36-315-Lec5

# 36-315: Statistical Graphics and Visualization
## Lecture 5

Meghan Hall Department of Statistics & Data Science Carnegie Mellon University June 2, 2021

---
layout: true
<div class="my-footer">cmu-36315.netlify.app</div>
---

# From last time

---

# Updates

---

# `object not found`

.large[The consequence of `group_by` and `summarize`]
 
.medium[Only grouping variables are available]

```r
mpg %>% 
  group_by(drv) %>% 
  summarize(mean_hwy_mpg = mean(hwy))
```

---

# `object not found`

.large[The consequence of `group_by` and `summarize`]
 
.medium[Only grouping variables are available]

```r
mpg %>%
  filter(year == 2008) %>%
  group_by(manufacturer)
  summarize(median_size = median(cty, na.rm = TRUE)) 
```

---

# Today

---

# Today's agenda

1. the basics of line graphs

2. using multiple groups, avoiding "spaghetti graphs"

3. working with `lubridate`

4. the line graph debate

5. slope graphs

]

---

# Today's agenda

1. **the basics of line graphs**

2. using multiple groups, avoiding "spaghetti graphs"

3. working with `lubridate`

4. the line graph debate

5. slope graphs

]

---

# When to use a line graph

.large[Most commonly: when looking at values over time]
 
.medium[can also be used with ordered categorical data (e.g., month)]
 
 
--
 
.large[`y`: values of whatever we're measuring]
 
.large[`x`: when the measurement was taken]
 
.medium[most often chronologically, or time via a different variable]
 
 
--
 
.large[Most important: meaningful relationship between successive points on the x]

---

# Axis of a line graph

.large[The axis of a bar graph **must** start at zero]
 
.medium[encoding data by the bars and comparing by length]
 
.medium[otherwise can be misleading]
 
 
--
 
 
.large[But what about a line graph?]
 
.medium[encoding data by position, not length]
 
.medium[include zero if it makes sense]

---

# Axis of a line graph

.huge[*Don't* start the axis at zero if:]
 
 
 
 
.large[the range of data is small but the distance from the bottom of the range to zero is large]

---

# Axis of a line graph

---

# Axis of a line graph

---

# Axis of a line graph

.huge[*Don't* start the axis at zero if:]
 
 
 
 
.large[the range of data is small but the distance from the bottom of the range to zero is large]
 
 
 
.large[the relationship to zero is insignificant]

---

# Axis of a line graph

---

# Axis of a line graph

---

# Today's data

.center[![F1](figs/Lec5/F1.png)]
 
 
.center[`constructor_pts`] 
.center[`BritishGP`] 
.center[`driver_pts`]

---

# Today's data

---

# Line graph basics

```r
constructor_pts %>% 
  filter(year == 2020 & name == "McLaren") %>% 
  ggplot(aes(x = round, y = points)) + 
  geom_line()
```

---

# Line graph basics

```r
constructor_pts %>% 
  filter(year == 2020 & name == "McLaren") %>% 
  ggplot(aes(x = round, y = points)) + 
  geom_area(fill = "#E0610E")
```

---

# Line graph basics

```r
constructor_pts %>% 
  filter(year == 2020 & name == "McLaren") %>% 
  ggplot(aes(x = round, y = points)) + 
  geom_line(color = "#E0610E", size = 1.5, linetype = "dotted")
```

---

# Line graph basics

```r
constructor_pts %>% 
  filter(year == 2020 & name == "McLaren") %>% 
  ggplot(aes(x = round, y = points)) + 
  geom_line(color = "#E0610E", size = 1.5) +
  geom_point(alpha = 0.5, color = "black", size = 2)
```

---

# Line graph basics

```r
constructor_pts %>% 
  filter(year == 2020 & name == "McLaren") %>% 
  ggplot(aes(x = round, y = points)) + 
  geom_line() +
* scale_x_continuous(breaks = seq(1, 17, 1))
```

---

# Today's agenda

1. the basics of line graphs

2. **using multiple groups, avoiding "spaghetti graphs"**

3. working with `lubridate`

4. the line graph debate

5. slope graphs

]

