'data.frame': 1000 obs. of 9 variables:
$ Age : num 67 22 49 45 53 35 53 35 61 28 ...
$ Gender : chr "male" "female" "male" "male" ...
$ Housing : chr "own" "own" "own" "free" ...
$ Saving accounts : chr NA "little" "little" "little" ...
$ Checking account: chr "little" "moderate" NA "little" ...
$ Credit amount : num 1169 5951 2096 7882 4870 ...
$ Duration : num 6 48 12 42 24 36 24 36 12 30 ...
$ Purpose : chr "radio/TV" "radio/TV" "education" "furniture/equipment" ...
$ Class Risk : num 1 2 1 1 2 1 1 1 1 2 ...Lab 3.2
Plot Revisions Based on Peer Feedback
Julie Milligan
Looking at the Data
- Structure of the dataset
Looking at the Data
- First 10 rows
Age Gender Housing Saving accounts Checking account Credit amount Duration
1 67 male own <NA> little 1169 6
2 22 female own little moderate 5951 48
3 49 male own little <NA> 2096 12
4 45 male free little little 7882 42
5 53 male free little little 4870 24
6 35 male free <NA> <NA> 9055 36
7 53 male own quite rich <NA> 2835 24
8 35 male rent little moderate 6948 36
9 61 male own rich <NA> 3059 12
10 28 male own little moderate 5234 30
Purpose Class Risk
1 radio/TV 1
2 radio/TV 2
3 education 1
4 furniture/equipment 1
5 car 2
6 education 1
7 furniture/equipment 1
8 car 1
9 radio/TV 1
10 car 2Looking at the Data
- Last 10 rows
Age Gender Housing Saving accounts Checking account Credit amount Duration
991 37 male own <NA> <NA> 3565 12
992 34 male own moderate <NA> 1569 15
993 23 male rent <NA> little 1936 18
994 30 male own little little 3959 36
995 50 male own <NA> <NA> 2390 12
996 31 female own little <NA> 1736 12
997 40 male own little little 3857 30
998 38 male own little <NA> 804 12
999 23 male free little little 1845 45
1000 27 male own moderate moderate 4576 45
Purpose Class Risk
991 education 1
992 radio/TV 1
993 radio/TV 1
994 furniture/equipment 1
995 car 1
996 furniture/equipment 1
997 car 1
998 radio/TV 1
999 radio/TV 2
1000 car 1Summary Statistics
- For the “german” dataset
Age Gender Housing Saving accounts
Min. :19.00 Length:1000 Length:1000 Length:1000
1st Qu.:27.00 Class :character Class :character Class :character
Median :33.00 Mode :character Mode :character Mode :character
Mean :35.55
3rd Qu.:42.00
Max. :75.00
Checking account Credit amount Duration Purpose
Length:1000 Min. : 250 Min. : 4.0 Length:1000
Class :character 1st Qu.: 1366 1st Qu.:12.0 Class :character
Mode :character Median : 2320 Median :18.0 Mode :character
Mean : 3271 Mean :20.9
3rd Qu.: 3972 3rd Qu.:24.0
Max. :18424 Max. :72.0
Class Risk
Min. :1.0
1st Qu.:1.0
Median :1.0
Mean :1.3
3rd Qu.:2.0
Max. :2.0 Creating a Table for Factor Variable
- Factor variable = “Housing”
free own rent
108 713 179 Building the Plot
- Mapping
Building the Plot
- Geom
Building the Plot
- Co-Ordinates & Scales
Final Plot (Revised)
- Labels and Guides
Interpretations and Findings
From an initial first look, it is easy to tell that there is a drastic amount of individuals that own a house compared others that are either renting or housing for free. However, all of the different data points for each housing type are scattered up and down the chart. There appears to be more of a relationship between credit amount and age in the 20-40 age range and those credit amounts range more heavily around 1,000 to just under 10,000. For younger ages around 20, this is not a bad starting credit amount at all.
Peer Feedback
- Feedback provided by Erick Maldonado Caballero and Lincoln Morphis
The only revision my group provided me with on my final chart was the incorporation of dollar signs on the X axis. Since we are dealing with the variable “Credit amount”, it is important to make it clear to the viewer that they are dealing with money rather than just random numbers.
I was also provided with help from Lincoln to set up my CSS as that was one other factor I forgot to add the first time around.