $ gdppcap08 : int NA 7715 8033 NA 5899 NA 14333 6070 35677 38152 ...
## $ gender_equal3 : chr "" "" "" "" ...
## $ gini04 : num NA 28.2 35.3 NA NA NA 52.2 37.9 35.2 30 ...
## $ gini08 : num NA 31.1 35.3 NA NA NA 51.3 33.8 35.2 29.1 ...
## $ hi_gdp : chr "" "Low GDP" "Low GDP" "" ...
## $ indy : int 1919 1991 1962 1278 1975 1981 1816 1991 1901 1156 ...
## $ oecd : chr "Not member" "Not member" "Not member" "Not member" ...
## $ old2006 : num NA 8.48 4.58 NA 2.45 ...
## $ old2003 : num NA 7.28 4.05 NA 2.93 ...
## $ pmat12_3 : chr "" "Low post-mat" "" "" ...
## $ pop03 : num NA 3169064 31832610 66000 13522110 ...
## $ pop08 : num 27.4 3.1 34.4 NA 18 NA 39.9 3.1 21 8.3 ...
## $ pop08_3 : chr ">=16.8 mil" "<=4.3 mil" ">=16.8 mil" "" ...
## $ popcat3 : chr "Moderate (1-29m)" "Moderate (1-29m)" "Moderate (1-29m)" "Small (under 1m)" ...
## $ pr_sys : chr "No" "No" "Yes" "No" ...
## $ protact3 : chr "" "Moderate" "" "" ...
## $ regime_type3 : chr "Dictatorship" "Parliamentary democ" "Dictatorship" "Parliamentary democ" ...
## $ region : chr "Middle East" "C&E Europe" "Africa" "W. Europe" ...
## $ sources : logi NA NA NA NA NA NA ...
## $ typerel : chr "Muslim" "Muslim" "Muslim" "Roman Catholic" ...
## $ unions : num NA NA NA NA NA ...
## $ urban03 : num NA 44.2 58.8 91.7 36.2 ...
## $ urban06 : num 23.3 46.1 63.9 90.3 54 ...
## $ vi_rel3 : chr "" "20-50%" ">50%" "" ...
## $ votevap00s : num NA 59.6 NA 20.9 NA ...
## $ women05 : num NA 6.4 NA 14.3 NA 10.5 33.7 5.3 24.7 33.9 ...
## $ women09 : num 27.7 16.4 7.7 35.7 37.3 10.5 41.6 8.4 26.7 27.9 ...
## $ womyear : int NA 1920 1962 1973 1975 1951 1947 1921 1902 1918 ...
## $ womyear2 : chr "" "1944 or before" "After 1944" "After 1944" ...
## $ yng2003 : num NA 27.3 33.9 NA 47.6 ...
## $ young06 : num NA 26.4 28.9 NA 46.3 ...
2. Summarizing categorical variables
The output from the str function above tells us that there are many factor variables in the data set. For example, the democ_regime variable is a factor variable (nominal-level). Summarize the information contained in this variable by creating a frequency table.
The typerel variable is another factor variable. This variable measures predominant religion in a given country. Create a frequency table for this variable.
table( world.data $ typerel )
##
## eastern Hindu Jewish Muslim Orthodox
## 15 2 1 50 13
## other Protestant Roman Catholic
## 12 35 63
Make this frequency table vertical using the data.frame function
data.frame( table( world.data $ typerel ) )
## Var1 Freq
## 1 eastern 15
## 2 Hindu 2
## 3 Jewish 1
## 4 Muslim 50
## 5 Orthodox 13
## 6 other 12
## 7 Protestant 35
## 8 Roman Catholic 63
Create a frequency table for the typerel variable
ft.typerel <- data.frame( table(world.data $ typerel) )
ft.typerel $ Percent <- round(prop.table(ft.typerel $ Freq) * 100, digits = 2)
colnames(ft.typerel)[colnames(ft.typerel) == "Var1"] <- "Religion"
ft.typerel
## Religion Freq Percent
## 1 eastern 15 7.85
## 2 Hindu 2 1.05
## 3 Jewish 1 0.52
## 4 Muslim 50 26.18
## 5 Orthodox 13 6.81
## 6 other 12 6.28
## 7 Protestant 35 18.32
## 8 Roman Catholic 63 32.98
Which religion is the most “popular” in the world? That is, what is the mode of the “typerel” variable?
Roman Catholic is the most popular
What is the percentage of countries where muslim is the majority?
26.18%
Create a frequency table for democ_regime
ft.dr <- data.frame( table(world.data $ democ_regime) )
ft.dr $ Percent <- round(prop.table(ft.dr $ Freq) * 100, digits = 2)
colnames(ft.dr)[colnames(ft.dr) == "Var1"] <- "Democracy?"
ft.dr
## Democracy? Freq Percent
## 1 No 75 39.68
## 2 Yes 114 60.32
What percentage of countries have a democratic regime?