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  ggplot(aes(x = round, y = points, color = name)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1))
```

---

# Multiple groups

**one way to reorder a legend**

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  ggplot(aes(x = round, y = points, color = reorder(name, -points))) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  labs(color = "")
```

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
* mutate(mclaren = ifelse(name == "McLaren","highlight","normal")) %>%
  ggplot(aes(x = round, y = points, color = mclaren)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1))
```

---

# Multiple groups

**when color doesn't distinguish all lines, need group**

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
* mutate(mclaren = ifelse(name == "McLaren","highlight","normal")) %>%
  ggplot(aes(x = round, y = points, color = mclaren, group = name)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
* scale_color_manual(values = c("#E0610E", "black"))
```

---

# Multiple groups

---

# Multiple groups

**further distinguish highlighted group with size and alpha**

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  mutate(mclaren = ifelse(name == "McLaren","highlight","normal")) %>% 
  ggplot(aes(x = round, y = points, color = mclaren, group = name, 
*            size = mclaren, alpha = mclaren)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E", "dark grey")) +
* scale_size_manual(values = c(1.5, 0.75)) +
* scale_alpha_manual(values = c(1, 0.3))
```

---

# Multiple groups

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  mutate(mclaren = ifelse(name == "McLaren","highlight","normal")) %>% 
  ggplot(aes(x = round, y = points, color = mclaren, group = name, 
             size = mclaren, alpha = mclaren)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E", "dark grey")) +
  scale_size_manual(values = c(1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 0.3)) +
* labs(title = "Accumulated points by McLaren during the 2020 season",
*      x = "Race Round",
*      y = "Accumulated Points") +
* theme(legend.position = "none")
```

---

# Multiple groups

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
* mutate(third = ifelse(name %in%
*             c("McLaren","Renault","Racing Point"), name, "z")) %>%
  ggplot(aes(x = round, y = points, color = third, group = name, 
             size = third, alpha = third)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E","#FFF500",
                                "#F596C8","dark grey")) +
  scale_size_manual(values = c(1.5, 1.5, 1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 1, 1, 0.3)) +
  labs(title = "The race for third place during the 2020 season",
       x = "Race Round",
       y = "Accumulated Points")
```

---

# Multiple groups

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  mutate(third = ifelse(name %in% 
              c("McLaren","Renault","Racing Point"), name, "z")) %>% 
  ggplot(aes(x = round, y = points, color = third, group = name, 
             size = third, alpha = third)) + 
  geom_line() +
* geom_text(aes(label = name), size = 4) +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E","#F596C8",
                                "#FFF500","dark grey")) +
  scale_size_manual(values = c(1.5, 1.5, 1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 1, 1, 0.3)) +
  labs(title = "The race for third place during the 2020 season",
       x = "Race Round",
       y = "Accumulated Points") +
  theme(legend.position = "none")
```

---

# Multiple groups

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  mutate(third = ifelse(name %in% 
              c("McLaren","Renault","Racing Point"), name, "z"),
*        label = if_else(round == max(round) &
*                          third != "z", name, NA_character_)) %>%
  ggplot(aes(x = round, y = points, color = third, group = name, 
             size = third, alpha = third)) + 
  geom_line() +
* geom_text(aes(label = label), size = 4) +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E","#F596C8",
                                "#FFF500","dark grey")) +
  scale_size_manual(values = c(1.5, 1.5, 1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 1, 1, 0.3)) +
  labs(title = "The race for third place during the 2020 season",
       x = "Race Round",
       y = "Accumulated Points") +
  theme(legend.position = "none")
```

---

# Multiple groups

---

# Multiple groups

