Import data
# excel file
data <- read_excel("../01_module4/data/myData.xlsx")
data
## # A tibble: 193 × 9
## hdi_rank_2023 country human_development_in…¹ life_expectancy_at_b…²
## <dbl> <chr> <dbl> <dbl>
## 1 1 Iceland 0.972 82.7
## 2 2 Norway 0.97 83.3
## 3 2 Switzerland 0.97 84.0
## 4 4 Denmark 0.962 81.9
## 5 5 Germany 0.959 81.4
## 6 5 Sweden 0.959 83.3
## 7 7 Australia 0.958 83.9
## 8 8 Hong Kong, China… 0.955 85.5
## 9 8 Netherlands 0.955 82.2
## 10 10 Belgium 0.951 82.1
## # ℹ 183 more rows
## # ℹ abbreviated names: ¹human_development_index_hdi, ²life_expectancy_at_birth
## # ℹ 5 more variables: expected_years_of_schooling <dbl>,
## # mean_years_of_schooling <dbl>, gross_national_income_gni_per_capita <dbl>,
## # gni_per_capita_rank_minus_hdi_rank <dbl>, hdi_rank_2022 <chr>
Apply the following dplyr verbs to your data
Filter rows
filter(data, human_development_index_hdi >= 0.9)
## # A tibble: 37 × 9
## hdi_rank_2023 country human_development_in…¹ life_expectancy_at_b…²
## <dbl> <chr> <dbl> <dbl>
## 1 1 Iceland 0.972 82.7
## 2 2 Norway 0.97 83.3
## 3 2 Switzerland 0.97 84.0
## 4 4 Denmark 0.962 81.9
## 5 5 Germany 0.959 81.4
## 6 5 Sweden 0.959 83.3
## 7 7 Australia 0.958 83.9
## 8 8 Hong Kong, China… 0.955 85.5
## 9 8 Netherlands 0.955 82.2
## 10 10 Belgium 0.951 82.1
## # ℹ 27 more rows
## # ℹ abbreviated names: ¹human_development_index_hdi, ²life_expectancy_at_birth
## # ℹ 5 more variables: expected_years_of_schooling <dbl>,
## # mean_years_of_schooling <dbl>, gross_national_income_gni_per_capita <dbl>,
## # gni_per_capita_rank_minus_hdi_rank <dbl>, hdi_rank_2022 <chr>
Arrange rows
arrange(data,desc( gross_national_income_gni_per_capita))
## # A tibble: 193 × 9
## hdi_rank_2023 country human_development_in…¹ life_expectancy_at_b…²
## <dbl> <chr> <dbl> <dbl>
## 1 17 Liechtenstein 0.938 83.6
## 2 2 Norway 0.97 83.3
## 3 13 Singapore 0.946 83.7
## 4 43 Qatar 0.886 82.4
## 5 11 Ireland 0.949 82.4
## 6 25 Luxembourg 0.922 82.2
## 7 2 Switzerland 0.97 84.0
## 8 4 Denmark 0.962 81.9
## 9 60 Brunei Darussalam 0.837 75.3
## 10 17 United States 0.938 79.3
## # ℹ 183 more rows
## # ℹ abbreviated names: ¹human_development_index_hdi, ²life_expectancy_at_birth
## # ℹ 5 more variables: expected_years_of_schooling <dbl>,
## # mean_years_of_schooling <dbl>, gross_national_income_gni_per_capita <dbl>,
## # gni_per_capita_rank_minus_hdi_rank <dbl>, hdi_rank_2022 <chr>
Select columns
select(data, country, human_development_index_hdi, gross_national_income_gni_per_capita, life_expectancy_at_birth)
## # A tibble: 193 × 4
## country human_development_in…¹ gross_national_incom…² life_expectancy_at_b…³
## <chr> <dbl> <dbl> <dbl>
## 1 Iceland 0.972 69117. 82.7
## 2 Norway 0.97 112710. 83.3
## 3 Switzer… 0.97 81949. 84.0
## 4 Denmark 0.962 76008. 81.9
## 5 Germany 0.959 64053. 81.4
## 6 Sweden 0.959 66102. 83.3
## 7 Austral… 0.958 58277. 83.9
## 8 Hong Ko… 0.955 69436. 85.5
## 9 Netherl… 0.955 68344. 82.2
## 10 Belgium 0.951 63582. 82.1
## # ℹ 183 more rows
## # ℹ abbreviated names: ¹human_development_index_hdi,
## # ²gross_national_income_gni_per_capita, ³life_expectancy_at_birth
Add columns
mutate(data,
gni_rank = hdi_rank_2023 + gni_per_capita_rank_minus_hdi_rank) %>%
select(hdi_rank_2023, country, gni_rank)
## # A tibble: 193 × 3
## hdi_rank_2023 country gni_rank
## <dbl> <chr> <dbl>
## 1 1 Iceland 13
## 2 2 Norway 2
## 3 2 Switzerland 7
## 4 4 Denmark 8
## 5 5 Germany 18
## 6 5 Sweden 15
## 7 7 Australia 21
## 8 8 Hong Kong, China (SAR) 12
## 9 8 Netherlands 14
## 10 10 Belgium 19
## # ℹ 183 more rows