Reading and Graphing Data Using Scatterplot
Achmad Fahry Baihaki
2022-10-18
Name: Achmad Fahry Baihaki
NIM: 2206065110100
Institute: Maulana Malik Ibrahim Islamic State University of Malang
Departement: Computer Science
library(mosaicCalc)## Loading required package: mosaic## Registered S3 method overwritten by 'mosaic':
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## DIntro to scatterplot
Sometimes, the mathematical model that we’ll create will be motivated by data. Like statistical modelling. We’ll learn to process of setting parameters of a mathematical function to make the function a close representation of some data. It’s called curve fitting.
That means, we’ll have to learn how to access data in computer files, how data are stored and how to visualize the data. To do it all, R and the mosaic package make this straightforward.
The data files we’ll be using are stored as spreadsheets on the internet. Usually, the spreadsheet will have multiple variables; each variable is stored as one column. (The rows are “cases”, sometimes called “data points”). To read the data in to R, you need to know the name of the file and its location. Often, the location will be an address on the Internet.
In this case, we’ll work with “Income-housing.csv” data, which is located at “http://www.mosaic-web.org/go/datasets/Income-Housing.csv”. This data contains information from a survey on housing conditions for people in different income brackets in the US.
Here’s how to read it into R:
Housing = read.csv("http://www.mosaic-web.org/go/datasets/Income-Housing.csv")This is how we look the data:
Housing## Income IncomePercentile CrimeProblem AbandonedBuildings IncompleteBathroom
## 1 3914 5 39.6 12.6 2.6
## 2 10817 15 32.4 10.0 3.3
## 3 21097 30 26.7 7.1 2.3
## 4 34548 50 23.9 4.1 2.1
## 5 51941 70 21.4 2.3 2.4
## 6 72079 90 19.9 1.2 2.0
## NoCentralHeat ExposedWires AirConditioning TwoBathrooms MotorVehicle
## 1 32.3 5.5 52.3 13.9 57.3
## 2 34.7 5.0 55.4 16.9 82.1
## 3 28.1 2.4 61.7 24.8 91.7
## 4 21.4 2.1 69.8 39.6 97.0
## 5 14.9 1.4 73.9 51.2 98.0
## 6 9.6 1.0 76.7 73.2 99.0
## TwoVehicles ClothesWasher ClothesDryer Dishwasher Telephone
## 1 17.3 57.8 37.5 16.5 68.7
## 2 34.3 61.4 38.0 16.0 79.7
## 3 56.4 78.6 62.0 25.8 90.8
## 4 75.3 84.4 75.2 41.6 96.5
## 5 86.6 92.8 88.9 58.2 98.3
## 6 92.9 97.1 95.6 79.7 99.5
## DoctorVisitsUnder7 DoctorVisits7To18 NoDoctorVisitUnder7 NoDoctorVisit7To18
## 1 3.6 2.6 13.7 31.2
## 2 3.7 2.6 14.9 32.0
## 3 3.6 2.1 13.8 31.4
## 4 4.0 2.3 10.4 27.3
## 5 4.0 2.5 7.7 23.9
## 6 4.7 3.1 5.3 17.5We can access the names of all variabels sing names(objectName) syntax:
names(Housing)## [1] "Income" "IncomePercentile" "CrimeProblem"
## [4] "AbandonedBuildings" "IncompleteBathroom" "NoCentralHeat"
## [7] "ExposedWires" "AirConditioning" "TwoBathrooms"
## [10] "MotorVehicle" "TwoVehicles" "ClothesWasher"
## [13] "ClothesDryer" "Dishwasher" "Telephone"
## [16] "DoctorVisitsUnder7" "DoctorVisits7To18" "NoDoctorVisitUnder7"
## [19] "NoDoctorVisit7To18"Also we can access one of the variables. Just use objectName$variableName. Example:
Housing$AbandonedBuildings## [1] 12.6 10.0 7.1 4.1 2.3 1.2Making scatterplot
One of the most familiar graphical forms is the scatter-plot, a format in which each “case” or “data point” is plotted as a dot at the coordinate location given by two variables.
For instance, here’s a scatter plot of the fraction of household that have two vehicles , versus the median income in their bracket
gf_point(TwoVehicles ~ Income, data = Housing )
Because graphs are constructed in layers. If we want to plot a math function over the data, we need to use plotting function to make another layer. Then use %>% (pipe symbol) to display layers in the same plot. Note that %>% can never go at the start of a new line.
gf_point(
TwoVehicles ~ Income, data=Housing ) %>%
slice_plot(
20 - Income/2000 ~ Income, color = "blue")
If we prefer to set the limits of the axes to something of own choices, we can do this. For instance:
gf_point(
TwoVehicles ~ Income, data = Housing) %>%
slice_plot(
20-Income/2000 ~ Income, color = "blue") %>%
gf_lims(
x = range(0,40000),
y=range(0,100))## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 48 row(s) containing missing values (geom_path).
Even though math function drawn isn’t a very good match to the data, but the important thing is about how to draw graphs, not how to choose a family of functions or find parameters.
Properly made scientific graphics should have informative axis names. We can set the axis names directly using gf_labs:
gf_point(
TwoVehicles ~ Income, data=Housing) %>%
gf_labs(x= "Income Bracket ($US per household)/year",
y = "Fraction of Households",
main = "Two Vehicles") %>%
gf_lims(x = range(0,40000), y = range(0,100))## Warning: Removed 2 rows containing missing values (geom_point).