r format(Sys.Date())
suppressPackageStartupMessages(library(dplyr))
library(tidyr)
library(ggplot2)
library(readr)
gap_dat <- read_tsv("07_gap-merged-with-continent.tsv") %>%
mutate(country = factor(country),
continent = factor(continent)) %>%
select(country, year, pop, gdpPercap, lifeExp, continent)
gap_dat %>% str()## Classes 'tbl_df', 'tbl' and 'data.frame': 3313 obs. of 6 variables:
## $ country : Factor w/ 187 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
During data exploration, I learned that most countries have data every five years, e.g. 1952, 1957, 1962, and so on. Let's make that official.
gap_dat <- gap_dat %>%
filter(year %% 5 == 2)
gap_dat %>% str()## Classes 'tbl_df', 'tbl' and 'data.frame': 2013 obs. of 6 variables:
## $ country : Factor w/ 187 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
Number of distinct values for year.
(n_years <- gap_dat$year %>% n_distinct())## [1] 12
Does every country contribute data for all years?
country_freq <- gap_dat %>%
count(country)
country_freq$n %>% table()## .
## 1 4 7 8 9 10 11 12
## 3 13 3 12 5 7 2 142
No.
ggplot(country_freq, aes(x = n)) + geom_histogram(binwidth = 1)
\
Most countries do contribute data for 12 years. Who contributes less?
country_freq %>%
filter(n < 12) %>%
arrange(n) %>%
print(n = nrow(.))## Source: local data frame [45 x 2]
##
## country n
## (fctr) (int)
## 1 French Guiana 1
## 2 Guadeloupe 1
## 3 Martinique 1
## 4 Armenia 4
## 5 Azerbaijan 4
## 6 Belarus 4
## 7 Estonia 4
## 8 Kazakhstan 4
## 9 Lithuania 4
## 10 Moldova 4
## 11 Russia 4
## 12 Tajikistan 4
## 13 Timor-Leste 4
## 14 Turkmenistan 4
## 15 Ukraine 4
## 16 Uzbekistan 4
## 17 Samoa 7
## 18 Tonga 7
## 19 Vanuatu 7
## 20 Aruba 8
## 21 Bhutan 8
## 22 Brunei 8
## 23 Cyprus 8
## 24 Grenada 8
## 25 Macao, China 8
## 26 Maldives 8
## 27 Micronesia, Fed. Sts. 8
## 28 Netherlands Antilles 8
## 29 Qatar 8
## 30 Suriname 8
## 31 United Arab Emirates 8
## 32 French Polynesia 9
## 33 Georgia 9
## 34 Latvia 9
## 35 New Caledonia 9
## 36 Solomon Islands 9
## 37 Bahamas 10
## 38 Barbados 10
## 39 Belize 10
## 40 Fiji 10
## 41 Guyana 10
## 42 Malta 10
## 43 Papua New Guinea 10
## 44 Cape Verde 11
## 45 Luxembourg 11
I will let these countries go.
gap_dat <- country_freq %>%
filter(n > 11) %>%
left_join(gap_dat) %>%
select(-n) %>%
droplevels() %>%
arrange(country, year)## Joining by: "country"
gap_dat %>% str()## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## match variable order of the past
gap_dat <- gap_dat %>%
select(country, continent, year, lifeExp, pop, gdpPercap)
write_tsv(gap_dat,"08_gap-every-five-years.tsv")