R: mapped_discrete` 对象只能从数字向量创建
R: mapped_discrete` objects can only be created from numeric vectors
我在 R 中有以下数据：
df <- structure(list(t0 = c(3.82, -4.88, NA, -3.83, -3.99, NA, NA,
NA, 6.35, 2.47, 0.28, 0.3, NA, 8.31, NA, NA, NA, 2.76, NA, 1.38
), t1 = c(NA, NA, NA, NA, NA, NA, -1.23, 2.19, 4.13, 3.49, -0.42,
NA, 3.78, 2.7, 1.17, NA, NA, NA, NA, NA), t2 = c(-1.85, NA, 1.46,
0.17, NA, NA, -2.81, 1.75, NA, 2.32, -3.08, -1.39, NA, 7.53,
1.77, NA, 0.1, NA, NA, -2.61), t3 = c(-2.05, 3.73, -2.04, -0.22,
-4.29, NA, NA, -0.11, 0.43, NA, -0.78, 3.24, NA, NA, -1.13, 1.09,
NA, NA, 2.7, NA), t4 = c(1.01, -2.77, NA, -3.05, -2.33, 3.78,
NA, NA, NA, NA, -2.04, -4.01, -2.32, 4, -0.28, NA, NA, 9.04,
NA, -4.12), t5 = c(1.56, NA, 4.89, NA, NA, NA, NA, NA, 0.88,
3.15, NA, NA, 2.59, NA, 2.04, NA, NA, NA, -0.26, NA), t6 = c(0.34,
-0.99, NA, 1.93, NA, NA, NA, NA, 0.35, NA, -6.46, NA, NA, NA,
2.57, NA, NA, 4.89, NA, -5.63), t7 = c(0.52, NA, 0.5, 1.85, -6.23,
NA, NA, 1.59, 7.82, 0.82, NA, NA, -1.77, NA, NA, NA, 2.01, NA,
0.7, -1.55), t8 = c(NA, NA, 4.9, -3.93, -8.13, 3.14, 0.03, 1.67,
3.55, NA, -1.55, 2.57, -0.87, NA, 0.71, -0.1, NA, NA, 2.04, NA
), t9 = c(-1.09, NA, -0.52, NA, NA, NA, NA, NA, NA, 2.05, -5.21,
-0.89, -0.03, NA, 0.66, 3.72, -1.96, NA, NA, NA)), row.names = c(NA,
20L), class = "data.frame")
使用以下教程 (https://jenslaufer.com/data/analysis/visualize_missing_values_with_ggplot.html)，我正在尝试制作显示缺失数据百分比的可视化：
library(dplyr)
library(ggplot2)
library(tidyverse)
row.plot <- df %>%
mutate(id = row_number()) %>%
gather(-id, key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
ggplot(aes(key, id, fill = isna)) +
geom_raster(alpha=0.8) +
scale_fill_manual(name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")) +
scale_x_discrete(limits = levels) +
labs(x = "Variable",
y = "Row Number", title = "Missing values in rows") +
coord_flip()
当我尝试查看结果时，这是我得到的错误：
row.plot
Error in `new_mapped_discrete()`:
! `mapped_discrete` objects can only be created from numeric vectors
Run `rlang::last_error()` to see where the error occurred.
Warning messages:
1: In structure(in_domain, pos = match(in_domain, breaks)) :
Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
Consider 'structure(list(), *)' instead.
2: In structure(in_domain, pos = match(in_domain, breaks)) :
Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
Consider 'structure(list(), *)' instead.
3: Removed 200 rows containing missing values (geom_raster).
