R Crash Course
Aditya Ranganath
2/15/2022
Introduction
Please load the following packages:
library(tidyverse)
library(stargazer)
library(ggeffects)
library(psych)
library(janitor)
library(fastDummies)
library(gtsummary)Loading data
Reading in data from the working directory
# Read in persson and tabellini dataset from working directory
persson_tabellini_original<-read_csv("persson_tabellini_workshop.csv")Reading data directly from an online source (Alternative method)
# Read in persson and tabellini dataset from Github repo
persson_tabellini_original<-read_csv("https://raw.githubusercontent.com/aranganath24/r_primer/main/workshop_data/persson_tabellini_workshop.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## country = col_character(),
## continent = col_character()
## )
## ℹ Use `spec()` for the full column specifications.Make a copy of the dataset
# Make a copy of the dataset so we don't alter the original dataset; then, view
# the copied dataset
pt_copy<-persson_tabellini_original# Print contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## oecd country pind pindo ctrycd col_uk t_indep col_uka col_espa col_otha
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 Argentina 0 0 213 0 183 0 0.268 0
## 2 1 Australia 1 1 193 1 98 0.608 0 0
## 3 1 Austria 0 0 122 0 250 0 0 0
## 4 0 Bahamas 1 1 313 1 26 0.896 0 0
## 5 0 Bangladesh 1 1 513 0 28 0 0 0.888
## 6 0 Barbados 1 1 316 1 33 0.868 0 0
## 7 0 Belarus 1 1 913 0 8 0 0 0.968
## 8 1 Belgium 0 0 124 0 169 0 0 0.324
## 9 0 Belize 1 1 339 1 18 0.928 0 0
## 10 0 Bolivia 0.116 0.116 218 0 174 0 0.304 0
## # … with 75 more rows, and 65 more variables: legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>,
## # catho80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>, graft <dbl>,
## # logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, engfrac <dbl>,
## # eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>, cgexp <dbl>,
## # cgrev <dbl>, ssw <dbl>, rgdph <dbl>, trade <dbl>, prop1564 <dbl>,
## # prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, yearele <dbl>, …View(pt_copy)| oecd | country | pind | pindo | ctrycd | col_uk | t_indep | col_uka | col_espa | col_otha | legor_uk | legor_so | legor_fr | legor_ge | legor_sc | prot80 | catho80 | confu | avelf | govef | graft | logyl | loga | yrsopen | gadp | engfrac | eurfrac | frankrom | latitude | gastil | cgexp | cgrev | ssw | rgdph | trade | prop1564 | prop65 | federal | eduger | spropn | yearele | yearreg | seats | maj | pres | lyp | semi | majpar | majpres | propres | dem_age | lat01 | age | polityIV | spl | cpi9500 | du_60ctry | magn | sdm | oecd.x | mining_gdp | gini_8090 | con2150 | con5180 | con81 | list | maj_bad | maj_gin | maj_old | pres_bad | pres_gin | pres_old | propar | lpop | continent |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Argentina | 0 | 0 | 213 | 0 | 183 | 0.000 | 0.268 | 0.000 | 0 | 0 | 1 | 0 | 0 | 2.7 | 91.6 | 0 | 0.1769318 | 4.475911 | 5.549095 | 9.60270 | 8.39295 | 0.089 | 0.579 | 0.000 | 0.836 | 1.723 | -36.676 | 2.333333 | 14.00048 | 12.31048 | 6.7540784 | 5831.075 | 18.42008 | 61.37738 | 9.293244 | 1 | 94.8000 | 0.0 | 1983 | 1983 | 257.11111 | 0 | 1 | 8.670957 | 0 | 0.0000000 | 0.0000000 | 1 | 1983 | 0.4075111 | 0.085 | 7.000000 | -0.5086034 | 6.506667 | 1 | 0.0933475 | 0.1889764 | 0 | 1.8993865 | NA | 0 | 0 | 1 | 257.1111 | 0.000000 | NA | 0.000 | 2.333333 | NA | 0.085 | 0 | 17.35051 | laam |
| 1 | Australia | 1 | 1 | 193 | 1 | 98 | 0.608 | 0.000 | 0.000 | 1 | 0 | 0 | 0 | 0 | 23.5 | 29.6 | 0 | 0.1127971 | 2.082613 | 1.797832 | 10.30421 | 8.67103 | 0.689 | 0.931 | 0.950 | 0.950 | 1.404 | -32.219 | 1.000000 | 25.78327 | 24.22912 | 8.6001902 | 15499.723 | 38.79273 | 66.80672 | 11.663297 | 1 | 111.3375 | 0.0 | 1901 | 1901 | 147.66667 | 1 | 0 | 9.648578 | 0 | 1.0000000 | 0.0000000 | 0 | 1901 | 0.3579889 | 0.495 | 10.000000 | -0.6793869 | 1.340000 | 1 | 1.0000000 | 0.1904762 | 1 | 4.4933643 | 39.860 | 0 | 0 | 0 | 0.0000 | 1.000000 | 39.860 | 0.495 | 0.000000 | 0 | 0.000 | 0 | 16.69928 | other |
| 1 | Austria | 0 | 0 | 122 | 0 | 250 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 1 | 0 | 6.5 | 88.8 | 0 | 0.0332123 | 2.562550 | 2.085785 | 10.13165 | 8.80521 | 0.778 | 0.949 | 0.000 | 0.980 | 3.601 | 48.231 | 1.000000 | 40.14563 | 36.20243 | 17.8820381 | 13313.135 | 78.26205 | 67.45173 | 15.100343 | 1 | 103.9571 | 0.0 | 1945 | 1945 | 183.00000 | 0 | 0 | 9.496507 | 0 | 0.0000000 | 0.0000000 | 0 | 1945 | 0.5359000 | 0.275 | 10.000000 | -4.4101000 | 2.478333 | 1 | 0.0491803 | 0.0728745 | 1 | 0.3989157 | 29.000 | 1 | 0 | 0 | 183.0000 | 0.000000 | 0.000 | 0.000 | 0.000000 | 0 | 0.000 | 1 | 15.89130 | other |
| 0 | Bahamas | 1 | 1 | 313 | 1 | 26 | 0.896 | 0.000 | 0.000 | 1 | 0 | 0 | 0 | 0 | 47.2 | 25.5 | 0 | 0.0000000 | 4.052546 | 4.005234 | NA | NA | NA | 0.614 | 0.865 | 0.865 | 3.638 | 24.700 | 1.722222 | 18.80976 | 17.46011 | 0.9501425 | 11768.360 | 101.83389 | 63.95686 | 4.389884 | 0 | 95.3000 | 0.0 | 1973 | 1973 | 47.00000 | 1 | 0 | 9.373170 | 0 | 1.0000000 | 0.0000000 | 0 | 1973 | 0.2744445 | 0.135 | NA | -2.3542581 | NA | 1 | 1.0000000 | 1.0000000 | 0 | 0.5927405 | 45.000 | 0 | 1 | 0 | 0.0000 | 1.722222 | 45.000 | 0.135 | 0.000000 | 0 | 0.000 | 0 | 12.51776 | laam |
| 0 | Bangladesh | 1 | 1 | 513 | 0 | 28 | 0.000 | 0.000 | 0.888 | 1 | 0 | 0 | 0 | 0 | 0.2 | 0.2 | 0 | 0.0000000 | 6.129216 | 5.578219 | 8.41382 | 8.65712 | 0.000 | 0.313 | 0.000 | 0.000 | 2.333 | 23.880 | 3.166667 | 12.56501 | 13.79131 | 0.1292526 | 1611.785 | 25.35251 | 53.81113 | 3.232165 | 0 | 43.6000 | 0.1 | 1991 | 1991 | 330.00000 | 1 | 0 | 7.385097 | 0 | 0.8888889 | 0.1111111 | 0 | 1991 | 0.2653333 | 0.045 | 4.777778 | 1.1960450 | 7.710000 | 0 | 0.9090909 | 1.0000000 | 0 | 0.0184373 | 33.635 | 0 | 0 | 1 | 0.0000 | 3.166667 | 33.635 | 0.045 | 0.000000 | 0 | 0.000 | 0 | NA | asiae |
| 0 | Barbados | 1 | 1 | 316 | 1 | 33 | 0.868 | 0.000 | 0.000 | 1 | 0 | 0 | 0 | 0 | 33.2 | 5.9 | 0 | 0.0733333 | NA | NA | 9.56870 | 8.54757 | 1.000 | 0.739 | 1.000 | 1.000 | 4.027 | 13.179 | 1.000000 | 32.36219 | NA | NA | 7094.703 | 116.35070 | 65.17910 | 11.119803 | 0 | NA | 0.0 | 1966 | 1966 | 27.88889 | 1 | 0 | 8.867104 | 0 | 1.0000000 | 0.0000000 | 0 | 1966 | 0.1464333 | 0.170 | NA | NA | NA | 1 | 1.0000000 | 1.0000000 | 0 | 0.5067325 | 49.000 | 0 | 1 | 0 | 0.0000 | 1.000000 | 49.000 | 0.170 | 0.000000 | 0 | 0.000 | 0 | 12.47983 | laam |
Summary statistics
Generating a table of summary statistics
# Generate summary statistics for "pt_copy" and assign to new object named "pt_copy_summarystats1"
pt_copy_summarystats1<-describe(pt_copy)# Print contents of "pt_copy_summarystats1"
pt_copy_summarystats1## vars n mean sd median trimmed mad min max
## oecd 1 85 0.29 0.46 0.00 0.25 0.00 0.00 1.00
## country* 2 85 43.00 24.68 43.00 43.00 31.13 1.00 85.00
## pind 3 85 0.46 0.47 0.42 0.45 0.62 0.00 1.00
## pindo 4 85 0.61 0.46 0.90 0.63 0.15 0.00 1.00
## ctrycd 5 85 430.88 284.89 299.00 406.86 240.18 111.00 968.00
## col_uk 6 85 0.35 0.48 0.00 0.32 0.00 0.00 1.00
## t_indep 7 85 119.73 89.76 92.00 117.48 114.16 6.00 250.00
## col_uka 8 85 0.28 0.39 0.00 0.24 0.00 0.00 0.93
## col_espa 9 85 0.06 0.13 0.00 0.03 0.00 0.00 0.79
## col_otha 10 85 0.22 0.36 0.00 0.16 0.00 0.00 0.98
## legor_uk 11 85 0.39 0.49 0.00 0.36 0.00 0.00 1.00
## legor_so 12 85 0.13 0.34 0.00 0.04 0.00 0.00 1.00
## legor_fr 13 85 0.35 0.48 0.00 0.32 0.00 0.00 1.00
## legor_ge 14 85 0.07 0.26 0.00 0.00 0.00 0.00 1.00
## legor_sc 15 85 0.06 0.24 0.00 0.00 0.00 0.00 1.00
## prot80 16 85 17.46 25.50 2.70 12.24 3.85 0.00 97.80
## catho80 17 85 40.69 38.66 27.60 38.92 39.88 0.00 97.30
## confu 18 85 0.07 0.26 0.00 0.00 0.00 0.00 1.00
## avelf 19 85 0.29 0.26 0.18 0.26 0.21 0.00 0.84
## govef 20 81 4.21 1.75 4.48 4.25 1.99 0.84 7.26
## graft 21 81 4.17 1.89 4.24 4.26 2.18 0.74 6.92
## logyl 22 74 9.23 0.90 9.29 9.30 1.15 6.95 10.48
## loga 23 73 8.17 0.61 8.37 8.23 0.58 6.28 9.02
## yrsopen 24 75 0.52 0.54 0.42 0.47 0.53 0.00 4.09
## gadp 25 75 0.69 0.20 0.68 0.69 0.24 0.31 1.00
## engfrac 26 78 0.14 0.32 0.00 0.06 0.00 0.00 1.00
## eurfrac 27 78 0.40 0.44 0.08 0.38 0.12 0.00 1.00
## frankrom 28 78 2.87 0.84 2.91 2.86 0.78 0.94 5.64
## latitude 29 78 17.96 27.87 17.30 19.13 33.67 -36.89 63.89
## gastil 30 85 2.44 1.23 2.28 2.36 1.65 1.00 4.89
## cgexp 31 82 28.82 10.49 28.90 28.50 13.04 9.74 51.18
## cgrev 32 78 26.49 10.12 24.16 26.05 10.98 8.92 50.85
## ssw 33 71 8.15 6.67 7.17 7.65 8.60 0.13 22.38
## rgdph 34 85 6688.63 5495.17 4400.03 6130.45 4371.13 530.22 20782.81
## trade 35 85 78.77 47.34 68.45 73.50 34.38 17.56 343.39
## prop1564 36 84 62.07 5.76 63.94 62.49 5.31 49.05 71.70
## prop65 37 84 8.45 4.89 6.47 8.26 5.68 2.30 17.43
## federal 38 83 0.16 0.37 0.00 0.07 0.00 0.00 1.00
## eduger 39 82 88.58 17.70 93.48 90.55 14.33 40.05 117.11
## spropn 40 77 0.13 0.25 0.00 0.07 0.00 0.00 1.00
## yearele 41 81 1965.55 36.85 1981.00 1972.62 16.31 1800.00 1994.00
## yearreg 42 81 1961.48 40.16 1978.00 1969.65 20.76 1800.00 1994.00
## seats 43 85 215.45 162.54 166.00 195.25 127.17 15.00 656.00
## maj 44 85 0.39 0.49 0.00 0.36 0.00 0.00 1.00
## pres 45 85 0.39 0.49 0.00 0.36 0.00 0.00 1.00
## lyp 46 85 8.41 0.97 8.39 8.45 1.25 6.27 9.94
## semi 47 85 0.11 0.31 0.00 0.01 0.00 0.00 1.00
## majpar 48 85 0.25 0.42 0.00 0.19 0.00 0.00 1.00
## majpres 49 85 0.13 0.33 0.00 