Visualizing time

class: center, middle, inverse, title-slide

.title[
# Visualizing time
]
.author[
### DS 2020
]

---

# NASA data

- `nasa` is a data set in the `GGally` package

- it consists of atmospheric measurements across a grid of locations in Middle America

```r
data(nasa, package="GGally")
head(nasa)
```

```
##   time y x   lat      long       date cloudhigh cloudlow cloudmid ozone
## 1    1 1 1 -21.2 -113.8000 1995-01-01       0.5     31.0      2.0   260
## 2    1 1 2 -21.2 -111.2957 1995-01-01       1.5     31.5      2.5   260
## 3    1 1 3 -21.2 -108.7913 1995-01-01       1.5     32.5      3.5   260
## 4    1 1 4 -21.2 -106.2870 1995-01-01       1.0     39.0      4.0   258
## 5    1 1 5 -21.2 -103.7826 1995-01-01       0.5     48.0      4.5   258
## 6    1 1 6 -21.2 -101.2783 1995-01-01       0.0     50.0      2.5   258
##   pressure surftemp temperature  id day month year
## 1     1000    297.4       296.9 1-1   0     1 1995
## 2     1000    297.4       296.5 2-1   0     1 1995
## 3     1000    297.4       296.0 3-1   0     1 1995
## 4     1000    296.9       296.5 4-1   0     1 1995
## 5     1000    296.5       295.5 5-1   0     1 1995
## 6     1000    296.5       295.0 6-1   0     1 1995
```

---

# Quick overview

```r
nasa %>% group_by(x, y) %>% tally() %>% head()
```

```
## # A tibble: 6 × 3
## # Groups:   x [1]
##       x     y     n
##   <int> <int> <int>
## 1     1     1    72
## 2     1     2    72
## 3     1     3    72
## 4     1     4    72
## 5     1     5    72
## 6     1     6    72
```

```r
nasa %>% ggplot(aes(x =x , y = y)) + geom_point()
```

![](01_time-series_files/figure-html/unnamed-chunk-2-1.png)
---
# A date variable

```r
str(nasa$date)
```

```
##  POSIXct[1:41472], format: "1995-01-01" "1995-01-01" "1995-01-01" "1995-01-01" "1995-01-01" ...
```

```r
summary(nasa$date)
```

```
##                  Min.               1st Qu.                Median 
## "1995-01-01 00:00:00" "1996-06-23 12:00:00" "1997-12-16 12:00:00" 
##                  Mean               3rd Qu.                  Max. 
## "1997-12-15 22:40:00" "1999-06-08 12:00:00" "2000-12-01 00:00:00"
```

---
# Time series

- for each observational unit we have multiple measurements:

```r
nasa %>% filter(x == 1, y == 1) %>%
  ggplot(aes(x = date, y = temperature)) + geom_point()
```

![](01_time-series_files/figure-html/unnamed-chunk-4-1.png)

---

# Time series (2)

- for each observational unit we have multiple measurements,

- which we connect by a line:

```r
nasa %>% filter(x == 1, y == 1) %>%
  ggplot(aes(x = date, y = temperature)) + geom_line()
```

![](01_time-series_files/figure-html/unnamed-chunk-5-1.png)

---

# Time series (3)

- for each observational unit we have multiple measurements,

- which we connect by a line.

- Each observational unit forms a group, we only connect points within a group by a line.

```r
nasa %>% filter(x == 1, y %in% c(1, 10)) %>%
  ggplot(aes(x = date, y = temperature, group=id)) + geom_line()
```

![](01_time-series_files/figure-html/unnamed-chunk-6-1.png)

---
class: inverse
# Your Turn (6 mins)

- Load the `nasa` data from the package `GGally`,

- for one location, draw a time line of Ozone over the time frame (`date`).

- Plot separate lines for each of the years, i.e. put `month` on the x-axis and `ozone` on the y-axis for the same location. Is there a seasonal pattern apparent?

- Pick a location with x in 1:10 and y in 7:10. Plot temperature over time. Comment on the result.