# Chapter 13 Revisiting earlier graphs

Now we’ve learned more about coding and transforming data(sets) we can recreate earlier graphs but through calculating the means ourselves, rather than have ggplot do it for us.

## 13.1 Recreating mean-plots

Remember this plot:

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_bar(stat = "summary", fun = "mean")``````

Let’s create the means ourselves:

``````# Create new dataframe mpg_means
mpg_means <- mpg %>%
group_by(manufacturer) %>% # Group the data by manufacturer
summarize(mean_cty = mean(cty)) # Create new variable which is the mean of cty
mpg_means``````
``````## # A tibble: 15 × 2
##    manufacturer mean_cty
##    <chr>           <dbl>
##  1 audi             17.6
##  2 chevrolet        15
##  3 dodge            13.1
##  4 ford             14
##  5 honda            24.4
##  6 hyundai          18.6
##  7 jeep             13.5
##  8 land rover       11.5
##  9 lincoln          11.3
## 10 mercury          13.2
## 11 nissan           18.1
## 12 pontiac          17
## 13 subaru           19.3
## 14 toyota           18.5
## 15 volkswagen       20.9``````

Now that we have done the work ourselves, we can plot the means! Importantly, we are not going to specify that we will create a “summary” with `stat="summary"` and neither will we specify a function `fun="mean"`. Of course, we need to tell ggplot that we’ll make use of our new dataset, and also that we’ll be using the `mean_cty` variable from this new dataset.

``````ggplot(mpg_means, aes(x = manufacturer, y = mean_cty)) +
geom_bar()``````
``Error: stat_count() must not be used with a y aesthetic.``

Hhmm, an error message. This is because `geom_bar()` apparently calculates some `stat`, so we can’t use our own y-variable. It’s further good to realise that `geom_bar()` as a default has `stat = "count"` (see `?geom_bar`). The trick here is to specify the `stat` as being `identity`; this means that the numbers that are explicitely specified should be used (they define the “identity” of the bars).

``````ggplot(mpg_means, aes(x = manufacturer, y = mean_cty)) +
geom_bar(stat = "identity")``````

Now we have reproduced our earlier plot! We’ve already seen two ways of creating this plot, but now we have a third way. This last option in which we calculate means ourselves is most laborious, but also gives us most flexibililty. Particularly if we want to do something more complex to our dataset than just calculating means.

Let’s also try to recreate the following graph:

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_point(stat = "summary", fun = "mean")``````

Et voila:

``````ggplot(mpg_means, aes(x = manufacturer, y = mean_cty)) +
geom_point()``````

Why do we need a `stat="identity"` when we use `geom_bar()` but not when we use `geom_point()`?

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_bar(stat = "summary", fun = "mean") +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96))``````

Let’s recreate errorbars by creating a new dataset. This time, in addition to calculating the mean, we will also calculate the standard error (which equates to the standard deviation divided by the square root of N).

``````# Create new dataframe mpg_means_se
mpg_means_se <- mpg %>%
group_by(manufacturer) %>% # Group the data by manufacturer
summarize(
mean_cty = mean(cty), # Create variable with mean of cty per group
sd_cty = sd(cty), # Create variable with sd of cty per group
N_cty = n(), # Create new variable N of cty per group
se = sd_cty / sqrt(N_cty), # Create variable with se of cty per group
upper_limit = mean_cty + se, # Upper limit
lower_limit = mean_cty - se # Lower limit
)
mpg_means_se``````
``````## # A tibble: 15 × 7
##    manufacturer mean_cty sd_cty N_cty    se upper_limit lower_limit
##    <chr>           <dbl>  <dbl> <int> <dbl>       <dbl>       <dbl>
##  1 audi             17.6  1.97     18 0.465        18.1        17.1
##  2 chevrolet        15    2.92     19 0.671        15.7        14.3
##  3 dodge            13.1  2.49     37 0.409        13.5        12.7
##  4 ford             14    1.91     25 0.383        14.4        13.6
##  5 honda            24.4  1.94      9 0.648        25.1        23.8
##  6 hyundai          18.6  1.50     14 0.401        19.0        18.2
##  7 jeep             13.5  2.51      8 0.886        14.4        12.6
##  8 land rover       11.5  0.577     4 0.289        11.8        11.2
##  9 lincoln          11.3  0.577     3 0.333        11.7        11
## 10 mercury          13.2  0.5       4 0.25         13.5        13
## 11 nissan           18.1  3.43     13 0.950        19.0        17.1
## 12 pontiac          17    1         5 0.447        17.4        16.6
## 13 subaru           19.3  0.914    14 0.244        19.5        19.0
## 14 toyota           18.5  4.05     34 0.694        19.2        17.8
## 15 volkswagen       20.9  4.56     27 0.877        21.8        20.0``````

Now let’s use this information to add an errorbar with the help of `geom_errorbar`, which requires as input a minimim boundary `ymin` and a maximum boundary `ymax`.