我的问题：有人可以告诉我我做错了什么吗？我该如何解决这个错误？最后想得到这样一张图：
谢谢！
您似乎想要为每一行的缺失数据而不是为每个变量生成此图（尽管我在此处提供了这两种数据）。主要问题是没有提供 levels，所以我们可以在这里创建它，然后作为一个因素提供给 scale_x_discrete。
library(tidyverse)
output <- df %>%
mutate(id = row_number()) %>%
pivot_longer(-id, names_to = "key", values_to = "val") %>%
select(-key) %>%
group_by(id) %>%
mutate(isna = is.na(val),
total = n()) %>%
group_by(id, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- output %>% filter(isna == T) %>% arrange(desc(pct)) %>% pull(id)
row.plot <- output %>%
ggplot() +
geom_bar(aes(
x = reorder(id, desc(pct)),
y = pct,
fill = isna
),
stat = 'identity',
alpha = 0.8) +
scale_x_discrete(limits = factor(levels)) +
scale_fill_manual(
name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")
) +
coord_flip() +
labs(title = "Percentage of missing values", x =
'Row Number', y = "% of missing values")
输出
或者如果你想通过变量来做，那么：
output <- df %>%
pivot_longer(everything(), names_to = "key", values_to = "val") %>%
group_by(key) %>%
mutate(isna = is.na(val),
total = n()) %>%
group_by(key, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- output %>% filter(isna == T) %>% arrange(desc(pct)) %>% pull(key)
row.plot <- output %>%
ggplot() +
geom_bar(aes(
x = reorder(key, desc(pct)),
y = pct,
fill = isna
),
stat = 'identity',
alpha = 0.8) +
scale_x_discrete(limits = levels) +
scale_fill_manual(
name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")
) +
coord_flip() +
labs(title = "Percentage of missing values", x =
'Variable', y = "% of missing values")
输出
错误是由scale_x_discrete引起的。
您不需要它，因为在您的示例中 id 是数字并且没有 levels 作为 factor 会：
df %>%
mutate(id = row_number()) %>%
gather(-id, key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
ggplot(aes(key, id, fill = isna)) +
geom_raster(alpha=0.8) +
scale_fill_manual(name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")) +
#scale_x_discrete(limits = levels)
labs(x = "Variable",
y = "Row Number", title = "Missing values in rows") +
coord_flip()
当我运行你教程中的代码和你的数据时，没有错误。也许你想要这样的东西：
library(tidyverse)
missing.values <- df %>%
gather(key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
group_by(key) %>%
mutate(total = n()) %>%
group_by(key, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- (missing.values %>% filter(isna == T) %>% arrange(desc(pct)))$key
percentage.plot <- missing.values %>%
ggplot() +
geom_bar(aes(x = reorder(key, desc(pct)), y = pct, fill=isna), stat = 'identity', alpha=0.8, width = 1) +
scale_x_discrete(limits = levels) +
scale_fill_manual(name = "", values = c('goldenrod3', 'firebrick3'), labels = c("Present", "Missing")) +
coord_flip() +
labs(title = "Percentage of missing values", x = 'Variable', y = "% of missing values") +
theme_bw() +
theme(panel.grid = element_blank(),
panel.border = element_blank())
输出：
我在 R 中有以下数据：
df <- structure(list(t0 = c(3.82, -4.88, NA, -3.83, -3.99, NA, NA,
NA, 6.35, 2.47, 0.28, 0.3, NA, 8.31, NA, NA, NA, 2.76, NA, 1.38
), t1 = c(NA, NA, NA, NA, NA, NA, -1.23, 2.19, 4.13, 3.49, -0.42,
NA, 3.78, 2.7, 1.17, NA, NA, NA, NA, NA), t2 = c(-1.85, NA, 1.46,
0.17, NA, NA, -2.81, 1.75, NA, 2.32, -3.08, -1.39, NA, 7.53,
1.77, NA, 0.1, NA, NA, -2.61), t3 = c(-2.05, 3.73, -2.04, -0.22,
-4.29, NA, NA, -0.11, 0.43, NA, -0.78, 3.24, NA, NA, -1.13, 1.09,
NA, NA, 2.7, NA), t4 = c(1.01, -2.77, NA, -3.05, -2.33, 3.78,
NA, NA, NA, NA, -2.04, -4.01, -2.32, 4, -0.28, NA, NA, 9.04,
NA, -4.12), t5 = c(1.56, NA, 4.89, NA, NA, NA, NA, NA, 0.88,
3.15, NA, NA, 2.59, NA, 2.04, NA, NA, NA, -0.26, NA), t6 = c(0.34,
-0.99, NA, 1.93, NA, NA, NA, NA, 0.35, NA, -6.46, NA, NA, NA,
2.57, NA, NA, 4.89, NA, -5.63), t7 = c(0.52, NA, 0.5, 1.85, -6.23,
NA, NA, 1.59, 7.82, 0.82, NA, NA, -1.77, NA, NA, NA, 2.01, NA,
0.7, -1.55), t8 = c(NA, NA, 4.9, -3.93, -8.13, 3.14, 0.03, 1.67,
3.55, NA, -1.55, 2.57, -0.87, NA, 0.71, -0.1, NA, NA, 2.04, NA
), t9 = c(-1.09, NA, -0.52, NA, NA, NA, NA, NA, NA, 2.05, -5.21,
-0.89, -0.03, NA, 0.66, 3.72, -1.96, NA, NA, NA)), row.names = c(NA,
20L), class = "data.frame")
使用以下教程 (https://jenslaufer.com/data/analysis/visualize_missing_values_with_ggplot.html)，我正在尝试制作显示缺失数据百分比的可视化：
library(dplyr)
library(ggplot2)
library(tidyverse)
row.plot <- df %>%
mutate(id = row_number()) %>%
gather(-id, key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
ggplot(aes(key, id, fill = isna)) +
geom_raster(alpha=0.8) +
scale_fill_manual(name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")) +
scale_x_discrete(limits = levels) +
labs(x = "Variable",
y = "Row Number", title = "Missing values in rows") +
coord_flip()
当我尝试查看结果时，这是我得到的错误：
row.plot
Error in `new_mapped_discrete()`:
! `mapped_discrete` objects can only be created from numeric vectors
Run `rlang::last_error()` to see where the error occurred.