```r
constructor_pts %>% 
  filter(year == 2020) %>% 
  mutate(third = ifelse(name %in% 
              c("McLaren","Renault","Racing Point"), name, "z"),
         label = if_else(round == max(round) & 
                           third != "z", name, NA_character_)) %>% 
  ggplot(aes(x = round, y = points, color = third, group = name, 
             size = third, alpha = third)) + 
  geom_line() +
* geom_label_repel(aes(label = label), size = 4) +
  scale_x_continuous(breaks = seq(1, 17, 1)) +
  scale_color_manual(values = c("#E0610E","#F596C8",
                                "#FFF500","dark grey")) +
  scale_size_manual(values = c(1.5, 1.5, 1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 1, 1, 0.3)) +
  labs(title = "The race for third place during the 2020 season",
       x = "Race Round",
       y = "Accumulated Points") +
  theme(legend.position = "none")
```

---

# Multiple groups

---

# Today's agenda

1. the basics of line graphs

2. using multiple groups, avoiding "spaghetti graphs"

3. **working with `lubridate`**

4. the line graph debate

5. slope graphs

]

---

# Today's data

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> race_name </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> surname </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> name </th>
 <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
 <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> round </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> q1 </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> q2 </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> q3 </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Hamilton </td>
 <td style="text-align:left;"> Mercedes </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:25.900 </td>
 <td style="text-align:left;"> 1:25.347 </td>
 <td style="text-align:left;"> 1:24.303 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Bottas </td>
 <td style="text-align:left;"> Mercedes </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:25.801 </td>
 <td style="text-align:left;"> 1:25.015 </td>
 <td style="text-align:left;"> 1:24.616 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Verstappen </td>
 <td style="text-align:left;"> Red Bull </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.115 </td>
 <td style="text-align:left;"> 1:26.144 </td>
 <td style="text-align:left;"> 1:25.325 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Leclerc </td>
 <td style="text-align:left;"> Ferrari </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.550 </td>
 <td style="text-align:left;"> 1:26.203 </td>
 <td style="text-align:left;"> 1:25.427 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Norris </td>
 <td style="text-align:left;"> McLaren </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.855 </td>
 <td style="text-align:left;"> 1:26.420 </td>
 <td style="text-align:left;"> 1:25.782 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Stroll </td>
 <td style="text-align:left;"> Racing Point </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.243 </td>
 <td style="text-align:left;"> 1:26.501 </td>
 <td style="text-align:left;"> 1:25.839 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Sainz </td>
 <td style="text-align:left;"> McLaren </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.715 </td>
 <td style="text-align:left;"> 1:26.149 </td>
 <td style="text-align:left;"> 1:25.965 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Ricciardo </td>
 <td style="text-align:left;"> Renault </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.677 </td>
 <td style="text-align:left;"> 1:26.339 </td>
 <td style="text-align:left;"> 1:26.009 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Ocon </td>
 <td style="text-align:left;"> Renault </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.396 </td>
 <td style="text-align:left;"> 1:26.252 </td>
 <td style="text-align:left;"> 1:26.209 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> British Grand Prix </td>
 <td style="text-align:left;"> Vettel </td>
 <td style="text-align:left;"> Ferrari </td>
 <td style="text-align:right;"> 2020 </td>
 <td style="text-align:right;"> 4 </td>
 <td style="text-align:left;"> 1:26.469 </td>
 <td style="text-align:left;"> 1:26.455 </td>
 <td style="text-align:left;"> 1:26.339 </td>
 </tr>
</tbody>
</table>

---

# Working with `lubridate`

**how do lap times change through qualifying?**

```r
BritishGP %>% 
  filter(q3 != "\\N") %>% 
  pivot_longer(q1:q3, values_to = "time", names_to = "qround")
```

---

# Working with `lubridate`

**how do lap times change through qualifying?**

```r
BritishGP %>% 
  filter(q3 != "\\N") %>% 
  pivot_longer(q1:q3, values_to = "time", names_to = "qround") %>% 
* ggplot(aes(x = qround, y = time, color = name, group = surname)) +
  geom_line()
```

---

# Working with `lubridate`

```r
class(BritishGP$q1)
```

```
## [1] "character"
```

.center[![lubridate](figs/Lec5/lubridate.png)]
 
.center[.medium[working with dates & times in R:]]
 
.center[[https://lubridate.tidyverse.org/](https://lubridate.tidyverse.org/)]

---

# Working with `lubridate`

**how do lap times change through qualifying?**

