- 1 Lab 4 Deliverable
- 1.1 Part 1: Tidying
- 1.2 Cleaning the fields
- 1.3 Binding rows
- 1.4 Converting years into “narrow” format
- 1.5 Making a graph
- 1.6 Part 2: Gapminder
- 1.7 The maximum and minimum GDP per capita for all continents:
- 1.8 Changes in GDP per capita over time on all continents
- 1.9 The spread of GDP per capita across countries within the continents
- 1.10 How does life expectancy vary across different continents?
- 1.11 Making table of countries, where life expectancy under 40 years old
- 2 Finding countries, where life exepctancy lower than worldwide median
- 2.1 The correlation between GDP per capita and life expectancy across African countries
- 2.2 The graph of GDP changes over time in African countries
- 2.3 Life Expectancy changes over years in African countries
- 2.4 Correlation between GDP nad Life Expectancy in African countries
- 2.5 Life Expectancy changes over years in European countries
- 2.6 Correlation between GDP nad Life Expectancy in European countries
Lab 4 Deliverable
Mike Kolesnikov
4/22/2020
-- Attaching packages -------------------------------------- tidyverse 1.2.1 --v ggplot2 3.3.0 v purrr 0.3.2
v tibble 2.1.3 v dplyr 0.8.3
v tidyr 1.0.2 v stringr 1.4.0
v readr 1.3.1 v forcats 0.4.0Warning: package 'ggplot2' was built under R version 3.6.3Warning: package 'tidyr' was built under R version 3.6.3-- Conflicts ----------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Warning: package 'gapminder' was built under R version 3.6.3Warning: package 'ggbeeswarm' was built under R version 3.6.3Warning: package 'skimr' was built under R version 3.6.3Warning: package 'janitor' was built under R version 3.6.3
Attaching package: 'janitor'The following objects are masked from 'package:stats':
chisq.test, fisher.testWarning: package 'ggridges' was built under R version 3.6.3data<-read_csv(here::here("downloads", "data.csv"))Parsed with column specification:
cols(
`Fixed broadband Internet subscribers (per 100 people)` = col_character(),
`1998` = col_double(),
`1999` = col_double(),
`2000` = col_double(),
`2001` = col_double(),
`2002` = col_double(),
`2003` = col_double(),
`2004` = col_double(),
`2005` = col_double(),
`2006` = col_double(),
`2007` = col_double(),
`2008` = col_double(),
`2009` = col_double(),
`2010` = col_double(),
`2011` = col_logical()
)glimpse(data)Observations: 213
Variables: 15
$ `Fixed broadband Internet subscribers (per 100 people)` <chr> "Afgha...
$ `1998` <dbl> NA, NA...
$ `1999` <dbl> NA, NA...
$ `2000` <dbl> NA, NA...
$ `2001` <dbl> 0.0000...
$ `2002` <dbl> 0.0000...
$ `2003` <dbl> 0.0000...
$ `2004` <dbl> 6.8802...
$ `2005` <dbl> 7.3566...
$ `2006` <dbl> 0.0016...
$ `2007` <dbl> 0.0015...
$ `2008` <dbl> 0.0015...
$ `2009` <dbl> 0.0029...
$ `2010` <dbl> 0.0043...
$ `2011` <lgl> NA, NA...1 Lab 4 Deliverable
1.1 Part 1: Tidying
data_tidy<- data %>%
bind_rows()
glimpse(data_tidy)Observations: 213
Variables: 15
$ `Fixed broadband Internet subscribers (per 100 people)` <chr> "Afgha...
$ `1998` <dbl> NA, NA...
$ `1999` <dbl> NA, NA...
$ `2000` <dbl> NA, NA...
$ `2001` <dbl> 0.0000...
$ `2002` <dbl> 0.0000...
$ `2003` <dbl> 0.0000...
$ `2004` <dbl> 6.8802...
$ `2005` <dbl> 7.3566...
$ `2006` <dbl> 0.0016...
$ `2007` <dbl> 0.0015...
$ `2008` <dbl> 0.0015...
$ `2009` <dbl> 0.0029...
$ `2010` <dbl> 0.0043...
$ `2011` <lgl> NA, NA...skim(data_tidy) %>%
summary()| Name | data_tidy |
| Number of rows | 213 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| logical | 1 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
1.2 Cleaning the fields
names(data_tidy)[1] <- "country"
names(data_tidy)[2] <- "1998"
names(data_tidy)[3] <- "1999"
names(data_tidy)[4] <- "2000"
names(data_tidy)[5] <- "2001"
names(data_tidy)[6] <- "2002"
names(data_tidy)[7] <- "2003"
names(data_tidy)[8] <- "2004"
names(data_tidy)[9] <- "2005"
names(data_tidy)[10] <- "2006"
names(data_tidy)[11] <- "2007"
names(data_tidy)[12] <- "2008"
names(data_tidy)[13] <- "2009"
names(data_tidy)[14] <- "2010"
names(data_tidy)[15] <- "2011"
glimpse(data_tidy)Observations: 213
Variables: 15
$ country <chr> "Afghanistan", "Albania", "Algeria", "American Samoa",...
$ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, 0.0000000, NA, NA, NA, 0.0...
$ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.6368611,...
$ `2000` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.37781227...
$ `2001` <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000...
$ `2002` <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000...
$ `2003` <dbl> 0.000000e+00, 0.000000e+00, 5.640253e-02, NA, 4.987327...
$ `2004` <dbl> 6.880265e-04, 0.000000e+00, 1.111247e-01, NA, 8.343516...
$ `2005` <dbl> 7.356639e-04, 8.657458e-03, 4.104785e-01, NA, 1.327676...
$ `2006` <dbl> 0.001625928, NA, 0.509104678, NA, 18.298820640, 0.0438...
$ `2007` <dbl> 0.001581161, 0.315490754, 0.846557772, NA, 22.76201007...
$ `2008` <dbl> 0.001537626, 2.011694862, 1.408735929, NA, 25.03360500...
$ `2009` <dbl> 0.00299058, 2.88155283, 2.34047516, NA, 27.41613586, 0...
$ `2010` <dbl> 0.004362367, 3.292324493, 2.537498590, NA, 28.87207768...
$ `2011` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...group_by(data_tidy, country)# A tibble: 213 x 15
# Groups: country [213]
country `1998` `1999` `2000` `2001` `2002` `2003` `2004` `2005`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghan~ NA NA NA 0. 0. 0. 6.88e-4 7.36e-4
2 Albania NA NA NA 0. 0. 0. 0. 8.66e-3
3 Algeria NA NA NA 0. 0. 5.64e-2 1.11e-1 4.10e-1
4 Americ~ NA NA NA 0. 0. NA NA NA
5 Andorra NA NA NA NA 1.66e+0 4.99e+0 8.34e+0 1.33e+1
6 Angola NA NA NA 0. 0. 0. 0. 0.
7 Antigu~ NA NA NA 0. 0. 0. 1.21e-1 9.38e-1
8 Argent~ 0 NA NA 2.52e-1 3.93e-1 6.81e-1 1.42e+0 2.40e+0
9 Armenia NA NA NA 1.96e-4 2.61e-4 3.27e-4 3.27e-2 6.49e-2
10 Aruba NA NA NA 0. 0. 1.44e+0 7.03e+0 1.22e+1
# ... with 203 more rows, and 6 more variables: `2006` <dbl>,
# `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <lgl>skim(data_tidy)| Name | data_tidy |
| Number of rows | 213 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| logical | 1 |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 4 | 32 | 0 | 213 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| 2011 | 213 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| 1998 | 156 | 0.27 | 0.03 | 0.10 | 0 | 0.00 | 0.00 | 0.00 | 0.48 | ▇▁▁▁▁ |
| 1999 | 171 | 0.20 | 0.16 | 0.40 | 0 | 0.00 | 0.00 | 0.01 | 1.91 | ▇▁▁▁▁ |
| 2000 | 146 | 0.31 | 0.60 | 1.47 | 0 | 0.00 | 0.01 | 0.38 | 8.23 | ▇▁▁▁▁ |
| 2001 | 20 | 0.91 | 0.53 | 1.80 | 0 | 0.00 | 0.00 | 0.07 | 16.48 | ▇▁▁▁▁ |
| 2002 | 11 | 0.95 | 0.98 | 2.66 | 0 | 0.00 | 0.00 | 0.34 | 21.84 | ▇▁▁▁▁ |
| 2003 | 21 | 0.90 | 1.69 | 3.74 | 0 | 0.00 | 0.03 | 0.89 | 23.35 | ▇▁▁▁▁ |
| 2004 | 23 | 0.89 | 2.71 | 5.09 | 0 | 0.00 | 0.11 | 2.22 | 24.81 | ▇▁▁▁▁ |
| 2005 | 18 | 0.92 | 4.02 | 7.00 | 0 | 0.01 | 0.43 | 3.36 | 29.08 | ▇▁▁▁▁ |
| 2006 | 27 | 0.87 | 5.58 | 8.68 | 0 | 0.03 | 1.04 | 5.89 | 37.06 | ▇▁▁▁▁ |
| 2007 | 13 | 0.94 | 6.34 | 9.70 | 0 | 0.05 | 1.47 | 8.25 | 45.00 | ▇▁▁▁▁ |
| 2008 | 15 | 0.93 | 7.76 | 10.93 | 0 | 0.09 | 2.35 | 10.73 | 55.18 | ▇▁▁▁▁ |
| 2009 | 23 | 0.89 | 9.20 | 12.02 | 0 | 0.21 | 3.58 | 13.36 | 62.11 | ▇▂▁▁▁ |
| 2010 | 17 | 0.92 | 9.71 | 12.33 | 0 | 0.30 | 4.25 | 14.71 | 63.83 | ▇▂▂▁▁ |
glimpse(data_tidy)Observations: 213
Variables: 15
$ country <chr> "Afghanistan", "Albania", "Algeria", "American Samoa",...
$ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, 0.0000000, NA, NA, NA, 0.0...
$ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.6368611,...
$ `2000` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.37781227...
$ `2001` <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000...
$ `2002` <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0000000000...
$ `2003` <dbl> 0.000000e+00, 0.000000e+00, 5.640253e-02, NA, 4.987327...
$ `2004` <dbl> 6.880265e-04, 0.000000e+00, 1.111247e-01, NA, 8.343516...
$ `2005` <dbl> 7.356639e-04, 8.657458e-03, 4.104785e-01, NA, 1.327676...
$ `2006` <dbl> 0.001625928, NA, 0.509104678, NA, 18.298820640, 0.0438...
$ `2007` <dbl> 0.001581161, 0.315490754, 0.846557772, NA, 22.76201007...
$ `2008` <dbl> 0.001537626, 2.011694862, 1.408735929, NA, 25.03360500...
$ `2009` <dbl> 0.00299058, 2.88155283, 2.34047516, NA, 27.41613586, 0...
$ `2010` <dbl> 0.004362367, 3.292324493, 2.537498590, NA, 28.87207768...
