使用R中的OECD时间序列比较不同的德国内陆货运类别

基于我最近的OECD软件包相关文章（http://www.supplychaindataanalytics.com/oecd-package-interface-in-r-reading-german-freight-transport-data-from-oecd-direct-in-r/）通过在ggplot图表中比较不同的内陆货运类别，我扩展了对德国运输量发展的分析。再次使用OECD R包查询数据。

library(OECD)

从上一篇文章中，我们已经知道了与运输相关的数据集的ID密钥。使用get_dataset（来自OECD数据库的R函数），我通过R中的OECD接口提取数据。

data_df <- as.data.frame(get_dataset(dataset = "ITF_GOODS_TRANSPORT"))

使用dplyr筛选出感兴趣的数据条目：

library(dplyr)

colnames(data_df) <- c("country","variable","timeformat","unit","powercode","obsTime","obsValue","obsStatus")
data_df <- dplyr::filter(data_df,country=="DEU") 
data_df <- dplyr::filter(data_df,timeformat=="P1Y")
data_df <- dplyr::filter(data_df,unit=="TONNEKM") 
data_df <- data_df[is.na(data_df$obsStatus),]

过滤后，条目仍由变量指示符区分。为了使我能够解释这些数据，我使用R中OECD包中的get_data_structure函数来提取数据结构：

data_struct <- get_data_structure("ITF_GOODS_TRANSPORT")
data_struct$VARIABLE

##                  id                                      label
## 1  T-GOODS-TOT-INLD             Total inland freight transport
## 2    T-GOODS-RL-TOT                     Rail freight transport
## 3    T-GOODS-RD-TOT                     Road freight transport
## 4    T-GOODS-RD-REW Road freight transport for hire and reward
## 5    T-GOODS-RD-OWN      Road freight transport on own account
## 6    T-GOODS-IW-TOT         Inland waterways freight transport
## 7    T-GOODS-PP-TOT                        Pipelines transport
## 8         T-SEA-CAB      Coastal shipping (national transport)
## 9             T-SEA                         Maritime transport
## 10    T-CONT-RL-TEU            Rail containers transport (TEU)
## 11           T-CONT                       Containers transport
## 12    T-CONT-RL-TON         Rail containers transport (weight)
## 13   T-CONT-SEA-TEU        Maritime containers transport (TEU)
## 14   T-CONT-SEA-TON     Maritime containers transport (weight)

我现在可以创建一个ggplot路径图，比较以下感兴趣的类别：1）内陆货运2）内陆铁路货运3）内陆管道运输4）内陆水路运输

library(ggplot2)
ggplot(data_df[data_df$variable == c("T-GOODS-IW-TOT",
                                     "T-GOODS-PP-TOT",
                                     "T-GOODS-RD-TOT",
                                     "T-GOODS-RL-TOT"),]) + 
  geom_path(mapping = aes(x=as.numeric(obsTime),y=obsValue, color=variable)) +
  ggtitle("German inland freight development by considered category") +
  xlab("year") +
  ylab("in millions of TONNEKM")