0.04 0.00 0.00 1.00
## propres 50 85 0.26 0.44 0.00 0.20 0.00 0.00 1.00
## dem_age 51 85 1958.34 43.74 1978.00 1967.00 19.27 1800.00 1994.00
## lat01 52 78 0.32 0.19 0.28 0.31 0.20 0.00 0.71
## age 53 85 0.21 0.22 0.11 0.17 0.10 0.03 1.00
## polityIV 54 80 7.17 3.64 8.11 7.92 2.80 -6.00 10.00
## spl 55 74 -2.18 3.48 -1.69 -2.22 2.85 -11.36 12.59
## cpi9500 56 71 4.81 2.38 5.29 4.95 2.46 0.27 8.25
## du_60ctry 57 85 0.71 0.46 1.00 0.75 0.00 0.00 1.00
## magn 58 84 0.47 0.40 0.26 0.46 0.31 0.01 1.00
## sdm 59 77 0.35 0.39 0.16 0.32 0.15 0.01 1.00
## oecd.x 60 85 0.27 0.45 0.00 0.22 0.00 0.00 1.00
## mining_gdp 61 77 4.26 6.72 1.29 2.82 1.51 0.02 37.20
## gini_8090 62 72 39.20 10.41 37.52 38.71 12.23 19.49 62.30
## con2150 63 85 0.11 0.31 0.00 0.01 0.00 0.00 1.00
## con5180 64 85 0.29 0.46 0.00 0.25 0.00 0.00 1.00
## con81 65 85 0.49 0.50 0.00 0.49 0.00 0.00 1.00
## list 66 84 114.48 129.55 77.67 93.16 115.15 0.00 510.33
## maj_bad 67 85 1.06 1.60 0.00 0.77 0.00 0.00 4.89
## maj_gin 68 72 16.40 20.96 0.00 13.86 0.00 0.00 62.00
## maj_old 69 85 0.08 0.19 0.00 0.04 0.00 0.00 1.00
## pres_bad 70 85 1.21 1.69 0.00 0.97 0.00 0.00 4.89
## pres_gin 71 72 16.66 22.90 0.00 13.83 0.00 0.00 62.00
## pres_old 72 85 0.06 0.16 0.00 0.02 0.00 0.00 1.00
## propar 73 85 0.35 0.48 0.00 0.32 0.00 0.00 1.00
## lpop 74 60 15.90 1.92 15.94 15.95 1.47 11.61 20.63
## continent* 75 85 3.04 1.06 3.00 3.16 1.48 1.00 4.00
## range skew kurtosis se
## oecd 1.00 0.89 -1.23 0.05
## country* 84.00 0.00 -1.24 2.68
## pind 1.00 0.13 -1.89 0.05
## pindo 1.00 -0.46 -1.69 0.05
## ctrycd 857.00 0.63 -1.08 30.90
## col_uk 1.00 0.60 -1.65 0.05
## t_indep 244.00 0.26 -1.54 9.74
## col_uka 0.93 0.71 -1.44 0.04
## col_espa 0.79 2.68 9.07 0.01
## col_otha 0.98 1.16 -0.42 0.04
## legor_uk 1.00 0.45 -1.82 0.05
## legor_so 1.00 2.17 2.74 0.04
## legor_fr 1.00 0.60 -1.65 0.05
## legor_ge 1.00 3.29 8.96 0.03
## legor_sc 1.00 3.68 11.71 0.03
## prot80 97.80 1.65 1.99 2.77
## catho80 97.30 0.38 -1.59 4.19
## confu 1.00 3.29 8.96 0.03
## avelf 0.84 0.76 -0.79 0.03
## govef 6.42 -0.22 -1.11 0.19
## graft 6.17 -0.37 -1.14 0.21
## logyl 3.52 -0.57 -0.53 0.10
## loga 2.73 -0.85 0.12 0.07
## yrsopen 4.09 3.72 22.32 0.06
## gadp 0.69 0.11 -1.19 0.02
## engfrac 1.00 1.97 2.04 0.04
## eurfrac 1.00 0.31 -1.80 0.05
## frankrom 4.70 0.25 0.60 0.10
## latitude 100.78 -0.31 -0.98 3.16
## gastil 3.89 0.39 -1.16 0.13
## cgexp 41.44 0.17 -0.99 1.16
## cgrev 41.93 0.34 -0.84 1.15
## ssw 22.26 0.41 -1.16 0.79
## rgdph 20252.59 0.77 -0.71 596.03
## trade 325.82 2.37 9.85 5.13
## prop1564 22.66 -0.60 -0.87 0.63
## prop65 15.13 0.23 -1.60 0.53
## federal 1.00 1.86 1.46 0.04
## eduger 77.06 -0.94 0.59 1.95
## spropn 1.00 2.22 4.33 0.03
## yearele 194.00 -2.13 5.16 4.09
## yearreg 194.00 -1.85 3.28 4.46
## seats 641.00 1.06 0.18 17.63
## maj 1.00 0.45 -1.82 0.05
## pres 1.00 0.45 -1.82 0.05
## lyp 3.67 -0.24 -1.04 0.11
## semi 1.00 2.52 4.39 0.03
## majpar 1.00 1.12 -0.69 0.05
## majpres 1.00 2.17 2.75 0.04
## propres 1.00 1.08 -0.84 0.05
## dem_age 194.00 -1.65 2.05 4.74
## lat01 0.71 0.27 -1.10 0.02
## age 0.97 1.65 2.05 0.02
## polityIV 16.00 -1.62 2.20 0.41
## spl 23.95 0.88 3.85 0.40
## cpi9500 7.98 -0.49 -1.12 0.28
## du_60ctry 1.00 -0.89 -1.23 0.05
## magn 0.99 0.39 -1.64 0.04
## sdm 0.99 0.93 -0.95 0.04
## oecd.x 1.00 1.01 -0.98 0.05
## mining_gdp 37.18 2.41 6.61 0.77
## gini_8090 42.81 0.35 -0.80 1.23
## con2150 1.00 2.52 4.39 0.03
## con5180 1.00 0.89 -1.23 0.05
## con81 1.00 0.02 -2.02 0.05
## list 510.33 1.19 0.79 14.14
## maj_bad 4.89 1.23 0.04 0.17
## maj_gin 62.00 0.63 -1.30 2.47
## maj_old 1.00 3.05 9.57 0.02
## pres_bad 4.89 0.91 -0.81 0.18
## pres_gin 62.00 0.71 -1.33 2.70
## pres_old 1.00 4.26 18.75 0.02
## propar 1.00 0.60 -1.65 0.05
## lpop 9.02 -0.16 -0.32 0.25
## continent* 3.00 -0.72 -0.82 0.12| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| oecd | 1 | 85 | 0.2941176 | 0.4583492 | 0.0000000 | 0.2463768 | 0.0000000 | 0 | 1 | 1 | 0.8877955 | -1.225827 | 0.0497150 |
| country* | 2 | 85 | 43.0000000 | 24.6813020 | 43.0000000 | 43.0000000 | 31.1346000 | 1 | 85 | 84 | 0.0000000 | -1.242428 | 2.6770631 |
| pind | 3 | 85 | 0.4600902 | 0.4659720 | 0.4188713 | 0.4508357 | 0.6210186 | 0 | 1 | 1 | 0.1317631 | -1.887050 | 0.0505418 |
| pindo | 4 | 85 | 0.6061366 | 0.4573739 | 0.9000000 | 0.6307480 | 0.1482600 | 0 | 1 | 1 | -0.4581688 | -1.691084 | 0.0496092 |
| ctrycd | 5 | 85 | 430.8823529 | 284.8886755 | 299.0000000 | 406.8550725 | 240.1812000 | 111 | 968 | 857 | 0.6276472 | -1.082973 | 30.9005154 |
| col_uk | 6 | 85 | 0.3529412 | 0.4807207 | 0.0000000 | 0.3188406 | 0.0000000 | 0 | 1 | 1 | 0.6046284 | -1.653463 | 0.0521415 |
Exporting a summary statistics table as a CSV file
# add rownames to pt_copy_summarystats1
pt_copy_summarystats1$variable<-rownames(pt_copy_summarystats1)
# make "variables" field the first one in the dataset
pt_copy_summarystats1<-pt_copy_summarystats1 %>% relocate(variable)# write out summary statistics table as CSV file
write_csv(pt_copy_summarystats1, "/Users/adra7980/Documents/git_repositories/r_primer/written_data/pt_copy_summarystats1.csv")Using stargazer to create and export summary statistics
# Make the summary stats into a data frame
pt_copy_df<-as.data.frame(pt_copy)# Use stargazer to export summary statistics as a text file
stargazer(pt_copy_df, type="text", title="Descriptive Statistics", digits=1, out="summary_stats.txt")# Use stargazer to export summary statistics as an html file
stargazer(pt_copy_df, type="text", title="Descriptive Statistics", digits=1, out="summary_stats.html")Summary statistics and grouping variables
Subsetting summary statistics by group with the describe function
# Creates summary statistics for each continent grouping, and puts results in list named "summary_stats_by_continent"
summary_stats_by_continent<-describeBy(pt_copy, pt_copy$continent)# Accessing continent-level summary statistics for africa from the "summary_stats_by_continent" list
summary_stats_by_continent[["africa"]]## vars n mean sd median trimmed mad min max
## oecd 1 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## country* 2 11 6.00 3.32 6.00 6.00 4.45 1.00 11.00
## pind 3 11 0.77 0.42 1.00 0.83 0.00 0.00 1.00
## pindo 4 11 0.77 0.42 1.00 0.83 0.00 0.00 1.00
## ctrycd 5 11 647.55 154.90 684.00 685.56 56.34 199.00 754.00
## col_uk 6 11 0.82 0.40 1.00 0.89 0.00 0.00 1.00
## t_indep 7 11 36.64 19.77 35.00 33.89 5.93 9.00 89.00
## col_uka 8 11 0.69 0.35 0.86 0.74 0.02 0.00 0.92
## col_espa 9 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## col_otha 10 11 0.15 0.33 0.00 0.07 0.00 0.00 0.96
## legor_uk 11 11 0.82 0.40 1.00 0.89 0.00 0.00 1.00
## legor_so 12 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_fr 13 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## legor_ge 14 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_sc 15 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## prot80 16 11 22.17 20.23 25.80 19.96 19.57 0.10 64.20
## catho80 17 11 19.46 13.67 18.70 18.07 13.20 1.90 49.60
## confu 18 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## avelf 19 11 0.71 0.14 0.73 0.73 0.15 0.38 0.84
## govef 20 11 5.37 0.82 5.02 5.25 0.68 4.56 7.26
## graft 21 11 5.11 0.77 5.39 5.12 0.80 3.93 6.23
## logyl 22 11 7.93 0.78 7.75 7.90 0.53 6.95 9.13
## loga 23 11 7.38 0.66 7.33 7.37 0.55 6.28 8.58
## yrsopen 24 11 0.21 0.29 0.16 0.15 0.18 0.00 1.00
## gadp 25 11 0.55 0.12 0.54 0.55 0.12 0.37 0.74
## engfrac 26 11 0.02 0.04 0.00 0.02 0.00 0.00 0.09
## eurfrac 27 11 0.07 0.17 0.00 0.03 0.00 0.00 0.57
## frankrom 28 11 2.90 0.51 2.94 2.86 0.56 2.19 3.95
## latitude 29 11 -9.14 15.17 -15.81 -9.58 8.49 -29.13 14.77
## gastil 30 11 3.59 1.16 4.00 3.66 1.32 1.61 4.89
## cgexp 31 10 27.00 7.63 25.50 27.10 8.58 14.65 38.57
## cgrev 32 9 26.15 10.36 23.81 26.15 6.14 17.24 50.85
## ssw 33 6 1.67 1.46 0.94 1.67 0.58 0.44 3.80
## rgdph 34 11 1899.87 1832.60 1116.28 1522.39 738.30 530.22 6666.77
## trade 35 11 77.34 32.13 69.17 76.87 27.13 30.83 128.12
## prop1564 36 11 54.23 4.91 53.23 53.51 2.96 49.05 65.95
## prop65 37 11 3.28 1.16 2.80 3.06 0.65 2.34 6.26
## federal 38 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## eduger 39 11 73.95 23.54 73.55 73.64 25.47 40.05 110.67
## spropn 40 10 0.27 0.42 0.00 0.21 0.00 0.00 1.00
## yearele 41 8 1982.50 13.48 1990.50 1982.50 5.19 1965.00 1994.00
## yearreg 42 8 1982.50 13.48 1990.50 1982.50 5.19 1965.00 1994.00
## seats 43 11 151.20 109.96 122.22 136.21 86.65 37.33 400.00
## maj 44 11 0.73 0.47 1.00 0.78 0.00 0.00 1.00
## pres 45 11 0.64 0.50 1.00 0.67 0.00 0.00 1.00
## lyp 46 11 7.22 0.81 7.02 7.15 0.88 6.27 8.80
## semi 47 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## majpar 48 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## majpres 49 11 0.55 0.52 1.00 0.56 0.00 0.00 1.00
## propres 50 11 0.09 0.30 0.00 0.00 0.00 0.00 1.00
## dem_age 51 11 1975.82 24.77 1989.00 1981.11 7.41 1910.00 1994.00
## lat01 52 11 0.17 0.08 0.18 0.17 0.05 0.00 0.32
## age 53 11 0.12 0.12 0.05 0.09 0.04 0.03 0.45
## polityIV 54 11 2.34 5.56 0.22 2.42 6.75 -6.00 10.00
## spl 55 8 -1.55 4.52 -1.54 -1.55 1.91 -6.77 8.23
## cpi9500 56 9 5.70 1.15 5.90 5.70 1.14 3.93 7.55
## du_60ctry 57 11 0.27 0.47 0.00 0.22 0.00 0.00 1.00
## magn 58 11 0.71 0.41 1.00 0.75 0.00 0.02 1.00
## sdm 59 9 0.71 0.45 1.00 0.71 