Nations_chart <- read.csv("nations.csv")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
nations_gdp_t <- Nations_chart %>%
mutate(gdp_trillions = gdp_percap * population / 10^12)
desired_countries <- c("China", "United States", "Germany", "Japan", nations_gdp_t)
desired_countries2 <- nations_gdp_t %>%
filter(country %in% desired_countries)
desired_countries2
## iso2c iso3c country year gdp_percap population birth_rate
## 1 CN CHN China 1992 1260.162 1164970000 18.270000
## 2 CN CHN China 2005 5053.379 1303720000 12.400000
## 3 CN CHN China 2000 2915.415 1262645000 14.030000
## 4 CN CHN China 1991 1091.449 1150780000 19.680000
## 5 CN CHN China 2013 12218.521 1357380000 12.080000
## 6 CN CHN China 1999 2649.745 1252735000 14.640000
## 7 CN CHN China 2014 13254.643 1364270000 12.400000
## 8 CN CHN China 2003 3933.599 1288400000 12.410000
## 9 CN CHN China 2004 4422.650 1296075000 12.290000
## 10 CN CHN China 1993 1453.159 1178440000 18.090000
## 11 CN CHN China 2009 8290.089 1331260000 12.130000
## 12 CN CHN China 2010 9238.755 1337705000 11.900000
## 13 CN CHN China 2011 10274.494 1344130000 11.930000
## 14 CN CHN China 1996 2060.030 1217550000 16.980000
## 15 CN CHN China 1997 2265.314 1230075000 16.570000
## 16 CN CHN China 1998 2446.150 1241935000 15.640000
## 17 CN CHN China 1990 979.979 1135185000 21.060000
## 18 CN CHN China 2001 3205.927 1271850000 13.380000
## 19 CN CHN China 2002 3527.352 1280400000 12.860000
## 20 CN CHN China 2006 5836.833 1311020000 12.090000
## 21 CN CHN China 1994 1659.313 1191835000 17.700000
## 22 CN CHN China 2008 7569.680 1324655000 12.140000
## 23 CN CHN China 1995 1859.830 1204855000 17.120000
## 24 CN CHN China 2012 11219.928 1350695000 12.100000
## 25 CN CHN China 2007 6807.112 1317885000 12.100000
## 26 DE DEU Germany 1997 24183.862 82034771 9.900000
## 27 DE DEU Germany 1990 19032.696 79433029 11.400000
## 28 DE DEU Germany 1996 23655.820 81914831 9.700000
## 29 DE DEU Germany 1992 21230.248 80624598 10.000000
## 30 DE DEU Germany 1993 21386.501 81156363 9.800000
## 31 DE DEU Germany 2003 29362.425 82534176 8.600000
## 32 DE DEU Germany 1995 23093.601 81678051 9.400000
## 33 DE DEU Germany 1994 22300.868 81438348 9.500000
## 34 DE DEU Germany 2010 39639.480 81776930 8.300000
## 35 DE DEU Germany 2007 36777.783 82266372 8.300000
## 36 DE DEU Germany 2005 32184.055 82469422 8.300000
## 37 DE DEU Germany 2013 44184.823 82132753 8.300000
## 38 DE DEU Germany 2009 37112.881 81902307 8.100000
## 39 DE DEU Germany 1998 24871.241 82047195 9.600000
## 40 DE DEU Germany 2011 42142.549 81797673 8.100000
## 41 DE DEU Germany 2001 27725.110 82349925 8.900000
## 42 DE DEU Germany 2002 28435.901 82488495 8.700000
## 43 DE DEU Germany 1991 20520.739 80013896 10.400000
## 44 DE DEU Germany 2006 34690.333 82376451 8.200000
## 45 DE DEU Germany 2004 30694.902 82516260 8.600000
## 46 DE DEU Germany 2012 43600.113 80425823 8.400000
## 47 DE DEU Germany 2014 46393.877 80982500 8.600000
## 48 DE DEU Germany 2008 38438.741 82110097 8.300000
## 49 DE DEU Germany 2000 26630.511 82211508 9.300000
## 50 DE DEU Germany 1999 25798.280 82100243 9.400000
## 51 JP JPN Japan 1992 21052.319 124229000 9.800000
## 52 JP JPN Japan 2002 27241.103 127445000 9.300000
