data-raw/03_extract-from-excel-gdpPercap.md
In YTLogos/gapminder: Data from Gapminder
r format(Sys.Date())
Cleaning history
- 2010: The first time I documented cleaning this dataset. I started with
delimited files I exported from Excel.
- 2014: I re-cleaned the data and (mostly) forced myself to pull it straight
out of the spreadsheets. Used the gdata package.
- 2015: I revisited the cleaning and switched to the readxl and readr
packages.
suppressPackageStartupMessages(library(dplyr))
library(tidyr)
library(ggplot2)
library(readr)
Extract the GDP per capita data from the Excel file downloaded 2009-04-30
from gapminder.org.
2015-12-29 NOTE: I am punting here for the moment and basing intake on
manually exported delimited text. For population and life expectancy, I have
re-implemented the Excel extraction programmatically using gdata::read.xls()
(2014) and then readxl (2015), which I plan to try here as well. But I dread
doing GDP per capita because the spreadsheet is transposed relative to the
other two. In Excel, I ruthlessly deleted tons of columns at front and up to
1950, then saved as tab-delimited text file gdpPercap.txt.
gdp_xls <- read_tsv("xls-manual-extract/gdpPercap.txt")
gdp_xls %>% str(list.len = 4)
## Classes 'tbl_df', 'tbl' and 'data.frame': 259 obs. of 59 variables:
## $ Area: chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ 1950: num NA 757 NA 1532 2429 ...
## $ 1951: num NA 767 NA 1598 2398 ...
## $ 1952: num NA 779 NA 1601 2449 ...
## [list output truncated]
gdp_xls %>% head()
## Source: local data frame [6 x 59]
##
## Area 1950 1951 1952 1953 1954
## (chr) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 Abkhazia NA NA NA NA NA
## 2 Afghanistan 757.3188 766.7522 779.4453 812.8563 815.3595
## 3 Akrotiri and Dhekelia NA NA NA NA NA
## 4 Albania 1532.3539 1598.4927 1601.0561 1665.7947 1714.6888
## 5 Algeria 2429.2137 2397.5311 2449.0082 2436.3400 2557.8185
## 6 American Samoa 4465.1447 NA NA NA NA
## Variables not shown: 1955 (dbl), 1956 (dbl), 1957 (dbl), 1958 (dbl), 1959
## (dbl), 1960 (dbl), 1961 (dbl), 1962 (dbl), 1963 (dbl), 1964 (dbl), 1965
## (dbl), 1966 (dbl), 1967 (dbl), 1968 (dbl), 1969 (dbl), 1970 (dbl), 1971
## (dbl), 1972 (dbl), 1973 (dbl), 1974 (dbl), 1975 (dbl), 1976 (dbl), 1977
## (dbl), 1978 (dbl), 1979 (dbl), 1980 (dbl), 1981 (dbl), 1982 (dbl), 1983
## (dbl), 1984 (dbl), 1985 (dbl), 1986 (dbl), 1987 (dbl), 1988 (dbl), 1989
## (dbl), 1990 (dbl), 1991 (dbl), 1992 (dbl), 1993 (dbl), 1994 (dbl), 1995
## (dbl), 1996 (dbl), 1997 (dbl), 1998 (dbl), 1999 (dbl), 2000 (dbl), 2001
## (dbl), 2002 (dbl), 2003 (dbl), 2004 (dbl), 2005 (dbl), 2006 (dbl), 2007
## (dbl).
Sadly, this file is transposed relative to population and life expectancy.
Each row is a country and the columns give the GDP data for different years.
WHY?!?
Reshape the data by gathering all the year variables.
gdp_tidy <- gdp_xls %>%
gather(key = "Xyear", value = "gdpPercap", -Area)
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 15022 obs. of 3 variables:
## $ Area : chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ Xyear : Factor w/ 58 levels "1950","1951",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gdpPercap: num NA 757 NA 1532 2429 ...
Rename Area --> country and fix the years.
gdp_tidy <- gdp_tidy %>%
rename(country = Area) %>%
mutate(Xyear = levels(Xyear)[as.numeric(Xyear)],
year = gsub("X", "", Xyear) %>% as.integer(),
Xyear = NULL)
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 15022 obs. of 3 variables:
## $ country : chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ gdpPercap: num NA 757 NA 1532 2429 ...
## $ year : int 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
Filter rows where gdpPercap is NA.
gdp_tidy <- gdp_tidy %>%
filter(!is.na(gdpPercap))
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 10911 obs. of 3 variables:
## $ country : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ gdpPercap: num 757 1532 2429 4465 3363 ...
## $ year : int 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
Look into the coverage by year.
summary(gdp_tidy$year)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1950 1967 1981 1981 1995 2007
(p <- ggplot(gdp_tidy, aes(x = year)) + geom_histogram(binwidth = 1))
\
Unlike population and life expectancy, there's no obvious reason to filter on
year at this point.
