R for Marketing Research and Analytics

Chris Chapman and Elea McDonnell Feit
January 2016

Chapter 3: Describing Data (Descriptive Statistics)

Website for all data files:
http://r-marketing.r-forge.r-project.org/data.html

Load the data

The book walks through simulation of nearly all the data sets … check that out, as there is much more about R in those sections.

For today, we'll just load the data from the website:

store.df <- read.csv("http://goo.gl/QPDdMl")
summary(store.df)

    storeNum          Year          Week          p1sales   
 Min.   :101.0   Min.   :1.0   Min.   : 1.00   Min.   : 73  
 1st Qu.:105.8   1st Qu.:1.0   1st Qu.:13.75   1st Qu.:113  
 Median :110.5   Median :1.5   Median :26.50   Median :129  
 Mean   :110.5   Mean   :1.5   Mean   :26.50   Mean   :133  
 3rd Qu.:115.2   3rd Qu.:2.0   3rd Qu.:39.25   3rd Qu.:150  
 Max.   :120.0   Max.   :2.0   Max.   :52.00   Max.   :263  

    p2sales         p1price         p2price         p1prom   
 Min.   : 51.0   Min.   :2.190   Min.   :2.29   Min.   :0.0  
 1st Qu.: 84.0   1st Qu.:2.290   1st Qu.:2.49   1st Qu.:0.0  
 Median : 96.0   Median :2.490   Median :2.59   Median :0.0  
 Mean   :100.2   Mean   :2.544   Mean   :2.70   Mean   :0.1  
 3rd Qu.:113.0   3rd Qu.:2.790   3rd Qu.:2.99   3rd Qu.:0.0  
 Max.   :225.0   Max.   :2.990   Max.   :3.19   Max.   :1.0  

     p2prom       country 
 Min.   :0.0000   AU:104  
 1st Qu.:0.0000   BR:208  
 Median :0.0000   CN:208  
 Mean   :0.1385   DE:520  
 3rd Qu.:0.0000   GB:312  
 Max.   :1.0000   JP:416  
                  US:312

Descriptives 1

table() for categorical variable

table(store.df$p1price)


2.19 2.29 2.49 2.79 2.99 
 395  444  423  443  375

The counts can be converted to proportions with prop.table()

prop.table(table(store.df$p1price))


     2.19      2.29      2.49      2.79      2.99 
0.1899038 0.2134615 0.2033654 0.2129808 0.1802885

Table as an object

Tables are objects that can be assigned and indexed:

p1.table <- table(store.df$p1price)
p1.table


2.19 2.29 2.49 2.79 2.99 
 395  444  423  443  375 

p1.table[3]

2.49 
 423 

str(p1.table)

 'table' int [1:5(1d)] 395 444 423 443 375
 - attr(*, "dimnames")=List of 1
  ..$ : chr [1:5] "2.19" "2.29" "2.49" "2.79" ...

Draw the map

Once the data is “mapped” to the locations, we can draw the visualization:

mapCountryData(p1sales.map, nameColumnToPlot="x", 
               mapTitle="Total P1 sales by Country",
               colourPalette=brewer.pal(7, "Greens"), 
               catMethod="fixedWidth", addLegend=FALSE)

plot of chunk unnamed-chunk-33

That's all for Chapter 3!

Break time

Notes

This presentation is based on Chapter 6 of Chapman and Feit, R for Marketing Research and Analytics © 2015 Springer. http://r-marketing.r-forge.r-project.org/

Exercises here use the Salaries data set from the car package, John Fox and Sanford Weisberg (2011). An R Companion to Applied Regression, Second Edition. Thousand Oaks CA: Sage. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion

All code in the presentation is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\ Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.