## R Lesson

### Guided exercises

The following is based on Chapter 12, “R for Data Science”.

We have several tables (built-in data in tidyr) containing the same information:

• table1
• table2
• table3
• table4a and table4b
• table5

Of these, only table1 is tidy: Each column is a variable and each row is an observation.

relative_rates


> table 1
# 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


We will now proceed to convert the remaining tables into tidy tables.

### Tidying table2

The tibble table2 is not tidy because observations are spread across multiple rows. This calls for the verb spread().


> table2
# 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

# 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


### Tidying table3

The tibble table3 is not because there are two variables contained in the column rate. This calls for the verb separate().


> table3
# A tibble: 6 x 3
country  year              rate
*       <chr> <int>             <chr>
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

> table3 %>% separate(rate, into=c("cases", "population"), sep = "/")
# A tibble: 6 x 4
country  year  cases population
*       <chr> <int>  <chr>      <chr>
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

## Notice how cases and population are **characters**? We should force the tidyverse to re-evaluate
> table3 %>% separate(rate, into=c("cases", "population"), sep = "/", convert = TRUE)
# 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


### Tidying table4a, table4b

The tibbles table4a and table4b together contain all information in table1. Each of these is not tidy because it misuses values as variables, which calls for the verb gather(). Further, to recreate table1, we must use the dplyr function left_join().


> table4a
# A tibble: 3 x 3
country 1999 2000
*       <chr>  <int>  <int>
1 Afghanistan    745   2666
2      Brazil  37737  80488
3       China 212258 213766

### Note the back-ticks to call columns that are **numbers**
> table4a %>% gather(year, cases, 1999:2000) -> tidy4a
> tidy4a
# A tibble: 6 x 3
country  year  cases
<chr> <chr>  <int>
1 Afghanistan  1999    745
2      Brazil  1999  37737
3       China  1999 212258
4 Afghanistan  2000   2666
5      Brazil  2000  80488
6       China  2000 213766

> table4b
# A tibble: 3 x 3
country     1999     2000
*       <chr>      <int>      <int>
1 Afghanistan   19987071   20595360
2      Brazil  172006362  174504898
3       China 1272915272 1280428583

### Note the back-ticks to call columns that are **numbers**
> table4b %>% gather(year, population, 1999:2000) -> tidy4b
# A tibble: 6 x 3
country  year population
<chr> <chr>      <int>
1 Afghanistan  1999   19987071
2      Brazil  1999  172006362
3       China  1999 1272915272
4 Afghanistan  2000   20595360
5      Brazil  2000  174504898
6       China  2000 1280428583

### Finally, join together
> left_join(tidy4a, tidy4b)

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


### Tidying table5

The tibble table5 is not tidy because two variables are split into two columns (century and year). This calls for the verb unite(). We also have a similar situation as table3 with the rate column.


>table5
># A tibble: 6 x 4
country century  year              rate
*       <chr>   <chr> <chr>             <chr>
1 Afghanistan      19    99      745/19987071
2 Afghanistan      20    00     2666/20595360
3      Brazil      19    99   37737/172006362
4      Brazil      20    00   80488/174504898
5       China      19    99 212258/1272915272
6       China      20    00 213766/1280428583

> table5 %>% unite(Year, century, year, sep="")
country  Year              rate
*       <chr> <chr>             <chr>
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

### Finish it up as in table3
> table5 %>%
unite(Year, century, year, sep="") %>%
separate(rate, into=c("cases", "population"), sep = "/", convert=TRUE)
# A tibble: 6 x 4
country  Year  cases population
*       <chr> <chr>  <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