tidydata.rmd

# tidyr - Pivoting

References: 
* https://tidyr.tidyverse.org/reference/pivot_longer.html 
* https://tidyr.tidyverse.org/articles/pivot.html

Always need to load the proper library for what functions you plan to use. In this case, tidyverse will be loaded.
```{r}
library(tidyverse)
```

## How to use pivot_longer (replaces gather command in tidyr)

pivot_longer() makes datasets longer by increasing the number of rows and decreasing the number of columns. I don’t believe it makes sense to describe a dataset as being in “long form”.


```{r pivot_longer1}
table4a
ggplot(table4a,mapping=aes(country,1999)) +
  geom_point()

pivot_longer(table4a,c('1999','2000'),names_to='year',values_to='cases')

table4a <- table4a %>%
  pivot_longer(c('1999','2000'),names_to='year',values_to='cases')

ggplot(table4a,mapping=aes(year,cases,color=country)) +
  geom_point() 

```

```{r pivot_longer2}
table4b

pivot_longer(table4b,c('1999','2000'),names_to="year",values_to="popluation")

table4b <- pivot_longer(table4b,c('1999','2000'),names_to="year",values_to="popluation")

JoinedTable <- left_join(table4a,table4b)
JoinedTable$year <- as.integer(JoinedTable$year)
```


## How to pivot_wider (replaces spreading)

pivot_wider() is the opposite of pivot_longer(): it makes a dataset wider by increasing the number of columns and decreasing the number of rows. It’s relatively rare to need pivot_wider() to make tidy data, but it’s often useful for creating summary tables for presentation, or data in a format needed by other tools

```{r pivot_wider}
table2
table2 <- pivot_wider(table2,names_from="type",values_from="count")
```

```{r separating}
table3
table3 <- separate(table3,rate,into=c("cases","populatoin"),sep='/',convert=TRUE)
```