Warning messages:
1: In structure(in_domain, pos = match(in_domain, breaks)) :
Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
Consider 'structure(list(), *)' instead.
2: In structure(in_domain, pos = match(in_domain, breaks)) :
Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
Consider 'structure(list(), *)' instead.
3: Removed 200 rows containing missing values (geom_raster).
我的问题：有人可以告诉我我做错了什么吗？我该如何解决这个错误？最后想得到这样一张图：
谢谢！
您似乎想要为每一行的缺失数据而不是为每个变量生成此图（尽管我在此处提供了这两种数据）。主要问题是没有提供 levels，所以我们可以在这里创建它，然后作为一个因素提供给 scale_x_discrete。
library(tidyverse)
output <- df %>%
mutate(id = row_number()) %>%
pivot_longer(-id, names_to = "key", values_to = "val") %>%
select(-key) %>%
group_by(id) %>%
mutate(isna = is.na(val),
total = n()) %>%
group_by(id, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- output %>% filter(isna == T) %>% arrange(desc(pct)) %>% pull(id)
row.plot <- output %>%
ggplot() +
geom_bar(aes(
x = reorder(id, desc(pct)),
y = pct,
fill = isna
),
stat = 'identity',
alpha = 0.8) +
scale_x_discrete(limits = factor(levels)) +
scale_fill_manual(
name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")
) +
coord_flip() +
labs(title = "Percentage of missing values", x =
'Row Number', y = "% of missing values")
输出
或者如果你想通过变量来做，那么：
output <- df %>%
pivot_longer(everything(), names_to = "key", values_to = "val") %>%
group_by(key) %>%
mutate(isna = is.na(val),
total = n()) %>%
group_by(key, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- output %>% filter(isna == T) %>% arrange(desc(pct)) %>% pull(key)
row.plot <- output %>%
ggplot() +
geom_bar(aes(
x = reorder(key, desc(pct)),
y = pct,
fill = isna
),
stat = 'identity',
alpha = 0.8) +
scale_x_discrete(limits = levels) +
scale_fill_manual(
name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")
) +
coord_flip() +
labs(title = "Percentage of missing values", x =
'Variable', y = "% of missing values")
输出
错误是由scale_x_discrete引起的。
您不需要它，因为在您的示例中 id 是数字并且没有 levels 作为 factor 会：
df %>%
mutate(id = row_number()) %>%
gather(-id, key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
ggplot(aes(key, id, fill = isna)) +
geom_raster(alpha=0.8) +
scale_fill_manual(name = "",
values = c('steelblue', 'tomato3'),
labels = c("Present", "Missing")) +
#scale_x_discrete(limits = levels)
labs(x = "Variable",
y = "Row Number", title = "Missing values in rows") +
coord_flip()
当我运行你教程中的代码和你的数据时，没有错误。也许你想要这样的东西：
library(tidyverse)
missing.values <- df %>%
gather(key = "key", value = "val") %>%
mutate(isna = is.na(val)) %>%
group_by(key) %>%
mutate(total = n()) %>%
group_by(key, total, isna) %>%
summarise(num.isna = n()) %>%
mutate(pct = num.isna / total * 100)
levels <- (missing.values %>% filter(isna == T) %>% arrange(desc(pct)))$key
percentage.plot <- missing.values %>%
ggplot() +
geom_bar(aes(x = reorder(key, desc(pct)), y = pct, fill=isna), stat = 'identity', alpha=0.8, width = 1) +
scale_x_discrete(limits = levels) +
scale_fill_manual(name = "", values = c('goldenrod3', 'firebrick3'), labels = c("Present", "Missing")) +
coord_flip() +
labs(title = "Percentage of missing values", x = 'Variable', y = "% of missing values") +
theme_bw() +
theme(panel.grid = element_blank(),
panel.border = element_blank())
输出：