$ `2011` <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...1.3 Binding rows
data_tidy %>%
bind_rows()# A tibble: 213 x 15
country `1998` `1999` `2000` `2001` `2002` `2003` `2004` `2005`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghan~ NA NA NA 0. 0. 0. 6.88e-4 7.36e-4
2 Albania NA NA NA 0. 0. 0. 0. 8.66e-3
3 Algeria NA NA NA 0. 0. 5.64e-2 1.11e-1 4.10e-1
4 Americ~ NA NA NA 0. 0. NA NA NA
5 Andorra NA NA NA NA 1.66e+0 4.99e+0 8.34e+0 1.33e+1
6 Angola NA NA NA 0. 0. 0. 0. 0.
7 Antigu~ NA NA NA 0. 0. 0. 1.21e-1 9.38e-1
8 Argent~ 0 NA NA 2.52e-1 3.93e-1 6.81e-1 1.42e+0 2.40e+0
9 Armenia NA NA NA 1.96e-4 2.61e-4 3.27e-4 3.27e-2 6.49e-2
10 Aruba NA NA NA 0. 0. 1.44e+0 7.03e+0 1.22e+1
# ... with 203 more rows, and 6 more variables: `2006` <dbl>,
# `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <lgl>1.4 Converting years into “narrow” format
data_years <- data_tidy %>%
gather(key="year", value="number_of_users", "1998":"2011")
glimpse(data_years)Observations: 2,982
Variables: 3
$ country <chr> "Afghanistan", "Albania", "Algeria", "American...
$ year <chr> "1998", "1998", "1998", "1998", "1998", "1998"...
$ number_of_users <dbl> NA, NA, NA, NA, NA, NA, NA, 0.0000000, NA, NA,...data_years %>%
group_by(country) %>%
select_if(is.numeric)%>%
skim()| Name | Piped data |
| Number of rows | 2982 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | country |
Variable type: numeric
| skim_variable | country | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| number_of_users | Afghanistan | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▅▁▂▂ |
| number_of_users | Albania | 5 | 0.64 | 0.95 | 1.38 | 0.00 | 0.00 | 0.01 | 2.01 | 3.29 | ▇▁▁▁▂ |
| number_of_users | Algeria | 4 | 0.71 | 0.82 | 0.96 | 0.00 | 0.07 | 0.46 | 1.27 | 2.54 | ▇▃▂▁▃ |
| number_of_users | American Samoa | 12 | 0.14 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Andorra | 5 | 0.64 | 16.74 | 10.10 | 1.66 | 8.34 | 18.30 | 25.03 | 28.87 | ▅▂▂▅▇ |
| number_of_users | Angola | 4 | 0.71 | 0.04 | 0.05 | 0.00 | 0.00 | 0.02 | 0.08 | 0.11 | ▇▁▂▂▅ |
| number_of_users | Antigua and Barbuda | 4 | 0.71 | 2.66 | 3.19 | 0.00 | 0.03 | 1.38 | 4.96 | 8.09 | ▇▃▁▂▃ |
| number_of_users | Argentina | 3 | 0.79 | 3.82 | 3.72 | 0.00 | 0.54 | 2.40 | 7.31 | 9.56 | ▇▂▂▂▅ |
| number_of_users | Armenia | 5 | 0.64 | 0.48 | 0.91 | 0.00 | 0.00 | 0.06 | 0.36 | 2.75 | ▇▁▁▁▁ |
| number_of_users | Aruba | 4 | 0.71 | 10.24 | 7.47 | 0.00 | 2.84 | 12.87 | 16.91 | 17.79 | ▆▂▁▃▇ |
| number_of_users | Australia | 5 | 0.64 | 12.18 | 10.29 | 0.63 | 2.60 | 9.88 | 23.20 | 24.15 | ▇▂▁▂▆ |
| number_of_users | Austria | 1 | 0.93 | 11.42 | 8.65 | 0.00 | 3.99 | 10.65 | 19.54 | 23.86 | ▇▃▃▂▇ |
| number_of_users | Azerbaijan | 6 | 0.57 | 0.89 | 1.74 | 0.00 | 0.02 | 0.11 | 0.79 | 5.08 | ▇▁▁▁▁ |
| number_of_users | Bahamas | 4 | 0.71 | 5.12 | 2.48 | 1.37 | 3.66 | 4.65 | 6.96 | 9.34 | ▅▇▂▇▂ |
| number_of_users | Bahrain | 1 | 0.93 | 2.98 | 2.88 | 0.00 | 0.18 | 2.23 | 5.36 | 7.30 | ▇▃▂▃▅ |
| number_of_users | Bangladesh | 5 | 0.64 | 0.02 | 0.02 | 0.00 | 0.00 | 0.00 | 0.03 | 0.04 | ▇▁▁▂▅ |
| number_of_users | Barbados | 4 | 0.71 | 12.42 | 7.57 | 0.00 | 10.35 | 13.06 | 17.90 | 21.01 | ▃▁▆▂▇ |
| number_of_users | Belarus | 4 | 0.71 | 3.59 | 6.14 | 0.00 | 0.00 | 0.07 | 4.17 | 17.55 | ▇▁▁▁▁ |
| number_of_users | Belgium | 1 | 0.93 | 15.17 | 11.56 | 0.11 | 4.47 | 15.54 | 25.55 | 30.96 | ▇▃▂▃▇ |
| number_of_users | Belize | 4 | 0.71 | 1.55 | 1.10 | 0.00 | 0.51 | 2.06 | 2.47 | 2.58 | ▅▂▁▂▇ |
| number_of_users | Benin | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.01 | 0.02 | 0.04 | ▇▃▃▁▂ |
| number_of_users | Bermuda | 6 | 0.57 | 36.03 | 24.98 | 0.00 | 21.81 | 41.03 | 55.19 | 62.11 | ▅▁▅▂▇ |
| number_of_users | Bhutan | 4 | 0.71 | 0.19 | 0.38 | 0.00 | 0.00 | 0.00 | 0.22 | 1.19 | ▇▂▁▁▁ |
| number_of_users | Bolivia | 4 | 0.71 | 0.37 | 0.40 | 0.00 | 0.07 | 0.17 | 0.71 | 0.98 | ▇▁▁▁▃ |
| number_of_users | Bosnia and Herzegovina | 1 | 0.93 | 1.79 | 2.82 | 0.00 | 0.00 | 0.18 | 2.24 | 8.18 | ▇▁▁▂▁ |
| number_of_users | Botswana | 3 | 0.79 | 0.17 | 0.23 | 0.00 | 0.00 | 0.09 | 0.32 | 0.60 | ▇▁▁▁▂ |
| number_of_users | Brazil | 1 | 0.93 | 2.22 | 2.43 | 0.00 | 0.19 | 1.72 | 4.01 | 6.81 | ▇▃▁▁▂ |
| number_of_users | Brunei | 4 | 0.71 | 2.69 | 1.77 | 0.57 | 1.27 | 2.33 | 4.06 | 5.44 | ▇▇▂▂▅ |
| number_of_users | Bulgaria | 4 | 0.71 | 5.32 | 5.74 | 0.00 | 0.02 | 3.57 | 10.11 | 14.44 | ▇▂▂▂▃ |
| number_of_users | Burkina Faso | 4 | 0.71 | 0.03 | 0.04 | 0.00 | 0.00 | 0.01 | 0.06 | 0.09 | ▇▁▁▁▂ |
| number_of_users | Burundi | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▁▁▁▃ |
| number_of_users | Cambodia | 4 | 0.71 | 0.07 | 0.10 | 0.00 | 0.00 | 0.01 | 0.11 | 0.25 | ▇▁▁▁▂ |
| number_of_users | Cameroon | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | ▇▂▂▂▆ |
| number_of_users | Canada | 1 | 0.93 | 17.08 | 11.00 | 0.46 | 9.12 | 16.93 | 27.56 | 30.46 | ▅▃▃▂▇ |
| number_of_users | Cape Verde | 4 | 0.71 | 1.06 | 1.18 | 0.00 | 0.02 | 0.86 | 1.64 | 3.22 | ▇▁▅▂▂ |
| number_of_users | Cayman Islands | 9 | 0.36 | 19.61 | 17.91 | 0.00 | 0.00 | 31.70 | 32.81 | 33.53 | ▅▁▁▁▇ |
| number_of_users | Central African Rep. | 6 | 0.57 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Chad | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▁▁▁▂ |
| number_of_users | Channel Islands | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Chile | 2 | 0.86 | 4.49 | 3.90 | 0.00 | 1.00 | 3.66 | 7.93 | 10.45 | ▇▃▃▂▆ |
| number_of_users | China | 2 | 0.86 | 3.20 | 3.30 | 0.00 | 0.20 | 2.39 | 5.34 | 9.44 | ▇▃▃▂▃ |
| number_of_users | Colombia | 2 | 0.86 | 1.57 | 1.96 | 0.00 | 0.07 | 0.52 | 2.86 | 5.60 | ▇▁▂▁▁ |
| number_of_users | Comoros | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 | ▇▁▂▁▁ |
| number_of_users | Congo, Dem. Rep. | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | ▇▁▁▁▂ |
| number_of_users | Congo, Rep. | 5 | 0.64 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▁▁▁▁ |
| number_of_users | Costa Rica | 4 | 0.71 | 1.88 | 1.94 | 0.00 | 0.43 | 1.47 | 2.31 | 6.19 | ▇▅▁▂▂ |
| number_of_users | Cote d’Ivoire | 4 | 0.71 | 0.03 | 0.03 | 0.00 | 0.00 | 0.02 | 0.05 | 0.05 | ▇▁▁▂▆ |
| number_of_users | Croatia | 4 | 0.71 | 6.32 | 6.89 | 0.00 | 0.20 | 4.14 | 11.06 | 18.19 | ▇▂▂▂▃ |
| number_of_users | Cuba | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.03 | ▇▁▁▂▁ |
| number_of_users | Cyprus | 4 | 0.71 | 6.93 | 6.77 | 0.26 | 1.17 | 4.55 | 12.55 | 17.63 | ▇▂▂▂▃ |
| number_of_users | Czech Rep. | 1 | 0.93 | 6.11 | 6.81 | 0.00 | 0.06 | 2.31 | 12.92 | 16.88 | ▇▁▁▂▃ |
| number_of_users | Denmark | 2 | 0.86 | 20.68 | 14.78 | 0.00 | 7.41 | 21.81 | 34.88 | 37.72 | ▅▃▂▂▇ |
| number_of_users | Djibouti | 5 | 0.64 | 0.22 | 0.33 | 0.00 | 0.00 | 0.02 | 0.29 | 0.91 | ▇▁▁▁▁ |
| number_of_users | Dominica | 2 | 0.86 | 5.77 | 4.79 | 0.00 | 2.49 | 4.82 | 9.80 | 13.81 | ▆▇▂▃▃ |
| number_of_users | Dominican Rep. | 4 | 0.71 | 1.29 | 1.31 | 0.00 | 0.23 | 0.87 | 2.16 | 3.63 | ▇▂▂▂▃ |
| number_of_users | Ecuador | 4 | 0.71 | 0.56 | 0.62 | 0.02 | 0.06 | 0.27 | 1.01 | 1.69 | ▇▁▁▁▂ |
| number_of_users | Egypt | 4 | 0.71 | 0.55 | 0.62 | 0.00 | 0.08 | 0.27 | 0.89 | 1.79 | ▇▁▁▁▁ |
| number_of_users | El Salvador | 4 | 0.71 | 1.13 | 1.02 | 0.00 | 0.37 | 0.86 | 1.88 | 2.83 | ▇▃▂▂▃ |
| number_of_users | Equatorial Guinea | 4 | 0.71 | 0.03 | 0.05 | 0.00 | 0.00 | 0.03 | 0.03 | 0.17 | ▇▁▁▁▁ |
| number_of_users | Eritrea | 1 | 0.93 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▁▁▁▁ |
| number_of_users | Estonia | 4 | 0.71 | 14.07 | 8.35 | 1.28 | 7.57 | 15.84 | 20.45 | 25.10 | ▅▅▂▅▇ |
| number_of_users | Ethiopia | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▂▁▁▂ |
| number_of_users | Faeroe Islands | 4 | 0.71 | 17.34 | 14.62 | 0.12 | 2.26 | 19.62 | 30.41 | 33.40 | ▆▂▁▁▇ |
| number_of_users | Fiji | 4 | 0.71 | 0.90 | 0.91 | 0.00 | 0.00 | 0.94 | 1.47 | 2.70 | ▇▃▆▁▂ |
