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本文作者：天善智能讲师邬书豪 。一入数据之门，当须精益求精。他日纵横捭阖，根据即在年轻！
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附视频链接：天善智能Kaggle十大案例精讲（连载中）提供案例代码和数据，可以实操。欢迎学习！（数据集和代码在链接公告处提供下载）
2017年8月26日，全球最大的数据科学社群Kaggle发布了数据科学／机器学习业界现状全行业调查的数据集。调查问卷数据从2017年8月7日~8月25日收集。受访者囊括了来自50多个国家的16，716+位从业者，根据kaggle的调查问卷数据集，我们挖掘一些有营养的信息。
################# =========== 导入数据+简单清洗 ============== #################
library(data.table)# fread()
library(dplyr)# group_by() / %>% / summaries()
library(ggplot2)# ggplot()
responses = fread("D:/R/天善智能/书豪十大案例/数据科学调查\\multipleChoiceResponses.csv")
## 把Country列中的表示中国的特征值改为中国
responses$Country <- ifelse(responses$Country =="Republic of China"|
responses$Country =="People 's Republic of China",
"China", responses$Country)
################# =========== 国家+年龄统计 ================ ##################
## 探索数据科学从业者的年龄中位数最大的十个国家
# 创建绘图所需的数据源（按照Country进行统计Age的中位数，并且按照Age进行降序排列）
df_country_age <- responses %>%
group_by(Country) %>%# 按照Country进行统计
summarise(AgeMedian = median(Age, na.rm = T)) %>%# 统计Age的中位数
arrange(desc(AgeMedian))# 按照Age进行降序排列
# reorder(Country, AgeMedian)--按照AgeMedian的升序排列其对应的Country
# head(df_country, 10)--选取数据源的前10行
# x参数中传入图中的x轴所需数据，y参数同理
# geom_bar()--绘制条形图的子函数
# fill = Country--按照Country填充条形图颜色
# stat(统计转换)参数设置为'identity'，即对原始数据集不作任何统计变换
# geom_text()--添加文本注释的子函数
# label = AgeMedian--添加AgeMedian中的内容
# hjust--控制横向对齐（0：底部对齐， 0.5：居中， 1：顶部对齐）
# colour--控制注释颜色
# theme_minimal()--是ggplot的一种主背景主题
ggplot(head(df_country_age,10), aes(x = reorder(Country, AgeMedian), y = AgeMedian)) +
geom_bar(aes(fill = Country),stat='identity') +
labs(x ='Country', y ='AgeMedian') +
geom_text(aes(label = AgeMedian), hjust =1.5, colour ='white') +
coord_flip() +
theme_minimal() +
theme(legend.position ='none')# 移除图例
# 封装绘图函数
fun1 <-function(data, xlab, ylab, xname, yname) {
ggplot(data, aes(xlab, ylab)) +
geom_bar(aes(fill = xlab),stat='identity') +
labs(x = xname, y = yname) +
geom_text(aes(label = ylab), hjust =1.5, colour ='white') +
coord_flip() +
theme_minimal() +
theme(legend.position ='none')
}
data <- head(df_country_age,10)
xname <-'Country'
yname <-'AgeMedian'
fun1(data, reorder(data$Country, data$AgeMedian), data$AgeMedian, xname, yname)
## 探索数据科学从业者的年龄中位数最小的十个国家
################# =========== 职位统计 ==================== ####################
## 探索kaggler的当前职位
# 创建绘图所需的数据源（按照CurrentJobTitleSelect统计其个数，并按照个数进行降序排列）
df_CJT <- responses %>%
filter(CurrentJobTitleSelect !='') %>%# 筛选CurrentJobTitleSelect不为空的观测
group_by(CurrentJobTitleSelect) %>%# 按照CurrentJobTitleSelect统计
summarise(Count = n()) %>%# 统计其特征值的个数（Count）
arrange(desc(Count))# 按照个数（Count）进行降序排列
data <- head(df_CJT,10)
xname <-'Country'
yname <-'Count'
fun1(data, reorder(data$CurrentJobTitleSelect, data$Count), data$Count, xname, yname)
## 探索美国kaggler的当前职位
# 创建绘图所需的数据源（按照CurrentJobTitleSelect统计其个数，并按照个数进行降序排列）
df_CJT_USA <- responses %>%
# 筛选CurrentJobTitleSelect不为空且美国kaggler的观测
filter(CurrentJobTitleSelect !=''& Country =='United States') %>%
group_by(CurrentJobTitleSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_CJT_USA,10)
xname <-'CurrentJobTitleSelect'
yname <-'Count'
fun1(data, reorder(data$CurrentJobTitleSelect, data$Count), data$Count, xname, yname)
## 探索中国kaggler的当前职位
df_CJT_China <- responses %>%
filter(CurrentJobTitleSelect !=''& Country =='China') %>%
group_by(CurrentJobTitleSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_CJT_China,10)
xname <-'CurrentJobTitleSelect'
yname <-'Count'
fun1(data, reorder(data$CurrentJobTitleSelect, data$Count), data$Count, xname, yname)
################# =========== 明年将学习的机器学习工具 ============== ##########
## 探索kaggler明年将学习的机器学习工具
# 创建绘图所需数据源（按照MLToolNextYearSelect统计其个数，比按照其个数降序排列）
df_MLT <- responses %>%
filter(MLToolNextYearSelect !='') %>%# 筛选出MLToolNextYearSelect不为空的观测