0.00 0.03 1.00
## oecd.x 60 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## mining_gdp 61 10 8.43 11.70 4.10 5.89 5.71 0.02 37.20
## gini_8090 62 9 50.25 9.95 54.00 50.25 11.86 35.36 62.30
## con2150 63 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## con5180 64 11 0.27 0.47 0.00 0.22 0.00 0.00 1.00
## con81 65 11 0.73 0.47 1.00 0.78 0.00 0.00 1.00
## list 66 11 49.83 119.87 0.00 16.46 0.00 0.00 400.00
## maj_bad 67 11 2.73 2.05 3.83 2.80 1.56 0.00 4.89
## maj_gin 68 9 37.31 22.84 41.35 37.31 18.75 0.00 62.00
## maj_old 69 11 0.06 0.07 0.04 0.06 0.06 0.00 0.17
## pres_bad 70 11 2.63 2.18 3.83 2.67 1.56 0.00 4.89
## pres_gin 71 9 26.72 26.59 35.36 26.72 39.50 0.00 62.00
## pres_old 72 11 0.04 0.05 0.03 0.03 0.04 0.00 0.17
## propar 73 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## lpop 74 3 13.99 0.15 13.92 13.99 0.05 13.88 14.17
## continent* 75 11 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## range skew kurtosis se
## oecd 0.00 NaN NaN 0.00
## country* 10.00 0.00 -1.53 1.00
## pind 1.00 -1.06 -0.79 0.13
## pindo 1.00 -1.06 -0.79 0.13
## ctrycd 555.00 -2.13 3.44 46.70
## col_uk 1.00 -1.43 0.08 0.12
## t_indep 80.00 1.38 1.88 5.96
## col_uka 0.92 -1.31 -0.14 0.10
## col_espa 0.00 NaN NaN 0.00
## col_otha 0.96 1.58 0.79 0.10
## legor_uk 1.00 -1.43 0.08 0.12
## legor_so 0.00 NaN NaN 0.00
## legor_fr 1.00 1.43 0.08 0.12
## legor_ge 0.00 NaN NaN 0.00
## legor_sc 0.00 NaN NaN 0.00
## prot80 64.10 0.46 -0.80 6.10
## catho80 47.70 0.71 -0.39 4.12
## confu 0.00 NaN NaN 0.00
## avelf 0.46 -1.15 0.44 0.04
## govef 2.70 0.97 -0.17 0.25
## graft 2.30 -0.17 -1.62 0.23
## logyl 2.18 0.42 -1.43 0.23
## loga 2.29 0.03 -0.91 0.20
## yrsopen 1.00 1.72 2.15 0.09
## gadp 0.37 0.28 -1.38 0.04
## engfrac 0.09 0.95 -1.09 0.01
## eurfrac 0.57 2.24 3.76 0.05
## frankrom 1.77 0.54 -0.69 0.15
## latitude 43.90 0.44 -1.52 4.57
## gastil 3.28 -0.48 -1.45 0.35
## cgexp 23.92 0.06 -1.30 2.41
## cgrev 33.61 1.40 0.71 3.45
## ssw 3.36 0.52 -1.87 0.60
## rgdph 6136.54 1.50 1.28 552.55
## trade 97.29 0.31 -1.40 9.69
## prop1564 16.90 1.19 0.34 1.48
## prop65 3.92 1.47 1.16 0.35
## federal 0.00 NaN NaN 0.00
## eduger 70.62 0.08 -1.50 7.10
## spropn 1.00 0.92 -1.07 0.13
## yearele 29.00 -0.41 -2.00 4.77
## yearreg 29.00 -0.41 -2.00 4.77
## seats 362.67 0.92 -0.20 33.16
## maj 1.00 -0.88 -1.31 0.14
## pres 1.00 -0.49 -1.91 0.15
## lyp 2.53 0.53 -1.18 0.25
## semi 1.00 1.43 0.08 0.12
## majpar 1.00 1.43 0.08 0.12
## majpres 1.00 -0.16 -2.15 0.16
## propres 1.00 2.47 4.52 0.09
## dem_age 84.00 -1.57 1.64 7.47
## lat01 0.32 -0.28 -0.38 0.03
## age 0.42 1.57 1.64 0.04
## polityIV 16.00 0.07 -1.63 1.68
## spl 15.00 0.98 0.05 1.60
## cpi9500 3.61 0.01 -1.45 0.38
## du_60ctry 1.00 0.88 -1.31 0.14
## magn 0.98 -0.58 -1.70 0.12
## sdm 0.97 -0.67 -1.63 0.15
## oecd.x 0.00 NaN NaN 0.00
## mining_gdp 37.18 1.39 0.79 3.70
## gini_8090 26.94 -0.19 -1.71 3.32
## con2150 0.00 NaN NaN 0.00
## con5180 1.00 0.88 -1.31 0.14
## con81 1.00 -0.88 -1.31 0.14
## list 400.00 2.22 3.64 36.14
## maj_bad 4.89 -0.32 -1.81 0.62
## maj_gin 62.00 -0.71 -1.17 7.61
## maj_old 0.17 0.69 -1.39 0.02
## pres_bad 4.89 -0.30 -1.92 0.66
## pres_gin 62.00 0.04 -1.97 8.86
## pres_old 0.17 1.66 2.10 0.02
## propar 1.00 1.43 0.08 0.12
## lpop 0.28 0.36 -2.33 0.09
## continent* 0.00 NaN NaN 0.00# Accessing continent-level summary statistics for africa from the "summary_stats_by_continent" list; alternate syntax
summary_stats_by_continent %>% pluck("africa")## vars n mean sd median trimmed mad min max
## oecd 1 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## country* 2 11 6.00 3.32 6.00 6.00 4.45 1.00 11.00
## pind 3 11 0.77 0.42 1.00 0.83 0.00 0.00 1.00
## pindo 4 11 0.77 0.42 1.00 0.83 0.00 0.00 1.00
## ctrycd 5 11 647.55 154.90 684.00 685.56 56.34 199.00 754.00
## col_uk 6 11 0.82 0.40 1.00 0.89 0.00 0.00 1.00
## t_indep 7 11 36.64 19.77 35.00 33.89 5.93 9.00 89.00
## col_uka 8 11 0.69 0.35 0.86 0.74 0.02 0.00 0.92
## col_espa 9 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## col_otha 10 11 0.15 0.33 0.00 0.07 0.00 0.00 0.96
## legor_uk 11 11 0.82 0.40 1.00 0.89 0.00 0.00 1.00
## legor_so 12 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_fr 13 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## legor_ge 14 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_sc 15 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## prot80 16 11 22.17 20.23 25.80 19.96 19.57 0.10 64.20
## catho80 17 11 19.46 13.67 18.70 18.07 13.20 1.90 49.60
## confu 18 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## avelf 19 11 0.71 0.14 0.73 0.73 0.15 0.38 0.84
## govef 20 11 5.37 0.82 5.02 5.25 0.68 4.56 7.26
## graft 21 11 5.11 0.77 5.39 5.12 0.80 3.93 6.23
## logyl 22 11 7.93 0.78 7.75 7.90 0.53 6.95 9.13
## loga 23 11 7.38 0.66 7.33 7.37 0.55 6.28 8.58
## yrsopen 24 11 0.21 0.29 0.16 0.15 0.18 0.00 1.00
## gadp 25 11 0.55 0.12 0.54 0.55 0.12 0.37 0.74
## engfrac 26 11 0.02 0.04 0.00 0.02 0.00 0.00 0.09
## eurfrac 27 11 0.07 0.17 0.00 0.03 0.00 0.00 0.57
## frankrom 28 11 2.90 0.51 2.94 2.86 0.56 2.19 3.95
## latitude 29 11 -9.14 15.17 -15.81 -9.58 8.49 -29.13 14.77
## gastil 30 11 3.59 1.16 4.00 3.66 1.32 1.61 4.89
## cgexp 31 10 27.00 7.63 25.50 27.10 8.58 14.65 38.57
## cgrev 32 9 26.15 10.36 23.81 26.15 6.14 17.24 50.85
## ssw 33 6 1.67 1.46 0.94 1.67 0.58 0.44 3.80
## rgdph 34 11 1899.87 1832.60 1116.28 1522.39 738.30 530.22 6666.77
## trade 35 11 77.34 32.13 69.17 76.87 27.13 30.83 128.12
## prop1564 36 11 54.23 4.91 53.23 53.51 2.96 49.05 65.95
## prop65 37 11 3.28 1.16 2.80 3.06 0.65 2.34 6.26
## federal 38 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## eduger 39 11 73.95 23.54 73.55 73.64 25.47 40.05 110.67
## spropn 40 10 0.27 0.42 0.00 0.21 0.00 0.00 1.00
## yearele 41 8 1982.50 13.48 1990.50 1982.50 5.19 1965.00 1994.00
## yearreg 42 8 1982.50 13.48 1990.50 1982.50 5.19 1965.00 1994.00
## seats 43 11 151.20 109.96 122.22 136.21 86.65 37.33 400.00
## maj 44 11 0.73 0.47 1.00 0.78 0.00 0.00 1.00
## pres 45 11 0.64 0.50 1.00 0.67 0.00 0.00 1.00
## lyp 46 11 7.22 0.81 7.02 7.15 0.88 6.27 8.80
## semi 47 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## majpar 48 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## majpres 49 11 0.55 0.52 1.00 0.56 0.00 0.00 1.00
## propres 50 11 0.09 0.30 0.00 0.00 0.00 0.00 1.00
## dem_age 51 11 1975.82 24.77 1989.00 1981.11 7.41 1910.00 1994.00
## lat01 52 11 0.17 0.08 0.18 0.17 0.05 0.00 0.32
## age 53 11 0.12 0.12 0.05 0.09 0.04 0.03 0.45
## polityIV 54 11 2.34 5.56 0.22 2.42 6.75 -6.00 10.00
## spl 55 8 -1.55 4.52 -1.54 -1.55 1.91 -6.77 8.23
## cpi9500 56 9 5.70 1.15 5.90 5.70 1.14 3.93 7.55
## du_60ctry 57 11 0.27 0.47 0.00 0.22 0.00 0.00 1.00
## magn 58 11 0.71 0.41 1.00 0.75 0.00 0.02 1.00
## sdm 59 9 0.71 0.45 1.00 0.71 0.00 0.03 1.00
## oecd.x 60 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## mining_gdp 61 10 8.43 11.70 4.10 5.89 5.71 0.02 37.20
## gini_8090 62 9 50.25 9.95 54.00 50.25 11.86 35.36 62.30
## con2150 63 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## con5180 64 11 0.27 0.47 0.00 0.22 0.00 0.00 1.00
## con81 65 11 0.73 0.47 1.00 0.78 0.00 0.00 1.00
## list 66 11 49.83 119.87 0.00 16.46 0.00 0.00 400.00
## maj_bad 67 11 2.73 2.05 3.83 2.80 1.56 0.00 4.89
## maj_gin 68 9 37.31 22.84 41.35 37.31 18.75 0.00 62.00
## maj_old 69 11 0.06 0.07 0.04 0.06 0.06 0.00 0.17
## pres_bad 70 11 2.63 2.18 3.83 2.67 1.56 0.00 4.89
## pres_gin 71 9 26.72 26.59 35.36 26.72 39.50 0.00 62.00
## pres_old 72 11 0.04 0.05 0.03 0.03 0.04 0.00 0.17
## propar 73 11 0.18 0.40 0.00 0.11 0.00 0.00 1.00
## lpop 74 3 13.99 0.15 13.92 13.99 0.05 13.88 14.17
## continent* 75 11 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## range skew kurtosis se
## oecd 0.00 NaN NaN 0.00
## country* 10.00 0.00 -1.53 1.00
## pind 1.00 -1.06 -0.79 0.13
## pindo 1.00 -1.06 -0.79 0.13
## ctrycd 555.00 -2.13 3.44 46.70
## col_uk 1.00 -1.43 0.08 0.12
## t_indep 80.00 1.38 1.88 5.96
## col_uka 0.92 -1.31 -0.14 0.10
## col_espa 0.00 NaN NaN 0.00
## col_otha 0.96 1.58 0.79 0.10
## legor_uk 1.00 -1.43 0.08 0.12
## legor_so 0.00 NaN NaN 0.00
## legor_fr 1.00 1.43 0.08 0.12
## legor_ge 0.00 NaN NaN 0.00
## legor_sc 0.00 NaN NaN 0.00
## prot80 64.10 0.46 -0.80 6.10
## catho80 47.70 0.71 -0.39 4.12
## confu 0.00 NaN NaN 0.00
## avelf 0.46 -1.15 0.44 0.04
## govef 2.70 0.97 -0.17 0.25
## graft 2.30 -0.17 -1.62 0.23
## logyl 2.18 0.42 -1.43 0.23
## loga 2.29 0.03 -0.91 0.20
## yrsopen 1.00 1.72 2.15 0.09
## gadp 0.37 0.28 -1.38 0.04
## engfrac 0.09 0.95 -1.09 0.01
## eurfrac 0.57 2.24 3.76 0.05
## frankrom 1.77 0.54 -0.69 0.15
## latitude 43.90 0.44 -1.52 4.57
## gastil 3.28 -0.48 -1.45 0.35
## cgexp 23.92 0.06 -1.30 2.41
## cgrev 33.61 1.40 0.71 3.45
## ssw 3.36 0.52 -1.87 0.60
## rgdph 6136.54 1.50 1.28 552.55
## trade 97.29 0.31 -1.40 9.69
## prop1564 16.90 1.19 0.34 1.48
## prop65 3.92 1.47 1.16 0.35
## federal 0.00 NaN NaN 0.00
## eduger 70.62 0.08 -1.50 7.10
## spropn 1.00 0.92 -1.07 0.13
## yearele 29.00 -0.41 -2.00 4.77
## yearreg 29.00 -0.41 -2.00 4.77
## seats 362.67 0.92 -0.20 33.16
## maj 1.00 -0.88 -1.31 0.14
## pres 1.00 -0.49 -1.91 0.15
## lyp 2.53 0.53 -1.18 0.25
## semi 1.00 1.43 0.08 0.12
## majpar 1.00 1.43 0.08 0.12
## majpres 1.00 -0.16 -2.15 0.16
## propres 1.00 2.47 4.52 0.09
## dem_age 84.00 -1.57 1.64 7.47
## lat01 0.32 -0.28 -0.38 0.03
## age 0.42 1.57 1.64 0.04
## polityIV 16.00 0.07 -1.63 1.68
## spl 15.00 0.98 0.05 1.60
## cpi9500 3.61 0.01 -1.45 0.38
## du_60ctry 1.00 0.88 -1.31 0.14
## magn 0.98 -0.58 -1.70 0.12
## sdm 0.97 -0.67 -1.63 0.15
## oecd.x 0.00 NaN NaN 0.00
## mining_gdp 37.18 1.39 0.79 3.70
## gini_8090 26.94 -0.19 -1.71 3.32
## con2150 0.00 NaN NaN 0.00
## con5180 1.00 0.88 -1.31 0.14
## con81 1.00 -0.88 -1.31 0.14
## list 400.00 2.22 3.64 36.14
## maj_bad 4.89 -0.32 -1.81 0.62
## maj_gin 62.00 -0.71 -1.17 7.61
## maj_old 0.17 0.69 -1.39 0.02
## pres_bad 4.89 -0.30 -1.92 0.66
## pres_gin 62.00 0.04 -1.97 8.86
## pres_old 0.17 1.66 2.10 0.02
## propar 1.00 1.43 0.08 0.12
## lpop 0.28 0.36 -2.33 0.09
## continent* 0.00 NaN NaN 0.00# Group-level summary statistics can be assigned to their own object for easy retrieval
asia_europe_summary_statistics<-summary_stats_by_continent %>% pluck("asiae")# retrieve summary statistics for Asia/Europe