## 53 JP JPN Japan 2008 33495.178 128063000 8.700000
## 54 JP JPN Japan 2001 26559.532 127149000 9.300000
## 55 JP JPN Japan 2007 33313.865 128001000 8.630000
## 56 JP JPN Japan 1998 24328.820 126400000 9.600000
## 57 JP JPN Japan 1993 21536.856 124536000 9.600000
## 58 JP JPN Japan 1999 24606.921 126631000 9.300000
## 59 JP JPN Japan 1997 24626.417 126057000 9.500000
## 60 JP JPN Japan 2005 30441.348 127773000 8.413292
## 61 JP JPN Japan 2014 36577.211 127131800 8.000000
## 62 JP JPN Japan 2004 29377.344 127761000 8.693605
## 63 JP JPN Japan 2000 25938.198 126843000 9.400000
## 64 JP JPN Japan 2012 35735.617 127561489 8.200000
## 65 JP JPN Japan 1991 20466.647 123921000 9.900000
## 66 JP JPN Japan 2010 33760.977 128070000 8.500000
## 67 JP JPN Japan 2011 34335.304 127817277 8.300000
## 68 JP JPN Japan 2009 31857.372 128047000 8.500000
## 69 JP JPN Japan 2013 36618.306 127338621 8.200000
## 70 JP JPN Japan 1996 23888.613 125757000 9.600000
## 71 JP JPN Japan 1990 19229.666 123537000 10.000000
## 72 JP JPN Japan 1994 22109.681 124961000 10.000000
## 73 JP JPN Japan 2003 27941.176 127718000 9.200000
## 74 JP JPN Japan 2006 31790.655 127854000 8.650000
## 75 JP JPN Japan 1995 22921.541 125439000 9.540000
## 76 US USA United States 2001 37273.618 284968955 14.100000
## 77 US USA United States 2008 48401.427 304093966 14.000000
## 78 US USA United States 2002 38166.038 287625193 14.000000
## 79 US USA United States 1999 34620.929 279040000 14.200000
## 80 US USA United States 2009 47001.555 306771529 13.500000
## 81 US USA United States 2007 48061.538 301231207 14.300000
## 82 US USA United States 2003 39677.198 290107933 14.100000
## 83 US USA United States 2000 36449.855 282162411 14.400000
## 84 US USA United States 1998 32949.198 275854000 14.300000
## 85 US USA United States 1996 30068.231 269394000 14.400000
## 86 US USA United States 1990 23954.479 249623000 16.700000
## 87 US USA United States 1991 24405.165 252981000 16.200000
## 88 US USA United States 2012 51433.047 314102623 12.600000
## 89 US USA United States 2013 52660.295 316427395 12.400000
## 90 US USA United States 2010 48374.087 309346863 13.000000
## 91 US USA United States 1997 31572.690 272657000 14.200000
## 92 US USA United States 1992 25492.952 256514000 15.800000
## 93 US USA United States 1993 26464.853 259919000 15.400000
## 94 US USA United States 2006 46437.067 298379912 14.300000
## 95 US USA United States 2014 54398.460 318907401 12.500000
## 96 US USA United States 2011 49781.801 311718857 12.700000
## 97 US USA United States 2004 41921.810 292805298 14.000000
## 98 US USA United States 1994 27776.636 263126000 15.000000
## 99 US USA United States 1995 28782.175 266278000 14.600000
## 100 US USA United States 2005 44307.921 295516599 14.000000
## neonat_mortal_rate region income gdp_trillions
## 1 29.4 East Asia & Pacific Upper middle income 1.468052
## 2 14.0 East Asia & Pacific Upper middle income 6.588191
## 3 21.2 East Asia & Pacific Upper middle income 3.681134
## 4 29.7 East Asia & Pacific Upper middle income 1.256017
## 5 6.3 East Asia & Pacific Upper middle income 16.585176
## 6 22.2 East Asia & Pacific Upper middle income 3.319429
## 7 5.9 East Asia & Pacific Upper middle income 