```r
BritishGP %>% 
  filter(q3 != "\\N") %>% 
  pivot_longer(q1:q3, values_to = "time", names_to = "qround") %>% 
* mutate(time_format = ms(time),
*        seconds = seconds(time_format),
*        duration = as.duration(time_format))
```

```
## # A tibble: 5 x 6
## surname qround time time_format seconds duration 
## <chr> <chr> <chr> <Period> <Period> <Duration> 
## 1 Hamilton q1 1:25.900 1M 25.9S 85.9S 85.9s (~1.43 minutes) 
## 2 Hamilton q2 1:25.347 1M 25.347S 85.347S 85.347s (~1.42 minutes)
## 3 Hamilton q3 1:24.303 1M 24.303S 84.303S 84.303s (~1.41 minutes)
## 4 Bottas q1 1:25.801 1M 25.801S 85.801S 85.801s (~1.43 minutes)
## 5 Bottas q2 1:25.015 1M 25.015S 85.015S 85.015s (~1.42 minutes)
```

---

# Working with `lubridate`

**how do lap times change through qualifying?**

---

# Working with `lubridate`

---

# Working with `lubridate`

**how do lap times change through qualifying?**

---

# Working with `lubridate`

---

# Working with `lubridate`

```r
BritishGP %>% 
  filter(q3 != "\\N") %>% 
  pivot_longer(q1:q3, values_to = "time", names_to = "qround") %>% 
  mutate(time_format = ms(time),
      seconds = seconds(time_format), 
      duration = as.duration(time_format),
      mercedes = ifelse(name == "Mercedes","highlight","normal"),
*     qround = fct_recode(qround, "Qualifying 1" = "q1",
*                 "Qualifying 2" = "q2", "Qualifying 3" = "q3"),
*     label = ifelse(surname == "Hamilton" & qround == "Qualifying 3",
*                       name, NA_character_)) %>%
  ggplot(aes(x = qround, y = duration, group = surname,
             size = mercedes, alpha = mercedes, color = mercedes)) +
  scale_color_manual(values = c("#00D2BE","black")) +
  scale_size_manual(values = c(1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 0.3)) +
  geom_line() +
* geom_label_repel(aes(label = label), size = 4) +
  theme(legend.position = "none")
```

---

# Working with `lubridate`

---

# Working with `lubridate`

```r
*BritishGP_cleaned %>%
  ggplot(aes(x = qround, y = duration, group = surname,
             size = mercedes, alpha = mercedes, color = mercedes)) + 
  scale_color_manual(values = c("#00D2BE","black")) +
  scale_size_manual(values = c(1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 0.3)) +
  geom_line() +
  geom_label_repel(aes(label = label), size = 4) + 
* scale_y_continuous(trans = "reverse", breaks = seq(84, 87, 0.5),
*                    labels = c("1:24.0","1:24.5","1:25.0","1:25.5",
*                               "1:26.0","1:26.5","1:27.0")) +
  labs(y = "Lap Time (sec)",
       x = "",
       title = "Qualifying Times in the 2020 British Grand Prix") +
  theme(legend.position = "none")
```

---

# Working with `lubridate`

---

# Working with `lubridate`

**a preview of annotations**

