library(readxl)
data <- read_excel("C:/Users/24966/Desktop/Excercise/307676931_按文本_电影文化挪用效果调查问卷_256_256.xlsx")
names(data)[which(names(data) == "3、您对电影《花木兰》的满意度有多高？")] <- "mulan_satisfaction"
names(data)[which(names(data) == "4、您认为电影《花木兰》多大程度上体现出花木兰历史故事的文化内涵？")] <- "mulan_culture"
names(data)[which(names(data) == "5、您认为电影《花木兰》中中国文化元素的含量有多高")] <- "mulan_elements"回归满意度影响
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Running Code
数据读取
花木兰单变量回归
model_1 <- lm(mulan_satisfaction ~ mulan_culture, data = data)
model_2 <- lm(mulan_satisfaction ~ mulan_elements, data = data)花木兰多元回归
model_3 <- lm(mulan_satisfaction ~ mulan_culture+mulan_elements, data = data)功夫熊猫单变量回归
names(data)[which(names(data) == "6、您对电影《功夫熊猫》的满意度有多高？")] <- "panda_satisfaction"
names(data)[which(names(data) == "7、您认为电影《功夫熊猫》多大程度上体现出中国功夫的文化内涵")] <- "panda_kungfu"
names(data)[which(names(data) == "8、您认为电影《功夫熊猫》中中国文化元素的含量有多高")] <- "panda_elements"
# 去除缺失值
df <- na.omit(data[, c("panda_satisfaction", "panda_kungfu", "panda_elements")])
# 多元线性回归模型
model_4 <- lm(panda_satisfaction ~ panda_kungfu , data = df)
model_5 <- lm(panda_satisfaction ~ panda_elements, data = df)功夫熊猫多元回归
model_6 <- lm(panda_satisfaction ~ panda_kungfu+panda_elements , data = df)# 重命名列（推荐，可避免中文出错）
names(data)[which(names(data) == "9、您接触或学习外来文化的兴趣有多高？")] <- "interest_foreign"
names(data)[which(names(data) == "10、您认为外来文化对社会的重要性如何？")] <- "importance_foreign"
names(data)[which(names(data) == "11、您认为文化交融会促进本土文化的发展吗？")] <- "integration_effect"
# 计算这三列的行平均值，创建新变量
data$culture_openness <- rowMeans(data[, c("interest_foreign", "importance_foreign", "integration_effect")], na.rm = TRUE)
# 查看结果
head(data[, c("interest_foreign", "importance_foreign", "integration_effect", "culture_openness")])# A tibble: 6 × 4
interest_foreign importance_foreign integration_effect culture_openness
<dbl> <dbl> <dbl> <dbl>
1 7 7 7 7
2 4 4 4 4
3 6 5 7 6
4 5 3 4 4
5 6 6 7 6.33
6 6 4 4 4.67包容度和满意度回归(功夫熊猫）
model_7 <- lm(panda_satisfaction ~ culture_openness, data = data)model_8 <- lm(mulan_satisfaction ~ culture_openness, data = data)summary(model_1)
Call:
lm(formula = mulan_satisfaction ~ mulan_culture, data = data)
Residuals:
Min 1Q Median 3Q Max
-3.9470 -0.2433 0.0530 0.2011 3.7567
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.68781 0.15024 4.578 7.34e-06 ***
mulan_culture 0.85184 0.03119 27.308 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7302 on 255 degrees of freedom
Multiple R-squared: 0.7452, Adjusted R-squared: 0.7442
F-statistic: 745.7 on 1 and 255 DF, p-value: < 2.2e-16summary(model_2)
Call:
lm(formula = mulan_satisfaction ~ mulan_elements, data = data)
Residuals:
Min 1Q Median 3Q Max
-3.7219 -0.5588 0.1151 0.4412 3.9520
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.53713 0.20107 2.671 0.00804 **
mulan_elements 0.83695 0.03988 20.984 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.876 on 255 degrees of freedom
Multiple R-squared: 0.6333, Adjusted R-squared: 0.6318
F-statistic: 440.3 on 1 and 255 DF, p-value: < 2.2e-16summary(model_3)
Call:
lm(formula = mulan_satisfaction ~ mulan_culture + mulan_elements,
data = data)
Residuals:
Min 1Q Median 3Q Max
-3.2661 -0.2134 0.0352 0.2367 3.3365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.23349 0.15825 1.475 0.141
mulan_culture 0.61706 0.04776 12.921 < 2e-16 ***
mulan_elements 0.31577 0.05090 6.204 2.22e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6818 on 254 degrees of freedom
Multiple R-squared: 0.7787, Adjusted R-squared: 0.777
F-statistic: 446.9 on 2 and 254 DF, p-value: < 2.2e-16summary(model_4)
Call:
lm(formula = panda_satisfaction ~ panda_kungfu, data = df)
Residuals:
Min 1Q Median 3Q Max
-5.5984 -0.2740 0.0638 0.4016 2.7127
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.96288 0.25758 7.621 4.97e-13 ***
panda_kungfu 0.66222 0.04655 14.227 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8408 on 255 degrees of freedom
Multiple R-squared: 0.4425, Adjusted R-squared: 0.4403
F-statistic: 202.4 on 1 and 255 DF, p-value: < 2.2e-16summary(model_5)
Call:
lm(formula = panda_satisfaction ~ panda_elements, data = df)
Residuals:
Min 1Q Median 3Q Max
-5.5330 -0.2435 0.1118 0.4670 2.4012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0198 0.2672 7.558 7.36e-13 ***
