Based on my recent OECD-package related post (http://www.supplychaindataanalytics.com/oecd-package-interface-in-r-reading-german-freight-transport-data-from-oecd-directly-in-r/) I extend my recenet analysis of German transport volume development by comparing different inland freight categories in a ggplot-chart. The data, again, i querried using the OECD-package in R.

library(OECD)

From the previous post we already know the ID-key of the transport-related dataset of interest. Using get_dataset (R function from the OECD database) I pull the data via the OECD interface in R.

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

Using dplyr I filter out the data entries of interest:

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),] 

After filtering entries are still distinguished by variables indicators. In order for me to be able to interpret those I pull the data structure, using the get_data_structure function from the OECD package in R:

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)

I can now create a ggplot path plot, comparing the following categories of interest to me: – Inland road freight – Inland rail freight – Inland pipeline transport – Inland waterway transport

library(ggplot2)
ggplot(data_df[data_df$variable == c("T-GOODS-IW-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")

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python