Is country ok as is?
n_distinct(gdp_tidy$country)
## [1] 229
unique(gdp_tidy$country)
## [1] "Afghanistan" "Albania"
## [3] "Algeria" "American Samoa"
## [5] "Angola" "Argentina"
## [7] "Armenia" "Australia"
## [9] "Austria" "Azerbaijan"
## [11] "Bahamas" "Bahrain"
## [13] "Bangladesh" "Barbados"
## [15] "Belarus" "Belgium"
## [17] "Belize" "Benin"
## [19] "Bermuda" "Bhutan"
## [21] "Bolivia" "Bosnia and Herzegovina"
## [23] "Botswana" "Brazil"
## [25] "Brunei" "Bulgaria"
## [27] "Burkina Faso" "Burundi"
## [29] "Cambodia" "Cameroon"
## [31] "Canada" "Cape Verde"
## [33] "Central African Rep." "Chad"
## [35] "Chile" "China"
## [37] "Colombia" "Comoros"
## [39] "Congo, Dem. Rep." "Congo, Rep."
## [41] "Costa Rica" "Cote d'Ivoire"
## [43] "Croatia" "Cuba"
## [45] "Cyprus" "Czech Rep."
## [47] "Denmark" "Djibouti"
## [49] "Dominica" "Dominican Rep."
## [51] "Ecuador" "Egypt"
## [53] "El Salvador" "Equatorial Guinea"
## [55] "Eritrea" "Estonia"
## [57] "Ethiopia" "Finland"
## [59] "France" "French Guiana"
## [61] "French Polynesia" "Gabon"
## [63] "Gambia" "Germany"
## [65] "Ghana" "Greece"
## [67] "Grenada" "Guadeloupe"
## [69] "Guatemala" "Guinea"
## [71] "Guinea-Bissau" "Guyana"
## [73] "Haiti" "Honduras"
## [75] "Hong Kong, China" "Hungary"
## [77] "Iceland" "India"
## [79] "Indonesia" "Iran"
## [81] "Iraq" "Ireland"
## [83] "Isle of Man" "Israel"
## [85] "Italy" "Jamaica"
## [87] "Japan" "Jordan"
## [89] "Kazakhstan" "Kenya"
## [91] "Korea, Dem. Rep." "Korea, Rep."
## [93] "Kuwait" "Kyrgyzstan"
## [95] "Laos" "Lebanon"
## [97] "Lesotho" "Liberia"
## [99] "Libya" "Lithuania"
## [101] "Luxembourg" "Macao, China"
## [103] "Macedonia, FYR" "Madagascar"
## [105] "Malawi" "Malaysia"
## [107] "Maldives" "Mali"
## [109] "Malta" "Martinique"
## [111] "Mauritania" "Mauritius"
## [113] "Mexico" "Moldova"
## [115] "Mongolia" "Montenegro"
## [117] "Morocco" "Mozambique"
## [119] "Myanmar" "Namibia"
## [121] "Nepal" "Netherlands"
## [123] "New Caledonia" "New Zealand"
## [125] "Nicaragua" "Niger"
## [127] "Nigeria" "Northern Mariana Islands"
## [129] "Norway" "Oman"
## [131] "Pakistan" "Panama"
## [133] "Papua New Guinea" "Paraguay"
## [135] "Peru" "Philippines"
## [137] "Poland" "Portugal"
## [139] "Puerto Rico" "Reunion"
## [141] "Romania" "Russia"
## [143] "Rwanda" "Saint Lucia"
## [145] "Saint Vincent and the Grenadines" "Sao Tome and Principe"
## [147] "Saudi Arabia" "Senegal"
## [149] "Serbia" "Seychelles"
## [151] "Sierra Leone" "Singapore"
## [153] "Slovak Republic" "Slovenia"
## [155] "Somalia" "South Africa"
## [157] "Spain" "Sri Lanka"
## [159] "Sudan" "Suriname"
## [161] "Swaziland" "Sweden"
## [163] "Switzerland" "Syria"
## [165] "Taiwan" "Tajikistan"
## [167] "Tanzania" "Thailand"
## [169] "Timor-Leste" "Togo"
## [171] "Trinidad and Tobago" "Tunisia"
## [173] "Turkey" "Turkmenistan"
## [175] "Uganda" "Ukraine"
