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R 如何删除显示 NA 的栏

How to get rid of that NA column? Is there a better way to deal with missing values in this case, than drop_na that I used? enter image description here

我的代码是:

plot2 <- plot2_longer %>%
  mutate(
    variable = recode(
      variable,
      "item28" = "item28",
      "item29" = "item29",
      "item30" = "item30",
      "item31" = "item31",
      "item32" = "item32",
      "item33" = "item33",
      "item34" = "item34")) %>%
  #drop_na() %>% #writing this out produces the NA colum
  mutate(value = recode(value, "1" = "option 1", "2" = "option 2", "3" = "option 3",
                        "4" = "option 4", "5" = "option 5", "6" = "option 6",
                        "7" = "option 7")) %>%
  count(variable, value) %>% # tai |>
  group_by(variable) %>%  # tai |>
  mutate(pct = n / sum(n))


plot2$value <- factor(plot2$value,
                      levels = plot2$value[order(plot2$pct, decreasing = FALSE)])


ggplot(plot2, mapping = aes(x = value, y = pct)) +
  geom_col(aes(fill = value),
           width = 0.30
  ) +
  scale_y_continuous(
    breaks = seq(from = 0, to = 1, by = 0.2),
    minor_breaks = seq(from = 0, to = 1, by = 0.1),
    labels = seq(from = 0, to = 100, by = 20),
    limits = c(0, 1),
    expand = c(0, 0)
  ) +
  scale_fill_manual(
    values = c('option 1' = "blue", 
               'option 2' = "blue", 
               'option 3' = "blue",
               'option 4' = "blue",
               'option 5' = "blue",
               'option 6' = "blue",
               'option 7' = "blue"),
    drop = FALSE
  ) +
  labs(x = "", y = "%") +
  guides(
    fill = "none"
  ) +
  theme(
    plot.margin = margin(t = 50, r =  30, b = 20, l = 5.5),
    plot.title = element_text(size = 20, face = "bold"),
    panel.background = element_blank(),
    panel.grid.major = element_line(colour = "grey"),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_line(colour = "lightgrey"),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_line(colour = "grey"),
    axis.text.y = element_text(
      size = 13,
      face = "bold",
      hjust = 0
    ), 
    axis.text.x = element_text(
      size = 13,
      face = "bold",
      hjust = 0
    ),
    axis.title.x = element_text(size = 20),
  ) +
  coord_flip() +
  ggtitle("Title")

我的数据:

structure(list(variable = c("item28", "item28", "item29", "item29", 
"item30", "item30", "item31", "item31", "item32", "item32", "item33", 
"item33", "item34", "item34"), value = structure(c(2L, NA, 5L, 
NA, 6L, NA, 7L, NA, 3L, NA, 4L, NA, 1L, NA), .Label = c("option 7", 
"option 1", "option 5", "option 6", "option 2", "option 3", "option 4"
), class = c("ordered", "factor")), n = c(38L, 109L, 110L, 37L, 
111L, 36L, 121L, 26L, 45L, 102L, 70L, 77L, 19L, 128L), pct = c(0.258503401360544, 
0.741496598639456, 0.748299319727891, 0.251700680272109, 0.755102040816326, 
0.244897959183673, 0.82312925170068, 0.17687074829932, 0.306122448979592, 
0.693877551020408, 0.476190476190476, 0.523809523809524, 0.129251700680272, 
0.870748299319728)), row.names = c(NA, -14L), groups = structure(list(
    variable = c("item28", "item29", "item30", "item31", "item32", 
    "item33", "item34"), .rows = structure(list(1:2, 3:4, 5:6, 
        7:8, 9:10, 11:12, 13:14), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -7L), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