group_by(MLToolNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLT,10)
xname <-'ML Tool'
yname <-'Count'
fun1(data, reorder(data$MLToolNextYearSelect, data$Count), data$Count, xname, yname)
## 探索美国kaggler明年将学习的机器学习工具
# 创建绘图所需数据源（按照MLToolNextYearSelect统计其个数，比按照其个数降序排列）
df_MLT_USA <- responses %>%
# 筛选出MLToolNextYearSelect不为空且美国kaggler的观测
filter(MLToolNextYearSelect !=''& Country =='United States') %>%
group_by(MLToolNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLT_USA,10)
xname <-'ML Tool'
yname <-'Count'
fun1(data, reorder(data$MLToolNextYearSelect, data$Count), data$Count, xname, yname)
## 探索中国kaggler明年将学习的机器学习工具
# 创建绘图所需数据源（按照MLToolNextYearSelect统计其个数，比按照其个数降序排列）
df_MLT_China <- responses %>%
# 筛选出MLToolNextYearSelect不为空且中国kaggler的观测
filter(MLToolNextYearSelect !=''& Country =='China') %>%
group_by(MLToolNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLT_China,10)
xname <-'ML Tool'
yname <-'Count'
fun1(data, reorder(data$MLToolNextYearSelect, data$Count), data$Count, xname, yname)
################# =========== 明年将学习的机器学习方法 ============= ###########
## 探索kaggler明年将学习的机器学习方法
# 创建绘图所需数据源（按照MLMethodNextYearSelect统计其个数，比按照其个数降序排列）
df_MLM <- responses %>%
filter(MLMethodNextYearSelect !='') %>%# 筛选MLMethodNextYearSelect不为空的观测
group_by(MLMethodNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLM,10)
xname <-'ML Method'
yname <-'Count'
fun1(data, reorder(data$MLMethodNextYearSelect, data$Count), data$Count, xname, yname)
## 探索美国kaggler明年将学习的机器学习方法
# 创建绘图所需数据源（按照MLMethodNextYearSelect统计其个数，比按照其个数降序排列）
df_MLM_USA <- responses %>%
# 筛选MLMethodNextYearSelect不为空且美国kaggler的观测
filter(MLMethodNextYearSelect !=''& Country =='United States') %>%
group_by(MLMethodNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLM_USA,10)
xname <-'ML Method'
yname <-'Count'
fun1(data, reorder(data$MLMethodNextYearSelect, data$Count), data$Count, xname, yname)
## 探索中国kaggler明年将学习的机器学习方法
# 创建绘图所需数据源（按照MLMethodNextYearSelect统计其个数，比按照其个数降序排列）
df_MLM_China <- responses %>%
# 筛选MLMethodNextYearSelect不为空且中国kaggler的观测
filter(MLMethodNextYearSelect !=''& Country =='China') %>%
group_by(MLMethodNextYearSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_MLM_China,10)
xname <-'ML Method'
yname <-'Count'
fun1(data, reorder(data$MLMethodNextYearSelect, data$Count), data$Count, xname, yname)
################# =========== 受访者来自的国家 ============= #################
## 探索kaggler都来自哪些国家
# 创建绘图所需数据源（按照Country统计其个数，比按照其个数降序排列）
df_Country <- responses %>%
filter(Country !=''& Country !='Other') %>%
group_by(Country) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_Country,10)
xname <-'Country'
yname <-'Count'
fun1(data, reorder(data$Country, data$Count), data$Count, xname, yname)
################# =========== 受访者的学历水平 ===================== ###########
## 探索kaggler的学历水平
# 创建绘图所需数据源（按照FormalEducation统计其个数，比按照其个数降序排列）
df_Education <- responses %>%
filter(FormalEducation !='') %>%
group_by(FormalEducation) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_Education,10)
xname <-'FormalEducation'
yname <-'Count'
fun1(data, reorder(data$FormalEducation, data$Count), data$Count, xname, yname)
################# =========== 受访者的就业状况 ================ ################
## 探索kaggler的就业状况
# 创建绘图所需数据源（按照EmploymentStatus统计其个数，比按照其个数降序排列）
df_Employment <- responses %>%
group_by(EmploymentStatus) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- head(df_Employment,10)
xname <-'EmploymentStatus'
yname <-'Count'
fun1(data, reorder(data$EmploymentStatus, data$Count), data$Count, xname, yname)
################# =========== 受访者的学习平台 =============== #################
## 探索kaggler在什么平台学习数据科学
# 按照‘,’拆分字符串---把CoursePlatformSelect列的字符依据‘,’拆分
platform <- unlist(strsplit(responses$CoursePlatformSelect,','))
# 统计不同字符串（平台）的频次并转换成数据框
platform <-as.data.frame(table(platform))
data <- platform
