The aim of this activity is to perform convenient visualization for data describing the evolution of wages and wheat price for English workers from the 16th to the 19th century.
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Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante:
<!--
library(reshape2)
library(Hmisc)
-->
We will need to use the following libraries:
```{r, results=FALSE, message=FALSE}
# The environment
library(tidyverse)
library(ggplot2)
library(reshape2)
library(Hmisc)
```
## Build the data frame
From the following link we have downloaded the data we are going to work with in the form of a csv file, and make it into data/ forlder: <https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv>
From the following link we have downloaded the data we are going to work with in the form of a csv file, and make it into **data/** folder: <https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv>
<!-- describe(df) -->
We assign it to a data frame as follows:
We build the data frame as follows, and we print a couple of rows to have a look at its structure:
```{r, message=FALSE}
df <- read.csv("data/Wheat.csv",header=T)
df[c(1,2),]
...
...
@@ -41,14 +40,47 @@ df[c(1,2),]
## Clean the data frame
We observe that the first column indicates a sort of an identifier for each data sample. This is not an interesting parameter, so we can simply omit it:
```{r, message=FALSE}
#only keep columns from 2 to 4 (column 1 is omitted)
#only keep columns from 2 to 4 (column 1 is omitted)
df <- df[c(2:4)]
df[c(1,2),]
```
## Plotting
## Reproducing Playfair's graph
**!!TODO : perform required transformations in terms of wheat-price & salary**
