4.2 Arrange
Let’s start with arrange(). Arrange simply arranges the data in a particular order. Let’s order the SDG data by Gini coefficient. Note that the data is by default arranged from lowest to highest. We can alter this by specifying desc(), which will put the data in descending order.
We’ve used two pipes here – first we took the sdg dataset and piped it into the arrange function (and thus sorting the dataset by Gini coefficient). We then took that sorted dataset and piped it into head() to view the first six rows. In this way, chaining pipes is like building up a sentence. We would have to use a number of nested brackets to do this otherwise, which is much harder to follow.
#--- Arrange by Gini
sdg %>% arrange(gini) %>% head()## country code reg gdp gini pop delta.pop int.migrant
## 1 Ukraine UKR EUR 2185.728 25.5 45004645 -3.81 10.79
## 2 Iceland ISL EUR 59976.940 25.6 334252 10.03 11.39
## 3 Slovenia SVN EUR 21304.570 25.7 2064845 2.89 11.41
## 4 Czech Republic CZE EUR 18266.550 25.9 10561633 3.15 3.84
## 5 Slovak Republic SVK EUR 16495.990 26.1 5428704 1.04 3.27
## 6 Kazakhstan KAZ EUR 7510.077 26.5 17797032 16.26 20.12
## urb delta.urb emp.ratio slums pop.density largest.city sanitation
## 1 69.92 1.95 53.89 NA 77.69 9.43 97.4
## 2 94.23 1.07 71.13 NA 3.33 NA 98.7
## 3 49.63 -0.78 52.26 NA 102.52 NA 99.1
## 4 72.98 -0.55 57.00 NA 136.79 17.18 99.1
## 5 53.47 -1.94 53.49 NA 112.90 13.72 99.4
## 6 53.23 -1.29 67.71 NA 6.59 16.20 97.0
## water million tb urb.pov electric pollution urban.pov.hc primary
## 1 95.5 12.03 91.0 31.2 100 18.878440 NA 96.17551
## 2 100.0 NA 2.4 NA 100 7.789033 NA 99.05151
## 3 99.7 NA 7.2 22.1 100 20.349960 NA 97.73995
## 4 100.0 12.54 5.2 18.9 100 21.408990 NA NA
## 5 100.0 NA 6.5 NA 100 20.523670 NA NA
## 6 99.4 8.62 89.0 NA 100 19.670000 1.3 87.38210
## health.exp tb.cure case.d diarrhea.trt imm.dpt mat.mort nurse.mw
## 1 46.21547 72 74 69.3 19 12.5 6.677
## 2 17.47512 89 87 NA 91 NA 16.353
## 3 12.06582 77 87 NA 94 NA 8.611
## 4 14.32592 76 93 NA 96 5.5 8.326
## 5 22.53609 88 87 NA 96 3.6 6.078
## 6 45.13577 90 89 54.0 82 12.6 7.991
## beds ari lmic
## 1 9.000000 92.3 Lower middle income
## 2 3.200000 NA High income
## 3 4.552046 NA High income
## 4 6.800000 NA High income
## 5 6.000000 NA High income
## 6 7.200000 81.2 Upper middle income#--- Same code, no pipe
head(arrange(sdg, gini))## country code reg gdp gini pop delta.pop int.migrant
## 1 Ukraine UKR EUR 2185.728 25.5 45004645 -3.81 10.79
## 2 Iceland ISL EUR 59976.940 25.6 334252 10.03 11.39
## 3 Slovenia SVN EUR 21304.570 25.7 2064845 2.89 11.41
## 4 Czech Republic CZE EUR 18266.550 25.9 10561633 3.15 3.84
## 5 Slovak Republic SVK EUR 16495.990 26.1 5428704 1.04 3.27
## 6 Kazakhstan KAZ EUR 7510.077 26.5 17797032 16.26 20.12
## urb delta.urb emp.ratio slums pop.density largest.city sanitation
## 1 69.92 1.95 