`geom_text()`

pixellated? How can I make it more crisp?You should use `annotate(geom = "text")`

instead of `geom_text()`

for annotation.

In the following visualisation we have annotated a histogram with a red line and red text to mark the mean. Note that both the line and the text appears pixellated/fuzzy.

```
mean_hwy <- round(mean(mpg$hwy), 2)
ggplot(mpg, aes(x = hwy)) +
geom_histogram(binwidth = 2) +
geom_segment(
x = mean_hwy, xend = mean_hwy,
y = 0, yend = 35,
color = "red"
) +
geom_text(
x = mean_hwy, y = 40,
label = paste("mean\n", mean_hwy),
color = "red"
)
```

This is because `geom_text()`

draws the geom once per each row of the data frame, and plotting these on top of each other. For annotation (as opposed to plotting the data using text as geometric objects to represent each observation) use `annotate()`

instead.

`geom_text()`

fits in the bounds of the plot?Set `vjust = "inward"`

and `hjust = "inward"`

in `geom_text()`

.

Suppose you have the following data frame and visualization. The labels at the edges of the plot are cut off slightly.

```
df <- tibble::tribble(
~x, ~y, ~name,
2, 2, "two",
3, 3, "three",
4, 4, "four"
)
ggplot(df, aes(x = x, y = y, label = name)) +
geom_text(size = 10)
```

You could manually extend axis limits to avoid this, but a more straightforward approach is to set `vjust = "inward"`

and `hjust = "inward"`

in `geom_text()`

.

Either calculate the counts ahead of time and place them on bars using `geom_text()`

or let `ggplot()`

calculate them for you and then add them to the plot using `stat_coun()`

with `geom = "text"`

.

Suppose you have the following bar plot and you want to add the number of cars that fall into each `drv`

level on their respective bars.

One option is to calculate the counts with `dplyr::count()`

and then pass them to the `label`

mapping in `geom_text()`

. Note that we expanded the y axis limit to get the numbers to fit on the plot.

```
mpg %>%
dplyr::count(drv) %>%
ggplot(aes(x = drv, y = n)) +
geom_col() +
geom_text(aes(label = n), vjust = -0.5) +
coord_cartesian(ylim = c(0, 110))
```

Another option is to let `ggplot()`

do the counting for you, and access these counts with `..count..`

that is mapped to the labels to be placed on the plot with `stat_count()`

.

```
ggplot(mpg, aes(x = drv)) +
geom_bar() +
stat_count(geom = "text", aes(label = ..count..), vjust = -0.5) +
coord_cartesian(ylim = c(0, 110))
```

First calculate the counts for each segment (e.g. with `dplyr::count()`

) and then place them on the bars with `geom_text()`

using `position_stack(vjust = 0.5)`

in the `position`

argument to place the values in the middle of the segments.

Suppose you have the following stacked bar plot.

You can first calculate the counts for each segment with `dplyr::count()`

, which will place these values in a column called `n`

.

```
mpg %>%
count(class, drv)
#> # A tibble: 12 × 3
#> class drv n
#> <chr> <chr> <int>
#> 1 2seater r 5
#> 2 compact 4 12
#> 3 compact f 35
#> 4 midsize 4 3
#> 5 midsize f 38
#> 6 minivan f 11
#> 7 pickup 4 33
#> 8 subcompact 4 4
#> 9 subcompact f 22
#> 10 subcompact r 9
#> 11 suv 4 51
#> 12 suv r 11
```

You can then pass this result directly to `ggplot()`

, draw the segments with appropriate heights with `y = n`

in the `aes`

thetic mapping and `geom_col()`

to draw the bars, and finally place the counts on the plot with `geom_text()`

.

Either calculate the prpportions ahead of time and place them on bars using `geom_text()`

or let `ggplot()`

calculate them for you and then add them to the plot using `stat_coun()`

with `geom = "text"`

.

Suppose you have the following bar plot but you want to display the proportion of cars that fall into each `drv`

level, instead of the count.

One option is to calculate the proportions with `dplyr::count()`

and then use `geom_col()`

to draw the bars

```
mpg %>%
dplyr::count(drv) %>%
mutate(prop = n / sum(n)) %>%
ggplot(aes(x = drv, y = prop)) +
geom_col()
```

Another option is to let `ggplot()`

do the calculation of proportions for you, and access these counts with `..prop..`

. Note that we also need to the `group = 1`

mapping for this option.