asia_europe_summary_statistics## vars n mean sd median trimmed mad min max
## oecd 1 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## country* 2 13 7.00 3.89 7.00 7.00 4.45 1.00 13.00
## pind 3 13 0.87 0.28 1.00 0.94 0.00 0.00 1.00
## pindo 4 13 0.95 0.09 1.00 0.96 0.00 0.78 1.00
## ctrycd 5 13 623.00 141.16 564.00 605.64 32.62 513.00 924.00
## col_uk 6 13 0.54 0.52 1.00 0.55 0.00 0.00 1.00
## t_indep 7 13 74.31 78.67 51.00 62.91 13.34 24.00 250.00
## col_uka 8 13 0.45 0.44 0.79 0.45 0.17 0.00 0.90
## col_espa 9 13 0.06 0.22 0.00 0.00 0.00 0.00 0.79
## col_otha 10 13 0.39 0.44 0.00 0.38 0.00 0.00 0.90
## legor_uk 11 13 0.77 0.44 1.00 0.82 0.00 0.00 1.00
## legor_so 12 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_fr 13 13 0.08 0.28 0.00 0.00 0.00 0.00 1.00
## legor_ge 14 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00
## legor_sc 15 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## prot80 16 13 9.48 18.15 1.40 5.89 1.78 0.00 58.40
## catho80 17 13 11.38 23.52 2.80 5.81 3.56 0.00 84.10
## confu 18 13 0.38 0.51 0.00 0.36 0.00 0.00 1.00
## avelf 19 13 0.46 0.28 0.45 0.47 0.29 0.00 0.80
## govef 20 12 4.60 1.75 4.86 4.79 1.90 0.84 6.49
## graft 21 12 4.76 1.56 5.29 4.93 1.38 1.10 6.71
## logyl 22 12 8.83 0.66 8.62 8.80 0.82 7.92 9.97
## loga 23 12 8.05 0.52 8.01 8.03 0.58 7.30 8.90
## yrsopen 24 13 0.72 1.09 0.41 0.48 0.60 0.00 4.09
## gadp 25 12 0.61 0.17 0.62 0.61 0.20 0.31 0.86
## engfrac 26 13 0.01 0.02 0.00 0.00 0.00 0.00 0.09
## eurfrac 27 13 0.01 0.02 0.00 0.00 0.00 0.00 0.09
## frankrom 28 13 2.52 0.61 2.59 2.52 0.45 1.19 3.89
## latitude 29 13 14.28 16.21 13.92 15.08 16.83 -17.83 37.55
## gastil 30 13 3.52 0.75 3.33 3.54 0.74 2.17 4.67
## cgexp 31 13 20.44 5.76 18.48 20.22 4.29 12.57 30.81
## cgrev 32 13 19.54 6.81 18.45 19.06 6.91 9.74 34.67
## ssw 33 11 1.27 1.29 0.65 1.05 0.77 0.13 4.39
## rgdph 34 13 4601.57 4223.17 2441.69 3994.20 1919.26 1147.17 14737.04
## trade 35 13 97.78 84.61 80.63 82.35 45.68 21.88 343.39
## prop1564 36 12 60.79 6.17 60.00 60.41 7.40 53.78 71.70
## prop65 37 12 4.27 1.16 3.86 4.23 1.15 2.79 6.23
## federal 38 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00
## eduger 39 12 74.16 20.58 77.72 74.91 20.56 42.15 98.74
## spropn 40 10 0.09 0.12 0.04 0.07 0.06 0.00 0.36
## yearele 41 12 1981.94 14.46 1989.00 1984.10 4.45 1950.00 1992.22
## yearreg 42 12 1979.00 17.19 1988.00 1980.80 5.19 1948.00 1992.00
## seats 43 13 229.54 126.01 202.89 217.97 141.01 66.00 520.33
## maj 44 13 0.69 0.48 1.00 0.73 0.00 0.00 1.00
## pres 45 13 0.31 0.48 0.00 0.27 0.00 0.00 1.00
## lyp 46 13 8.08 0.86 7.80 8.03 0.79 7.05 9.60
## semi 47 13 0.23 0.44 0.00 0.18 0.00 0.00 1.00
## majpar 48 13 0.57 0.49 0.89 0.59 0.16 0.00 1.00
## majpres 49 13 0.15 0.35 0.00 0.09 0.00 0.00 1.00
## propres 50 13 0.16 0.37 0.00 0.10 0.00 0.00 1.00
## dem_age 51 13 1977.92 16.91 1988.00 1979.36 5.93 1948.00 1992.00
## lat01 52 13 0.20 0.13 0.20 0.20 0.16 0.02 0.42
## age 53 13 0.11 0.08 0.06 0.10 0.03 0.04 0.26
## polityIV 54 13 6.24 3.08 6.22 6.65 2.14 -2.00 10.00
## spl 55 13 -1.52 5.32 -1.70 -2.27 4.30 -7.36 12.59
## cpi9500 56 9 5.89 2.16 6.90 5.89 1.42 1.00 7.86
## du_60ctry 57 13 0.62 0.51 1.00 0.64 0.00 0.00 1.00
## magn 58 12 0.76 0.32 0.94 0.80 0.08 0.10 1.00
## sdm 59 11 0.52 0.45 0.39 0.53 0.57 0.01 1.00
## oecd.x 60 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## mining_gdp 61 11 2.86 5.07 1.04 1.64 0.96 0.02 16.74
## gini_8090 62 11 38.28 7.66 37.55 38.05 8.62 29.03 49.67
## con2150 63 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00
## con5180 64 13 0.31 0.48 0.00 0.27 0.00 0.00 1.00
## con81 65 13 0.62 0.51 1.00 0.64 0.00 0.00 1.00
## list 66 12 25.74 65.45 0.00 8.39 0.00 0.00 224.89
## maj_bad 67 13 2.50 1.83 3.17 2.53 1.65 0.00 4.67
## maj_gin 68 11 28.37 19.49 32.00 29.15 20.02 0.00 49.67
## maj_old 69 13 0.08 0.09 0.05 0.07 0.07 0.00 0.25
## pres_bad 70 13 1.05 1.71 0.00 0.85 0.00 0.00 4.28
## pres_gin 71 11 10.43 18.14 0.00 7.70 0.00 0.00 45.50
## pres_old 72 13 0.03 0.07 0.00 0.02 0.00 0.00 0.26
## propar 73 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00
## lpop 74 8 16.97 2.07 16.83 16.97 1.68 13.57 20.63
## continent* 75 13 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## range skew kurtosis se
## oecd 0.00 NaN NaN 0.00
## country* 12.00 0.00 -1.48 1.08
## pind 1.00 -2.31 4.47 0.08
## pindo 0.22 -1.12 -0.77 0.03
## ctrycd 411.00 1.15 -0.50 39.15
## col_uk 1.00 -0.14 -2.13 0.14
## t_indep 226.00 1.64 0.87 21.82
## col_uka 0.90 -0.12 -2.11 0.12
## col_espa 0.79 2.82 6.44 0.06
## col_otha 0.90 0.15 -2.10 0.12
## legor_uk 1.00 -1.13 -0.76 0.12
## legor_so 0.00 NaN NaN 0.00
## legor_fr 1.00 2.82 6.44 0.08
## legor_ge 1.00 1.70 0.99 0.10
## legor_sc 0.00 NaN NaN 0.00
## prot80 58.40 1.77 1.64 5.04
## catho80 84.10 2.28 4.10 6.52
## confu 1.00 0.42 -1.96 0.14
## avelf 0.80 -0.31 -1.35 0.08
## govef 5.65 -0.69 -0.68 0.51
## graft 5.60 -0.85 -0.04 0.45
## logyl 2.05 0.30 -1.39 0.19
## loga 1.60 0.04 -1.32 0.15
## yrsopen 4.09 2.19 4.17 0.30
## gadp 0.55 -0.17 -1.31 0.05
## engfrac 0.09 2.63 5.72 0.01
## eurfrac 0.09 2.63 5.72 0.01
## frankrom 2.70 0.07 0.65 0.17
## latitude 55.38 -0.41 -1.06 4.49
## gastil 2.50 0.04 -1.21 0.21
## cgexp 18.25 0.47 -1.34 1.60
## cgrev 24.94 0.62 -0.47 1.89
## ssw 4.26 1.23 0.40 0.39
## rgdph 13589.87 1.11 -0.03 1171.30
## trade 321.50 1.79 2.64 23.47
## prop1564 17.92 0.47 -1.20 1.78
## prop65 3.44 0.37 -1.50 0.33
## federal 1.00 1.70 0.99 0.10
## eduger 56.59 -0.40 -1.42 5.94
## spropn 0.36 1.04 -0.24 0.04
## yearele 42.22 -1.19 -0.18 4.17
## yearreg 44.00 -0.89 -1.10 4.96
## seats 454.33 0.74 -0.21 34.95
## maj 1.00 -0.74 -1.56 0.13
## pres 1.00 0.74 -1.56 0.13
## lyp 2.55 0.40 -1.50 0.24
## semi 1.00 1.13 -0.76 0.12
## majpar 1.00 -0.29 -1.97 0.13
## majpres 1.00 1.69 1.02 0.10
## propres 1.00 1.68 0.94 0.10
## dem_age 44.00 -0.73 -1.30 4.69
## lat01 0.40 0.03 -1.44 0.04
## age 0.22 0.73 -1.30 0.02
## polityIV 12.00 -1.16 1.31 0.86
## spl 19.95 1.19 1.10 1.48
## cpi9500 6.87 -1.11 0.08 0.72
## du_60ctry 1.00 -0.42 -1.96 0.14
## magn 0.90 -0.87 -0.91 0.09
## sdm 0.99 0.06 -2.06 0.14
## oecd.x 0.00 NaN NaN 0.00
## mining_gdp 16.72 1.87 2.21 1.53
## gini_8090 20.64 0.27 -1.64 2.31
## con2150 1.00 1.70 0.99 0.10
## con5180 1.00 0.74 -1.56 0.13
## con81 1.00 -0.42 -1.96 0.14
## list 224.89 2.32 4.17 18.89
## maj_bad 4.67 -0.45 -1.56 0.51
## maj_gin 49.67 -0.52 -1.45 5.88
## maj_old 0.25 0.79 -0.93 0.02
## pres_bad 4.28 0.97 -0.90 0.48
## pres_gin 45.50 0.97 -1.02 5.47
## pres_old 0.26 2.22 4.11 0.02
## propar 1.00 1.70 0.99 0.10
## lpop 7.06 0.10 -0.80 0.73
## continent* 0.00 NaN NaN 0.00The “vars” column in the summary statistics table is an index variable; it can be removed with the following:
# removes "vars" indexing variable from "asia_europe_summary_statistics"
asia_europe_summary_statistics<-asia_europe_summary_statistics %>% select(-vars)# Prints contents of "asia_europe_summary_statistics"
asia_europe_summary_statistics## n mean sd median trimmed mad min max range
