library(tidyverse)
## -- Attaching packages ----------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts -------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
df_wide <-read.csv2("https://raw.githubusercontent.com/johnsuh23/DATA-621/master/unicef-u5mr.csv",header = TRUE, sep = ",")
head(df_wide)
## CountryName U5MR.1950 U5MR.1951 U5MR.1952 U5MR.1953 U5MR.1954 U5MR.1955
## 1 Afghanistan
## 2 Albania
## 3 Algeria 251 249.9
## 4 Andorra
## 5 Angola
## 6 Antigua & Barbuda
## U5MR.1956 U5MR.1957 U5MR.1958 U5MR.1959 U5MR.1960 U5MR.1961 U5MR.1962
## 1 356.5 350.6
## 2
## 3 249 248 247.5 246.7 246.3 246.1 246.2
## 4
## 5
## 6
## U5MR.1963 U5MR.1964 U5MR.1965 U5MR.1966 U5MR.1967 U5MR.1968 U5MR.1969
## 1 345 339.7 334.1 328.7 323.3 318.1 313
## 2
## 3 246.8 247.4 248.2 248.7 248.4 247.4 245.3
## 4
## 5
## 6
## U5MR.1970 U5MR.1971 U5MR.1972 U5MR.1973 U5MR.1974 U5MR.1975 U5MR.1976
## 1 307.8 302.1 296.4 290.8 284.9 279.4 273.6
## 2
## 3 241.7 236.5 230 222.5 214.2 205 195.2
## 4
## 5
## 6
## U5MR.1977 U5MR.1978 U5MR.1979 U5MR.1980 U5MR.1981 U5MR.1982 U5MR.1983
## 1 267.8 261.6 255.5 249.1 242.7 236.2 229.7
## 2 91.1 84.7 78.6 73 67.8 62.8
## 3 184.9 173.8 161.8 148.1 132.5 115.8 99.2
## 4
## 5 234.1 232.8 231.5 230.2
## 6
## U5MR.1984 U5MR.1985 U5MR.1986 U5MR.1987 U5MR.1988 U5MR.1989 U5MR.1990
## 1 222.9 216 209.2 202.1 195 187.8 181
## 2 58.3 54.3 50.7 47.6 44.9 42.5 40.6
## 3 83.8 71.2 61.9 55.4 51.2 48.5 46.8
## 4 8.5
## 5 229.1 228.3 227.5 226.9 226.5 226.2 226
## 6 25.5
## U5MR.1991 U5MR.1992 U5MR.1993 U5MR.1994 U5MR.1995 U5MR.1996 U5MR.1997
## 1 174.2 167.8 162 156.8 152.3 148.6 145.5
## 2 38.8 37.3 36 34.6 33.2 31.8 30.3
## 3 45.7 44.9 44.1 43.3 42.5 41.8 41.1
## 4 7.9 7.4 6.9 6.4 6 5.7 5.3
## 5 225.9 226 225.8 225.5 224.8 224 222.6
## 6 24.2 23.1 21.9 20.8 19.7 18.8 17.9
## U5MR.1998 U5MR.1999 U5MR.2000 U5MR.2001 U5MR.2002 U5MR.2003 U5MR.2004
## 1 142.6 139.9 137 133.8 130.3 126.8 123.2
## 2 28.9 27.5 26.2 24.9 23.6 22.5 21.5
## 3 40.6 40.2 39.7 38.9 37.8 36.5 35.1
## 4 5 4.8 4.6 4.4 4.2 4.1 4
## 5 220.8 218.9 216.7 214.1 211.7 209.2 206.7
## 6 17 16.2 15.5 14.8 14.1 13.5 12.9
## U5MR.2005 U5MR.2006 U5MR.2007 U5MR.2008 U5MR.2009 U5MR.2010 U5MR.2011
## 1 119.6 116.3 113.2 110.4 107.6 105 102.3
## 2 20.5 19.5 18.7 17.9 17.3 16.6 16
## 3 33.6 32.1 30.7 29.4 28.3 27.3 26.6
## 4 3.9 3.7 3.6 3.5 3.4 3.3 3.2
## 5 203.9 200.5 196.4 192 187.3 182.5 177.3
## 6 12.4 11.8 11.3 10.9 10.4 9.9 9.5
## U5MR.2012 U5MR.2013 U5MR.2014 U5MR.2015
## 1 99.5 96.7 93.9 91.1
## 2 15.5 14.9 14.4 14
## 3 26.1 25.8 25.6 25.5
## 4 3.1 3 2.9 2.8
## 5 172.2 167.1 162.2 156.9
## 6 9.1 8.7 8.4 8.1
df_long <- gather(df_wide, year, measurement, U5MR.1950:U5MR.2015, factor_key=TRUE)
## Warning: attributes are not identical across measure variables;
## they will be dropped
df_long$year<-str_remove_all(df_long$year,"U5MR.")
df_long<-filter(df_long,measurement !="")
head(df_long)
## CountryName year measurement
## 1 Australia 1950 31.6
## 2 Canada 1950 48.7
## 3 Benin 1950 348.2
## 4 Denmark 1950 34.1
## 5 Dominican Republic 1950 156
## 6 Fiji 1950 135.7