较长的数据:

structure(list(variable = c("item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34", "item28", "item29", "item30", "item31", 
"item32", "item33", "item34"), value = structure(c(NA, 2L, 3L, 
4L, NA, NA, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, NA, NA, 4L, NA, 
NA, NA, NA, NA, NA, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 
NA, 4L, NA, 6L, NA, NA, NA, 3L, NA, NA, NA, NA, NA, NA, 3L, 4L, 
NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, NA, NA, 6L, 
7L, NA, NA, 3L, 4L, NA, 6L, NA, 1L, NA, 3L, NA, NA, NA, NA, NA, 
2L, 3L, 4L, 5L, NA, 7L, NA, 2L, 3L, NA, NA, NA, NA, NA, NA, 3L, 
4L, NA, NA, NA, NA, 2L, 3L, NA, NA, NA, NA, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, NA, 2L, NA, NA, NA, 6L, NA, NA, NA, NA, 4L, 5L, NA, NA, 
NA, NA, NA, 4L, NA, NA, NA, NA, 2L, 3L, NA, NA, NA, NA, NA, 2L, 
NA, 4L, NA, 6L, NA, NA, NA, 3L, 4L, NA, NA, NA, NA, 2L, 3L, 4L, 
5L, NA, 7L, 1L, 2L, 3L, 4L, NA, 6L, NA, NA, NA, NA, 4L, NA, NA, 
NA, NA, NA, 3L, 4L, NA, NA, NA, NA, 2L, 3L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 2L, 3L, 4L, NA, 6L, 7L, NA, NA, 3L, 
4L, NA, NA, 7L, NA, 2L, NA, NA, NA, 6L, NA, 1L, 2L, 3L, 4L, 5L, 
6L, NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, NA, NA, 4L, NA, 6L, NA, 
1L, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, NA, 
3L, NA, NA, NA, NA, NA, 2L, NA, 4L, 5L, NA, NA, NA, NA, 3L, 4L, 
5L, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 3L, NA, NA, NA, 
NA, 1L, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, 
2L, 3L, 4L, 5L, 6L, NA, 1L, 2L, 3L, 4L, 5L, 6L, NA, NA, 2L, NA, 
4L, NA, 6L, NA, 1L, NA, 3L, 4L, 5L, 6L, NA, NA, 2L, 3L, 4L, 5L, 
6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 3L, 4L, NA, NA, NA, 
1L, 2L, 3L, 4L, NA, NA, 7L, NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 
NA, 4L, NA, 6L, NA, 1L, 2L, 3L, 4L, 5L, NA, 7L, 1L, 2L, 3L, 4L, 
NA, NA, NA, NA, 2L, NA, 4L, 5L, NA, NA, NA, 2L, 3L, NA, 5L, 6L, 
NA, NA, 2L, 3L, 4L, 5L, NA, NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 
2L, 3L, NA, NA, NA, 7L, NA, NA, NA, 4L, 5L, 6L, NA, NA, 2L, 3L, 
NA, NA, NA, NA, NA, 2L, NA, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, NA, 
6L, 7L, 1L, 2L, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, NA, NA, 7L, 
NA, NA, 3L, 4L, NA, 6L, NA, 1L, NA, 3L, 4L, 5L, 6L, NA, NA, NA, 
NA, 4L, NA, NA, NA, NA, 2L, NA, 4L, 5L, 6L, NA, 1L, NA, 3L, 4L, 
NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 3L, NA, 5L, 6L, 
NA, NA, NA, 3L, NA, NA, NA, NA, NA, 2L, 3L, 4L, NA, 6L, NA, 1L, 
2L, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, 5L, 6L, NA, NA, 2L, 3L, 
4L, NA, 6L, NA, NA, NA, 3L, 4L, NA, 6L, NA, 1L, 2L, 3L, 4L, 5L, 
6L, NA, NA, NA, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, 5L, 6L, NA, 
NA, 2L, 3L, 4L, 5L, 6L, 7L, NA, 2L, NA, 4L, NA, NA, NA, NA, 2L, 
NA, 4L, NA, NA, NA, NA, 2L, 3L, 4L, 5L, NA, NA, NA, NA, 3L, NA, 
NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, 7L, 1L, 2L, NA, NA, NA, 6L, 
NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 3L, 4L, 5L, 6L, 7L, NA, 
2L, 3L, 4L, 5L, 6L, NA, NA, 2L, 3L, 4L, NA, 6L, NA, 1L, NA, 3L, 
4L, NA, NA, NA, 1L, 2L, 3L, NA, 5L, NA, NA, 1L, 2L, 3L, 4L, 5L, 
6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, NA, NA, 4L, NA, NA, NA, 
1L, 2L, 3L, 4L, NA, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, NA, NA, NA, 
NA, 4L, NA, NA, NA, NA, 2L, 3L, 4L, 5L, NA, NA, NA, 2L, 3L, 4L, 
NA, NA, NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, NA, 4L, 5L, NA, 
NA, NA, NA, NA, 4L, 5L, 6L, NA, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 
2L, 3L, 4L, 5L, 6L, NA, NA, 2L, NA, 4L, NA, NA, NA, NA, NA, NA, 
4L, NA, 6L, NA, NA, 2L, NA, 4L, NA, NA, NA, NA, 2L, 3L, 4L, 5L, 
NA, NA, NA, 2L, 3L, 4L, NA, NA, NA, NA, 2L, 3L, NA, 5L, 6L, NA, 
NA, 2L, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, NA, NA, NA, NA, NA, 2L, 
3L, 4L, NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 1L, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 3L, 4L, NA, 6L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 3L, NA, 5L, NA, NA, 1L, 
2L, NA, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, NA, 
4L, NA, 6L, NA, 1L, 2L, 3L, 4L, 5L, 6L, NA, 1L, 2L, 3L, 4L, NA, 
6L, NA, NA, 2L, 3L, 4L, 5L, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, 
NA, NA, 3L, 4L, 5L, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, 1L, 2L, 
NA, 4L, 5L, NA, NA, 1L, 2L, 3L, 4L, 5L, 6L, NA, NA, 2L, NA, 4L, 
NA, 6L, NA, NA, NA, 3L, 4L, 5L, NA, 7L, 1L, 2L, 3L, 4L, NA, 6L, 
7L, NA, NA, 3L, 4L, NA, NA, NA, NA, 2L, 3L, 4L, 5L, 6L, NA, NA, 
2L, 3L, 4L, NA, NA, 7L, NA, 2L, 3L, 4L, NA, 6L, NA, NA, 2L, 3L, 
4L, NA, 6L, NA, NA, 2L, 3L, 4L, NA, NA, NA, 1L, 2L, 3L, 4L, 5L, 
6L, NA), .Label = c("1", "2", "3", "4", "5", "6", "7"), class = c("ordered", 
"factor"))), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-1029L))

推荐答案

只要通过remove_missing(plot2)分，而不是plot2分到ggplot分

ggplot(remove_missing(plot2), mapping = aes(pct, value)) +
  geom_col(fill = '#97afc7', width = 0.30) +
  scale_x_continuous(breaks = 0:5/5, minor_breaks = 0:10/10,
                     labels = ~.x*100, limits = c(0, 1), expand = c(0, 0)) +
  labs(x = "%", y = NULL, title = "Title") +
  theme(plot.margin = margin(t = 50, r =  30, b = 20, l = 5.5),
        plot.title = element_text(size = 20, face = "bold"),
        panel.background = element_blank(),
        panel.grid.major = element_line(colour = "grey"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_line(colour = "lightgrey"),
        axis.ticks.y = element_blank(),
        axis.ticks.x = element_line(colour = "grey"),
        axis.text.x = element_text(size = 13, face = "bold", hjust = 0), 
        axis.text.y = element_text(size = 13, face = "bold", hjust = 0),
        axis.title.y = element_text(size = 20))

enter image description here

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