## oecd 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## country* 13 7.00 3.89 7.00 7.00 4.45 1.00 13.00 12.00
## pind 13 0.87 0.28 1.00 0.94 0.00 0.00 1.00 1.00
## pindo 13 0.95 0.09 1.00 0.96 0.00 0.78 1.00 0.22
## ctrycd 13 623.00 141.16 564.00 605.64 32.62 513.00 924.00 411.00
## col_uk 13 0.54 0.52 1.00 0.55 0.00 0.00 1.00 1.00
## t_indep 13 74.31 78.67 51.00 62.91 13.34 24.00 250.00 226.00
## col_uka 13 0.45 0.44 0.79 0.45 0.17 0.00 0.90 0.90
## col_espa 13 0.06 0.22 0.00 0.00 0.00 0.00 0.79 0.79
## col_otha 13 0.39 0.44 0.00 0.38 0.00 0.00 0.90 0.90
## legor_uk 13 0.77 0.44 1.00 0.82 0.00 0.00 1.00 1.00
## legor_so 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## legor_fr 13 0.08 0.28 0.00 0.00 0.00 0.00 1.00 1.00
## legor_ge 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00 1.00
## legor_sc 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## prot80 13 9.48 18.15 1.40 5.89 1.78 0.00 58.40 58.40
## catho80 13 11.38 23.52 2.80 5.81 3.56 0.00 84.10 84.10
## confu 13 0.38 0.51 0.00 0.36 0.00 0.00 1.00 1.00
## avelf 13 0.46 0.28 0.45 0.47 0.29 0.00 0.80 0.80
## govef 12 4.60 1.75 4.86 4.79 1.90 0.84 6.49 5.65
## graft 12 4.76 1.56 5.29 4.93 1.38 1.10 6.71 5.60
## logyl 12 8.83 0.66 8.62 8.80 0.82 7.92 9.97 2.05
## loga 12 8.05 0.52 8.01 8.03 0.58 7.30 8.90 1.60
## yrsopen 13 0.72 1.09 0.41 0.48 0.60 0.00 4.09 4.09
## gadp 12 0.61 0.17 0.62 0.61 0.20 0.31 0.86 0.55
## engfrac 13 0.01 0.02 0.00 0.00 0.00 0.00 0.09 0.09
## eurfrac 13 0.01 0.02 0.00 0.00 0.00 0.00 0.09 0.09
## frankrom 13 2.52 0.61 2.59 2.52 0.45 1.19 3.89 2.70
## latitude 13 14.28 16.21 13.92 15.08 16.83 -17.83 37.55 55.38
## gastil 13 3.52 0.75 3.33 3.54 0.74 2.17 4.67 2.50
## cgexp 13 20.44 5.76 18.48 20.22 4.29 12.57 30.81 18.25
## cgrev 13 19.54 6.81 18.45 19.06 6.91 9.74 34.67 24.94
## ssw 11 1.27 1.29 0.65 1.05 0.77 0.13 4.39 4.26
## rgdph 13 4601.57 4223.17 2441.69 3994.20 1919.26 1147.17 14737.04 13589.87
## trade 13 97.78 84.61 80.63 82.35 45.68 21.88 343.39 321.50
## prop1564 12 60.79 6.17 60.00 60.41 7.40 53.78 71.70 17.92
## prop65 12 4.27 1.16 3.86 4.23 1.15 2.79 6.23 3.44
## federal 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00 1.00
## eduger 12 74.16 20.58 77.72 74.91 20.56 42.15 98.74 56.59
## spropn 10 0.09 0.12 0.04 0.07 0.06 0.00 0.36 0.36
## yearele 12 1981.94 14.46 1989.00 1984.10 4.45 1950.00 1992.22 42.22
## yearreg 12 1979.00 17.19 1988.00 1980.80 5.19 1948.00 1992.00 44.00
## seats 13 229.54 126.01 202.89 217.97 141.01 66.00 520.33 454.33
## maj 13 0.69 0.48 1.00 0.73 0.00 0.00 1.00 1.00
## pres 13 0.31 0.48 0.00 0.27 0.00 0.00 1.00 1.00
## lyp 13 8.08 0.86 7.80 8.03 0.79 7.05 9.60 2.55
## semi 13 0.23 0.44 0.00 0.18 0.00 0.00 1.00 1.00
## majpar 13 0.57 0.49 0.89 0.59 0.16 0.00 1.00 1.00
## majpres 13 0.15 0.35 0.00 0.09 0.00 0.00 1.00 1.00
## propres 13 0.16 0.37 0.00 0.10 0.00 0.00 1.00 1.00
## dem_age 13 1977.92 16.91 1988.00 1979.36 5.93 1948.00 1992.00 44.00
## lat01 13 0.20 0.13 0.20 0.20 0.16 0.02 0.42 0.40
## age 13 0.11 0.08 0.06 0.10 0.03 0.04 0.26 0.22
## polityIV 13 6.24 3.08 6.22 6.65 2.14 -2.00 10.00 12.00
## spl 13 -1.52 5.32 -1.70 -2.27 4.30 -7.36 12.59 19.95
## cpi9500 9 5.89 2.16 6.90 5.89 1.42 1.00 7.86 6.87
## du_60ctry 13 0.62 0.51 1.00 0.64 0.00 0.00 1.00 1.00
## magn 12 0.76 0.32 0.94 0.80 0.08 0.10 1.00 0.90
## sdm 11 0.52 0.45 0.39 0.53 0.57 0.01 1.00 0.99
## oecd.x 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## mining_gdp 11 2.86 5.07 1.04 1.64 0.96 0.02 16.74 16.72
## gini_8090 11 38.28 7.66 37.55 38.05 8.62 29.03 49.67 20.64
## con2150 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00 1.00
## con5180 13 0.31 0.48 0.00 0.27 0.00 0.00 1.00 1.00
## con81 13 0.62 0.51 1.00 0.64 0.00 0.00 1.00 1.00
## list 12 25.74 65.45 0.00 8.39 0.00 0.00 224.89 224.89
## maj_bad 13 2.50 1.83 3.17 2.53 1.65 0.00 4.67 4.67
## maj_gin 11 28.37 19.49 32.00 29.15 20.02 0.00 49.67 49.67
## maj_old 13 0.08 0.09 0.05 0.07 0.07 0.00 0.25 0.25
## pres_bad 13 1.05 1.71 0.00 0.85 0.00 0.00 4.28 4.28
## pres_gin 11 10.43 18.14 0.00 7.70 0.00 0.00 45.50 45.50
## pres_old 13 0.03 0.07 0.00 0.02 0.00 0.00 0.26 0.26
## propar 13 0.15 0.38 0.00 0.09 0.00 0.00 1.00 1.00
## lpop 8 16.97 2.07 16.83 16.97 1.68 13.57 20.63 7.06
## continent* 13 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00
## skew kurtosis se
## oecd NaN NaN 0.00
## country* 0.00 -1.48 1.08
## pind -2.31 4.47 0.08
## pindo -1.12 -0.77 0.03
## ctrycd 1.15 -0.50 39.15
## col_uk -0.14 -2.13 0.14
## t_indep 1.64 0.87 21.82
## col_uka -0.12 -2.11 0.12
## col_espa 2.82 6.44 0.06
## col_otha 0.15 -2.10 0.12
## legor_uk -1.13 -0.76 0.12
## legor_so NaN NaN 0.00
## legor_fr 2.82 6.44 0.08
## legor_ge 1.70 0.99 0.10
## legor_sc NaN NaN 0.00
## prot80 1.77 1.64 5.04
## catho80 2.28 4.10 6.52
## confu 0.42 -1.96 0.14
## avelf -0.31 -1.35 0.08
## govef -0.69 -0.68 0.51
## graft -0.85 -0.04 0.45
## logyl 0.30 -1.39 0.19
## loga 0.04 -1.32 0.15
## yrsopen 2.19 4.17 0.30
## gadp -0.17 -1.31 0.05
## engfrac 2.63 5.72 0.01
## eurfrac 2.63 5.72 0.01
## frankrom 0.07 0.65 0.17
## latitude -0.41 -1.06 4.49
## gastil 0.04 -1.21 0.21
## cgexp 0.47 -1.34 1.60
## cgrev 0.62 -0.47 1.89
## ssw 1.23 0.40 0.39
## rgdph 1.11 -0.03 1171.30
## trade 1.79 2.64 23.47
## prop1564 0.47 -1.20 1.78
## prop65 0.37 -1.50 0.33
## federal 1.70 0.99 0.10
## eduger -0.40 -1.42 5.94
## spropn 1.04 -0.24 0.04
## yearele -1.19 -0.18 4.17
## yearreg -0.89 -1.10 4.96
## seats 0.74 -0.21 34.95
## maj -0.74 -1.56 0.13
## pres 0.74 -1.56 0.13
## lyp 0.40 -1.50 0.24
## semi 1.13 -0.76 0.12
## majpar -0.29 -1.97 0.13
## majpres 1.69 1.02 0.10
## propres 1.68 0.94 0.10
## dem_age -0.73 -1.30 4.69
## lat01 0.03 -1.44 0.04
## age 0.73 -1.30 0.02
## polityIV -1.16 1.31 0.86
## spl 1.19 1.10 1.48
## cpi9500 -1.11 0.08 0.72
## du_60ctry -0.42 -1.96 0.14
## magn -0.87 -0.91 0.09
## sdm 0.06 -2.06 0.14
## oecd.x NaN NaN 0.00
## mining_gdp 1.87 2.21 1.53
## gini_8090 0.27 -1.64 2.31
## con2150 1.70 0.99 0.10
## con5180 0.74 -1.56 0.13
## con81 -0.42 -1.96 0.14
## list 2.32 4.17 18.89
## maj_bad -0.45 -1.56 0.51
## maj_gin -0.52 -1.45 5.88
## maj_old 0.79 -0.93 0.02
## pres_bad 0.97 -0.90 0.48
## pres_gin 0.97 -1.02 5.47
## pres_old 2.22 4.11 0.02
## propar 1.70 0.99 0.10
## lpop 0.10 -0.80 0.73
## continent* NaN NaN 0.00Generating group-level summary statistics with the summarize function from dplyr
# Generate a table that displays summary statistics for trade at the continent level and assign to object named "trade_age_by_continent"
trade_age_by_continent<-pt_copy %>% group_by(continent) %>%
summarise(meanTrade=mean(trade),sdTrade=sd(trade),
meanAge=mean(age), sdAge=sd(age),
n=n())# prints contents of "trade_age_by_continent"
trade_age_by_continent## # A tibble: 4 × 6
## continent meanTrade sdTrade meanAge sdAge n
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 africa 77.3 32.1 0.121 0.124 11
## 2 asiae 97.8 84.6 0.110 0.0846 13
## 3 laam 68.6 32.8 0.139 0.153 23
## 4 other 78.8 40.7 0.309 0.263 38Generating crosstabs
Crosstabs with tabyl
# Creates cross-tab showing the breakdown of federal/non federal across continents
crosstab_federal_continent<-pt_copy %>% tabyl(federal, continent)# Prints contents of "crosstab_federal_continent"
crosstab_federal_continent## federal africa asiae laam other
## 0 11 11 19 29
## 1 0 2 4 7
## NA 0 0 0 2# Creates cross-tab showing the breakdown of majoritarian/nonmajoritarian across continents
crosstab_majoritarian_continent<-pt_copy %>% tabyl(maj, continent)# prints contents of "crosstab_majoritarian_continent"
crosstab_majoritarian_continent## maj africa asiae laam other
## 0 3 4 16 29
## 1 8 9 7 9Crosstabs with tbl_cross
# Uses "tbl_cross" function to create crosstab showing breakdown of federal/non-federal by continent
tbl_cross(pt_copy, row=federal, col=continent)| Characteristic | continent | Total | |||
|---|---|---|---|---|---|
| africa | asiae | laam | other | ||
| federal | |||||
| 0 | 11 | 11 | 19 | 29 | 70 |
| 1 | 0 | 2 | 4 | 7 | 13 |
| Unknown | 0 | 0 | 0 | 2 | 2 |
| Total | 11 | 13 | 23 | 38 | 85 |
# Uses "tbl_cross" function to create crosstab showing breakdown of majoritarian/non majoritarian by continent
tbl_cross(pt_copy, row=maj, col=continent)| Characteristic | continent | Total | |||
|---|---|---|---|---|---|
| africa | asiae | laam | other | ||
| maj | |||||
| 0 | 3 | 4 | 16 | 29 | 52 |
| 1 | 8 | 9 | 7 | 9 | 33 |
| Total | 11 | 13 | 23 | 38 | 85 |
Basic Data Cleaning and Preparation Tasks
Rearranging columns
# Prints contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## oecd country pind pindo ctrycd col_uk t_indep col_uka col_espa col_otha
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 Argentina 0 0 213 0 183 0 0.268 0
## 2 1 Australia 1 1 193 1 98 0.608 0 0
## 3 1 Austria 0 0 122 0 250 0 0 0
## 4 0 Bahamas 1 1 313 1 26 0.896 0 0
## 5 0 Bangladesh 1 1 513 0 28 0 0 0.888
## 6 0 Barbados 1 1 316 1 33 0.868 0 0
## 7 0 Belarus 1 1 913 0 8 0 0 0.968
## 8 1 Belgium 0 0 124 0 169 0 0 0.324
## 9 0 Belize 1 1 339 1 18 0.928 0 0
## 10 0 Bolivia 0.116 0.116 218 0 174 0 0.304 0
## # … with 75 more rows, and 65 more variables: legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>,
## # catho80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>, graft <dbl>,
## # logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, engfrac <dbl>,
## # eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>, cgexp <dbl>,
## # cgrev <dbl>, ssw <dbl>, rgdph <dbl>, trade <dbl>, prop1564 <dbl>,
## # prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, yearele <dbl>, …# bring the "country" column to the front of the dataset
pt_copy<-pt_copy %>% relocate(country)
pt_copy## # A tibble: 85 × 75
## country oecd pind pindo ctrycd col_uk t_indep col_uka col_espa col_otha
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Argentina 0 0 0 213 0 183 0 0.268 0
## 2 Australia 1 1 1 193 1 98 0.608 0 0
## 3 Austria 1 0 0 122 0 250 0 0 0
## 4 Bahamas 0 1 1 313 1 26 0.896 0 0
## 5 Bangladesh 0 1 1 513 0 28 0 0 0.888
## 6 Barbados 0 1 1 316 1 33 0.868 0 0
## 7 Belarus 0 1 1 913 0 8 0 0 0.968
## 8 Belgium 1 0 0 124 0 169 0 0 0.324
## 9 Belize 0 1 1 339 1 18 0.928 0 0
## 10 Bolivia 0 0.116 0.116 218 0 174 0 0.304 0
## # … with 75 more rows, and 65 more variables: legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>,
## # catho80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>, graft <dbl>,
## # logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, engfrac <dbl>,
## # eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>, cgexp <dbl>,
## # cgrev <dbl>, ssw <dbl>, rgdph <dbl>, trade <dbl>, prop1564 <dbl>,
## # prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, yearele <dbl>, …# bring the "country", "list", "trade", "oecd" columns to the front of the dataset
pt_copy<-pt_copy %>% relocate(country, list, trade, oecd)
