Spreading and gathering

table2 to table1

table2 %>% 
  spread(type, count)

## # A tibble: 6 x 4
##   country      year  cases population
##   <chr>       <int>  <int>      <int>
## 1 Afghanistan  1999    745   19987071
## 2 Afghanistan  2000   2666   20595360
## 3 Brazil       1999  37737  172006362
## 4 Brazil       2000  80488  174504898
## 5 China        1999 212258 1272915272
## 6 China        2000 213766 1280428583

table1 to table2

table1 %>% 
  gather(type, count, cases, population)

## # A tibble: 12 x 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  2000 cases            2666
##  3 Brazil       1999 cases           37737
##  4 Brazil       2000 cases           80488
##  5 China        1999 cases          212258
##  6 China        2000 cases          213766
##  7 Afghanistan  1999 population   19987071
##  8 Afghanistan  2000 population   20595360
##  9 Brazil       1999 population  172006362
## 10 Brazil       2000 population  174504898
## 11 China        1999 population 1272915272
## 12 China        2000 population 1280428583

table2 %>% 
  gather(type, count, -country, -year)

## # A tibble: 24 x 4
##    country      year type  count     
##    <chr>       <int> <chr> <chr>     
##  1 Afghanistan  1999 type  cases     
##  2 Afghanistan  1999 type  population
##  3 Afghanistan  2000 type  cases     
##  4 Afghanistan  2000 type  population
##  5 Brazil       1999 type  cases     
##  6 Brazil       1999 type  population
##  7 Brazil       2000 type  cases     
##  8 Brazil       2000 type  population
##  9 China        1999 type  cases     
## 10 China        1999 type  population
## # ... with 14 more rows

Are the two calls symmetrical?

No, we need to arrange this result:

table1 %>% 
  gather(type, count, -country, -year) %>% 
  arrange(country, year, type)

## # A tibble: 12 x 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  1999 population   19987071
##  3 Afghanistan  2000 cases            2666
##  4 Afghanistan  2000 population   20595360
##  5 Brazil       1999 cases           37737
##  6 Brazil       1999 population  172006362
##  7 Brazil       2000 cases           80488
##  8 Brazil       2000 population  174504898
##  9 China        1999 cases          212258
## 10 China        1999 population 1272915272
## 11 China        2000 cases          213766
## 12 China        2000 population 1280428583

Plot table-x

For showing one measurement:

table1 %>%
  ggplot(aes(country, population, fill = factor(year))) +
  geom_col(position = "dodge")

For mapping measurement type to an aesthetic:

table2 %>%
  ggplot(aes(country, count, fill = type)) +
  geom_col() +
  facet_wrap(~year)

For mapping both measurements to two aesthetics:

table1 %>%
  ggplot(aes(population, cases, color = country)) +
  geom_point() +
  facet_wrap(~year)

Can also use table1 to show only one measurement:

table2 %>%
  filter(type == "cases") %>%
  ggplot() +
  geom_col(aes(country, count, fill = type)) +
  facet_wrap(~year)

Binding

cases_tbl <-
  table4a %>%
  gather(year, count, -country) %>%
  mutate(type = "cases")

population_tbl <-
  table4b %>% 
  gather(year, count, -country) %>%
  mutate(type = "population")

bind_rows(cases_tbl, population_tbl) %>%
  select(country, year, everything()) %>% 
  arrange(country, year, type)

## # A tibble: 12 x 4
##    country     year       count type      
##    <chr>       <chr>      <int> <chr>     
##  1 Afghanistan 1999         745 cases     
##  2 Afghanistan 1999    19987071 population
##  3 Afghanistan 2000        2666 cases     
##  4 Afghanistan 2000    20595360 population
##  5 Brazil      1999       37737 cases     
##  6 Brazil      1999   172006362 population
##  7 Brazil      2000       80488 cases     
##  8 Brazil      2000   174504898 population
##  9 China       1999      212258 cases     
## 10 China       1999  1272915272 population
## 11 China       2000      213766 cases     
## 12 China       2000  1280428583 population

With just one call to gather():

cases_tbl <-
  table4a %>%
  mutate(type = "cases")

population_tbl <-
  table4b %>% 
  mutate(type = "population")

bind_rows(cases_tbl, population_tbl) %>%
  gather(year, count, -country, -type) %>%
  select(country, year, everything()) %>% 
  arrange(country, year, type)

## # A tibble: 12 x 4
##    country     year  type            count
##    <chr>       <chr> <chr>           <int>
##  1 Afghanistan 1999  cases             745
##  2 Afghanistan 1999  population   19987071
##  3 Afghanistan 2000  cases            2666
##  4 Afghanistan 2000  population   20595360
##  5 Brazil      1999  cases           37737
##  6 Brazil      1999  population  172006362
##  7 Brazil      2000  cases           80488
##  8 Brazil      2000  population  174504898
##  9 China       1999  cases          212258
## 10 China       1999  population 1272915272
## 11 China       2000  cases          213766
## 12 China       2000  population 1280428583

Economies

mpg %>%
  select(displ, hwy, cty) %>%
  gather(cty_hwy, value, hwy, cty) %>%
  ggplot() +
  geom_jitter(aes(displ, value, color = cty_hwy))