```r
BritishGP_cleaned %>%
  ggplot(aes(x = qround, y = duration, group = surname,
             size = mercedes, alpha = mercedes, color = mercedes)) + 
  scale_color_manual(values = c("#00D2BE","black")) +
  scale_size_manual(values = c(1.5, 0.75)) +
  scale_alpha_manual(values = c(1, 0.3)) +
  geom_line() +
  geom_label_repel(aes(label = label), size = 4) + 
  scale_y_continuous(trans = "reverse", breaks = seq(84, 87, 0.5), 
                     labels = c("1:24.0","1:24.5","1:25.0","1:25.5",
                                "1:26.0","1:26.5","1:27.0")) +
  labs(y = "Lap Time (sec)", x = "",
       title = "Qualifying Times in the 2020 British Grand Prix") +
  theme(legend.position = "none") +
* annotate("text", x = 0.65, y = 84.2, label = "Faster",
*          size = 3.5, fontface = 3) +
* annotate("segment", x = 0.65, xend = 0.65, y = 84.7, yend = 84.3,
*     color = "black", size = 1, arrow = arrow(length = unit(2, "mm")))
```

---

# Working with `lubridate`

---

# Today's agenda

1. the basics of line graphs

2. using multiple groups, avoiding "spaghetti graphs"

3. working with `lubridate`

4. **the line graph debate**

5. slope graphs

]

---

# When to use a line graph

.large[Your graph can be thought of as a function (e.g., temperature as a function of time)] 
.medium[the line can be reliably used to estimate data between points]

---

# When to use a line graph

---

# When to use a line graph

.large[Your graph can be thought of as a function (e.g., temperature as a function of time)] 
.medium[the line can be reliably used to estimate data between points] 
 
 
.large[You're at the most discrete level of data and are looking for a trend] 
.medium[the line is mostly a guide for the eye] 
.medium[there isn't really a clearer way to display this]

---

# When to use a line graph

---

# When to use a line graph

---

# What about this?

---

# The line graph debate

**how to view the number of total points for two constructors, 2015 to 2020?**

```r
constructor_pts %>% 
  filter(name %in% c("Mercedes","Red Bull") & year != 2021) %>% 
  group_by(year, name) %>% 
  summarize(points = max(points), 
            rounds = max(round))
```

<table class="table" style="font-size: 16px; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> year </th>
 <th style="text-align:left;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> name </th>
 <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> points </th>
 <th style="text-align:right;font-weight: bold;color: white !important;background-color: #bb0000 !important;"> rounds </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 2015 </td>
 <td style="text-align:left;"> Mercedes </td>
 <td style="text-align:right;"> 703 </td>
 <td style="text-align:right;"> 19 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2015 </td>
 <td style="text-align:left;"> Red Bull </td>
 <td style="text-align:right;"> 187 </td>
 <td style="text-align:right;"> 19 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2016 </td>
 <td style="text-align:left;"> Mercedes </td>
 <td style="text-align:right;"> 765 </td>
 <td style="text-align:right;"> 21 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2016 </td>
 <td style="text-align:left;"> Red Bull </td>
 <td style="text-align:right;"> 468 </td>
 <td style="text-align:right;"> 21 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2017 </td>
 <td style="text-align:left;"> Mercedes </td>
 <td style="text-align:right;"> 668 </td>
 <td style="text-align:right;"> 20 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2017 </td>
 <td style="text-align:left;"> Red Bull </td>
 <td style="text-align:right;"> 368 </td>
 <td style="text-align:right;"> 20 </td>
 </tr>
</tbody>
</table>

---

# The line graph debate

**how to view the number of total points for two constructors, 2015 to 2020?**

```r
constructor_pts %>% 
  filter(name %in% c("Mercedes","Red Bull") & year != 2021) %>% 
  group_by(year, name) %>% 
  summarize(points = max(points), 
            rounds = max(round)) %>% 
* mutate(axis = paste0(year, "\n", rounds, " races"),
*        label = ifelse(year == 2015, name, NA_character_)) %>%
  ggplot(aes(x = axis, y = points, color = name, group = name)) +
  geom_line() +
  scale_color_manual(values = c("#00D2BE","#1E41FF")) +
* geom_label_repel(aes(label = label)) +
  theme(legend.position = "none") +
  labs(x = "",
       y = "Accumulated Points",
       title = "Total points for Mercedes & Red Bull, 2015 to 2020")
```

---

# The line graph debate

---

# The line graph debate

.large[A line between two points implies the presence of data—is this a problem?] 
.medium[Does the data need to be measured nearly continuously for the lines to be logical interpretations?] 
.medium[Or are we used to lines representing time?] 
 
--
 
.large[My view (only one opinion!)] 
.medium[Be very careful as to what your lines are representing] 
.medium[Consider the time between measurements and the total # of measurements] 
.medium[If each value "fills up from zero" at each interval, consider a bar chart instead]

---

# The line graph debate

**how to view the number of total points for two constructors, 2015 to 2020?**