# prints updated contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## country list trade oecd pind pindo ctrycd col_uk t_indep col_uka col_espa
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Argenti… 257. 18.4 0 0 0 213 0 183 0 0.268
## 2 Austral… 0 38.8 1 1 1 193 1 98 0.608 0
## 3 Austria 183 78.3 1 0 0 122 0 250 0 0
## 4 Bahamas 0 102. 0 1 1 313 1 26 0.896 0
## 5 Banglad… 0 25.4 0 1 1 513 0 28 0 0
## 6 Barbados 0 116. 0 1 1 316 1 33 0.868 0
## 7 Belarus 0 117. 0 1 1 913 0 8 0 0
## 8 Belgium 184. 132. 1 0 0 124 0 169 0 0
## 9 Belize 0 113. 0 1 1 339 1 18 0.928 0
## 10 Bolivia 115. 48.9 0 0.116 0.116 218 0 174 0 0.304
## # … with 75 more rows, and 64 more variables: col_otha <dbl>, legor_uk <dbl>,
## # legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>,
## # prot80 <dbl>, catho80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>,
## # engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>,
## # cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>, prop1564 <dbl>,
## # prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, yearele <dbl>, …Renaming variables
## Renaming a variable (renames "list" to "party_list")
pt_copy<-pt_copy %>% rename(party_list=list)
# prints updated contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## country party_list trade oecd pind pindo ctrycd col_uk t_indep col_uka
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Argentina 257. 18.4 0 0 0 213 0 183 0
## 2 Australia 0 38.8 1 1 1 193 1 98 0.608
## 3 Austria 183 78.3 1 0 0 122 0 250 0
## 4 Bahamas 0 102. 0 1 1 313 1 26 0.896
## 5 Bangladesh 0 25.4 0 1 1 513 0 28 0
## 6 Barbados 0 116. 0 1 1 316 1 33 0.868
## 7 Belarus 0 117. 0 1 1 913 0 8 0
## 8 Belgium 184. 132. 1 0 0 124 0 169 0
## 9 Belize 0 113. 0 1 1 339 1 18 0.928
## 10 Bolivia 115. 48.9 0 0.116 0.116 218 0 174 0
## # … with 75 more rows, and 65 more variables: col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, catho80 <dbl>, confu <dbl>, avelf <dbl>,
## # govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>,
## # gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>,
## # gastil <dbl>, cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>,
## # prop1564 <dbl>, prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, …Sorting a dataset in ascending or descending order with respect to a variable
# sorting in ascending (low to high) order with respect to the "trade" variable
pt_copy<-pt_copy %>% arrange(trade)
# prints updated contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## country party_list trade oecd pind pindo ctrycd col_uk t_indep col_uka
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Brazil 510. 17.6 0 0 1 223 0 177 0
## 2 Argentina 257. 18.4 0 0 0 213 0 183 0
## 3 Japan 67.7 18.8 1 0.867 0.867 158 0 250 0
## 4 India 0 21.9 0 1 1 534 1 52 0.792
## 5 USA 0 23.0 1 1 1 111 0 250 0
## 6 Bangladesh 0 25.4 0 1 1 513 0 28 0
## 7 Peru 153. 25.9 0 0 0 293 0 178 0
## 8 Uganda 0 30.8 0 1 1 746 1 37 0.852
## 9 Colombia 157. 34.8 0 0 0 233 0 189 0
## 10 Pakistan 0 38.7 0 1 1 564 1 52 0.792
## # … with 75 more rows, and 65 more variables: col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, catho80 <dbl>, confu <dbl>, avelf <dbl>,
## # govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>,
## # gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>,
## # gastil <dbl>, cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>,
## # prop1564 <dbl>, prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, …# sorting in descending (high to low) order with respect to the "trade" variable
pt_copy<-pt_copy %>% arrange(desc(trade))
# prints updated contents of "pt_copy"
pt_copy## # A tibble: 85 × 75
## country party_list trade oecd pind pindo ctrycd col_uk t_indep col_uka
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Singapore 0 343. 0 1 1 576 1 34 0.864
## 2 Malta 65.9 190. 0 0 1 181 1 35 0.860
## 3 Luxembourg 60 189. 1 0 1 137 0 160 0
## 4 Malaysia 0 176. 0 1 1 548 1 42 0.832
## 5 Estonia 101 154. 0 0 1 939 0 8 0
## 6 Belgium 184. 132. 1 0 0 124 0 169 0
## 7 Ireland 166 129. 1 0 1 178 1 78 0.688
## 8 Mauritius 0 128. 0 1 1 684 1 31 0.876
## 9 St. Vincent… 0 123. 0 1 1 364 1 20 0.920
## 10 Jamaica 0 122. 0 1 1 343 1 37 0.852
## # … with 75 more rows, and 65 more variables: col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, catho80 <dbl>, confu <dbl>, avelf <dbl>,
## # govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>,
## # gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>,
## # gastil <dbl>, cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>,
## # prop1564 <dbl>, prop65 <dbl>, federal <dbl>, eduger <dbl>, spropn <dbl>, …Creating new variables based on existing variables
# Create new variable named "non_catholic_80" that is calculated by substracting the Catholic share of the population in 1980 ("catho80") from 100 and relocates "country", "catho80", and the newly created "non_catholic_80" to the front of the dataset
pt_copy<-pt_copy %>% mutate(non_catholic_80=100-catho80) %>%
relocate(country, catho80, non_catholic_80)# prints updated contents of "pt_copy"
pt_copy## # A tibble: 85 × 76
## country catho80 non_catholic_80 party_list trade oecd pind pindo ctrycd
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Singapore 4.70 95.3 0 343. 0 1 1 576
## 2 Malta 97.3 2.70 65.9 190. 0 0 1 181
## 3 Luxembourg 93 7 60 189. 1 0 1 137
## 4 Malaysia 2.80 97.2 0 176. 0 1 1 548
## 5 Estonia 2 98 101 154. 0 0 1 939
## 6 Belgium 90 10 184. 132. 1 0 0 124
## 7 Ireland 95.3 4.70 166 129. 1 0 1 178
## 8 Mauritius 31.2 68.8 0 128. 0 1 1 684
## 9 St. Vincen… 19.4 80.6 0 123. 0 1 1 364
## 10 Jamaica 9.60 90.4 0 122. 0 1 1 343
## # … with 75 more rows, and 67 more variables: col_uk <dbl>, t_indep <dbl>,
## # col_uka <dbl>, col_espa <dbl>, col_otha <dbl>, legor_uk <dbl>,
## # legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>,
## # prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>, graft <dbl>,
## # logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, engfrac <dbl>,
## # eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>, cgexp <dbl>,
## # cgrev <dbl>, ssw <dbl>, rgdph <dbl>, prop1564 <dbl>, prop65 <dbl>, …Selecting or deleting variables
Selecting variables
# Selects "country", "cgexp", "cgrev", and "trade" variables from the "pt_copy" dataset
pt_copy %>% select(country, cgexp, cgrev, trade)## # A tibble: 85 × 4
## country cgexp cgrev trade
## <chr> <dbl> <dbl> <dbl>
## 1 Singapore 18.5 34.7 343.
## 2 Malta 41.0 35.0 190.
## 3 Luxembourg 40.2 45.5 189.
## 4 Malaysia 24.5 26.8 176.
## 5 Estonia 30.0 31.1 154.
## 6 Belgium 47.9 43.7 132.
## 7 Ireland 38.1 34.8 129.
## 8 Mauritius 22.5 21.6 128.
## 9 St. Vincent&G 34.8 28.7 123.
## 10 Jamaica NA NA 122.
## # … with 75 more rows# Selects "country", "cgexp", "cgrev", and "trade" variables from the "pt_copy" dataset and assigns the selection to a new object named "pt_copy_selection"
pt_copy_selection<-pt_copy %>% select(country, cgexp, cgrev, trade)# Prints Contents of "pt_copy_selection"
pt_copy_selection## # A tibble: 85 × 4
## country cgexp cgrev trade
## <chr> <dbl> <dbl> <dbl>
## 1 Singapore 18.5 34.7 343.
## 2 Malta 41.0 35.0 190.
## 3 Luxembourg 40.2 45.5 189.
## 4 Malaysia 24.5 26.8 176.
## 5 Estonia 30.0 31.1 154.
## 6 Belgium 47.9 43.7 132.
## 7 Ireland 38.1 34.8 129.
## 8 Mauritius 22.5 21.6 128.
## 9 St. Vincent&G 34.8 28.7 123.
## 10 Jamaica NA NA 122.
## # … with 75 more rowsDeleting variables
# Deletes "cgrev" variable from "pt_copy_selection" dataset
pt_copy_selection %>% select(-cgrev)## # A tibble: 85 × 3
## country cgexp trade
## <chr> <dbl> <dbl>
## 1 Singapore 18.5 343.
## 2 Malta 41.0 190.
## 3 Luxembourg 40.2 189.
## 4 Malaysia 24.5 176.
## 5 Estonia 30.0 154.
## 6 Belgium 47.9 132.
## 7 Ireland 38.1 129.
## 8 Mauritius 22.5 128.
## 9 St. Vincent&G 34.8 123.
## 10 Jamaica NA 122.
## # … with 75 more rows# Deletes "cgrev" AND "cgexp" variables from "pt_copy_selection" dataset
pt_copy_selection %>% select(-c(cgexp, cgrev))## # A tibble: 85 × 2
## country trade
## <chr> <dbl>
## 1 Singapore 343.
## 2 Malta 190.
## 3 Luxembourg 189.
## 4 Malaysia 176.
## 5 Estonia 154.
## 6 Belgium 132.
## 7 Ireland 129.
## 8 Mauritius 128.
## 9 St. Vincent&G 123.
## 10 Jamaica 122.
## # … with 75 more rows# Deletes "cgrev" AND "cgexp" variables from "pt_copy_selection" dataset and assigns the result to a new object named "pt_copy_trade"
pt_copy_trade<-pt_copy_selection %>% select(-c(cgexp, cgrev))# Prints contents of "pt_copy_trade_revexp"
pt_copy_trade## # A tibble: 85 × 2
## country trade
## <chr> <dbl>
## 1 Singapore 343.
## 2 Malta 190.
## 3 Luxembourg 189.
## 4 Malaysia 176.
## 5 Estonia 154.
## 6 Belgium 132.
## 7 Ireland 129.
## 8 Mauritius 128.
## 9 St. Vincent&G 123.
## 10 Jamaica 122.
## # … with 75 more rows# Deletes "cgrev" AND "cgexp" variables from "pt_copy_selection" dataset and assigns the result to "pt_copy_selection", thereby overwriting the existing version of "pt_copy_selection" with a new version that reflects these deletions
pt_copy_selection<-pt_copy_selection %>% select(-c(cgexp, cgrev))# prints updated contents of "pt_copy_selection"