``````ggplot(mpg_means_se, aes(x = manufacturer, y = mean_cty)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = lower_limit, ymax = upper_limit))``````

## 13.3 Using multiple datasets in one graph

We’ve also seen (a version of) this graph:

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_jitter(colour = "lightblue", alpha = 0.5, width = 0.1) +
geom_point(stat = "summary", fun = "mean") +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96), width = 0) +
labs(x = "Manufacturer", y = "Mileage per gallon of fuel (mean + 95%CI)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))``````

To recreate this graph with the ‘manual’ effort we put into calculating the averages, we have to use two datasets in one graph. In each layer in ggplot, we can add another dataset by specifying `data = name_of_other_dataset`/ Let’s first try to get the average in our graph that we’ve calculated ourselves in `mpg_means_se`:

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_jitter(colour = "lightblue", alpha = 0.5, width = 0.1) +
geom_point(data = mpg_means_se) +
labs(x = "Manufacturer", y = "Mileage per gallon of fuel (mean + 95%CI)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))``````
``````## Error in `geom_point()`:
## ! Problem while computing aesthetics.
## ℹ Error occurred in the 2nd layer.
## Caused by error:

The reason why this doesn’t work is because `cty` does not exist in `mpg_means_se`! In `mpg_means_se` only the variable `mean_cty` exists.

``````ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_jitter(colour = "lightblue", alpha = 0.5, width = 0.1) +
geom_point(data = mpg_means_se, aes(y = mean_cty)) +
labs(x = "Manufacturer", y = "Mileage per gallon of fuel (mean + 95%CI)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))``````

Why don’t we have to specify `x=manufacturer` within `aes()` the second time?

Now let’s add the errorbar as well. Again, we have to realise that we should put `y=cty` only in the layer where it’s appropriate:

``````ggplot(mpg, aes(x = manufacturer)) +
geom_jitter(aes(y = cty), colour = "lightblue", alpha = 0.5, width = 0.1) +
geom_point(data = mpg_means_se, aes(y = mean_cty)) +
geom_errorbar(data = mpg_means_se,
aes(ymin = lower_limit, ymax = upper_limit)) +
labs(x = "Manufacturer", y = "Mileage per gallon of fuel (mean + 95%CI)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))``````

### 13.3.1 A quick preview of labels

Now that we know that we can use different datasets, we can use this to our advantage. Let’s add a label to most fuel efficient car:

``````mpg_max <- mpg %>% filter(cty == max(cty)) # dataframe with most fuel efficient car

ggplot(mpg, aes(x = reorder(manufacturer, cty, FUN = mean), y = cty)) +
geom_jitter(colour = "lightblue", alpha = 0.5, width = 0.1) +
geom_point(stat = "summary", fun = "mean") +
geom_errorbar(stat = "summary", fun.data = "mean_se",
fun.args = list(mult = 1.96), width = 0) +
geom_text(data = mpg_max, aes(label = model), size = 5, colour = "blue") +
labs(x = "Manufacturer", y = "Mileage per gallon of fuel (mean + 95%CI)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))``````

## 13.4 Line graphs

Let’s try the same trick, of using multiple datasets to create a graph. This time 3 datasets! We’ll make use of the gapminder dataset again.

Let’s first calculate the average lifespan across the world across time:

``````lifespan <- gapminder %>%
group_by(year) %>%
summarise(mean_lifespan = mean(lifeExp))``````

Let’s also create a dataset that focuses only on the Netherlands:

``````# This code does the same: NL <- gapminder[gapminder\$country == "Netherlands", ]
NL <- gapminder %>% filter(country == "Netherlands")``````

Let’s now create a graph with a line for each country across time based on the original dataset `gapminder`, the average across time based on our dataset `lifespan`, and the line for the Netherlands based on our dataset `NL`.

``````ggplot(gapminder, aes(x = year, y = lifeExp)) +
geom_line(aes(group = country)) +
geom_line(data = lifespan, aes(y = mean_lifespan), colour = "blue", size = 2) +
geom_line(data = NL, colour = "orange")``````