| number_of_users | Finland | 2 | 0.86 | 16.72 | 12.49 | 0.00 | 4.59 | 18.84 | 28.61 | 30.57 | ▆▂▂▂▇ |
| number_of_users | France | 1 | 0.93 | 13.20 | 12.67 | 0.02 | 0.98 | 10.49 | 24.68 | 32.89 | ▇▁▁▂▃ |
| number_of_users | French Polynesia | 4 | 0.71 | 5.62 | 4.88 | 0.01 | 0.74 | 5.69 | 10.10 | 11.90 | ▇▂▂▂▆ |
| number_of_users | Gabon | 4 | 0.71 | 0.11 | 0.10 | 0.00 | 0.02 | 0.12 | 0.15 | 0.27 | ▇▁▇▁▃ |
| number_of_users | Gambia | 5 | 0.64 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.02 | ▇▂▁▁▇ |
| number_of_users | Georgia | 4 | 0.71 | 1.35 | 1.94 | 0.01 | 0.04 | 0.33 | 2.18 | 5.70 | ▇▁▂▁▁ |
| number_of_users | Germany | 2 | 0.86 | 13.81 | 12.10 | 0.00 | 3.55 | 10.78 | 24.84 | 31.90 | ▇▂▃▂▅ |
| number_of_users | Ghana | 4 | 0.71 | 0.06 | 0.07 | 0.00 | 0.00 | 0.03 | 0.09 | 0.21 | ▇▃▃▁▂ |
| number_of_users | Gibraltar | 10 | 0.29 | 16.86 | 19.54 | 0.00 | 0.00 | 15.89 | 32.75 | 35.68 | ▇▁▁▁▇ |
| number_of_users | Greece | 1 | 0.93 | 5.06 | 7.29 | 0.00 | 0.00 | 0.47 | 9.09 | 19.95 | ▇▁▁▁▂ |
| number_of_users | Greenland | 4 | 0.71 | 11.85 | 9.48 | 0.00 | 1.62 | 14.38 | 20.54 | 22.15 | ▆▂▂▂▇ |
| number_of_users | Grenada | 5 | 0.64 | 5.67 | 5.08 | 0.00 | 0.59 | 5.38 | 8.29 | 13.88 | ▇▅▅▁▅ |
| number_of_users | Guam | 9 | 0.36 | 1.51 | 0.24 | 1.11 | 1.54 | 1.56 | 1.68 | 1.69 | ▃▁▁▇▇ |
| number_of_users | Guatemala | 4 | 0.71 | 0.45 | 0.60 | 0.00 | 0.00 | 0.26 | 0.62 | 1.80 | ▇▂▁▁▁ |
| number_of_users | Guinea | 5 | 0.64 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | ▇▁▁▁▁ |
| number_of_users | Guinea-Bissau | 6 | 0.57 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Guyana | 5 | 0.64 | 0.41 | 0.52 | 0.00 | 0.00 | 0.27 | 0.68 | 1.48 | ▇▂▂▂▂ |
| number_of_users | Haiti | 6 | 0.57 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Honduras | 5 | 0.64 | 0.11 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| number_of_users | Hong Kong, China | 1 | 0.93 | 18.45 | 10.62 | 0.17 | 10.67 | 22.37 | 27.42 | 29.87 | ▂▂▁▂▇ |
| number_of_users | Hungary | 2 | 0.86 | 7.94 | 7.72 | 0.00 | 0.90 | 5.26 | 14.49 | 19.56 | ▇▃▁▃▅ |
| number_of_users | Iceland | 1 | 0.93 | 17.97 | 13.98 | 0.00 | 3.66 | 19.09 | 31.43 | 34.34 | ▆▂▃▂▇ |
| number_of_users | India | 1 | 0.93 | 0.20 | 0.29 | 0.00 | 0.00 | 0.02 | 0.27 | 0.90 | ▇▂▁▁▁ |
| number_of_users | Indonesia | 2 | 0.86 | 0.21 | 0.29 | 0.00 | 0.01 | 0.04 | 0.36 | 0.79 | ▇▁▂▁▂ |
| number_of_users | Iran | 5 | 0.64 | 0.23 | 0.26 | 0.00 | 0.02 | 0.14 | 0.41 | 0.68 | ▇▂▂▂▃ |
| number_of_users | Iraq | 7 | 0.50 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▃▃▃▇ |
| number_of_users | Ireland | 1 | 0.93 | 7.58 | 8.37 | 0.00 | 0.00 | 3.74 | 14.60 | 21.04 | ▇▁▁▂▃ |
| number_of_users | Isle of Man | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Israel | 1 | 0.93 | 12.27 | 10.28 | 0.00 | 0.68 | 14.39 | 21.29 | 24.71 | ▇▂▂▂▇ |
| number_of_users | Italy | 2 | 0.86 | 9.89 | 8.51 | 0.00 | 1.29 | 9.88 | 17.50 | 21.92 | ▇▂▂▃▅ |
| number_of_users | Jamaica | 4 | 0.71 | 2.17 | 1.66 | 0.12 | 0.51 | 2.13 | 3.58 | 4.32 | ▇▅▂▂▇ |
| number_of_users | Japan | 1 | 0.93 | 13.42 | 10.13 | 0.03 | 3.02 | 15.31 | 22.15 | 26.71 | ▇▂▃▃▇ |
| number_of_users | Jordan | 4 | 0.71 | 1.23 | 1.35 | 0.01 | 0.12 | 0.66 | 2.17 | 3.44 | ▇▂▂▂▃ |
| number_of_users | Kazakhstan | 4 | 0.71 | 1.85 | 2.90 | 0.00 | 0.01 | 0.11 | 3.13 | 8.74 | ▇▁▂▁▁ |
| number_of_users | Kenya | 5 | 0.64 | 0.02 | 0.02 | 0.00 | 0.00 | 0.01 | 0.02 | 0.05 | ▇▃▂▁▃ |
| number_of_users | Kiribati | 10 | 0.29 | 0.22 | 0.45 | 0.00 | 0.00 | 0.00 | 0.22 | 0.90 | ▇▁▁▁▂ |
| number_of_users | Korea, Dem. Rep. | 2 | 0.86 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Korea, Rep. | 1 | 0.93 | 21.59 | 11.91 | 0.03 | 16.48 | 24.81 | 30.36 | 35.18 | ▃▂▂▆▇ |
| number_of_users | Kosovo | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Kuwait | 4 | 0.71 | 1.10 | 0.52 | 0.25 | 0.69 | 1.19 | 1.53 | 1.70 | ▃▂▃▂▇ |
| number_of_users | Kyrgyzstan | 5 | 0.64 | 0.12 | 0.14 | 0.00 | 0.04 | 0.05 | 0.28 | 0.36 | ▇▁▁▂▁ |
| number_of_users | Laos | 4 | 0.71 | 0.05 | 0.07 | 0.00 | 0.00 | 0.01 | 0.10 | 0.19 | ▇▁▂▁▁ |
| number_of_users | Latvia | 1 | 0.93 | 6.26 | 8.08 | 0.00 | 0.14 | 2.12 | 14.09 | 19.42 | ▇▁▁▁▃ |
| number_of_users | Lebanon | 4 | 0.71 | 3.13 | 1.82 | 0.00 | 1.83 | 3.92 | 4.67 | 4.73 | ▃▂▂▂▇ |
| number_of_users | Lesotho | 5 | 0.64 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 | ▇▃▁▁▃ |
| number_of_users | Liberia | 8 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▇▁▁▂▅ |
| number_of_users | Libya | 6 | 0.57 | 0.40 | 0.48 | 0.00 | 0.00 | 0.16 | 0.82 | 1.15 | ▇▁▁▂▃ |
| number_of_users | Liechtenstein | 5 | 0.64 | 32.90 | 23.44 | 4.27 | 13.80 | 28.58 | 55.18 | 63.83 | ▇▂▅▁▇ |
| number_of_users | Lithuania | 1 | 0.93 | 7.12 | 7.74 | 0.00 | 0.07 | 3.76 | 15.04 | 20.81 | ▇▁▁▂▂ |
| number_of_users | Luxembourg | 1 | 0.93 | 13.05 | 13.52 | 0.00 | 0.28 | 7.97 | 26.81 | 33.21 | ▇▁▁▁▅ |
| number_of_users | Macao, China | 3 | 0.79 | 13.50 | 9.31 | 0.86 | 4.91 | 14.13 | 22.65 | 24.15 | ▆▃▂▂▇ |
| number_of_users | Macedonia, FYR | 5 | 0.64 | 4.36 | 5.04 | 0.00 | 0.00 | 1.79 | 8.82 | 12.47 | ▇▂▁▂▃ |
| number_of_users | Madagascar | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.03 | ▇▂▂▂▃ |
| number_of_users | Malawi | 5 | 0.64 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.03 | ▇▂▂▂▂ |
| number_of_users | Malaysia | 1 | 0.93 | 2.16 | 2.56 | 0.00 | 0.02 | 0.99 | 3.79 | 7.32 | ▇▂▁▁▂ |
| number_of_users | Maldives | 1 | 0.93 | 1.68 | 2.10 | 0.00 | 0.00 | 0.25 | 3.41 | 4.99 | ▇▁▁▁▃ |
| number_of_users | Mali | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.01 | 0.02 | 0.02 | ▇▁▁▂▆ |
| number_of_users | Malta | 3 | 0.79 | 13.19 | 9.75 | 0.43 | 5.08 | 12.73 | 21.72 | 28.02 | ▇▂▃▂▆ |
| number_of_users | Marshall Islands | 6 | 0.57 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Mauritania | 4 | 0.71 | 0.07 | 0.09 | 0.00 | 0.00 | 0.02 | 0.16 | 0.19 | ▇▁▁▁▃ |
| number_of_users | Mauritius | 4 | 0.71 | 2.00 | 2.34 | 0.00 | 0.12 | 1.10 | 3.32 | 6.18 | ▇▃▂▁▃ |
| number_of_users | Mayotte | 12 | 0.14 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Mexico | 1 | 0.93 | 2.73 | 3.50 | 0.00 | 0.05 | 1.01 | 4.09 | 9.98 | ▇▁▁▁▂ |
| number_of_users | Micronesia, Fed. Sts. | 6 | 0.57 | 0.15 | 0.31 | 0.00 | 0.00 | 0.03 | 0.09 | 0.90 | ▇▁▁▁▁ |
| number_of_users | Moldova | 4 | 0.71 | 1.85 | 2.65 | 0.01 | 0.03 | 0.45 | 2.79 | 7.55 | ▇▁▁▁▁ |
| number_of_users | Monaco | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Mongolia | 4 | 0.71 | 0.57 | 0.88 | 0.00 | 0.02 | 0.10 | 0.98 | 2.60 | ▇▁▂▁▁ |
| number_of_users | Montenegro | 8 | 0.43 | 4.32 | 3.61 | 0.00 | 1.56 | 4.01 | 7.58 | 8.42 | ▇▃▁▃▇ |
| number_of_users | Morocco | 4 | 0.71 | 0.85 | 0.72 | 0.00 | 0.06 | 1.05 | 1.53 | 1.56 | ▆▁▂▁▇ |
| number_of_users | Mozambique | 5 | 0.64 | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.05 | 0.06 | ▇▁▂▂▃ |
| number_of_users | Myanmar | 3 | 0.79 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | 0.02 | 0.03 | ▇▁▁▁▂ |
| number_of_users | Namibia | 4 | 0.71 | 0.05 | 0.13 | 0.00 | 0.00 | 0.01 | 0.01 | 0.42 | ▇▁▁▁▁ |
| number_of_users | Nepal | 4 | 0.71 | 0.03 | 0.06 | 0.00 | 0.00 | 0.00 | 0.04 | 0.20 | ▇▂▁▁▁ |
| number_of_users | Netherlands | 2 | 0.86 | 20.43 | 14.88 | 0.48 | 6.16 | 22.41 | 34.04 | 38.10 | ▆▂▂▂▇ |
| number_of_users | New Caledonia | 1 | 0.93 | 4.75 | 5.57 | 0.00 | 0.06 | 2.24 | 8.38 | 15.46 | ▇▁▂▁▂ |
| number_of_users | New Zealand | 1 | 0.93 | 8.98 | 9.88 | 0.00 | 0.44 | 4.69 | 20.17 | 24.93 | ▇▁▁▁▅ |
| number_of_users | Nicaragua | 3 | 0.79 | 0.33 | 0.32 | 0.01 | 0.06 | 0.19 | 0.57 | 0.82 | ▇▂▃▂▃ |
| number_of_users | Niger | 4 | 0.71 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | ▇▁▁▁▁ |
| number_of_users | Nigeria | 5 | 0.64 | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.04 | 0.06 | ▇▁▂▂▃ |