## [177] "United Kingdom" "United States"
## [179] "Uruguay" "Uzbekistan"
## [181] "Wallis et Futuna" "Venezuela"
## [183] "West Bank and Gaza" "Vietnam"
## [185] "Yemen, Rep." "Zambia"
## [187] "Zimbabwe" "United Arab Emirates"
## [189] "Fiji" "Georgia"
## [191] "Latvia" "Solomon Islands"
## [193] "Andorra" "Anguilla"
## [195] "Antigua and Barbuda" "Aruba"
## [197] "British Virgin Islands" "Cayman Islands"
## [199] "Cook Islands" "Greenland"
## [201] "Kiribati" "Liechtenstein"
## [203] "Micronesia, Fed. Sts." "Monaco"
## [205] "Montserrat" "Nauru"
## [207] "Netherlands Antilles" "Palau"
## [209] "Qatar" "Saint Kitts and Nevis"
## [211] "San Marino" "Turks and Caicos Islands"
## [213] "Tuvalu" "Guernsey"
## [215] "Marshall Islands" "Samoa"
## [217] "Tonga" "Vanuatu"
## [219] "Saint Helena" "Faeroe Islands"
## [221] "Falkland Islands (Malvinas)" "Gibraltar"
## [223] "Jersey" "Kosovo"
## [225] "Mayotte" "Niue"
## [227] "Northern Cyprus" "Saint-Pierre-et-Miquelon"
## [229] "Tokelau"
No obvious train wrecks.
Save for now.
write_tsv(gdp_tidy, "03_gdpPercap.tsv")
devtools::session_info()
## Session info --------------------------------------------------------------
## setting value
## version R version 3.2.3 (2015-12-10)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_CA.UTF-8
## tz America/Vancouver
## date 2015-12-29
## Packages ------------------------------------------------------------------
## package * version date source
## assertthat 0.1 2013-12-06 CRAN (R 3.2.0)
## colorspace 1.2-6 2015-03-11 CRAN (R 3.2.0)
## DBI 0.3.1 2014-09-24 CRAN (R 3.2.0)
## devtools 1.9.1.9000 2015-12-18 Github (hadley/devtools@9aaa3af)
## digest 0.6.8 2014-12-31 CRAN (R 3.2.0)
## dplyr * 0.4.3.9000 2015-11-24 Github (hadley/dplyr@4f2d7f8)
## evaluate 0.8 2015-09-18 CRAN (R 3.2.0)
## formatR 1.2.1 2015-09-18 CRAN (R 3.2.0)
## ggplot2 * 2.0.0 2015-12-18 CRAN (R 3.2.3)
## gtable 0.1.2 2012-12-05 CRAN (R 3.2.0)
## htmltools 0.2.6 2014-09-08 CRAN (R 3.2.0)
## knitr 1.11.16 2015-11-23 Github (yihui/knitr@6e8ce0c)
## labeling 0.3 2014-08-23 CRAN (R 3.2.0)
## lazyeval 0.1.10 2015-01-02 CRAN (R 3.2.0)
## magrittr 1.5 2014-11-22 CRAN (R 3.2.0)
## memoise 0.2.1 2014-04-22 CRAN (R 3.2.0)
## munsell 0.4.2 2013-07-11 CRAN (R 3.2.0)
## plyr 1.8.3 2015-06-12 CRAN (R 3.2.0)
## R6 2.1.1 2015-08-19 CRAN (R 3.2.0)
## Rcpp 0.12.2 2015-11-15 CRAN (R 3.2.2)
## readr * 0.2.2 2015-10-22 CRAN (R 3.2.0)
## rmarkdown 0.9 2015-12-22 CRAN (R 3.2.3)
## scales 0.3.0 2015-08-25 CRAN (R 3.2.0)
## stringi 1.0-1 2015-10-22 CRAN (R 3.2.0)
## stringr 1.0.0 2015-04-30 CRAN (R 3.2.0)
## tidyr * 0.3.1.9000 2015-12-29 Github (hadley/tidyr@d534fc7)
## yaml 2.1.13 2014-06-12 CRAN (R 3.2.0)
title: "03_extract-from-excel-gdpPercap.R"
author: "jenny"
date: "Tue Dec 29 21:51:53 2015"
YTLogos/gapminder documentation built on May 20, 2019, 1:47 p.m.
