Question

Why does this code fail? How can you fix it.**
```{r}
table4a %>% gather(1999,2000,key="year",value="cases")
```

Answer:

**2) Tidy the simple tibble below. Do you need to spread or gather it? What are the variables?**
```{r}
preg <-tribble(~pregnant, ~male, ~female,
"yes", NA,10,
"no" ,20,12)

preg
```

**3) What do the extra and fill arguments do in separate()? Experiment with the various options for the following two toy datasets?**
```{r}
tibble(x=c("a,b,c","d,e,f,g","h,i,j"))%>% separate(x,c("one","two","three"))

tibble(x=c("a,b,c","d,e","f,g,i"))%>%separate(x,c("one","two","three"))
```

**4) Both unite and separate have a remove argument. What does it do? Why would you set it to FASLE?**

Solve all please with explanations

Answer 1

Q1) Why does this code fail? How can you fix it.
```{r}
table4a %>% gather(1999,2000,key="year",value="cases")
```
Answer:---
Because `gather` can't find the columns names. You can't name columns w/ numbers in R without quoting them with tick marks.

Q2) Tidy the simple tibble below. Do you need to spread or gather it? What are the variables?**
```{r}
preg <-tribble(~pregnant, ~male, ~female,
"yes", NA,10,
"no" ,20,12)

preg
```
Answer:--------
The main objective of analysis here is whether pregnant or not (bc males can not be pregnant), so I would go for `gather`ing the gender column rather than spreading the pregnant column.

```{r}
preg %>%
gather(gender, values, -pregnant)
# the other way around:
preg %>%
gather(gender, values, -pregnant) %>%
spread(pregnant, values)
```

Q3) What do the extra and fill arguments do in separate()? Experiment with the various options for the following two toy datasets?**
```{r}
tibble(x=c("a,b,c","d,e,f,g","h,i,j"))%>% separate(x,c("one","two","three"))

tibble(x=c("a,b,c","d,e","f,g,i"))%>%separate(x,c("one","two","three"))
```

Answer:-----
It's simple. x has vectors with 3 and 4 characters but we specify 3 columns. `fill` has three values:
`warn`, `right` and `left`. Here I specify a fourth column to place the extra letter. The first fills the missing values with the extra character using the right most match. `left` does the same thing but without a warning. and left places the extra character empty in the first column

```{r}
tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three", "four"))

tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three", "four"), fill = "right")

tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three", "four"), fill = "left")
```
I've deleted the fourth column to see how this works. `extra` on the other hand, deals with either droping or merging the extra characters. `warn` drops the extra character and emits a warning messge.
`drop` does the same thing but without a warning and `merge` merges the extra character to it's closest end. No aparent option to `merge` with the first column rather than the last.

```{r}
tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three"), extra = "warn")

tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three"), extra = "drop")

tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
separate(x, c("one", "two", "three"), extra = "merge")

Q4) Both unite and separate have a remove argument. What does it do? Why would you set it to FASLE?**
Answer:-------
Because `unite` and `separate` receive columns and create new ones, `remove` allows you to remove the original columns that you unite/separate on. You might want to leave them as they are if you're checking whether the transformation was done correctly.