Compare Life Expectancy from an initial year 1952, and compare that expectancy to all further years,-CodePudding

In my R class, we are currently learning how to manipulate Tibbles. I have a homework problem where I need to grab the life expectancy from 1952 for a country and compare it to all its other expectancies for however many years of data the tibble has. For all countries within the table, in one line using pipes.

Background: this table is called gap

I have used the line:

gap %>% group_by(year, lifeExp) %>% filter(year == 1952)

To filter out the lifeExp for all countries during 1952, but from there I have no idea how to pipe back into the table and compare those initial values to the other specific country values. I know what all the basic dplyr functions do, just having trouble seeing the bigger picture with all the pipes.

If this wasn't enough to understand, I will edit! Thank you for any kind of support!

CodePudding user response：

I don't think you want to use filter(), as that removes rows from your dataframe and you need to keep those rows to make the comparisons. Perhaps using mutate() to create a new variable with the difference for each year compared to the 'first' year (1952) for each country would solve your problem? E.g.

library(tidyverse)
library(gapminder)

gapminder %>% group_by(country) %>% mutate(lifeExp_increase_vs_1952 = lifeExp - first(lifeExp)) %>% select(country, year, lifeExp, lifeExp_increase_vs_1952)

#> # A tibble: 1,704 × 4
#> # Groups:   country [142]
#>    country      year lifeExp lifeExp_increase_vs_1952
#>    <fct>       <int>   <dbl>                    <dbl>
#>  1 Afghanistan  1952    28.8                     0   
#>  2 Afghanistan  1957    30.3                     1.53
#>  3 Afghanistan  1962    32.0                     3.20
#>  4 Afghanistan  1967    34.0                     5.22
#>  5 Afghanistan  1972    36.1                     7.29
#>  6 Afghanistan  1977    38.4                     9.64
#>  7 Afghanistan  1982    39.9                    11.1 
#>  8 Afghanistan  1987    40.8                    12.0 
#>  9 Afghanistan  1992    41.7                    12.9 
#> 10 Afghanistan  1997    41.8                    13.0 
#> 11 Afghanistan  2002    42.1                    13.3 
#> 12 Afghanistan  2007    43.8                    15.0 
#> 13 Albania      1952    55.2                     0   
#> 14 Albania      1957    59.3                     4.05
#> 15 Albania      1962    64.8                     9.59
#> 16 Albania      1967    66.2                    11.0 
#> 17 Albania      1972    67.7                    12.5 
#> 18 Albania      1977    68.9                    13.7 
#> 19 Albania      1982    70.4                    15.2 
#> 20 Albania      1987    72                      16.8 
#> # … with 1,684 more rows

^{Created on 2021-09-23 by the reprex package (v2.0.1)}

CodePudding user response：

You can solve it with the help of mutate and match.

library(dplyr)

gapminder::gapminder %>% 
  group_by(country) %>% 
  mutate(difference = lifeExp - lifeExp[match(1952, year)]) %>%
  ungroup -> gap

gap

#   country     continent  year lifeExp      pop gdpPercap difference
#   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>      <dbl>
# 1 Afghanistan Asia       1952    28.8  8425333      779.       0   
# 2 Afghanistan Asia       1957    30.3  9240934      821.       1.53
# 3 Afghanistan Asia       1962    32.0 10267083      853.       3.20
# 4 Afghanistan Asia       1967    34.0 11537966      836.       5.22
# 5 Afghanistan Asia       1972    36.1 13079460      740.       7.29
# 6 Afghanistan Asia       1977    38.4 14880372      786.       9.64
# 7 Afghanistan Asia       1982    39.9 12881816      978.      11.1 
# 8 Afghanistan Asia       1987    40.8 13867957      852.      12.0 
# 9 Afghanistan Asia       1992    41.7 16317921      649.      12.9 
#10 Afghanistan Asia       1997    41.8 22227415      635.      13.0 
# … with 1,694 more rows