53.89 NA 77.69 9.43 97.4
## 2 94.23 1.07 71.13 NA 3.33 NA 98.7
## 3 49.63 -0.78 52.26 NA 102.52 NA 99.1
## 4 72.98 -0.55 57.00 NA 136.79 17.18 99.1
## 5 53.47 -1.94 53.49 NA 112.90 13.72 99.4
## 6 53.23 -1.29 67.71 NA 6.59 16.20 97.0
## water million tb urb.pov electric pollution urban.pov.hc primary
## 1 95.5 12.03 91.0 31.2 100 18.878440 NA 96.17551
## 2 100.0 NA 2.4 NA 100 7.789033 NA 99.05151
## 3 99.7 NA 7.2 22.1 100 20.349960 NA 97.73995
## 4 100.0 12.54 5.2 18.9 100 21.408990 NA NA
## 5 100.0 NA 6.5 NA 100 20.523670 NA NA
## 6 99.4 8.62 89.0 NA 100 19.670000 1.3 87.38210
## health.exp tb.cure case.d diarrhea.trt imm.dpt mat.mort nurse.mw
## 1 46.21547 72 74 69.3 19 12.5 6.677
## 2 17.47512 89 87 NA 91 NA 16.353
## 3 12.06582 77 87 NA 94 NA 8.611
## 4 14.32592 76 93 NA 96 5.5 8.326
## 5 22.53609 88 87 NA 96 3.6 6.078
## 6 45.13577 90 89 54.0 82 12.6 7.991
## beds ari lmic
## 1 9.000000 92.3 Lower middle income
## 2 3.200000 NA High income
## 3 4.552046 NA High income
## 4 6.800000 NA High income
## 5 6.000000 NA High income
## 6 7.200000 81.2 Upper middle income#--- Arrange by descending Gini
sdg %>% arrange(desc(gini)) %>% head()## country code reg gdp gini pop delta.pop int.migrant
## 1 South Africa ZAF AFR 5273.5940 63.4 55908865 15.91 5.77
## 2 Zambia ZMB AFR 1178.3880 57.1 16591390 33.98 0.79
## 3 Brazil BRA AMR 8649.9480 51.3 208000000 9.86 0.34
## 4 Colombia COL AMR 5805.6050 51.1 48653419 10.99 0.28
## 5 Panama PAN AMR 13680.2400 51.0 4034119 18.93 4.70
## 6 Rwanda RWA AFR 702.8356 50.4 11917508 29.45 3.80
## urb delta.urb emp.ratio slums pop.density largest.city sanitation
## 1 65.30 5.22 39.47 23.0 46.09 26.34 69.6
## 2 41.38 4.35 69.65 54.0 22.32 33.29 55.6
## 3 85.93 2.79 59.29 22.3 24.84 11.93 88.0
## 4 76.71 2.83 61.83 13.1 43.85 26.71 85.2
## 5 66.90 2.94 61.60 25.8 54.27 63.30 83.5
## 6 29.78 9.61 82.73 53.2 483.08 36.45 58.5
## water million tb urb.pov electric pollution urban.pov.hc primary
## 1 99.6 37.03 834 NA 94.06103 29.62863 NA NA
## 2 85.6 13.77 391 7.7 61.50000 26.74665 NA 87.40411
## 3 100.0 40.05 41 NA 99.94555 11.36829 NA 92.69713
## 4 96.8 43.12 31 NA 99.83837 17.99814 24.1 90.60457
## 5 97.7 42.34 50 NA 100.00000 13.19621 NA 93.40181
## 6 86.6 10.85 56 NA 71.80000 49.71362 22.1 95.09565
## health.exp tb.cure case.d diarrhea.trt imm.dpt mat.mort nurse.mw beds
## 1 6.491224 78 63 NA 66 200.00 5.113 NA
## 2 29.994880 85 58 56.2 91 398.00 0.714 2.0
## 3 25.469930 71 87 NA 86 58.20 7.444 2.3
## 4 15.355020 76 80 52.0 91 71.22 0.656 1.5
## 5 22.267520 79 80 52.7 73 80.50 2.330 2.2
## 6 28.130080 86 84 19.5 98 253.00 0.678 NA
## ari lmic
## 1 NA Upper middle income
## 2 69.7 Lower middle income
## 3 NA Upper middle income
## 4 65.3 Upper middle income
## 5 81.6 Upper middle income
## 6 53.9 Low incomeEXERCISE: Which country has the highest GDP? Which has the lowest TB cure rate?