xname <-'platform'
yname <-'Count'
fun1(data, reorder(data$platform, data$Freq), data$Freq, xname, yname)
## 探索kaggler何种方式开始学习数据科学的
# 创建绘图所需数据源（按照FirstTrainingSelect统计其个数，比按照其个数降序排列
df_FT <- responses %>%
filter(FirstTrainingSelect !='') %>%
group_by(FirstTrainingSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- df_FT
xname <-'FirstTrainingSelect'
yname <-'Count'
fun1(data, reorder(data$FirstTrainingSelect, data$Count), data$Count, xname, yname)
################# =========== 任职数据科学的时间 =============== ###############
## 探索kaggler任职数据科学的时间
# 创建绘图所需数据源（按照Tenure统计其个数，比按照其个数降序排列
df_Tenure <- responses %>%
filter(Tenure !='') %>%
group_by(Tenure) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- df_Tenure
xname <-'Tenure'
yname <-'Count'
fun1(data, reorder(data$Tenure, data$Count), data$Count, xname, yname)
################# =========== 现任职的满意度 ======= ###########################
## 探索kaggler对现职位的满意度
# 创建绘图所需数据源（按照JobSatisfaction统计其个数，比按照其个数降序排列
df_JS <- responses %>%
filter(JobSatisfaction !='') %>%
group_by(JobSatisfaction) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- df_JS
xname <-'JobSatisfaction'
yname <-'Count'
fun1(data, reorder(data$JobSatisfaction, data$Count), data$Count, xname, yname)
################# =========== 首推的数据科学语言 ========== ####################
## 探索kaggler的首选语言
# 创建绘图所需数据源（按照LanguageRecommendationSelect统计其个数，并按照其个数降序排列
df_LR <- responses %>%
filter(LanguageRecommendationSelect !='') %>%
group_by(LanguageRecommendationSelect) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
data <- df_LR
xname <-'LanguageRecommendationSelect'
yname <-'Count'
fun1(data, reorder(data$LanguageRecommendationSelect, data$Count), data$Count, xname, yname)
################# =========== BigData数据科学证书、R、Python、SQL的重要程度 ====== ##########
## 创建新数据框（按照JobSkillImportanceR统计其个数，并按照Count降序排列）
df_r <- responses %>%
filter(JobSkillImportanceR !='') %>%
group_by(JobSkillImportanceR) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
df_r$Tool<-'R'# 创建新列，并赋值为‘R’
names(df_r) <- c("Importance","Count","Tool")# 对数据框重命名
df_python <- responses %>%
filter(JobSkillImportancePython !='') %>%
group_by(JobSkillImportancePython) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
df_python$Tool<-'python'
names(df_python) = c("Importance","Count","Tool")
df_BigData <- responses %>%
filter(JobSkillImportanceBigData !='') %>%
group_by(JobSkillImportanceBigData) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
df_BigData$Tool<-'BigData'
names(df_BigData) = c("Importance","Count","Tool")
df_SQL <- responses %>%
filter(JobSkillImportanceSQL !='') %>%
group_by(JobSkillImportanceSQL) %>%
summarise(Count = n()) %>%
arrange(desc(Count))
df_SQL$Tool<-'SQL'
names(df_SQL) = c("Importance","Count","Tool")
df_end <- rbind(df_python, df_r, df_BigData, df_SQL)# 对四个数据框进行行合并
## 绘制百分比堆积柱状图
# position = 'fill'意味着绘制百分比堆积柱状图
ggplot(df_end, aes(x = Tool, y = Count, fill = Importance)) +
geom_bar(position ='fill',stat='identity') +
labs(y ='Percent') +
theme_minimal()
################# =========== 5个不同国家对SQL、R和Python的推荐 ======== #######
## 创建数据源（以下5个国家+以下3个语言的观测）
df_country_language <- responses[responses$Country%in%
c("United States","India","Russia","Japan","China") &
responses$LanguageRecommendationSelect%in% c("R","Python","SQL"), ]
# 创建绘图所需数据源（按照Country+LanguageRecommendationSelect的组合统计其个数
df_cl <- df_country_language %>%
group_by(Country, LanguageRecommendationSelect) %>%
summarise(Count = n())
# 绘制百分比堆积柱状图（5个国家首推3个语言对比）
ggplot(df_cl, aes(x = Country, y = Count, fill = LanguageRecommendationSelect)) +
geom_bar(stat='identity') +
labs(y ='Percent') +
theme_minimal()
本文作者：天善智能讲师邬书豪。一入数据之门，当须精益求精。他日纵横捭阖，根据即在年轻！
更多内容，请戳：Kaggle十大案例精讲（连载中）
关注天善智能，我们是专注于商业智能BI，人工智能AI，大数据分析与挖掘领域的垂直社区，学习，问答、求职一站式搞定！
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