panda_elements 0.6447 0.0478 13.487 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8603 on 255 degrees of freedom
Multiple R-squared: 0.4164, Adjusted R-squared: 0.4141
F-statistic: 181.9 on 1 and 255 DF, p-value: < 2.2e-16summary(model_6)
Call:
lm(formula = panda_satisfaction ~ panda_kungfu + panda_elements,
data = df)
Residuals:
Min 1Q Median 3Q Max
-5.6904 -0.3169 0.0418 0.3625 2.5063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.56466 0.26465 5.912 1.08e-08 ***
panda_kungfu 0.41163 0.07248 5.679 3.70e-08 ***
panda_elements 0.32062 0.07275 4.407 1.55e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.812 on 254 degrees of freedom
Multiple R-squared: 0.4821, Adjusted R-squared: 0.478
F-statistic: 118.2 on 2 and 254 DF, p-value: < 2.2e-16summary(model_7)
Call:
lm(formula = panda_satisfaction ~ culture_openness, data = data)
Residuals:
Min 1Q Median 3Q Max
-4.2634 -0.6128 0.0860 0.6883 2.7848
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6429 0.3490 7.573 6.71e-13 ***
culture_openness 0.5241 0.0619 8.467 2.02e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9949 on 255 degrees of freedom
Multiple R-squared: 0.2194, Adjusted R-squared: 0.2164
F-statistic: 71.68 on 1 and 255 DF, p-value: 2.015e-15summary(model_8)
Call:
lm(formula = mulan_satisfaction ~ culture_openness, data = data)
Residuals:
Min 1Q Median 3Q Max
-4.0855 -0.6374 0.1385 0.9145 3.2587
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.73324 0.49337 5.54 7.53e-08 ***
culture_openness 0.33603 0.08751 3.84 0.000155 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.406 on 255 degrees of freedom
Multiple R-squared: 0.05466, Adjusted R-squared: 0.05096
F-statistic: 14.75 on 1 and 255 DF, p-value: 0.0001553可视化部分
library(ggplot2)
df$fitted <- fitted(model_7)
df$residuals <- resid(model_7)
ggplot(data, aes(x = culture_openness, y = panda_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "文化开放度对《功夫熊猫》满意度的影响",
x = "文化开放度 (culture_openness)",
y = "满意度 (panda_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
df$fitted <- fitted(model_8)
df$residuals <- resid(model_8)
ggplot(data, aes(x = culture_openness, y = mulan_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "文化开放度对《花木兰》满意度的影响",
x = "文化开放度 (culture_openness)",
y = "满意度 (mulan_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
df$fitted <- fitted(model_5)
df$residuals <- resid(model_5)
ggplot(data, aes(x = panda_elements, y = panda_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "熊猫元素对功夫熊猫满意度的影响",
x = "熊猫元素 (culture_openness)",
y = "满意度 (mulan_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
df$fitted <- fitted(model_4)
df$residuals <- resid(model_4)
ggplot(data, aes(x = panda_kungfu, y = panda_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "功夫元素对功夫熊猫满意度的影响",
x = "功夫元素 (culture_openness)",
y = "满意度 (mulan_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
df$fitted <- fitted(model_2)
df$residuals <- resid(model_2)
ggplot(data, aes(x = mulan_elements, y = mulan_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "木兰元素对花木兰满意度的影响",
x = "木兰元素 (culture_openness)",
y = "满意度 (mulan_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
df$fitted <- fitted(model_1)
df$residuals <- resid(model_1)
ggplot(data, aes(x = mulan_culture, y = mulan_satisfaction)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue") +
labs(title = "木兰文化对花木兰满意度的影响",
x = "木兰文化(culture_openness)",
y = "满意度 (mulan_satisfaction)") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
# 加载库
library(readxl)
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union# 读取数据
# 替换掉列名中的中文，简化变量名
names(data)[which(names(data) == "1、您的年龄段是？")] <- "age_group"
# 创建文化开放度变量
data$culture_openness <- rowMeans(data[, c("interest_foreign", "importance_foreign", "integration_effect")], na.rm = TRUE)
# 分类文化开放度为“低中高”
data$openness_level <- cut(
data$culture_openness,
breaks = c(-Inf, 4.5, 6.5, Inf),
labels = c("低", "中", "高")
)
# 生成堆积图数据
plot_data <- data %>%
filter(!is.na(age_group) & !is.na(openness_level)) %>%
group_by(age_group, openness_level) %>%
summarise(count = n(), .groups = "drop")
# 画堆积柱状图
ggplot(plot_data, aes(x = age_group, y = count, fill = openness_level)) +
geom_bar(stat = "identity") +
labs(title = "不同年龄段的文化开放度分布",
x = "年龄段",
y = "人数",
fill = "文化开放度") +
theme_minimal()