```r
constructor_pts %>% 
  filter(name %in% c("Mercedes","Red Bull") & year != 2021) %>% 
  group_by(year, name) %>% 
  summarize(points = max(points), 
            rounds = max(round)) %>% 
  mutate(axis = paste0(year, "\n", rounds, " races"), 
         label = ifelse(year == 2015, name, NA_character_)) %>% 
  ggplot(aes(x = axis, y = points, fill = name)) +
* geom_bar(position = "dodge", stat = "identity") +
  scale_fill_manual(values = c("#00D2BE","#1E41FF")) +
  geom_label_repel(aes(label = label)) +
  theme(legend.position = "none") +
  labs(x = "",
       y = "Accumulated Points")
```

---

# The line graph debate

---

# Today's agenda

1. the basics of line graphs

2. using multiple groups, avoiding "spaghetti graphs"

3. working with `lubridate`

4. the line graph debate

5. **slope graphs**

]

---

# Slope graphs

---

# Today's data

.center[`driver_pts`] 
.center[**how did points change for drivers who switched teams from 2018 to 2019?**]

---

# Date to year

```r
driver_pts %>% 
  mutate(year = lubridate::year(date))
```

---

# Slope graphs

**how did points change for drivers who switched teams?**

```r
driver_pts %>% 
  mutate(year = lubridate::year(date)) %>% 
  filter(surname %in% c("Sainz","Gasly","Leclerc","Ricciardo"))  %>% 
  group_by(year, surname) %>% 
  summarize(points = max(points)) %>% 
* ggplot(aes(x = year, y = points, group = surname)) +
* geom_line(aes(color = surname), size = 2) +
* geom_point(aes(color = surname), size = 4)
```

---

# Slope graphs

---

# Slope graphs

**how did points change for drivers who switched teams?**

```r
driver_pts %>% 
  mutate(year = lubridate::year(date)) %>% 
  filter(surname %in% c("Sainz","Gasly","Leclerc","Ricciardo"))  %>% 
  group_by(year, surname) %>% 
  summarize(points = max(points)) %>% 
* ggplot(aes(x = as.character(year), y = points, group = surname)) +
  geom_line(aes(color = surname), size = 2) + 
  geom_point(aes(color = surname), size = 4) +
* scale_y_continuous(breaks = seq(25, 300, 25))
```

---

# Slope graphs

---

# Slope graphs

**how did points change for drivers who switched teams?**

```r
driver_pts %>% 
  mutate(year = lubridate::year(date)) %>% 
  filter(surname %in% c("Sainz","Gasly","Leclerc","Ricciardo"))  %>% 
  group_by(year, surname) %>% 
  summarize(points = max(points)) %>% 
* mutate(label = ifelse(year == 2019, surname, NA_character_)) %>%
  ggplot(aes(x = as.character(year), y = points, group = surname)) + 
  geom_line(aes(color = surname), size = 2) + 
  geom_point(aes(color = surname), size = 4) +
  scale_y_continuous(breaks = seq(25, 300, 25)) +
* geom_label_repel(aes(label = label), size = 4) +
* theme(legend.position = "none")
```

---

# Slope graphs

---

# Slope graphs

**how did points change for drivers who switched teams?**

```r
driver_pts %>% 
  mutate(year = lubridate::year(date)) %>% 
  filter(surname %in% c("Sainz","Gasly","Leclerc","Ricciardo"))  %>% 
  group_by(year, surname) %>% 
  summarize(points = max(points)) %>% 
  mutate(label = ifelse(year == 2019, surname, NA_character_)) %>% 
  ggplot(aes(x = as.character(year), y = points, group = surname)) + 
  geom_line(aes(color = surname), size = 2) + 
  geom_point(aes(color = surname), size = 4) +
  scale_y_continuous(breaks = seq(25, 300, 25)) +
  geom_label_repel(aes(label = label), size = 4) + 
  theme(legend.position = "none") +
  labs(y = "Total Points",  x = "",
       title = "Change in points, drivers who switched teams in 2019")
```

---

# Slope graphs

---

# Upcoming