| number_of_users | Northern Mariana Islands | 5 | 0.64 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Norway | 2 | 0.86 | 17.66 | 14.17 | 0.00 | 3.88 | 18.03 | 31.14 | 35.26 | ▇▂▂▃▇ |
| number_of_users | Oman | 4 | 0.71 | 0.66 | 0.65 | 0.00 | 0.01 | 0.67 | 1.12 | 1.63 | ▇▂▃▂▃ |
| number_of_users | Pakistan | 5 | 0.64 | 0.07 | 0.11 | 0.00 | 0.00 | 0.02 | 0.09 | 0.31 | ▇▁▁▁▁ |
| number_of_users | Palau | 5 | 0.64 | 0.48 | 0.35 | 0.00 | 0.37 | 0.50 | 0.55 | 1.17 | ▃▂▇▁▂ |
| number_of_users | Panama | 4 | 0.71 | 3.05 | 2.99 | 0.26 | 0.49 | 1.98 | 5.44 | 7.84 | ▇▁▃▂▃ |
| number_of_users | Papua New Guinea | 4 | 0.71 | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.03 | 0.09 | ▇▁▁▁▁ |
| number_of_users | Paraguay | 3 | 0.79 | 0.12 | 0.13 | 0.00 | 0.01 | 0.09 | 0.17 | 0.44 | ▇▅▃▁▂ |
| number_of_users | Peru | 1 | 0.93 | 1.15 | 1.18 | 0.00 | 0.09 | 0.84 | 2.02 | 3.14 | ▇▁▂▁▃ |
| number_of_users | Philippines | 1 | 0.93 | 0.47 | 0.70 | 0.00 | 0.01 | 0.11 | 0.56 | 1.88 | ▇▁▁▁▂ |
| number_of_users | Poland | 1 | 0.93 | 4.63 | 5.38 | 0.00 | 0.03 | 2.29 | 10.45 | 12.99 | ▇▁▁▁▃ |
| number_of_users | Portugal | 1 | 0.93 | 8.24 | 7.20 | 0.00 | 0.96 | 7.98 | 14.26 | 19.30 | ▇▂▃▅▃ |
| number_of_users | Puerto Rico | 4 | 0.71 | 5.25 | 5.08 | 0.06 | 1.61 | 3.43 | 9.24 | 13.86 | ▇▆▁▂▃ |
| number_of_users | Qatar | 1 | 0.93 | 3.22 | 3.61 | 0.00 | 0.00 | 1.53 | 7.38 | 8.84 | ▇▁▁▁▅ |
| number_of_users | Romania | 4 | 0.71 | 5.56 | 5.73 | 0.03 | 0.59 | 3.40 | 10.91 | 13.90 | ▇▂▁▂▅ |
| number_of_users | Russia | 1 | 0.93 | 2.62 | 3.84 | 0.00 | 0.00 | 0.47 | 3.45 | 11.08 | ▇▁▁▁▂ |
| number_of_users | Rwanda | 4 | 0.71 | 0.01 | 0.01 | 0.00 | 0.00 | 0.01 | 0.02 | 0.03 | ▇▁▇▂▇ |
| number_of_users | Saint Kitts and Nevis | 5 | 0.64 | 15.47 | 9.27 | 1.07 | 9.06 | 17.67 | 21.73 | 28.08 | ▅▂▂▇▅ |
| number_of_users | Saint Lucia | 5 | 0.64 | 5.67 | 4.24 | 0.00 | 2.34 | 4.99 | 9.09 | 11.60 | ▇▇▃▇▇ |
| number_of_users | Saint Martin | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Saint Vincent and the Grenadines | 1 | 0.93 | 3.83 | 4.29 | 0.00 | 0.08 | 1.22 | 7.31 | 11.47 | ▇▁▁▂▂ |
| number_of_users | Samoa | 4 | 0.71 | 0.05 | 0.04 | 0.00 | 0.00 | 0.05 | 0.08 | 0.11 | ▇▂▂▂▆ |
| number_of_users | San Marino | 6 | 0.57 | 12.41 | 12.76 | 2.07 | 3.92 | 4.99 | 19.75 | 32.03 | ▇▁▂▁▃ |
| number_of_users | Sao Tome and Principe | 5 | 0.64 | 0.07 | 0.13 | 0.00 | 0.00 | 0.00 | 0.08 | 0.35 | ▇▁▁▁▁ |
| number_of_users | Saudi Arabia | 2 | 0.86 | 1.57 | 2.10 | 0.00 | 0.14 | 0.29 | 2.83 | 5.45 | ▇▁▁▁▂ |
| number_of_users | Senegal | 4 | 0.71 | 0.24 | 0.22 | 0.00 | 0.04 | 0.21 | 0.38 | 0.63 | ▇▂▃▃▂ |
| number_of_users | Serbia | 8 | 0.43 | 5.43 | 4.19 | 0.45 | 2.33 | 5.32 | 7.61 | 11.77 | ▇▃▃▃▃ |
| number_of_users | Seychelles | 4 | 0.71 | 2.27 | 2.40 | 0.00 | 0.11 | 2.03 | 3.31 | 7.22 | ▇▁▅▂▂ |
| number_of_users | Sierra Leone | 8 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | Singapore | 1 | 0.93 | 12.19 | 8.95 | 0.25 | 3.65 | 13.08 | 19.53 | 24.99 | ▇▃▂▆▆ |
| number_of_users | Slovak Republic | 4 | 0.71 | 5.37 | 4.99 | 0.00 | 0.68 | 4.51 | 9.40 | 12.79 | ▇▂▂▃▃ |
| number_of_users | Slovenia | 4 | 0.71 | 12.04 | 8.93 | 0.28 | 3.62 | 11.89 | 20.05 | 24.02 | ▇▂▅▂▇ |
| number_of_users | Solomon Islands | 4 | 0.71 | 0.15 | 0.15 | 0.00 | 0.01 | 0.12 | 0.27 | 0.38 | ▇▃▂▂▃ |
| number_of_users | Somalia | 6 | 0.57 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| number_of_users | South Africa | 5 | 0.64 | 0.59 | 0.50 | 0.01 | 0.13 | 0.70 | 0.87 | 1.49 | ▇▂▇▂▂ |
| number_of_users | Spain | 2 | 0.86 | 10.50 | 8.67 | 0.00 | 2.55 | 9.78 | 18.32 | 22.87 | ▇▃▂▃▆ |
| number_of_users | Sri Lanka | 4 | 0.71 | 0.31 | 0.38 | 0.00 | 0.04 | 0.11 | 0.45 | 1.09 | ▇▁▁▁▁ |
| number_of_users | Sudan | 4 | 0.71 | 0.10 | 0.15 | 0.00 | 0.00 | 0.00 | 0.11 | 0.38 | ▇▂▁▁▂ |
| number_of_users | Suriname | 4 | 0.71 | 0.75 | 0.96 | 0.00 | 0.05 | 0.37 | 1.04 | 2.99 | ▇▂▁▁▁ |
| number_of_users | Swaziland | 1 | 0.93 | 0.03 | 0.06 | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | ▇▁▁▁▂ |
| number_of_users | Sweden | 1 | 0.93 | 17.49 | 12.96 | 0.00 | 6.60 | 15.68 | 30.34 | 31.85 | ▃▃▁▁▇ |
| number_of_users | Switzerland | 1 | 0.93 | 17.20 | 14.68 | 0.00 | 1.94 | 16.61 | 31.53 | 37.16 | ▇▂▂▃▆ |
| number_of_users | Syria | 4 | 0.71 | 0.06 | 0.11 | 0.00 | 0.00 | 0.02 | 0.05 | 0.33 | ▇▁▁▁▁ |
| number_of_users | Tajikistan | 7 | 0.50 | 0.03 | 0.03 | 0.00 | 0.00 | 0.05 | 0.06 | 0.07 | ▆▁▁▁▇ |
| number_of_users | Tanzania | 4 | 0.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | ▇▁▂▃▆ |
| number_of_users | Thailand | 4 | 0.71 | 0.63 | 1.46 | 0.00 | 0.00 | 0.05 | 0.15 | 4.61 | ▇▁▁▁▁ |
| number_of_users | Timor-Leste | 4 | 0.71 | 0.01 | 0.02 | 0.00 | 0.00 | 0.00 | 0.01 | 0.04 | ▇▁▁▁▂ |
| number_of_users | Togo | 4 | 0.71 | 0.02 | 0.02 | 0.00 | 0.00 | 0.00 | 0.03 | 0.06 | ▇▁▁▁▁ |
| number_of_users | Tonga | 4 | 0.71 | 0.50 | 0.38 | 0.00 | 0.10 | 0.63 | 0.75 | 0.97 | ▆▂▁▇▃ |
| number_of_users | Trinidad and Tobago | 4 | 0.71 | 3.21 | 4.13 | 0.00 | 0.13 | 1.19 | 5.48 | 10.81 | ▇▁▁▁▂ |
| number_of_users | Tunisia | 3 | 0.79 | 1.08 | 1.63 | 0.00 | 0.00 | 0.18 | 1.57 | 4.57 | ▇▁▁▁▁ |
| number_of_users | Turkey | 1 | 0.93 | 3.17 | 3.87 | 0.00 | 0.02 | 0.86 | 6.79 | 9.73 | ▇▁▁▁▃ |
| number_of_users | Turkmenistan | 11 | 0.21 | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | 0.01 | ▇▁▁▇▇ |
| number_of_users | Turks and Caicos Islands | 14 | 0.00 | NaN | NaN | NA | NA | NA | NA | NA | |
| number_of_users | Tuvalu | 5 | 0.64 | 1.51 | 1.40 | 0.00 | 0.00 | 1.55 | 2.77 | 3.26 | ▇▁▂▂▆ |
| number_of_users | Uganda | 4 | 0.71 | 0.02 | 0.05 | 0.00 | 0.00 | 0.00 | 0.01 | 0.16 | ▇▁▁▁▁ |
| number_of_users | Ukraine | 5 | 0.64 | 1.91 | 2.30 | 0.00 | 0.00 | 1.11 | 3.46 | 6.44 | ▇▂▂▂▂ |
| number_of_users | United Arab Emirates | 1 | 0.93 | 3.69 | 4.09 | 0.00 | 0.27 | 1.53 | 7.03 | 10.47 | ▇▁▁▁▃ |
| number_of_users | United Kingdom | 3 | 0.79 | 15.55 | 12.32 | 0.09 | 3.76 | 16.44 | 26.86 | 31.46 | ▇▂▂▂▇ |
| number_of_users | United States | 1 | 0.93 | 13.59 | 10.13 | 0.26 | 4.49 | 12.76 | 23.31 | 27.71 | ▇▃▂▃▇ |
| number_of_users | Uruguay | 4 | 0.71 | 3.64 | 4.03 | 0.00 | 0.20 | 2.14 | 6.26 | 10.95 | ▇▂▂▂▃ |
| number_of_users | Uzbekistan | 4 | 0.71 | 0.10 | 0.13 | 0.00 | 0.01 | 0.03 | 0.20 | 0.32 | ▇▁▁▁▂ |
| number_of_users | Vanuatu | 4 | 0.71 | 0.07 | 0.08 | 0.00 | 0.01 | 0.04 | 0.08 | 0.21 | ▇▃▂▁▃ |
| number_of_users | Venezuela | 2 | 0.86 | 1.92 | 2.05 | 0.00 | 0.27 | 1.07 | 3.53 | 5.40 | ▇▂▁▁▃ |
| number_of_users | Vietnam | 4 | 0.71 | 1.28 | 1.62 | 0.00 | 0.02 | 0.44 | 2.19 | 4.18 | ▇▁▁▁▂ |
| number_of_users | Virgin Islands (U.S.) | 5 | 0.64 | 3.63 | 3.55 | 0.00 | 0.00 | 2.71 | 6.75 | 8.27 | ▇▂▁▂▆ |
| number_of_users | West Bank and Gaza | 7 | 0.50 | 0.34 | 0.56 | 0.00 | 0.00 | 0.00 | 0.47 | 1.45 | ▇▁▂▁▂ |
| number_of_users | Yemen, Rep. | 4 | 0.71 | 0.08 | 0.12 | 0.00 | 0.00 | 0.01 | 0.10 | 0.35 | ▇▁▁▁▁ |
| number_of_users | Zambia | 3 | 0.79 | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.04 | 0.08 | ▇▂▁▁▂ |
| number_of_users | Zimbabwe | 4 | 0.71 | 0.11 | 0.08 | 0.01 | 0.06 | 0.08 | 0.14 | 0.26 | ▇▇▅▁▅ |
1.5 Making a graph
library(ggplot2)
#install.packages("ggrepel") - if needed
library(ggrepel)Warning: package 'ggrepel' was built under R version 3.6.3ggplot(data_years, aes(x=year, y=number_of_users))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=country, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="Number of internet users per 100 people")+
guides(color=FALSE)+
ggtitle("Number of internet users per 100 people")`geom_smooth()` using method = 'loess' and formula 'y ~ x'Warning: Removed 874 rows containing non-finite values (stat_smooth).Warning: Removed 789 row(s) containing missing values (geom_path).Warning: Removed 874 rows containing missing values (geom_point).
1.6 Part 2: Gapminder
Recall the Task Menu:
# install.packages("gapminder") - if needed
library(gapminder)