18.082912
## 8 17.1 East Asia & Pacific Upper middle income 5.068050
## 9 15.5 East Asia & Pacific Upper middle income 5.732087
## 10 28.8 East Asia & Pacific Upper middle income 1.712461
## 11 9.1 East Asia & Pacific Upper middle income 11.036264
## 12 8.2 East Asia & Pacific Upper middle income 12.358729
## 13 7.5 East Asia & Pacific Upper middle income 13.810256
## 14 25.7 East Asia & Pacific Upper middle income 2.508189
## 15 24.4 East Asia & Pacific Upper middle income 2.786506
## 16 23.3 East Asia & Pacific Upper middle income 3.037959
## 17 29.7 East Asia & Pacific Upper middle income 1.112457
## 18 20.1 East Asia & Pacific Upper middle income 4.077459
## 19 18.6 East Asia & Pacific Upper middle income 4.516421
## 20 12.6 East Asia & Pacific Upper middle income 7.652205
## 21 28.0 East Asia & Pacific Upper middle income 1.977627
## 22 10.1 East Asia & Pacific Upper middle income 10.027214
## 23 26.9 East Asia & Pacific Upper middle income 2.240826
## 24 6.9 East Asia & Pacific Upper middle income 15.154700
## 25 11.3 East Asia & Pacific Upper middle income 8.970991
## 26 2.9 Europe & Central Asia High income: OECD 1.983918
## 27 3.4 Europe & Central Asia High income: OECD 1.511825
## 28 3.0 Europe & Central Asia High income: OECD 1.937762
## 29 3.5 Europe & Central Asia High income: OECD 1.711680
## 30 3.3 Europe & Central Asia High income: OECD 1.735651
## 31 2.7 Europe & Central Asia High income: OECD 2.423404
## 32 3.1 Europe & Central Asia High income: OECD 1.886240
## 33 3.2 Europe & Central Asia High income: OECD 1.816146
## 34 2.3 Europe & Central Asia High income: OECD 3.241595
## 35 2.5 Europe & Central Asia High income: OECD 3.025575
## 36 2.6 Europe & Central Asia High income: OECD 2.654200
## 37 2.2 Europe & Central Asia High income: OECD 3.629021
## 38 2.4 Europe & Central Asia High income: OECD 3.039631
## 39 2.9 Europe & Central Asia High income: OECD 2.040616
## 40 2.3 Europe & Central Asia High income: OECD 3.447162
## 41 2.7 Europe & Central Asia High income: OECD 2.283161
## 42 2.7 Europe & Central Asia High income: OECD 2.345635
## 43 3.5 Europe & Central Asia High income: OECD 1.641944
## 44 2.6 Europe & Central Asia High income: OECD 2.857667
## 45 2.6 Europe & Central Asia High income: OECD 2.532829
## 46 2.3 Europe & Central Asia High income: OECD 3.506575
## 47 2.2 Europe & Central Asia High income: OECD 3.757092
## 48 2.4 Europe & Central Asia High income: OECD 3.156209
## 49 2.8 Europe & Central Asia High income: OECD 2.189334
## 50 2.8 Europe & Central Asia High income: OECD 2.118045
## 51 2.4 East Asia & Pacific High income: OECD 2.615309
## 52 1.7 East Asia & Pacific High income: OECD 3.471742
## 53 1.2 East Asia & Pacific High income: OECD 4.289493
## 54 1.7 East Asia & Pacific High income: OECD 3.377018
## 55 1.3 East Asia & Pacific High income: OECD 4.264208
## 56 1.9 East Asia & Pacific High income: OECD 3.075163
## 57 2.3 East Asia & Pacific High income: OECD 2.682114
## 58 1.8 East Asia & Pacific High income: OECD 3.115999
## 59 2.0 East Asia & Pacific High income: OECD 3.104332
## 60 1.4 East Asia & Pacific High income: OECD 3.889582
## 61 1.0 East Asia & Pacific High income: OECD 4.650127
## 62 1.5 East Asia & Pacific High income: OECD 3.753279
## 63 1.8 East Asia & Pacific High income: OECD 3.290079
## 64 1.0 East Asia & Pacific High income: OECD 4.558488
## 65 2.5 East Asia & Pacific High income: OECD 2.536247
## 66 1.1 East Asia & Pacific High income: OECD 4.323768
## 67 1.0 East Asia & Pacific High income: OECD 4.388645
## 68 1.2 East Asia & Pacific High income: OECD 4.079241
## 69 1.0 East Asia & Pacific High income: OECD 4.662925
## 70 2.1 East Asia & Pacific High income: OECD 3.004160
## 71 2.5 East Asia & Pacific High income: OECD 2.375575
## 72 2.2 East Asia & Pacific High income: OECD 2.762848
## 73 1.6 East Asia & Pacific High income: OECD 3.568591
## 74 1.3 East Asia & Pacific High income: OECD 4.064562
## 75 2.2 East Asia & Pacific High income: OECD 2.875255
## 76 4.6 North America High income: OECD 10.621824
## 77 4.3 North America High income: OECD 14.718582
## 78 4.6 North America High income: OECD 10.977514
## 79 4.6 North America High income: OECD 9.660624
## 80 4.2 North America High income: OECD 14.418739
## 81 4.3 North America High income: OECD 14.477635
## 82 4.6 North America High income: OECD 11.510670
## 83 4.6 North America High income: OECD 10.284779
## 84 4.7 North America High income: OECD 9.089168
## 85 4.9 North America High income: OECD 8.100201
## 86 5.8 North America High income: OECD 5.979589
## 87 5.6 North America High income: OECD 6.174043
## 88 3.9 North America High income: OECD 16.155255
## 89 3.8 North America High income: OECD 16.663160
## 90 4.1 North America High income: OECD 14.964372
## 91 4.8 North America High income: OECD 8.608515
## 92 5.4 North America High income: OECD 6.539299
## 93 5.2 North America High income: OECD 6.878718
## 94 4.4 North America High income: OECD 13.855888
## 95 3.7 North America High income: OECD 17.348072
## 96 4.0 North America High income: OECD 15.517926
## 97 4.5 North America High income: OECD 12.274928
## 98 5.1 North America High income: OECD 7.308755
## 99 5.0 North America High income: OECD 7.664060
## 100 4.5 North America High income: OECD 13.093726
ggplot(desired_countries2, aes(x = year, y = gdp_trillions, color = country)) +
geom_point() +
geom_line() +
scale_color_brewer(palette = "Set1") +
labs (title = "GDP per capita over time",
x = "year",
y = "GDP per capita in trillions")
Gdp_world <- nations_gdp_t %>%
group_by(year, region) %>%
summarize(total_GDP = sum(gdp_trillions, na.rm = TRUE))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Gdp_world
## # A tibble: 175 × 3
## # Groups: year [25]
## year region total_GDP
## <int> <chr> <dbl>
## 1 1990 East Asia & Pacific 5.52
## 2 1990 Europe & Central Asia 9.36
## 3 1990 Latin America & Caribbean 2.40
## 4 1990 Middle East & North Africa 1.66
## 5 1990 North America 6.54
## 6 1990 South Asia 1.35
## 7 1990 Sub-Saharan Africa 0.787
## 8 1991 East Asia & Pacific 6.03
## 9 1991 Europe & Central Asia 9.71
## 10 1991 Latin America & Caribbean 2.55
## # ℹ 165 more rows
ggplot(Gdp_world, aes(x = year, y = total_GDP, fill = region)) +
geom_area(color = "white", size = .7) +
scale_color_brewer(palette = "Set2") +
labs(title = "Total GDP Over Time by Region",
x = "Year",
y = "Total GDP (Trillions of dollars)")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
