r format(Sys.Date())
library(plyr)
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)
library(readr)
gap_dat <- read_tsv("05_gap-merged-with-china-1952.tsv") %>%
mutate(country = factor(country),
continent = factor(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 ...
## $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
gap_dat$continent %>% summary()## Africa Americas Asia Europe FSU Oceania NA's
## 613 343 558 1302 122 74 301
Hmmmmm .... I've never heard of the continent of FSU.
tmp <- gap_dat %>%
filter(continent == "FSU") %>%
droplevels()
tmp$country %>% levels()## [1] "Belarus" "Kazakhstan" "Latvia" "Lithuania" "Russia"
## [6] "Ukraine"
Aha. FSU = Former Soviet Union. Which countries do not have continent data?
tmp <- gap_dat %>%
filter(is.na(continent)) %>%
droplevels()
tmp$country %>% levels()## [1] "Armenia" "Aruba"
## [3] "Australia" "Bahamas"
## [5] "Barbados" "Belize"
## [7] "Canada" "French Guiana"
## [9] "French Polynesia" "Georgia"
## [11] "Grenada" "Guadeloupe"
## [13] "Haiti" "Hong Kong, China"
## [15] "Maldives" "Martinique"
## [17] "Micronesia, Fed. Sts." "Netherlands Antilles"
## [19] "New Caledonia" "Papua New Guinea"
## [21] "Reunion" "Samoa"
## [23] "Sao Tome and Principe" "Tonga"
## [25] "Uzbekistan" "Vanuatu"
Populate missing values of continent.
cont_dat <- frame_data(
~country, ~continent,
'Armenia', 'FSU',
'Aruba', 'Americas',
'Australia', 'Oceania',
'Bahamas', 'Americas',
'Barbados', 'Americas',
'Belize', 'Americas',
'Canada', 'Americas',
'French Guiana', 'Americas',
'French Polynesia', 'Oceania',
'Georgia', 'FSU',
'Grenada', 'Americas',
'Guadeloupe', 'Americas',
'Haiti', 'Americas',
'Hong Kong, China', 'Asia',
'Maldives', 'Asia',
'Martinique', 'Americas',
'Micronesia, Fed. Sts.', 'Oceania',
'Netherlands Antilles', 'Americas',
'New Caledonia', 'Oceania',
'Papua New Guinea', 'Oceania',
'Reunion', 'Africa',
'Samoa', 'Oceania',
'Sao Tome and Principe', 'Africa',
'Tonga', 'Oceania',
'Uzbekistan', 'FSU',
'Vanuatu', 'Oceania')
gap_dat <- gap_dat %>%
## 2015-12-29
## dplyr bug means we can't use inner_join right now
## https://github.com/hadley/dplyr/issues/1559
#left_join(cont_dat, by = "country") %>%
merge(cont_dat, by = "country", all = TRUE) %>%
tbl_df() %>%
mutate(continent = factor(ifelse(is.na(continent.y),
as.character(continent.x),
as.character(continent.y))),
continent.x = NULL,
continent.y = NULL) %>%
arrange(country, year)
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 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
gap_dat$continent %>% summary()## Africa Americas Asia Europe FSU Oceania
## 637 470 578 1302 139 187
my_vars <- c('country', 'continent', 'year',
'lifeExp', 'pop', 'gdpPercap')
gap_dat <- gap_dat[my_vars]
write_tsv(gap_dat, "07_gap-merged-with-continent.tsv")