Visualizing summarized data

Introduction to the Tidyverse

David Robinson

Chief Data Scientist, DataCamp

Summarizing by year

by_year <- gapminder %>%
  group_by(year) %>%
  summarize(totalPop = sum(pop),
            meanLifeExp = mean(lifeExp))

by_year

# A tibble: 12 x 3
    year   totalPop meanLifeExp
   <int>      <dbl>       <dbl>
 1  1952 2406957150    49.05762
 2  1957 2664404580    51.50740
 3  1962 2899782974    53.60925
 4  1967 3217478384    55.67829
 5  1972 3576977158    57.64739
 6  1977 3930045807    59.57016
 7  1982 4289436840    61.53320
 8  1987 4691477418    63.21261
 9  1992 5110710260    64.16034
10  1997 5515204472    65.01468
11  2002 5886977579    65.69492
12  2007 6251013179    67.00742

Visualizing population over time

ggplot(by_year, aes(x = year, y = totalPop)) +
  geom_point()

Starting y-axis at zero

ggplot(by_year, aes(x = year, y = totalPop)) +
  geom_point() +
  expand_limits(y = 0)

Summarizing by year and continent

by_year_continent <- gapminder %>%
  group_by(year, continent) %>%
  summarize(totalPop = sum(pop),
            meanLifeExp = mean(lifeExp))

by_year_continent

# A tibble: 60 x 4
# Groups:   year [?]
    year continent   totalPop meanLifeExp
   <int>     <fct>      <dbl>       <dbl>
 1  1952    Africa  237640501    39.13550
 2  1952  Americas  345152446    53.27984
 3  1952      Asia 1395357351    46.31439
 4  1952    Europe  418120846    64.40850
 5  1952   Oceania   10686006    69.25500
 6  1957    Africa  264837738    41.26635
 7  1957  Americas  386953916    55.96028
 8  1957      Asia 1562780599    49.31854
 9  1957    Europe  437890351    66.70307
10  1957   Oceania   11941976    70.29500
# ... with 50 more rows

Visualizing population by year and continent

ggplot(by_year_continent, aes(x = year, y = totalPop, color = continent)) +
  geom_point() +
  expand_limits(y = 0)

Let's practice!

Introduction to the Tidyverse