slides.utf8

class: center, middle, inverse, title-slide

# <div class="line-block">Getting Started in R<br />
an introduction to data analysis and visualisation</div>
## babynames
### Réka Solymosi & Sam Langton
### 1 July 2019

---

### `babynames`
Names of boys and girls born each year in the U.S. since 1880

```r
library(babynames)
babynames
```

```
## # A tibble: 1,924,665 x 5
##     year sex   name          n   prop
##    <dbl> <chr> <chr>     <int>  <dbl>
##  1  1880 F     Mary       7065 0.0724
##  2  1880 F     Anna       2604 0.0267
##  3  1880 F     Emma       2003 0.0205
##  4  1880 F     Elizabeth  1939 0.0199
##  5  1880 F     Minnie     1746 0.0179
##  6  1880 F     Margaret   1578 0.0162
##  7  1880 F     Ida        1472 0.0151
##  8  1880 F     Alice      1414 0.0145
##  9  1880 F     Bertha     1320 0.0135
## 10  1880 F     Sarah      1288 0.0132
## # ... with 1,924,655 more rows
```

---
class: center, middle
<img src="img/baby_plot.png" width="500px" />

---

## The R code

```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```

---
class: center, middle, inverse
# Break it down
---

### Load the R packages
<br>
.pull-left[
```r
*library(tidyverse) ; library(babynames)

```
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
```

```
## -- Attaching packages ----------------------------------------------------- tidyverse 1.2.1 --
```

```
## v ggplot2 3.1.1     v purrr   0.3.2
## v tibble  2.1.1     v dplyr   0.8.1
## v tidyr   0.8.3     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
```

```
## -- Conflicts -------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

---
### Access the `babynames` data
<br>
.pull-left[
```r
library(tidyverse) ; library(babynames)

*babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[

babynames %>%
* filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[

```
## # A tibble: 187 x 5
##     year sex   name      n      prop
##    <dbl> <chr> <chr> <int>     <dbl>
##  1  1880 F     Mabel   808 0.00828  
##  2  1881 F     Mabel   893 0.00903  
##  3  1882 F     Mabel   997 0.00862  
##  4  1883 F     Mabel  1086 0.00905  
##  5  1883 M     Mabel     6 0.0000533
##  6  1884 F     Mabel  1270 0.00923  
##  7  1885 F     Mabel  1349 0.00950  
##  8  1885 M     Mabel     7 0.0000604
##  9  1886 F     Mabel  1422 0.00925  
## 10  1886 M     Mabel     6 0.0000504
## # ... with 177 more rows
```
]

---
### Initialise a `ggplot()` object, inherit the filtered data, and map variables to the x and y axes
.pull-left[
```r
library(tidyverse) ; library(babynames

babynames %>%
  filter(name == "Mabel") %>%
* ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-6-1.png)
]

---
### Use `geom_line()` to connect observations, map the `sex` variable to colour and set line thickness
.pull-left[
```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
* geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-7-1.png)
]

---
### Specify which colours the `sex` variable is mapped to

.pull-left[
```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
* scale_color_manual(values = c("#66c2a5", "#ff7f00"),
*                    labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-8-1.png)
]

---
### Add a title, label the axes, include a caption, and drop the legend title

.pull-left[
```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
* labs(title = "Babies named Mabel between 1880 and 2015",
*      x = "Year", 
*      y = "Frequency",
*      caption = "Source: Social Security Administration",
*      color = "") +
  theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-9-1.png)
]

---
### Use a `ggplot2` theme to format the chart
<br>
.pull-left[
```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
* theme_minimal()  +
  theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-10-1.png)
]

---
### Move the legend to the top of the plot
<br>
.pull-left[
```r
library(tidyverse) ; library(babynames)

babynames %>%
  filter(name == "Mabel") %>%
  ggplot(data = ., aes(x = year, y = n)) +
  geom_line(aes(color = sex), size = 2) +
  scale_color_manual(values = c("#66c2a5", "#ff7f00"),
                     labels = c("Female", "Male")) +
  labs(title = "Babies named Mabel between 1880 and 2015",
       x = "Year", 
       y = "Frequency",
       caption = "Source: Social Security Administration",
       color = "") +
  theme_minimal()  +
* theme(legend.position = "top")
ggsave("baby_plot.png", scale=1, dpi=300)
```
]

.pull-right[
![](slides_files/figure-html/unnamed-chunk-11-1.png)
]

---
### Save the plot as a png at a high resolution
<br>
.pull-left[
```r
library(tidyverse) ; library(babynames)

.pull-right[
![](slides_files/figure-html/unnamed-chunk-12-1.png)
]

---
class: center, middle, inverse
# Your turn