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r format(Sys.Date())
Cleaning history
- 2010: The first time I documented cleaning this dataset. I started with delimited files I exported from Excel.
- 2014: I re-cleaned the data and (mostly) forced myself to pull it straight out of the spreadsheets. Used the gdata package.
- 2015: I revisited the cleaning and switched to the readxl and readr packages.
suppressPackageStartupMessages(library(dplyr))
library(tidyr)
library(ggplot2)
library(readr)
Extract the GDP per capita data from the Excel file downloaded 2009-04-30 from gapminder.org.
2015-12-29 NOTE: I am punting here for the moment and basing intake on manually exported delimited text. For population and life expectancy, I have re-implemented the Excel extraction programmatically using gdata::read.xls() (2014) and then readxl (2015), which I plan to try here as well. But I dread doing GDP per capita because the spreadsheet is transposed relative to the other two. In Excel, I ruthlessly deleted tons of columns at front and up to 1950, then saved as tab-delimited text file gdpPercap.txt.
gdp_xls <- read_tsv("xls-manual-extract/gdpPercap.txt")
gdp_xls %>% str(list.len = 4)
## Classes 'tbl_df', 'tbl' and 'data.frame': 259 obs. of 59 variables:
## $ Area: chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ 1950: num NA 757 NA 1532 2429 ...
## $ 1951: num NA 767 NA 1598 2398 ...
## $ 1952: num NA 779 NA 1601 2449 ...
## [list output truncated]
gdp_xls %>% head()
## Source: local data frame [6 x 59]
##
## Area 1950 1951 1952 1953 1954
## (chr) (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 Abkhazia NA NA NA NA NA
## 2 Afghanistan 757.3188 766.7522 779.4453 812.8563 815.3595
## 3 Akrotiri and Dhekelia NA NA NA NA NA
## 4 Albania 1532.3539 1598.4927 1601.0561 1665.7947 1714.6888
## 5 Algeria 2429.2137 2397.5311 2449.0082 2436.3400 2557.8185
## 6 American Samoa 4465.1447 NA NA NA NA
## Variables not shown: 1955 (dbl), 1956 (dbl), 1957 (dbl), 1958 (dbl), 1959
## (dbl), 1960 (dbl), 1961 (dbl), 1962 (dbl), 1963 (dbl), 1964 (dbl), 1965
## (dbl), 1966 (dbl), 1967 (dbl), 1968 (dbl), 1969 (dbl), 1970 (dbl), 1971
## (dbl), 1972 (dbl), 1973 (dbl), 1974 (dbl), 1975 (dbl), 1976 (dbl), 1977
## (dbl), 1978 (dbl), 1979 (dbl), 1980 (dbl), 1981 (dbl), 1982 (dbl), 1983
## (dbl), 1984 (dbl), 1985 (dbl), 1986 (dbl), 1987 (dbl), 1988 (dbl), 1989
## (dbl), 1990 (dbl), 1991 (dbl), 1992 (dbl), 1993 (dbl), 1994 (dbl), 1995
## (dbl), 1996 (dbl), 1997 (dbl), 1998 (dbl), 1999 (dbl), 2000 (dbl), 2001
## (dbl), 2002 (dbl), 2003 (dbl), 2004 (dbl), 2005 (dbl), 2006 (dbl), 2007
## (dbl).
Sadly, this file is transposed relative to population and life expectancy. Each row is a country and the columns give the GDP data for different years. WHY?!?
Reshape the data by gathering all the year variables.
gdp_tidy <- gdp_xls %>%
gather(key = "Xyear", value = "gdpPercap", -Area)
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 15022 obs. of 3 variables:
## $ Area : chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ Xyear : Factor w/ 58 levels "1950","1951",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gdpPercap: num NA 757 NA 1532 2429 ...
Rename Area --> country and fix the years.
gdp_tidy <- gdp_tidy %>%
rename(country = Area) %>%
mutate(Xyear = levels(Xyear)[as.numeric(Xyear)],
year = gsub("X", "", Xyear) %>% as.integer(),
Xyear = NULL)
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 15022 obs. of 3 variables:
## $ country : chr "Abkhazia" "Afghanistan" "Akrotiri and Dhekelia" "Albania" ...
## $ gdpPercap: num NA 757 NA 1532 2429 ...
## $ year : int 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
Filter rows where gdpPercap is NA.
gdp_tidy <- gdp_tidy %>%
filter(!is.na(gdpPercap))
gdp_tidy %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 10911 obs. of 3 variables:
## $ country : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ gdpPercap: num 757 1532 2429 4465 3363 ...
## $ year : int 1950 1950 1950 1950 1950 1950 1950 1950 1950 1950 ...
Look into the coverage by year.
summary(gdp_tidy$year)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1950 1967 1981 1981 1995 2007
(p <- ggplot(gdp_tidy, aes(x = year)) + geom_histogram(binwidth = 1))
\
Unlike population and life expectancy, there's no obvious reason to filter on year at this point.