pt_copy_selection## # A tibble: 85 × 2
## country trade
## <chr> <dbl>
## 1 Singapore 343.
## 2 Malta 190.
## 3 Luxembourg 189.
## 4 Malaysia 176.
## 5 Estonia 154.
## 6 Belgium 132.
## 7 Ireland 129.
## 8 Mauritius 128.
## 9 St. Vincent&G 123.
## 10 Jamaica 122.
## # … with 75 more rowsRecoding variables
Creating Dummy Variables from Continuous Numeric Variables
# Creates a new dummy variable based on the existing "trade" variable named "trade_open" (which takes on a value of "1" if "trade" is greater than or equal to 77, and 0 otherwise) and then moves the newly created variable to the front of the dataset along with "country" and "trade"; all changes are assigned to "pt_copy", thereby overwriting the existing version of "pt_copy"
pt_copy<-pt_copy %>% mutate(trade_open=ifelse(trade>=77, 1, 0)) %>%
relocate(country, trade_open, trade)# prints updated contents of "pt_copy"; note the newly created dummy variable
pt_copy## # A tibble: 85 × 77
## country trade_open trade catho80 non_catholic_80 party_list oecd pind pindo
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Singap… 1 343. 4.70 95.3 0 0 1 1
## 2 Malta 1 190. 97.3 2.70 65.9 0 0 1
## 3 Luxemb… 1 189. 93 7 60 1 0 1
## 4 Malays… 1 176. 2.80 97.2 0 0 1 1
## 5 Estonia 1 154. 2 98 101 0 0 1
## 6 Belgium 1 132. 90 10 184. 1 0 0
## 7 Ireland 1 129. 95.3 4.70 166 1 0 1
## 8 Maurit… 1 128. 31.2 68.8 0 0 1 1
## 9 St. Vi… 1 123. 19.4 80.6 0 0 1 1
## 10 Jamaica 1 122. 9.60 90.4 0 0 1 1
## # … with 75 more rows, and 68 more variables: ctrycd <dbl>, col_uk <dbl>,
## # t_indep <dbl>, col_uka <dbl>, col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>,
## # engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>, gastil <dbl>,
## # cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>, prop1564 <dbl>, …Creating categorical variables from continuous numeric variables
# Creates a new variable in the "pt_copy" dataset named "trade_level" (that is coded as "Low Trade" when the "trade" variable is greater than 15 and less than 50, coded as "Intermediate Trade" when "trade" is greater than or equal to 50 and less than 100, and coded as "High TradE" when "trade" is greater than or equal to 100), and then reorders the dataset such that "country", "trade_level", and "trade" are the first three variables in the dataset
pt_copy<-pt_copy %>% mutate(trade_level=case_when(trade>15 & trade<50~"Low_Trade",
trade>=50 & trade<100~"Intermediate_Trade",
trade>=100~"High_Trade")) %>%
relocate(country, trade_level, trade)# prints updated contents of "pt_copy"; note the newly created categorical variable
pt_copy## # A tibble: 85 × 78
## country trade_level trade trade_open catho80 non_catholic_80 party_list oecd
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Singap… High_Trade 343. 1 4.70 95.3 0 0
## 2 Malta High_Trade 190. 1 97.3 2.70 65.9 0
## 3 Luxemb… High_Trade 189. 1 93 7 60 1
## 4 Malays… High_Trade 176. 1 2.80 97.2 0 0
## 5 Estonia High_Trade 154. 1 2 98 101 0
## 6 Belgium High_Trade 132. 1 90 10 184. 1
## 7 Ireland High_Trade 129. 1 95.3 4.70 166 1
## 8 Maurit… High_Trade 128. 1 31.2 68.8 0 0
## 9 St. Vi… High_Trade 123. 1 19.4 80.6 0 0
## 10 Jamaica High_Trade 122. 1 9.60 90.4 0 0
## # … with 75 more rows, and 70 more variables: pind <dbl>, pindo <dbl>,
## # ctrycd <dbl>, col_uk <dbl>, t_indep <dbl>, col_uka <dbl>, col_espa <dbl>,
## # col_otha <dbl>, legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>,
## # legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>,
## # govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>,
## # gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, latitude <dbl>,
## # gastil <dbl>, cgexp <dbl>, cgrev <dbl>, ssw <dbl>, rgdph <dbl>, …Creating dummmy variables from categorical variables
# Creates dummy variables from "trade_level" column, and relocates the new dummies to the front of the dataset
pt_copy<-pt_copy %>% dummy_cols("trade_level") %>%
relocate(country, trade_level, trade_level_High_Trade, trade_level_Intermediate_Trade, trade_level_Low_Trade)# Prints contents of "pt_copy"
pt_copy## # A tibble: 85 × 81
## country trade_level trade_level_Hig… trade_level_Int… trade_level_Low… trade
## <chr> <chr> <int> <int> <int> <dbl>
## 1 Singapo… High_Trade 1 0 0 343.
## 2 Malta High_Trade 1 0 0 190.
## 3 Luxembo… High_Trade 1 0 0 189.
## 4 Malaysia High_Trade 1 0 0 176.
## 5 Estonia High_Trade 1 0 0 154.
## 6 Belgium High_Trade 1 0 0 132.
## 7 Ireland High_Trade 1 0 0 129.
## 8 Mauriti… High_Trade 1 0 0 128.
## 9 St. Vin… High_Trade 1 0 0 123.
## 10 Jamaica High_Trade 1 0 0 122.
## # … with 75 more rows, and 75 more variables: trade_open <dbl>, catho80 <dbl>,
## # non_catholic_80 <dbl>, party_list <dbl>, oecd <dbl>, pind <dbl>,
## # pindo <dbl>, ctrycd <dbl>, col_uk <dbl>, t_indep <dbl>, col_uka <dbl>,
## # col_espa <dbl>, col_otha <dbl>, legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>, confu <dbl>,
## # avelf <dbl>, govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>,
## # yrsopen <dbl>, gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, …Subsetting rows data based on criteria
The filter function
# Extracts OECD observations in "pt_copy" and assigns to object named "oecd_countries"
oecd_countries<-pt_copy %>% filter(oecd==1) %>%
relocate(country, oecd)# Prints contents of "oecd_countries"
oecd_countries## # A tibble: 25 × 81
## country oecd trade_level trade_level_Hig… trade_level_Int… trade_level_Low…
## <chr> <dbl> <chr> <int> <int> <int>
## 1 Luxembo… 1 High_Trade 1 0 0
## 2 Belgium 1 High_Trade 1 0 0
## 3 Ireland 1 High_Trade 1 0 0
## 4 Netherl… 1 High_Trade 1 0 0
## 5 Austria 1 Intermedia… 0 1 0
## 6 Norway 1 Intermedia… 0 1 0
## 7 Switzer… 1 Intermedia… 0 1 0
## 8 Portugal 1 Intermedia… 0 1 0
## 9 Sweden 1 Intermedia… 0 1 0
## 10 Iceland 1 Intermedia… 0 1 0
## # … with 15 more rows, and 75 more variables: trade <dbl>, trade_open <dbl>,
## # catho80 <dbl>, non_catholic_80 <dbl>, party_list <dbl>, pind <dbl>,
## # pindo <dbl>, ctrycd <dbl>, col_uk <dbl>, t_indep <dbl>, col_uka <dbl>,
## # col_espa <dbl>, col_otha <dbl>, legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>, confu <dbl>,
## # avelf <dbl>, govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>,
## # yrsopen <dbl>, gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, …# Extracts observations for which cgrev (central government revenue as % of gdp)>40, and assigns to object named "high_revenues"
high_revenues<-pt_copy %>% filter(cgrev>40) %>%
relocate(country, cgrev)# Prints contents of "high_revenues"
high_revenues## # A tibble: 10 × 81
## country cgrev trade_level trade_level_Hig… trade_level_Int… trade_level_Low…
## <chr> <dbl> <chr> <int> <int> <int>
## 1 Luxembo… 45.5 High_Trade 1 0 0
## 2 Belgium 43.7 High_Trade 1 0 0
## 3 Netherl… 47.6 High_Trade 1 0 0
## 4 Botswana 50.8 Intermedia… 0 1 0
## 5 Hungary 45.6 Intermedia… 0 1 0
## 6 Norway 41.1 Intermedia… 0 1 0
## 7 Sweden 40.8 Intermedia… 0 1 0
## 8 Poland 40.3 Low_Trade 0 0 1
## 9 France 40.9 Low_Trade 0 0 1
## 10 Italy 41.2 Low_Trade 0 0 1
## # … with 75 more variables: trade <dbl>, trade_open <dbl>, catho80 <dbl>,
## # non_catholic_80 <dbl>, party_list <dbl>, oecd <dbl>, pind <dbl>,
## # pindo <dbl>, ctrycd <dbl>, col_uk <dbl>, t_indep <dbl>, col_uka <dbl>,
## # col_espa <dbl>, col_otha <dbl>, legor_uk <dbl>, legor_so <dbl>,
## # legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>, prot80 <dbl>, confu <dbl>,
## # avelf <dbl>, govef <dbl>, graft <dbl>, logyl <dbl>, loga <dbl>,
## # yrsopen <dbl>, gadp <dbl>, engfrac <dbl>, eurfrac <dbl>, frankrom <dbl>, …# Extracts observations for which the "catho80" variable is less than or equal to 50
minority_catholic<-pt_copy %>% filter(catho80<=50) %>%
relocate(country, catho80)# Prints contents of "minority_catholic"
minority_catholic## # A tibble: 53 × 81
## country catho80 trade_level trade_level_High_Trade trade_level_Interme…
## <chr> <dbl> <chr> <int> <int>
## 1 Singapore 4.70 High_Trade 1 0
## 2 Malaysia 2.80 High_Trade 1 0
## 3 Estonia 2 High_Trade 1 0
## 4 Mauritius 31.2 High_Trade 1 0
## 5 St. Vincent&G 19.4 High_Trade 1 0
## 6 Jamaica 9.60 High_Trade 1 0
## 7 Gambia 1.90 High_Trade 1 0
## 8 Fiji 9 High_Trade 1 0
## 9 Belarus 14 High_Trade 1 0
## 10 Barbados 5.90 High_Trade 1 0
## # … with 43 more rows, and 76 more variables: trade_level_Low_Trade <int>,
## # trade <dbl>, trade_open <dbl>, non_catholic_80 <dbl>, party_list <dbl>,
## # oecd <dbl>, pind <dbl>, pindo <dbl>, ctrycd <dbl>, col_uk <dbl>,
## # t_indep <dbl>, col_uka <dbl>, col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, …Using the & operator
# Extracts federal OECD countries (where oecd=1 AND federal=1) and assigns to a new object named "oecd_federal_countries"
oecd_federal_countries<-pt_copy %>% filter(oecd==1 & federal==1) %>%
relocate(country, oecd, federal)# prints contents of "oecd_federal_countries"
oecd_federal_countries## # A tibble: 7 × 81
## country oecd federal trade_level trade_level_Hig… trade_level_Int…
## <chr> <dbl> <dbl> <chr> <int> <int>
## 1 Austria 1 1 Intermediate_Trade 0 1
## 2 Switzerland 1 1 Intermediate_Trade 0 1
## 3 Canada 1 1 Intermediate_Trade 0 1
## 4 Germany 1 1 Low_Trade 0 0
## 5 Mexico 1 1 Low_Trade 0 0
## 6 Australia 1 1 Low_Trade 0 0
## 7 USA 1 1 Low_Trade 0 0
## # … with 75 more variables: trade_level_Low_Trade <int>, trade <dbl>,
## # trade_open <dbl>, catho80 <dbl>, non_catholic_80 <dbl>, party_list <dbl>,
## # pind <dbl>, pindo <dbl>, ctrycd <dbl>, col_uk <dbl>, t_indep <dbl>,
## # col_uka <dbl>, col_espa <dbl>, col_otha <dbl>, legor_uk <dbl>,
## # legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>, legor_sc <dbl>,
## # prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>, graft <dbl>,
## # logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, engfrac <dbl>, …Using the | operator
# Extracts observations that are in Africa ("africa") OR in Asia/Europe ("asiae) and assigns to an object named "asia_europe_africa"
asia_europe_africa<-pt_copy %>% filter(continent=="africa"|continent=="asiae") %>%
relocate(continent)# Prints contents of "asia_europe_africa"
asia_europe_africa## # A tibble: 24 × 81
## continent country trade_level trade_level_High… trade_level_Int…
## <chr> <chr> <chr> <int> <int>
## 1 asiae Singapore High_Trade 1 0
## 2 asiae Malaysia High_Trade 1 0
## 3 africa Mauritius High_Trade 1 0
## 4 africa Gambia High_Trade 1 0
## 5 asiae Fiji High_Trade 1 0
## 6 africa Namibia High_Trade 1 0
## 7 asiae Papua N. Guin High_Trade 1 0
## 8 asiae Taiwan Intermediate_Trade 0 1
## 9 africa Botswana Intermediate_Trade 0 1
## 10 asiae Thailand Intermediate_Trade 0 1
## # … with 14 more rows, and 76 more variables: trade_level_Low_Trade <int>,
## # trade <dbl>, trade_open <dbl>, catho80 <dbl>, non_catholic_80 <dbl>,
## # party_list <dbl>, oecd <dbl>, pind <dbl>, pindo <dbl>, ctrycd <dbl>,
## # col_uk <dbl>, t_indep <dbl>, col_uka <dbl>, col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, …Filtering for observations that do NOT meet a condition:
# Extracts all non-Africa observations and assigns to object named "pt_copy_sans_africa"
pt_copy_sans_africa<-pt_copy %>% filter(continent!="africa") %>% relocate(continent)# Prints contents of "pt_copy_sans_africa"
pt_copy_sans_africa## # A tibble: 74 × 81
## continent country trade_level trade_level_High_Trade trade_level_Int…
## <chr> <chr> <chr> <int> <int>
## 1 asiae Singapore High_Trade 1 0
## 2 other Malta High_Trade 1 0
## 3 other Luxembourg High_Trade 1 0
## 4 asiae Malaysia High_Trade 1 0
## 5 other Estonia High_Trade 1 0
## 6 other Belgium High_Trade 1 0
## 7 other Ireland High_Trade 1 0
## 8 laam St. Vincent&G High_Trade 1 0
## 9 laam Jamaica High_Trade 1 0
## 10 other Slovak Republic High_Trade 1 0
## # … with 64 more rows, and 76 more variables: trade_level_Low_Trade <int>,
## # trade <dbl>, trade_open <dbl>, catho80 <dbl>, non_catholic_80 <dbl>,
## # party_list <dbl>, oecd <dbl>, pind <dbl>, pindo <dbl>, ctrycd <dbl>,
## # col_uk <dbl>, t_indep <dbl>, col_uka <dbl>, col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, …Exploratory visualizations using ggplot
Bar charts
Basic bar chart
# Creates a bar chart of the "cgexp" variable (central government expenditure as a share of GDP) and assigns the plot to an object named "cgexp_viz1"
cgexp_viz1<-pt_copy %>%
drop_na(cgexp) %>%
ggplot()+
geom_col(aes(x=reorder(country, cgexp), y=cgexp))+
labs(title="Central Govt Expenditure as Pct of GDP (1990-1998 Average)", x="Country Name",
y="CGEXP")+
theme(plot.title=element_text(hjust=0.5),
axis.text.x = element_text(angle = 90))
# Prints contents of "cgexp_viz1"
cgexp_viz1
Inverted bar chart
# Creates an inverted bar chart of the "cgexp" variable (with countries on vertical axis) and assigns the result to an object named "cgexp_viz2"
cgexp_viz2<-pt_copy %>%
drop_na(cgexp) %>%