summary(gapminder) country continent year lifeExp
Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
Algeria : 12 Asia :396 Median :1980 Median :60.71
Angola : 12 Europe :360 Mean :1980 Mean :59.47
Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
Australia : 12 Max. :2007 Max. :82.60
(Other) :1632
pop gdpPercap
Min. :6.001e+04 Min. : 241.2
1st Qu.:2.794e+06 1st Qu.: 1202.1
Median :7.024e+06 Median : 3531.8
Mean :2.960e+07 Mean : 7215.3
3rd Qu.:1.959e+07 3rd Qu.: 9325.5
Max. :1.319e+09 Max. :113523.1
- Get the maximum and minimum of GDP per capita for all continents.
1.7 The maximum and minimum GDP per capita for all continents:
gapminder %>%
group_by(continent) %>%
select(gdpPercap) %>%
summarise(min=min(gdpPercap), max=max(gdpPercap)) %>%
knitr::kable()Adding missing grouping variables: `continent`| continent | min | max |
|---|---|---|
| Africa | 241.1659 | 21951.21 |
| Americas | 1201.6372 | 42951.65 |
| Asia | 331.0000 | 113523.13 |
| Europe | 973.5332 | 49357.19 |
| Oceania | 10039.5956 | 34435.37 |
1.8 Changes in GDP per capita over time on all continents
ggplot(gapminder, aes(x=year, y=gdpPercap))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=continent))+
geom_point(aes(color=continent),size=2)+
labs(x="Year",
y="GDP Per Capita")+
ggtitle("Changes in GDP per capita over time on all continents")`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Changes in GDP per capita in all countries over time
ggplot(gapminder, aes(x=year, y=gdpPercap))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=country, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="GDP Per Capita")+
guides(color=FALSE)+
ggtitle("Changes in GDP per capita in all countries over times")`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
- Look at the spread of GDP per capita across countries within the continents.
1.9 The spread of GDP per capita across countries within the continents
gapminder %>%
group_by(continent, country) %>%
select(gdpPercap) %>%
summarise(min=min(gdpPercap), max=max(gdpPercap)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Africa | Algeria | 2449.0082 | 6223.3675 |
| Africa | Angola | 2277.1409 | 5522.7764 |
| Africa | Benin | 949.4991 | 1441.2849 |
| Africa | Botswana | 851.2411 | 12569.8518 |
| Africa | Burkina Faso | 543.2552 | 1217.0330 |
| Africa | Burundi | 339.2965 | 631.6999 |
| Africa | Cameroon | 1172.6677 | 2602.6642 |
| Africa | Central African Republic | 706.0165 | 1193.0688 |
| Africa | Chad | 797.9081 | 1704.0637 |
| Africa | Comoros | 986.1479 | 1937.5777 |
| Africa | Congo, Dem. Rep. | 241.1659 | 905.8602 |
| Africa | Congo, Rep. | 2125.6214 | 4879.5075 |
| Africa | Cote d’Ivoire | 1388.5947 | 2602.7102 |
| Africa | Djibouti | 1895.0170 | 3694.2124 |
| Africa | Egypt | 1418.8224 | 5581.1810 |
| Africa | Equatorial Guinea | 375.6431 | 12154.0897 |
| Africa | Eritrea | 328.9406 | 913.4708 |
| Africa | Ethiopia | 362.1463 | 690.8056 |
| Africa | Gabon | 4293.4765 | 21745.5733 |
| Africa | Gambia | 485.2307 | 884.7553 |
| Africa | Ghana | 847.0061 | 1327.6089 |
| Africa | Guinea | 510.1965 | 945.5836 |
| Africa | Guinea-Bissau | 299.8503 | 838.1240 |
| Africa | Kenya | 853.5409 | 1463.2493 |
| Africa | Lesotho | 298.8462 | 1569.3314 |
| Africa | Liberia | 414.5073 | 803.0055 |
| Africa | Libya | 2387.5481 | 21951.2118 |
| Africa | Madagascar | 894.6371 | 1748.5630 |
| Africa | Malawi | 369.1651 | 759.3499 |
| Africa | Mali | 452.3370 | 1042.5816 |
| Africa | Mauritania | 743.1159 | 1803.1515 |
| Africa | Mauritius | 1967.9557 | 10956.9911 |
| Africa | Morocco | 1566.3535 | 3820.1752 |
| Africa | Mozambique | 389.8762 | 823.6856 |
| Africa | Namibia | 2423.7804 | 4811.0604 |
| Africa | Niger | 580.3052 | 1054.3849 |
| Africa | Nigeria | 1014.5141 | 2013.9773 |
| Africa | Reunion | 2718.8853 | 7670.1226 |
| Africa | Rwanda | 493.3239 | 881.5706 |
| Africa | Sao Tome and Principe | 860.7369 | 1890.2181 |
| Africa | Senegal | 1367.8994 | 1712.4721 |
| Africa | Sierra Leone | 574.6482 | 1465.0108 |
| Africa | Somalia | 882.0818 | 1450.9925 |
| Africa | South Africa | 4725.2955 | 9269.6578 |
| Africa | Sudan | 1492.1970 | 2602.3950 |
| Africa | Swaziland | 1148.3766 | 4513.4806 |
| Africa | Tanzania | 698.5356 | 1107.4822 |
| Africa | Togo | 859.8087 | 1649.6602 |
| Africa | Tunisia | 1395.2325 | 7092.9230 |
| Africa | Uganda | 617.7244 | 1056.3801 |
| Africa | Zambia | 1071.3538 | 1777.0773 |
| Africa | Zimbabwe | 406.8841 | 799.3622 |
| Americas | Argentina | 5911.3151 | 12779.3796 |
| Americas | Bolivia | 2127.6863 | 3822.1371 |
| Americas | Brazil | 2108.9444 | 9065.8008 |
| Americas | Canada | 11367.1611 | 36319.2350 |
| Americas | Chile | 3939.9788 | 13171.6388 |
| Americas | Colombia | 2144.1151 | 7006.5804 |
| Americas | Costa Rica | 2627.0095 | 9645.0614 |
| Americas | Cuba | 5180.7559 | 8948.1029 |
| Americas | Dominican Republic | 1397.7171 | 6025.3748 |
| Americas | Ecuador | 3522.1107 | 7429.4559 |
| Americas | El Salvador | 3048.3029 | 5728.3535 |
| Americas | Guatemala | 2428.2378 | 5186.0500 |
| Americas | Haiti | 1201.6372 | 2011.1595 |
| Americas | Honduras | 2194.9262 | 3548.3308 |
| Americas | Jamaica | 2898.5309 | 7433.8893 |
| Americas | Mexico | 3478.1255 | 11977.5750 |
| Americas | Nicaragua | 2170.1517 | 5486.3711 |
| Americas | Panama | 2480.3803 | 9809.1856 |
| Americas | Paraguay | 1952.3087 | 4258.5036 |
| Americas | Peru | 3758.5234 | 7408.9056 |
| Americas | Puerto Rico | 3081.9598 | 19328.7090 |
| Americas | Trinidad and Tobago | 3023.2719 | 18008.5092 |
| Americas | United States | 13990.4821 | 42951.6531 |
| Americas | Uruguay | 5444.6196 | 10611.4630 |
| Americas | Venezuela | 7689.7998 | 13143.9510 |
| Asia | Afghanistan | 635.3414 | 978.0114 |
| Asia | Bahrain | 9867.0848 | 29796.0483 |
| Asia | Bangladesh | 630.2336 | 1391.2538 |
| Asia | Cambodia | 368.4693 | 1713.7787 |
| Asia | China | 400.4486 | 4959.1149 |
| Asia | Hong Kong, China | 3054.4212 | 39724.9787 |
| Asia | India | 546.5657 | 2452.2104 |
| Asia | Indonesia | 749.6817 | 3540.6516 |
| Asia | Iran | 3035.3260 | 11888.5951 |
| Asia | Iraq | 3076.2398 | 14688.2351 |
| Asia | Israel | 4086.5221 | 25523.2771 |
| Asia | Japan | 3216.9563 | 31656.0681 |
| Asia | Jordan | 1546.9078 | 4519.4612 |
| Asia | Korea, Dem. Rep. | 1088.2778 | 4106.5253 |
| Asia | Korea, Rep. | 1030.5922 | 23348.1397 |
| Asia | Kuwait | 28118.4300 | 113523.1329 |
| Asia | Lebanon | 4834.8041 | 10461.0587 |
| Asia | Malaysia | 1810.0670 | 12451.6558 |
| Asia | Mongolia | 786.5669 | 3095.7723 |
| Asia | Myanmar | 331.0000 | 944.0000 |
| Asia | Nepal | 545.8657 | 1091.3598 |
| Asia | Oman | 1828.2303 | 22316.1929 |
| Asia | Pakistan | 684.5971 | 2605.9476 |
| Asia | Philippines | 1272.8810 | 3190.4810 |
| Asia | Saudi Arabia | 6459.5548 | 34167.7626 |
| Asia | Singapore | 2315.1382 | 47143.1796 |
| Asia | Sri Lanka | 1072.5466 | 3970.0954 |
| Asia | Syria | 1643.4854 | 4184.5481 |
| Asia | Taiwan | 1206.9479 | 28718.2768 |
| Asia | Thailand | 757.7974 | 7458.3963 |
| Asia | Vietnam | 605.0665 | 2441.5764 |
| Asia | West Bank and Gaza | 1515.5923 | 7110.6676 |
| Asia | Yemen, Rep. | 781.7176 | 2280.7699 |
| Europe | Albania | 1601.0561 | 5937.0295 |
| Europe | Austria | 6137.0765 | 36126.4927 |
| Europe | Belgium | 8343.1051 | 33692.6051 |
| Europe | Bosnia and Herzegovina | 973.5332 | 7446.2988 |
| Europe | Bulgaria | 2444.2866 | 10680.7928 |
| Europe | Croatia | 3119.2365 | 14619.2227 |
| Europe | Czech Republic | 6876.1403 | 22833.3085 |
| Europe | Denmark | 9692.3852 | 35278.4187 |
| Europe | Finland | 6424.5191 | 33207.0844 |
| Europe | France | 7029.8093 | 30470.0167 |
| Europe | Germany | 7144.1144 | 32170.3744 |
| Europe | Greece | 3530.6901 | 27538.4119 |
| Europe | Hungary | 5263.6738 | 18008.9444 |
| Europe | Iceland | 7267.6884 | 36180.7892 |
| Europe | Ireland | 5210.2803 | 40675.9964 |