Is country ok as is?
n_distinct(gdp_tidy$country)
## [1] 229
unique(gdp_tidy$country)
## [1] "Afghanistan" "Albania"
## [3] "Algeria" "American Samoa"
## [5] "Angola" "Argentina"
## [7] "Armenia" "Australia"
## [9] "Austria" "Azerbaijan"
## [11] "Bahamas" "Bahrain"
## [13] "Bangladesh" "Barbados"
## [15] "Belarus" "Belgium"
## [17] "Belize" "Benin"
## [19] "Bermuda" "Bhutan"
## [21] "Bolivia" "Bosnia and Herzegovina"
## [23] "Botswana" "Brazil"
## [25] "Brunei" "Bulgaria"
## [27] "Burkina Faso" "Burundi"
## [29] "Cambodia" "Cameroon"
## [31] "Canada" "Cape Verde"
## [33] "Central African Rep." "Chad"
## [35] "Chile" "China"
## [37] "Colombia" "Comoros"
## [39] "Congo, Dem. Rep." "Congo, Rep."
## [41] "Costa Rica" "Cote d'Ivoire"
## [43] "Croatia" "Cuba"
## [45] "Cyprus" "Czech Rep."
## [47] "Denmark" "Djibouti"
## [49] "Dominica" "Dominican Rep."
## [51] "Ecuador" "Egypt"
## [53] "El Salvador" "Equatorial Guinea"
## [55] "Eritrea" "Estonia"
## [57] "Ethiopia" "Finland"
## [59] "France" "French Guiana"
## [61] "French Polynesia" "Gabon"
## [63] "Gambia" "Germany"
## [65] "Ghana" "Greece"
## [67] "Grenada" "Guadeloupe"
## [69] "Guatemala" "Guinea"
## [71] "Guinea-Bissau" "Guyana"
## [73] "Haiti" "Honduras"
## [75] "Hong Kong, China" "Hungary"
## [77] "Iceland" "India"
## [79] "Indonesia" "Iran"
## [81] "Iraq" "Ireland"
## [83] "Isle of Man" "Israel"
## [85] "Italy" "Jamaica"
## [87] "Japan" "Jordan"
## [89] "Kazakhstan" "Kenya"
## [91] "Korea, Dem. Rep." "Korea, Rep."
## [93] "Kuwait" "Kyrgyzstan"
## [95] "Laos" "Lebanon"
## [97] "Lesotho" "Liberia"
## [99] "Libya" "Lithuania"
## [101] "Luxembourg" "Macao, China"
## [103] "Macedonia, FYR" "Madagascar"
## [105] "Malawi" "Malaysia"
## [107] "Maldives" "Mali"
## [109] "Malta" "Martinique"
## [111] "Mauritania" "Mauritius"
## [113] "Mexico" "Moldova"
## [115] "Mongolia" "Montenegro"
## [117] "Morocco" "Mozambique"
## [119] "Myanmar" "Namibia"
## [121] "Nepal" "Netherlands"
## [123] "New Caledonia" "New Zealand"
## [125] "Nicaragua" "Niger"
## [127] "Nigeria" "Northern Mariana Islands"
## [129] "Norway" "Oman"
## [131] "Pakistan" "Panama"
## [133] "Papua New Guinea" "Paraguay"
## [135] "Peru" "Philippines"
## [137] "Poland" "Portugal"
## [139] "Puerto Rico" "Reunion"
## [141] "Romania" "Russia"
## [143] "Rwanda" "Saint Lucia"
## [145] "Saint Vincent and the Grenadines" "Sao Tome and Principe"
## [147] "Saudi Arabia" "Senegal"
## [149] "Serbia" "Seychelles"
## [151] "Sierra Leone" "Singapore"
## [153] "Slovak Republic" "Slovenia"
## [155] "Somalia" "South Africa"
## [157] "Spain" "Sri Lanka"
## [159] "Sudan" "Suriname"
## [161] "Swaziland" "Sweden"
## [163] "Switzerland" "Syria"
## [165] "Taiwan" "Tajikistan"
## [167] "Tanzania" "Thailand"
## [169] "Timor-Leste" "Togo"
## [171] "Trinidad and Tobago" "Tunisia"
## [173] "Turkey" "Turkmenistan"