ggplot()+
geom_col(aes(x=reorder(country, cgexp), y=cgexp))+
coord_flip()+
labs(title="Central Govt Expenditure as Pct of GDP (1990-1998 Average) ", x="Country Name",
y="CGEXP")+
theme(plot.title=element_text(hjust=0.5))
# Prints contents of "cgexp_viz2"
cgexp_viz2
Scatterplots
Basic scatterplot
# Creates scatterplot with "cgexp" variable on x-axis and "trade" variiable on y-axis and assigns to object named "scatter_cgexp_trade"
scatter_cgexp_trade<-
pt_copy %>%
drop_na(cgexp) %>%
ggplot()+
geom_point(aes(x=cgexp, y=trade))+
labs(title="Trade Share of GDP \nas a function of\n Central Govt Expenditure (1990-1998 Average) ",
x="Central Government Expenditure (Pct of GDP)", y="Overall Trade (Pct of GDP)")+
theme(plot.title=element_text(hjust=0.5))
# Prints contents of "scatter_cgexp_trade"
scatter_cgexp_trade
Grouped scatterplot
# Creates scatterplot with "cgexp" variable on x-axis and "trade" variiable on y-axis, and uses different color points for different continents; plot is assigned to object named "scatter_cgexp_trade_grouped"
scatter_cgexp_trade_grouped<-
pt_copy %>%
drop_na(cgexp) %>%
ggplot()+
geom_point(aes(x=cgexp, y=trade, color=continent))+
labs(title="Trade Share of GDP \nas a function of\n Central Govt Expenditure (1990-1998 Average) ",
x="Central Government Expenditure (Pct of GDP)", y="Overall Trade (Pct of GDP)")+
theme(plot.title=element_text(hjust=0.5))
# Prints contents of "scatter_cgexp_trade_grouped"
scatter_cgexp_trade_grouped
Scatterplot with line of best fit
# Creates scatterplot with "cgexp" variable on x-axis and "trade" variiable on y-axis, adds line of best fit; plot assigned to object named "scatter_cgexp_trade_line"
scatter_cgexp_trade_line<-
pt_copy %>%
drop_na(cgexp) %>%
ggplot()+
geom_point(aes(x=cgexp, y=trade))+
geom_smooth(aes(x=cgexp, y=trade), method="lm")+
labs(title="Trade Share of GDP \nas a function of\n Central Govt Expenditure (1990-1998 Average) ",
x="Central Government Expenditure (Pct of GDP)", y="Overall Trade (Pct of GDP)")+
theme(plot.title=element_text(hjust=0.5))
# Prints contents of "scatter_cgexp_trade_line"
scatter_cgexp_trade_line## `geom_smooth()` using formula 'y ~ x'
test
Elementary Data Analysis
Computing correlations
Computing correlations between two variables in a dataset
# Prints correlation coefficient between "trade" and "cgexp" variables
cor.test(pt_copy$trade, pt_copy$cgexp, use="complete.obs")##
## Pearson's product-moment correlation
##
## data: pt_copy$trade and pt_copy$cgexp
## t = 1.8131, df = 80, p-value = 0.07356
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01915884 0.39850057
## sample estimates:
## cor
## 0.1986743Generating and exporting a correlation matrix
# Extracts variables for which we want a correlation matrix
desired_variables<-pt_copy %>% select(trade, cgexp, cgrev, catho80)# Creates correlation matrix from "desired_variables" object and assigns to object named "cor_matrix"
cor_matrix<-cor(desired_variables, use="complete.obs")# prints contents of "cor_matrix"
cor_matrix## trade cgexp cgrev catho80
## trade 1.00000000 0.1792884 0.3458730 -0.08442666
## cgexp 0.17928838 1.0000000 0.9094998 -0.07010910
## cgrev 0.34587298 0.9094998 1.0000000 -0.05923500
## catho80 -0.08442666 -0.0701091 -0.0592350 1.00000000# Exports correlation matrix assigned to "cor_matrix" object using stargazer
stargazer(cor_matrix, type="text", title="Correlation Matrix", digits=3, out="corr_table.html")##
## Correlation Matrix
## ====================================
## trade cgexp cgrev catho80
## ------------------------------------
## trade 1 0.179 0.346 -0.084
## cgexp 0.179 1 0.909 -0.070
## cgrev 0.346 0.909 1 -0.059
## catho80 -0.084 -0.070 -0.059 1
## ------------------------------------Basic regression analysis
Running a regression model
# Implements regression with "gexp" as DV, and assigns to object named "regression1"
regression1<-lm(cgexp~gastil+lyp+trade+prop1564+prop65+federal+oecd, data=pt_copy)# Prints regression table
summary(regression1)##
## Call:
## lm(formula = cgexp ~ gastil + lyp + trade + prop1564 + prop65 +
## federal + oecd, data = pt_copy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.9861 -4.6981 -0.5521 4.4482 16.1124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.17290 16.08572 3.243 0.00179 **
## gastil -2.15202 1.10609 -1.946 0.05561 .
## lyp -2.04441 2.00721 -1.019 0.31184
## trade 0.04978 0.01924 2.587 0.01170 *
## prop1564 -0.28482 0.26686 -1.067 0.28939
## prop65 1.58627 0.33548 4.728 1.09e-05 ***
## federal -4.58101 2.38015 -1.925 0.05822 .
## oecd 0.96969 2.97171 0.326 0.74514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.064 on 72 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.5865, Adjusted R-squared: 0.5463
## F-statistic: 14.59 on 7 and 72 DF, p-value: 1.137e-11Working with categorical variables in a regression model
Working with categorical variables by using factors
# Set Continent variable as factor
pt_copy$continent<-as.factor(pt_copy$continent)
# Examines levels of factor variable
levels(pt_copy$continent)## [1] "africa" "asiae" "laam" "other"# Relevels factor variable to set "other" as reference category
pt_copy$continent<-relevel(pt_copy$continent, ref="other")# Run regression with the continent variable and assign result to object named "regression2"
regression2<-lm(cgexp~gastil+lyp+trade+prop1564+prop65+federal+continent+col_espa+col_uka+col_otha+oecd, data=pt_copy)
# Prints regression table for "regression2"
summary(regression2)##
## Call:
## lm(formula = cgexp ~ gastil + lyp + trade + prop1564 + prop65 +
## federal + continent + col_espa + col_uka + col_otha + oecd,
## data = pt_copy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3617 -3.9886 -0.3921 4.6050 17.3752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.88833 17.56879 2.555 0.0129 *
## gastil -2.06438 1.13670 -1.816 0.0739 .
## lyp -0.12414 2.08305 -0.060 0.9527
## trade 0.03018 0.02069 1.459 0.1494
## prop1564 -0.25399 0.27421 -0.926 0.3577
## prop65 0.98675 0.45822 2.153 0.0349 *
## federal -4.73466 2.34235 -2.021 0.0473 *
## continentafrica -3.42365 4.58573 -0.747 0.4580
## continentasiae -7.72223 4.17322 -1.850 0.0687 .
## continentlaam -9.03522 4.25535 -2.123 0.0375 *
## col_espa 0.58034 8.05720 0.072 0.9428
## col_uka 2.68929 3.22769 0.833 0.4077
## col_otha -0.80223 3.02997 -0.265 0.7920
## oecd -2.37769 3.33814 -0.712 0.4788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.789 on 66 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.6499, Adjusted R-squared: 0.5809
## F-statistic: 9.424 on 13 and 66 DF, p-value: 1.21e-10Working with categorical variables by creating dummy variables
# Use "continent" field to make continent dummy variables
pt_copy<-pt_copy %>% dummy_cols("continent")# run regression with continent dummies, with "other" category excluded
regression2_alt<-lm(cgexp~gastil+lyp+trade+prop1564+prop65+federal+continent_africa+
continent_asiae+continent_laam+col_espa+col_uka+col_otha+oecd,
data=pt_copy)# Prints "regression2_alt" regression table
summary(regression2_alt)##
## Call:
## lm(formula = cgexp ~ gastil + lyp + trade + prop1564 + prop65 +
## federal + continent_africa + continent_asiae + continent_laam +
## col_espa + col_uka + col_otha + oecd, data = pt_copy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3617 -3.9886 -0.3921 4.6050 17.3752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.88833 17.56879 2.555 0.0129 *
## gastil -2.06438 1.13670 -1.816 0.0739 .
## lyp -0.12414 2.08305 -0.060 0.9527
## trade 0.03018 0.02069 1.459 0.1494
## prop1564 -0.25399 0.27421 -0.926 0.3577
## prop65 0.98675 0.45822 2.153 0.0349 *
## federal -4.73466 2.34235 -2.021 0.0473 *
## continent_africa -3.42365 4.58573 -0.747 0.4580
## continent_asiae -7.72223 4.17322 -1.850 0.0687 .
## continent_laam -9.03522 4.25535 -2.123 0.0375 *
## col_espa 0.58034 8.05720 0.072 0.9428
## col_uka 2.68929 3.22769 0.833 0.4077
## col_otha -0.80223 3.02997 -0.265 0.7920
## oecd -2.37769 3.33814 -0.712 0.4788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.789 on 66 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.6499, Adjusted R-squared: 0.5809
## F-statistic: 9.424 on 13 and 66 DF, p-value: 1.21e-10Working with interaction terms in regression models
# run regression with interaction term between "trade" and "federal"
trade_federal_interaction<-lm(cgexp~trade*federal, data=pt_copy)# prints "trade_federal_interaction" regression table
summary(trade_federal_interaction)##
## Call:
## lm(formula = cgexp ~ trade * federal, data = pt_copy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0774 -8.1325 0.5782 7.7004 21.0072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.708234 2.517787 10.608 <2e-16 ***
## trade 0.034512 0.026410 1.307 0.195
## federal -4.695595 5.512752 -0.852 0.397
## trade:federal 0.009965 0.076991 0.129 0.897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.42 on 77 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.05761, Adjusted R-squared: 0.02089
## F-statistic: 1.569 on 3 and 77 DF, p-value: 0.2037Plotting marginal effects
# Finds mean value of trade variable
mean(pt_copy$trade)## [1] 78.7659# Calculates marginal effects of federalism, with "trade" held at mean
marginal_effect_federalism<-ggpredict(trade_federal_interaction, terms="federal", condition=c(trade=78.7659))# Prints marginal effects table
marginal_effect_federalism## # Predicted values of cgexp
##
## federal | Predicted | 95% CI
## ------------------------------------
## 0 | 29.43 | [26.94, 31.91]
## 1 | 25.52 | [18.91, 32.12]# Plot marginal effects of federalism
ggpredict(trade_federal_interaction, terms="federal") %>%
ggplot(aes(x, predicted))+
geom_point()+
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),width=0.05)+
scale_x_continuous(breaks=(seq(0,1, by=1)))+
labs(title="Predicted Effects of Federalism on Gov't Expenditure\n(with trade set to mean)", y="Predicted Expenditure", x="Federalism")
Creating and exporting regression tables
# Put the regression models you want in your regression table in a list
model_list<-list(regression1,regression2)# Exporting table as text file
stargazer(model_list, type="text", out="cgexp_regressions.txt")# Exporting regression table as html file
stargazer(model_list, type="html", out="cgexp_regressions.html")Working with multiple datasets
Merging data
# Read in capital mobility from working directory
capital_mobility<-read_csv("chinn_eto_capitalopenness_summary.csv")Alternatively,
# Read in capital mobility data from Github repository
capital_mobility<-read_csv("https://raw.githubusercontent.com/aranganath24/r_primer/main/workshop_data/chinn_eto_capitalopenness_summary.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## ccode = col_character(),
## country_name = col_character(),
## ctrycd = col_double(),
## kaopen = col_double(),
## ka_open = col_double()
## )# View capital mobility data
capital_mobility## # A tibble: 182 × 5
## ccode country_name ctrycd kaopen ka_open
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ABW Aruba 314 -0.607 0.309
## 2 AFG Afghanistan 512 -1.92 0
## 3 AGO Angola 614 -1.57 0.0825
## 4 ALB Albania 914 -0.148 0.417
## 5 ANT Netherlands Antilles 353 -0.104 0.427
## 6 ARE United Arab Emirates 466 2.33 1
## 7 ARG Argentina 213 0.662 0.607
## 8 ARM Armenia 911 1.17 0.725
## 9 ATG Antigua and Barbuda 311 1.98 0.916
## 10 AUS Australia 193 2.16 0.960
## # … with 172 more rows# Joins "capital_mobility" to "pt_copy" using "ctrycd" as the join field (only keeps observations from "pt_copy"; countries in "capital_mobility" but not "pt_copy" are not included in the final joined dataset; joined dataset is assigned to an object named "pt_capitalmobility")
pt_capitalmobility<-inner_join(pt_copy, capital_mobility, by="ctrycd")# prints contents of "pt_capitalmobility"
pt_capitalmobility## # A tibble: 84 × 89
## country kaopen trade_level trade_level_High_Trade trade_level_Interme…
## <chr> <dbl> <chr> <int> <int>
## 1 Singapore 2.10 High_Trade 1 0
## 2 Malta -1.06 High_Trade 1 0
## 3 Malaysia 1.36 High_Trade 1 0
## 4 Estonia 2.08 High_Trade 1 0
## 5 Belgium 2.16 High_Trade 1 0
## 6 Ireland 1.36 High_Trade 1 0
## 7 Mauritius -0.0671 High_Trade 1 0
## 8 St. Vincent&G -0.505 High_Trade 1 0
## 9 Jamaica 0.245 High_Trade 1 0
## 10 Gambia 1.37 High_Trade 1 0
## # … with 74 more rows, and 84 more variables: trade_level_Low_Trade <int>,
## # trade <dbl>, trade_open <dbl>, catho80 <dbl>, non_catholic_80 <dbl>,
## # party_list <dbl>, oecd <dbl>, pind <dbl>, pindo <dbl>, ctrycd <dbl>,
## # col_uk <dbl>, t_indep <dbl>, col_uka <dbl>, col_espa <dbl>, col_otha <dbl>,
## # legor_uk <dbl>, legor_so <dbl>, legor_fr <dbl>, legor_ge <dbl>,
## # legor_sc <dbl>, prot80 <dbl>, confu <dbl>, avelf <dbl>, govef <dbl>,
## # graft <dbl>, logyl <dbl>, loga <dbl>, yrsopen <dbl>, gadp <dbl>, …