| Europe | Italy | 4931.4042 | 28569.7197 |
| Europe | Montenegro | 2647.5856 | 11732.5102 |
| Europe | Netherlands | 8941.5719 | 36797.9333 |
| Europe | Norway | 10095.4217 | 49357.1902 |
| Europe | Poland | 4029.3297 | 15389.9247 |
| Europe | Portugal | 3068.3199 | 20509.6478 |
| Europe | Romania | 3144.6132 | 10808.4756 |
| Europe | Serbia | 3581.4594 | 15870.8785 |
| Europe | Slovak Republic | 5074.6591 | 18678.3144 |
| Europe | Slovenia | 4215.0417 | 25768.2576 |
| Europe | Spain | 3834.0347 | 28821.0637 |
| Europe | Sweden | 8527.8447 | 33859.7484 |
| Europe | Switzerland | 14734.2327 | 37506.4191 |
| Europe | Turkey | 1969.1010 | 8458.2764 |
| Europe | United Kingdom | 9979.5085 | 33203.2613 |
| Oceania | Australia | 10039.5956 | 34435.3674 |
| Oceania | New Zealand | 10556.5757 | 25185.0091 |
| ## Changes i | n GDP in on all continents | over time |
ggplot(gapminder, aes(x=year, y=gdpPercap))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=continent))+
geom_point(aes(color=continent),size=2)+
labs(x="Year",
y="GDP per capita")+
ggtitle("GDP changes over years on all continents")`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
1.10 How does life expectancy vary across different continents?
gapminder %>%
group_by(continent, country) %>%
select(lifeExp) %>%
summarise(min=min(lifeExp), max=max(lifeExp)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Africa | Algeria | 43.077 | 72.301 |
| Africa | Angola | 30.015 | 42.731 |
| Africa | Benin | 38.223 | 56.728 |
| Africa | Botswana | 46.634 | 63.622 |
| Africa | Burkina Faso | 31.975 | 52.295 |
| Africa | Burundi | 39.031 | 49.580 |
| Africa | Cameroon | 38.523 | 54.985 |
| Africa | Central African Republic | 35.463 | 50.485 |
| Africa | Chad | 38.092 | 51.724 |
| Africa | Comoros | 40.715 | 65.152 |
| Africa | Congo, Dem. Rep. | 39.143 | 47.804 |
| Africa | Congo, Rep. | 42.111 | 57.470 |
| Africa | Cote d’Ivoire | 40.477 | 54.655 |
| Africa | Djibouti | 34.812 | 54.791 |
| Africa | Egypt | 41.893 | 71.338 |
| Africa | Equatorial Guinea | 34.482 | 51.579 |
| Africa | Eritrea | 35.928 | 58.040 |
| Africa | Ethiopia | 34.078 | 52.947 |
| Africa | Gabon | 37.003 | 61.366 |
| Africa | Gambia | 30.000 | 59.448 |
| Africa | Ghana | 43.149 | 60.022 |
| Africa | Guinea | 33.609 | 56.007 |
| Africa | Guinea-Bissau | 32.500 | 46.388 |
| Africa | Kenya | 42.270 | 59.339 |
| Africa | Lesotho | 42.138 | 59.685 |
| Africa | Liberia | 38.480 | 46.027 |
| Africa | Libya | 42.723 | 73.952 |
| Africa | Madagascar | 36.681 | 59.443 |
| Africa | Malawi | 36.256 | 49.420 |
| Africa | Mali | 33.685 | 54.467 |
| Africa | Mauritania | 40.543 | 64.164 |
| Africa | Mauritius | 50.986 | 72.801 |
| Africa | Morocco | 42.873 | 71.164 |
| Africa | Mozambique | 31.286 | 46.344 |
| Africa | Namibia | 41.725 | 61.999 |
| Africa | Niger | 37.444 | 56.867 |
| Africa | Nigeria | 36.324 | 47.472 |
| Africa | Reunion | 52.724 | 76.442 |
| Africa | Rwanda | 23.599 | 46.242 |
| Africa | Sao Tome and Principe | 46.471 | 65.528 |
| Africa | Senegal | 37.278 | 63.062 |
| Africa | Sierra Leone | 30.331 | 42.568 |
| Africa | Somalia | 32.978 | 48.159 |
| Africa | South Africa | 45.009 | 61.888 |
| Africa | Sudan | 38.635 | 58.556 |
| Africa | Swaziland | 39.613 | 58.474 |
| Africa | Tanzania | 41.215 | 52.517 |
| Africa | Togo | 38.596 | 58.420 |
| Africa | Tunisia | 44.600 | 73.923 |
| Africa | Uganda | 39.978 | 51.542 |
| Africa | Zambia | 39.193 | 51.821 |
| Africa | Zimbabwe | 39.989 | 62.351 |
| Americas | Argentina | 62.485 | 75.320 |
| Americas | Bolivia | 40.414 | 65.554 |
| Americas | Brazil | 50.917 | 72.390 |
| Americas | Canada | 68.750 | 80.653 |
| Americas | Chile | 54.745 | 78.553 |
| Americas | Colombia | 50.643 | 72.889 |
| Americas | Costa Rica | 57.206 | 78.782 |
| Americas | Cuba | 59.421 | 78.273 |
| Americas | Dominican Republic | 45.928 | 72.235 |
| Americas | Ecuador | 48.357 | 74.994 |
| Americas | El Salvador | 45.262 | 71.878 |
| Americas | Guatemala | 42.023 | 70.259 |
| Americas | Haiti | 37.579 | 60.916 |
| Americas | Honduras | 41.912 | 70.198 |
| Americas | Jamaica | 58.530 | 72.567 |
| Americas | Mexico | 50.789 | 76.195 |
| Americas | Nicaragua | 42.314 | 72.899 |
| Americas | Panama | 55.191 | 75.537 |
| Americas | Paraguay | 62.649 | 71.752 |
| Americas | Peru | 43.902 | 71.421 |
| Americas | Puerto Rico | 64.280 | 78.746 |
| Americas | Trinidad and Tobago | 59.100 | 69.862 |
| Americas | United States | 68.440 | 78.242 |
| Americas | Uruguay | 66.071 | 76.384 |
| Americas | Venezuela | 55.088 | 73.747 |
| Asia | Afghanistan | 28.801 | 43.828 |
| Asia | Bahrain | 50.939 | 75.635 |
| Asia | Bangladesh | 37.484 | 64.062 |
| Asia | Cambodia | 31.220 | 59.723 |
| Asia | China | 44.000 | 72.961 |
| Asia | Hong Kong, China | 60.960 | 82.208 |
| Asia | India | 37.373 | 64.698 |
| Asia | Indonesia | 37.468 | 70.650 |
| Asia | Iran | 44.869 | 70.964 |
| Asia | Iraq | 45.320 | 65.044 |
| Asia | Israel | 65.390 | 80.745 |
| Asia | Japan | 63.030 | 82.603 |
| Asia | Jordan | 43.158 | 72.535 |
| Asia | Korea, Dem. Rep. | 50.056 | 70.647 |
| Asia | Korea, Rep. | 47.453 | 78.623 |
| Asia | Kuwait | 55.565 | 77.588 |
| Asia | Lebanon | 55.928 | 71.993 |
| Asia | Malaysia | 48.463 | 74.241 |
| Asia | Mongolia | 42.244 | 66.803 |
| Asia | Myanmar | 36.319 | 62.069 |
| Asia | Nepal | 36.157 | 63.785 |
| Asia | Oman | 37.578 | 75.640 |
| Asia | Pakistan | 43.436 | 65.483 |
| Asia | Philippines | 47.752 | 71.688 |
| Asia | Saudi Arabia | 39.875 | 72.777 |
| Asia | Singapore | 60.396 | 79.972 |
| Asia | Sri Lanka | 57.593 | 72.396 |
| Asia | Syria | 45.883 | 74.143 |
| Asia | Taiwan | 58.500 | 78.400 |
| Asia | Thailand | 50.848 | 70.616 |
| Asia | Vietnam | 40.412 | 74.249 |
| Asia | West Bank and Gaza | 43.160 | 73.422 |
| Asia | Yemen, Rep. | 32.548 | 62.698 |
| Europe | Albania | 55.230 | 76.423 |
| Europe | Austria | 66.800 | 79.829 |
| Europe | Belgium | 68.000 | 79.441 |
| Europe | Bosnia and Herzegovina | 53.820 | 74.852 |
| Europe | Bulgaria | 59.600 | 73.005 |
| Europe | Croatia | 61.210 | 75.748 |
| Europe | Czech Republic | 66.870 | 76.486 |
| Europe | Denmark | 70.780 | 78.332 |
| Europe | Finland | 66.550 | 79.313 |
| Europe | France | 67.410 | 80.657 |
| Europe | Germany | 67.500 | 79.406 |
| Europe | Greece | 65.860 | 79.483 |
| Europe | Hungary | 64.030 | 73.338 |
| Europe | Iceland | 72.490 | 81.757 |
| Europe | Ireland | 66.910 | 78.885 |
| Europe | Italy | 65.940 | 80.546 |
| Europe | Montenegro | 59.164 | 75.445 |
| Europe | Netherlands | 72.130 | 79.762 |
| Europe | Norway | 72.670 | 80.196 |
| Europe | Poland | 61.310 | 75.563 |
| Europe | Portugal | 59.820 | 78.098 |
| Europe | Romania | 61.050 | 72.476 |
| Europe | Serbia | 57.996 | 74.002 |
| Europe | Slovak Republic | 64.360 | 74.663 |
| Europe | Slovenia | 65.570 | 77.926 |
| Europe | Spain | 64.940 | 80.941 |
| Europe | Sweden | 71.860 | 80.884 |
| Europe | Switzerland | 69.620 | 81.701 |
| Europe | Turkey | 43.585 | 71.777 |
| Europe | United Kingdom | 69.180 | 79.425 |
| Oceania | Australia | 69.120 | 81.235 |
| Oceania | New Zealand | 69.390 | 80.204 |
| ## Life expe | ctancy on all continents, a | ll countr | ies |
ggplot(gapminder, aes(x=year, y=lifeExp))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=continent))+
geom_point(aes(color=continent),size=2)+
labs(x="Year",
y="life expectancy")+
ggtitle("Life Expectancy changes over years in all countries")`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
* Report the absolute and/or relative abundance of countries with low life expectancy over time by continent: Compute some measure of worldwide life expectancy - you decide - a mean or median or some other quantile or perhaps your current age. Then determine how many countries on each continent have a life expectancy less than this benchmark, for each year.