## [175] "Uganda" "Ukraine"
## [177] "United Kingdom" "United States"
## [179] "Uruguay" "Uzbekistan"
## [181] "Wallis et Futuna" "Venezuela"
## [183] "West Bank and Gaza" "Vietnam"
## [185] "Yemen, Rep." "Zambia"
## [187] "Zimbabwe" "United Arab Emirates"
## [189] "Fiji" "Georgia"
## [191] "Latvia" "Solomon Islands"
## [193] "Andorra" "Anguilla"
## [195] "Antigua and Barbuda" "Aruba"
## [197] "British Virgin Islands" "Cayman Islands"
## [199] "Cook Islands" "Greenland"
## [201] "Kiribati" "Liechtenstein"
## [203] "Micronesia, Fed. Sts." "Monaco"
## [205] "Montserrat" "Nauru"
## [207] "Netherlands Antilles" "Palau"
## [209] "Qatar" "Saint Kitts and Nevis"
## [211] "San Marino" "Turks and Caicos Islands"
## [213] "Tuvalu" "Guernsey"
## [215] "Marshall Islands" "Samoa"
## [217] "Tonga" "Vanuatu"
## [219] "Saint Helena" "Faeroe Islands"
## [221] "Falkland Islands (Malvinas)" "Gibraltar"
## [223] "Jersey" "Kosovo"
## [225] "Mayotte" "Niue"
## [227] "Northern Cyprus" "Saint-Pierre-et-Miquelon"
## [229] "Tokelau"
No obvious train wrecks. Save for now.
write_tsv(gdp_tidy, "03_gdpPercap.tsv")
devtools::session_info()
## Session info --------------------------------------------------------------
## setting value
## version R version 3.2.3 (2015-12-10)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_CA.UTF-8
## tz America/Vancouver
## date 2015-12-29
## Packages ------------------------------------------------------------------
## package * version date source
## assertthat 0.1 2013-12-06 CRAN (R 3.2.0)
## colorspace 1.2-6 2015-03-11 CRAN (R 3.2.0)
## DBI 0.3.1 2014-09-24 CRAN (R 3.2.0)
## devtools 1.9.1.9000 2015-12-18 Github (hadley/devtools@9aaa3af)
## digest 0.6.8 2014-12-31 CRAN (R 3.2.0)
## dplyr * 0.4.3.9000 2015-11-24 Github (hadley/dplyr@4f2d7f8)
## evaluate 0.8 2015-09-18 CRAN (R 3.2.0)
## formatR 1.2.1 2015-09-18 CRAN (R 3.2.0)
## ggplot2 * 2.0.0 2015-12-18 CRAN (R 3.2.3)
## gtable 0.1.2 2012-12-05 CRAN (R 3.2.0)
## htmltools 0.2.6 2014-09-08 CRAN (R 3.2.0)
## knitr 1.11.16 2015-11-23 Github (yihui/knitr@6e8ce0c)
## labeling 0.3 2014-08-23 CRAN (R 3.2.0)
## lazyeval 0.1.10 2015-01-02 CRAN (R 3.2.0)
## magrittr 1.5 2014-11-22 CRAN (R 3.2.0)
## memoise 0.2.1 2014-04-22 CRAN (R 3.2.0)
## munsell 0.4.2 2013-07-11 CRAN (R 3.2.0)
## plyr 1.8.3 2015-06-12 CRAN (R 3.2.0)
## R6 2.1.1 2015-08-19 CRAN (R 3.2.0)
## Rcpp 0.12.2 2015-11-15 CRAN (R 3.2.2)
## readr * 0.2.2 2015-10-22 CRAN (R 3.2.0)
## rmarkdown 0.9 2015-12-22 CRAN (R 3.2.3)
## scales 0.3.0 2015-08-25 CRAN (R 3.2.0)
## stringi 1.0-1 2015-10-22 CRAN (R 3.2.0)
## stringr 1.0.0 2015-04-30 CRAN (R 3.2.0)
## tidyr * 0.3.1.9000 2015-12-29 Github (hadley/tidyr@d534fc7)
## yaml 2.1.13 2014-06-12 CRAN (R 3.2.0)
title: "03_extract-from-excel-gdpPercap.R" author: "jenny" date: "Tue Dec 29 21:51:53 2015"
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