60.32%
Create a bar chart to summarize the typerel variable
ggplot(world.data, aes(x = typerel)) + geom_bar() + theme_bw()
Create a bar chart to summarize the democ_regime variable
ggplot(world.data, aes(x = democ_regime)) + geom_bar() + theme_bw()
3. Making ggplot graphs look nicer
Create a bar chart for the typerel variable, and save it as a PDF file
g <- ggplot(world.data)
g <- g + aes(x = typerel)
g <- g + geom_bar()
g <- g + xlab("Predominant religion in countries")
g <- g + ylab("Number of countries")
g <- g + theme(axis.text.x = element_text(size = 12)) + theme_bw()
g
# ggsave(file = "religion_bar.pdf", width = 10, height = 8)
4. Summarizing numerical variables
Numerically summarize the gini04 variable.
That is, calculate and present the measures for central tendency and those for dispersion.
summary(world.data $ gini04)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 24.40 32.42 37.95 40.14 46.88 70.70 65
sd(world.data $ gini08, na.rm = TRUE)
## [1] 9.999242
Numerically summarize the gini08 variable.
That is, calculate and present the measures for central tendency and those for dispersion.
summary(world.data $ gini08)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 24.70 33.55 39.20 40.74 47.10 74.30 64
sd(world.data $ gini08, na.rm = TRUE)
## [1] 9.999242
Compare the distributions of gini04 and gini08.
Do you think that the level of economic inequality is getting worse, getting better, or neither? Why or why not?
Note: I’m not asking why it is getting better, worse, or neither; I’m asking on what basis you can conclude that it’s getting better or worse.
The level of economic inequality in the world seems to have deteriorated but only slightly. Although the quartile values of Gini coefficient got greater from 2004 to 2008, the mean value of Gini coefficient changed very little from 2004 and 2008 are very similar.
Create a histogram of gini04
Modify the axis labels accordingly to make them informative and intuitive. Save the graph as a PDF file.
g <- ggplot(world.data, aes(x = gini04))
g <- g + geom_histogram()
g <- g + theme(axis.text.x = element_text(size = 12))
g <- g + xlab("Gini coefficient")
g <- g + ylab("Number of countries") + theme_bw()
g
# ggsave(file = "gini04_hist.pdf", width = 10, height = 8)
Create a histogram of gini08
Modify the axis labels accordingly to make them informative and intuitive. Save the graph as a PDF file.
g<- ggplot(world.data, aes(x = gini08))
g <- g + geom_histogram()
g <- g + theme(axis.text.x = element_text(size = 12))
g <- g + xlab("Gini coefficient")
g <- g + ylab("Number of countries") + theme_bw()
g
# ggsave(file = "gini08_hist.pdf", width = 10, height = 8)
Compare the distributions of gini04 and gini08 graphically by placing the two PDF files you just created side by side.
Do you confirm the conclusion you derived previously?
Ans: The two histograms look very similar, which is consistent with the conclusion we derived above that the change was, if anything very subtle.
As we saw in the lecture, we sometimes create histograms for different values of a nominal-level variable. For example, we may want to create separate histograms of gini04 for countries in different regions.
How do we do it?
To do so, we use the facet_wrap option, as follows.
g <- ggplot(world.data, aes(x = gini04))
g <- g + geom_histogram()
g <- g + theme(axis.text.x = element_text(size = 12))
g <- g + xlab("Gini coefficient")
g <- g + ylab("Number of countries")
g <- g + facet_wrap( ~ region) + theme_bw()
g
We can see that Scandinavian countries and Western European countries have, on average, lower Gini coefficients (= they are more egalitarian), whereas countries in South America have relatively high values.
Create separate histograms of women09 for countries in different regions.
g <- ggplot(world.data, aes(x = women09))
g <- g + geom_histogram()
g <- g + theme(axis.text.x = element_text(size = 12))
g <- g + xlab("Percent women in congress")
g <- g + ylab("Number of countries")
g <- g + facet_wrap( ~ region) + theme_bw()
g
Calculate the standard deviation of gini04 for different regions using the by function
Hint: we still need to take care of the missing value problem.Use the na.rm = TRUE option.
by(world.data $ gini04, world.data $ region, sd, na.rm = TRUE)
## world.data$region: Africa
## [1] 11.06417
## ------------------------------------------------------------
## world.data$region: Asia-Pacific
## [1] 6.651417
## ------------------------------------------------------------
## world.data$region: C&E Europe
## [1] 5.167651
## ------------------------------------------------------------
## world.data$region: Middle East
## [1] 3.314901
## ------------------------------------------------------------
## world.data$region: N. America
## [1] 10.89327
## ------------------------------------------------------------
## world.data$region: S. America
## [1] 6.329504
## ------------------------------------------------------------
## world.data$region: Scandinavia
## [1] 0.9831921
## ------------------------------------------------------------
## world.data$region: W. Europe
## [1] 3.679761
Which region has the smallest dispersion?
Scandinavian countries have the smallest dispersion.