1.11 Making table of countries, where life expectancy under 40 years old
life_under_fourty <- gapminder %>%
group_by(continent, country) %>%
select(lifeExp) %>%
filter(lifeExp<40)Adding missing grouping variables: `continent`, `country`select(life_under_fourty, lifeExp) %>%
summarise(min=min(lifeExp), max=max(lifeExp)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Africa | Angola | 30.015 | 39.942 |
| Africa | Benin | 38.223 | 38.223 |
| Africa | Burkina Faso | 31.975 | 37.814 |
| Africa | Burundi | 39.031 | 39.031 |
| Africa | Cameroon | 38.523 | 38.523 |
| Africa | Central African Republic | 35.463 | 39.475 |
| Africa | Chad | 38.092 | 39.881 |
| Africa | Congo, Dem. Rep. | 39.143 | 39.143 |
| Africa | Djibouti | 34.812 | 39.693 |
| Africa | Equatorial Guinea | 34.482 | 38.987 |
| Africa | Eritrea | 35.928 | 38.047 |
| Africa | Ethiopia | 34.078 | 36.667 |
| Africa | Gabon | 37.003 | 38.999 |
| Africa | Gambia | 30.000 | 38.308 |
| Africa | Guinea | 33.609 | 38.842 |
| Africa | Guinea-Bissau | 32.500 | 39.327 |
| Africa | Liberia | 38.480 | 39.486 |
| Africa | Madagascar | 36.681 | 38.865 |
| Africa | Malawi | 36.256 | 39.487 |
| Africa | Mali | 33.685 | 39.977 |
| Africa | Mozambique | 31.286 | 38.113 |
| Africa | Niger | 37.444 | 39.487 |
| Africa | Nigeria | 36.324 | 39.360 |
| Africa | Rwanda | 23.599 | 36.087 |
| Africa | Senegal | 37.278 | 39.329 |
| Africa | Sierra Leone | 30.331 | 39.897 |
| Africa | Somalia | 32.978 | 39.658 |
| Africa | Sudan | 38.635 | 39.624 |
| Africa | Swaziland | 39.613 | 39.613 |
| Africa | Togo | 38.596 | 38.596 |
| Africa | Uganda | 39.978 | 39.978 |
| Africa | Zambia | 39.193 | 39.193 |
| Africa | Zimbabwe | 39.989 | 39.989 |
| Americas | Haiti | 37.579 | 37.579 |
| Asia | Afghanistan | 28.801 | 39.854 |
| Asia | Bangladesh | 37.484 | 39.348 |
| Asia | Cambodia | 31.220 | 39.417 |
| Asia | India | 37.373 | 37.373 |
| Asia | Indonesia | 37.468 | 39.918 |
| Asia | Myanmar | 36.319 | 36.319 |
| Asia | Nepal | 36.157 | 39.393 |
| Asia | Oman | 37.578 | 37.578 |
| Asia | Saudi Arabia | 39.875 | 39.875 |
| Asia | Yemen, Rep. | 32.548 | 39.848 |
2 Finding countries, where life exepctancy lower than worldwide median
low_life_exp<-gapminder %>%
group_by(continent, country) %>%
select(lifeExp) %>%
filter(lifeExp<median(lifeExp))Adding missing grouping variables: `continent`, `country`select(low_life_exp, lifeExp) %>%
summarise(min=min(lifeExp), max=max(lifeExp)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Africa | Algeria | 43.077 | 58.01400 |
| Africa | Angola | 30.015 | 39.48300 |
| Africa | Benin | 38.223 | 49.19000 |
| Africa | Botswana | 46.634 | 52.55600 |
| Africa | Burkina Faso | 31.975 | 46.13700 |
| Africa | Burundi | 39.031 | 44.73600 |
| Africa | Cameroon | 38.523 | 49.35500 |
| Africa | Central African Republic | 35.463 | 43.45700 |
| Africa | Chad | 38.092 | 47.38300 |
| Africa | Comoros | 40.715 | 50.93900 |
| Africa | Congo, Dem. Rep. | 39.143 | 44.96600 |
| Africa | Congo, Rep. | 42.111 | 52.97000 |
| Africa | Cote d’Ivoire | 40.477 | 47.99100 |
| Africa | Djibouti | 34.812 | 46.51900 |
| Africa | Egypt | 41.893 | 53.31900 |
| Africa | Equatorial Guinea | 34.482 | 42.02400 |
| Africa | Eritrea | 35.928 | 44.14200 |
| Africa | Ethiopia | 34.078 | 44.51000 |
| Africa | Gabon | 37.003 | 52.79000 |
| Africa | Gambia | 30.000 | 41.84200 |
| Africa | Ghana | 43.149 | 51.75600 |
| Africa | Guinea | 33.609 | 40.76200 |
| Africa | Guinea-Bissau | 32.500 | 37.46500 |
| Africa | Kenya | 42.270 | 53.55900 |
| Africa | Lesotho | 42.138 | 48.49200 |
| Africa | Liberia | 38.480 | 42.22100 |
| Africa | Libya | 42.723 | 57.44200 |
| Africa | Madagascar | 36.681 | 46.88100 |
| Africa | Malawi | 36.256 | 43.76700 |
| Africa | Mali | 33.685 | 41.71400 |
| Africa | Mauritania | 40.543 | 50.85200 |
| Africa | Mauritius | 50.986 | 64.93000 |
| Africa | Morocco | 42.873 | 55.73000 |
| Africa | Mozambique | 31.286 | 42.08200 |
| Africa | Namibia | 41.725 | 52.90600 |
| Africa | Niger | 37.444 | 41.29100 |
| Africa | Nigeria | 36.324 | 44.51400 |
| Africa | Reunion | 52.724 | 67.06400 |
| Africa | Rwanda | 23.599 | 43.41300 |
| Africa | Sao Tome and Principe | 46.471 | 58.55000 |
| Africa | Senegal | 37.278 | 48.87900 |
| Africa | Sierra Leone | 30.331 | 36.78800 |
| Africa | Somalia | 32.978 | 40.97300 |
| Africa | South Africa | 45.009 | 53.36500 |
| Africa | Sudan | 38.635 | 47.80000 |
| Africa | Swaziland | 39.613 | 46.63300 |
| Africa | Tanzania | 41.215 | 48.46600 |
| Africa | Togo | 38.596 | 52.88700 |
| Africa | Tunisia | 44.600 | 59.83700 |
| Africa | Uganda | 39.978 | 48.05100 |
| Africa | Zambia | 39.193 | 46.02300 |
| Africa | Zimbabwe | 39.989 | 52.35800 |
| Americas | Argentina | 62.485 | 68.48100 |
| Americas | Bolivia | 40.414 | 50.02300 |
| Americas | Brazil | 50.917 | 61.48900 |
| Americas | Canada | 68.750 | 74.21000 |
| Americas | Chile | 54.745 | 67.05200 |
| Americas | Colombia | 50.643 | 63.83700 |
| Americas | Costa Rica | 57.206 | 70.75000 |
| Americas | Cuba | 59.421 | 72.64900 |
| Americas | Dominican Republic | 45.928 | 61.78800 |
| Americas | Ecuador | 48.357 | 61.31000 |
| Americas | El Salvador | 45.262 | 56.69600 |
| Americas | Guatemala | 42.023 | 56.02900 |
| Americas | Haiti | 37.579 | 49.92300 |
| Americas | Honduras | 41.912 | 57.40200 |
| Americas | Jamaica | 58.530 | 70.11000 |
| Americas | Mexico | 50.789 | 65.03200 |
| Americas | Nicaragua | 42.314 | 57.47000 |
| Americas | Panama | 55.191 | 68.68100 |
| Americas | Paraguay | 62.649 | 66.35300 |
| Americas | Peru | 43.902 | 58.44700 |
| Americas | Puerto Rico | 64.280 | 73.44000 |
| Americas | Trinidad and Tobago | 59.100 | 68.30000 |
| Americas | United States | 68.440 | 73.38000 |
| Americas | Uruguay | 66.071 | 69.48100 |
| Americas | Venezuela | 55.088 | 67.45600 |
| Asia | Afghanistan | 28.801 | 38.43800 |
| Asia | Bahrain | 50.939 | 65.59300 |
| Asia | Bangladesh | 37.484 | 46.92300 |
| Asia | Cambodia | 31.220 | 45.41500 |
| Asia | China | 44.000 | 63.96736 |
| Asia | Hong Kong, China | 60.960 | 73.60000 |
| Asia | India | 37.373 | 54.20800 |
| Asia | Indonesia | 37.468 | 52.70200 |
| Asia | Iran | 44.869 | 57.70200 |
| Asia | Iraq | 45.320 | 57.04600 |
| Asia | Israel | 65.390 | 73.06000 |
| Asia | Japan | 63.030 | 75.38000 |
| Asia | Jordan | 43.158 | 61.13400 |
| Asia | Korea, Dem. Rep. | 50.056 | 66.66200 |
| Asia | Korea, Rep. | 47.453 | 64.76600 |
| Asia | Kuwait | 55.565 | 69.34300 |
| Asia | Lebanon | 55.928 | 66.09900 |
| Asia | Malaysia | 48.463 | 65.25600 |
| Asia | Mongolia | 42.244 | 55.49100 |
| Asia | Myanmar | 36.319 | 56.05900 |
| Asia | Nepal | 36.157 | 46.74800 |
| Asia | Oman | 37.578 | 57.36700 |
| Asia | Pakistan | 43.436 | 54.04300 |
| Asia | Philippines | 47.752 | 60.06000 |
| Asia | Saudi Arabia | 39.875 | 58.69000 |
| Asia | Singapore | 60.396 | 70.79500 |
| Asia | Sri Lanka | 57.593 | 65.94900 |
| Asia | Syria | 45.883 | 61.19500 |
| Asia | Taiwan | 58.500 | 70.59000 |
| Asia | Thailand | 50.848 | 62.49400 |
| Asia | Vietnam | 40.412 | 55.76400 |
| Asia | West Bank and Gaza | 43.160 | 60.76500 |
| Asia | Yemen, Rep. | 32.548 | 44.17500 |
| Europe | Albania | 55.230 | 68.93000 |
| Europe | Austria | 66.800 | 72.17000 |
| Europe | Belgium | 68.000 | 72.80000 |
| Europe | Bosnia and Herzegovina | 53.820 | 69.86000 |
| Europe | Bulgaria | 59.600 | 70.81000 |
| Europe | Croatia | 61.210 | 70.46000 |
| Europe | Czech Republic | 66.870 | 70.71000 |
| Europe | Denmark | 70.780 | 74.63000 |
| Europe | Finland | 66.550 | 72.52000 |
| Europe | France | 67.410 | 73.83000 |
| Europe | Germany | 67.500 | 72.50000 |
| Europe | Greece | 65.860 | 73.68000 |
| Europe | Hungary | 64.030 | 69.50000 |
| Europe | Iceland | 72.490 | 76.11000 |
| Europe | Ireland | 66.910 | 72.03000 |
| Europe | Italy | 65.940 | 73.48000 |
| Europe | Montenegro | 59.164 | 73.06600 |
| Europe | Netherlands | 72.130 | 75.24000 |
| Europe | Norway | 72.670 | 75.37000 |
| Europe | Poland | 61.310 | 70.85000 |
| Europe | Portugal | 59.820 | 70.41000 |
| Europe | Romania | 61.050 | 69.36000 |
| Europe | Serbia | 57.996 | 70.16200 |
| Europe | Slovak Republic | 64.360 | 70.80000 |
| Europe | Slovenia | 65.570 | 70.97000 |
| Europe | Spain | 64.940 | 74.39000 |
| Europe | Sweden | 71.860 | 75.44000 |
| Europe | Switzerland | 69.620 | 75.39000 |
| Europe | Turkey | 43.585 | 59.50700 |
| Europe | United Kingdom | 69.180 | 72.76000 |
| Oceania | Australia | 69.120 | 73.49000 |
| Oceania | New Zealand | 69.390 | 72.22000 |
2.1 The correlation between GDP per capita and life expectancy across African countries
2.1.0.1 Table
african_countries<-gapminder %>%
group_by(continent, country) %>%
filter(continent=="Africa")
select(african_countries, gdpPercap)%>%
summarise(min=min(gdpPercap), max=max(gdpPercap)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Africa | Algeria | 2449.0082 | 6223.3675 |
| Africa | Angola | 2277.1409 | 5522.7764 |
| Africa | Benin | 949.4991 | 1441.2849 |
| Africa | Botswana | 851.2411 | 12569.8518 |
| Africa | Burkina Faso | 543.2552 | 1217.0330 |
| Africa | Burundi | 339.2965 | 631.6999 |
| Africa | Cameroon | 1172.6677 | 2602.6642 |
| Africa | Central African Republic | 706.0165 | 1193.0688 |
| Africa | Chad | 797.9081 | 1704.0637 |
| Africa | Comoros | 986.1479 | 1937.5777 |
| Africa | Congo, Dem. Rep. | 241.1659 | 905.8602 |
| Africa | Congo, Rep. | 2125.6214 | 4879.5075 |
| Africa | Cote d’Ivoire | 1388.5947 | 2602.7102 |
| Africa | Djibouti | 1895.0170 | 3694.2124 |
| Africa | Egypt | 1418.8224 | 5581.1810 |
| Africa | Equatorial Guinea | 375.6431 | 12154.0897 |
| Africa | Eritrea | 328.9406 | 913.4708 |
| Africa | Ethiopia | 362.1463 | 690.8056 |
| Africa | Gabon | 4293.4765 | 21745.5733 |
| Africa | Gambia | 485.2307 | 884.7553 |
| Africa | Ghana | 847.0061 | 1327.6089 |
| Africa | Guinea | 510.1965 | 945.5836 |
| Africa | Guinea-Bissau | 299.8503 | 838.1240 |
| Africa | Kenya | 853.5409 | 1463.2493 |
| Africa | Lesotho | 298.8462 | 1569.3314 |
| Africa | Liberia | 414.5073 | 803.0055 |
| Africa | Libya | 2387.5481 | 21951.2118 |
| Africa | Madagascar | 894.6371 | 1748.5630 |
| Africa | Malawi | 369.1651 | 759.3499 |
| Africa | Mali | 452.3370 | 1042.5816 |
| Africa | Mauritania | 743.1159 | 1803.1515 |
| Africa | Mauritius | 1967.9557 | 10956.9911 |
| Africa | Morocco | 1566.3535 | 3820.1752 |
| Africa | Mozambique | 389.8762 | 823.6856 |
| Africa | Namibia | 2423.7804 | 4811.0604 |
| Africa | Niger | 580.3052 | 1054.3849 |
| Africa | Nigeria | 1014.5141 | 2013.9773 |
| Africa | Reunion | 2718.8853 | 7670.1226 |
| Africa | Rwanda | 493.3239 | 881.5706 |
| Africa | Sao Tome and Principe | 860.7369 | 1890.2181 |
| Africa | Senegal | 1367.8994 | 1712.4721 |
| Africa | Sierra Leone | 574.6482 | 1465.0108 |
| Africa | Somalia | 882.0818 | 1450.9925 |
| Africa | South Africa | 4725.2955 | 9269.6578 |
| Africa | Sudan | 1492.1970 | 2602.3950 |
| Africa | Swaziland | 1148.3766 | 4513.4806 |
| Africa | Tanzania | 698.5356 | 1107.4822 |
| Africa | Togo | 859.8087 | 1649.6602 |
| Africa | Tunisia | 1395.2325 | 7092.9230 |
| Africa | Uganda | 617.7244 | 1056.3801 |
| Africa | Zambia | 1071.3538 | 1777.0773 |
| Africa | Zimbabwe | 406.8841 | 799.3622 |
2.2 The graph of GDP changes over time in African countries
2.2.0.1 Companion Graph
ggplot(african_countries, aes(x=year, y=gdpPercap))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=country, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="GDP per capita")+
ggtitle("GDP changes over years in African countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.3 Life Expectancy changes over years in African countries
ggplot(african_countries, aes(x=year, y=lifeExp))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="life expectancy")+
ggtitle("Life Expectancy changes over years in African countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.4 Correlation between GDP nad Life Expectancy in African countries
ggplot(african_countries, aes(x=gdpPercap, y=lifeExp))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="GDP per capitar",
y="life expectancy")+
ggtitle("Correlation between GDP nad Life Expectancy in African countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.4.0.1 Description/Writeup
African continent has been in the center of heated debates with respect to econimical equality, development and how it affects longevity as well as quality of life.Some African countries show significant econimical development and progress since 1960’s, couple of them peaked at 1980’s with the majority of African countries are staying in the lowest GDP per capita. The life expectancy also correlates with GDP and overall trend in African countries shows the life expectancy declining, as the GDP decreases, although for some countries, as GDP was increasing past 10000, the life expectancy went down.This points out to the fact that GDP can not be used as a sole predictor of life expectancy, but there might be some other social/political factors, which affect the life longevity in African countries.
2.4.1 The correlation between GDP per capita and life expectancy across African countries
2.4.1.1 Table
european_countries<-gapminder %>%
group_by(continent, country) %>%
filter(continent=="Europe")
select(european_countries, gdpPercap)%>%
summarise(min=min(gdpPercap), max=max(gdpPercap)) %>%
knitr::kable()Adding missing grouping variables: `continent`, `country`| continent | country | min | max |
|---|---|---|---|
| Europe | Albania | 1601.0561 | 5937.030 |
| Europe | Austria | 6137.0765 | 36126.493 |
| Europe | Belgium | 8343.1051 | 33692.605 |
| Europe | Bosnia and Herzegovina | 973.5332 | 7446.299 |
| Europe | Bulgaria | 2444.2866 | 10680.793 |
| Europe | Croatia | 3119.2365 | 14619.223 |
| Europe | Czech Republic | 6876.1403 | 22833.309 |
| Europe | Denmark | 9692.3852 | 35278.419 |
| Europe | Finland | 6424.5191 | 33207.084 |
| Europe | France | 7029.8093 | 30470.017 |
| Europe | Germany | 7144.1144 | 32170.374 |
| Europe | Greece | 3530.6901 | 27538.412 |
| Europe | Hungary | 5263.6738 | 18008.944 |
| Europe | Iceland | 7267.6884 | 36180.789 |
| Europe | Ireland | 5210.2803 | 40675.996 |
| Europe | Italy | 4931.4042 | 28569.720 |
| Europe | Montenegro | 2647.5856 | 11732.510 |
| Europe | Netherlands | 8941.5719 | 36797.933 |
| Europe | Norway | 10095.4217 | 49357.190 |
| Europe | Poland | 4029.3297 | 15389.925 |
| Europe | Portugal | 3068.3199 | 20509.648 |
| Europe | Romania | 3144.6132 | 10808.476 |
| Europe | Serbia | 3581.4594 | 15870.879 |
| Europe | Slovak Republic | 5074.6591 | 18678.314 |
| Europe | Slovenia | 4215.0417 | 25768.258 |
| Europe | Spain | 3834.0347 | 28821.064 |
| Europe | Sweden | 8527.8447 | 33859.748 |
| Europe | Switzerland | 14734.2327 | 37506.419 |
| Europe | Turkey | 1969.1010 | 8458.276 |
| Europe | United Kingdom | 9979.5085 | 33203.261 |
2.4.1.2 GDP changes over years in European countrie
ggplot(european_countries, aes(x=year, y=gdpPercap))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=country, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="GDP per capita")+
ggtitle("GDP changes over years in European countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.5 Life Expectancy changes over years in European countries
ggplot(european_countries, aes(x=year, y=lifeExp))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="Year",
y="life expectancy")+
ggtitle("Life Expectancy changes over years in Europen countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.6 Correlation between GDP nad Life Expectancy in European countries
ggplot(european_countries, aes(x=gdpPercap, y=lifeExp))+
geom_smooth(se = FALSE, lwd = .5) +
geom_line(aes(group=continent, color=country))+
geom_point(aes(color=country),size=2)+
labs(x="GDP per capitar",
y="life expectancy")+
ggtitle("Correlation between GDP and Life Expectancy in European countries")+
guides(color=FALSE)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
2.6.0.1 Description/Writeup
The European countries went through a period of economical development and mostly steady increase in GDP per capita afte WWII. At the same time the life expectancy of people living in European countries also steadily increased. There appears to be a strong correlation between GDP and life expectancy.