The document is an assignment on data frames and scatterplots in R, explaining what a data frame is and how to create and manipulate it, as well as how to visualize data using scatterplots. It covers accessing columns and rows, subsetting data frames, and styling plots with different colors. Additionally, it introduces the ggplot2 package for more sophisticated plotting and provides resources for further learning.

1. DEPARTMENT OF COMPUTER
SCIENCE & APPLICALION
SESSION(2019-20)
ATAL BIHARI VAJPAYEE VISHWAVIDYALAYA
SUBMITTED TO
DR. jeetendra gupta
PROFESSOR
SUBMITTED BY
UJJWAL MATOLIYA
B.Sc. 1ST YEAR
Roll no. 37
Assignment
R: Data Frames and Scatterplots

2. What is a data frame?
• In the section on exploring Q→Q relationships, you loaded a “data
frame” called h
• A data frame is R’s representation of a table
• Columns and rows can have names
• Each column can contain data of a different type
• E.g. some columns might be numerical data, and some might be categorical
• Categorical variables in R are called factors
• By contrast, a matrix is a table in which all the data are the same type

3. Examining Data Frames
• In Rstudio, we can view a data frame by clicking on it in the
Environment tab

4. Examining the data frame
• In this example, the data frame has three columns called gender,
height, and weight
• We can see this in code by checking the column names:
> colnames(h)
[1] "gender" "height" "weight”
• And we can access a single column using the $ operator:
> head(h$gender)
[1] 0 0 0 1 1 1
• Note head(…) gives the first few elements of an object

5. Getting the size of the data frame
• We can get the number of rows, the number of columns, or the
dimension (i.e. both) of the data frame:
> nrow(h)
[1] 81
> ncol(h)
[1] 3
> dim(h)
[1] 81 3

6. Scatterplots
• The basic plot(…) command can take two numeric vectors, in which
case it will produce a scatter plot
• So here we plot the height of the subjects as the x-value and the
weight as the y-value:
plot(h$height, h$weight)

7. Styling the plot
• Create labels for the axes with the xlab and ylab
parameters
• Use the col parameter to change the color of the
points
• In the following code, we first plot all points in blue,
then replot the points for the females in red:
> plot(h$height,h$weight,
xlab="Height (inches)",
ylab="Weight (lbs)",col="blue")
>points(h$height[h$gender==1],h$wei
ght[h$gender==1],col="red")

8. Subsetting vectors
• Use square brackets […] to access elements of a vector or data frame:
> h$height[c(1,2,3)]
[1] 72 67 65
> h$height[1:3] #equivalent to above
[1] 72 67 65
• You can also use logical (TRUE or FALSE) values to subset a vector:
> head(h$gender)
[1] 0 0 0 1 1 1
> head(h$gender == 1)
[1] FALSE FALSE FALSE TRUE TRUE TRUE
> h$height[h$gender == 1] # heights of rows with gender==1
(female)
[1] 67 63 54 66 64 57 66 67 68 65 70 64 64 63 60 69 65 67 62 66
[21] 65 63 58 56

9. Subsetting data frames
• Use square brackets with two values to select specific rows and
columns from a data frame
• Leave one blank to select all rows or columns
• First three rows, height (2) and weight (3) columns:
> h[1:3,2:3]
height weight
1 72 155
2 67 145
3 65 125
• Only the females (gender == 1), all columns:
> h[h$gender==1, ]

10. Other (better) ways to color the plot
• The col parameter can take a list with the same length as the number of points
• So we could do:
> ptCol <- h$gender # copy the gender column to a new
object
> ptCol[ptCol==1] <- "red" # replace 1 with "red”
> ptCol[ptCol==0] <- "blue" # replace 0 with "blue”
> plot(h$height, h$weight, col=ptCol, xlab="Height
(inches)", ylab="Weight (lbs)")
• The ifelse(…) function takes three parameters: a test, a value if the test is
TRUE, and a value if the test is FALSE.
• So we could also do:
> plot(h$height, h$weight, col=ifelse(h$gender==1,
"red", "blue"), xlab="Height (inches)", ylab="Weight
(lbs)")

11. Creating data frames
• In the course, the data frame was already created in a R data file
• Had to originally come from somewhere!
• You can create a data frame with the data.frame(…) function
h2 <- data.frame(gender=c(0,0,0,1,1,1),
height=c(72,67,65), weight=c(155,145,125))
• Usually more practical to read the data frame from a file:
h2 <- read.csv(“heights.csv”)
• Get help on the read.csv function (or any other function) using
?read.csv

12. Plotting with ggplot2
• The ggplot2 package provides functionality for more sophisticated plots
• Install the package, if you haven’t already
install.packages(“ggplot2”)
• Load the library
library(“ggplot2”)
• Let’s just fix the gender column:
h2 <- h
h2$gender <- ifelse(h2$gender==1, “Female”, “Male”)
• Start a plot with the ggplot(…) function, specifying the data to use and
the aesthetics, which can include a mapping to the x-axis and y-axis:
ggplot(h2, aes(x=height, y=weight))
• This just creates a blank plot

13. Scatterplots with ggplot2
• Add layers to a ggplot2 plot by adding layers to the plot, using
the various geom_xxx functions
• To create a scatterplot, use geom_point():
ggplot(h2, aes(x=height, y=weight)) +
geom_point()
• To color the points, simply use the color parameter:
ggplot(h2, aes(x=height, y=weight)) +
geom_point(aes(color=gender))
• To specify the colors, use scale_color_manual():
ggplot(h2, aes(x=height, y=weight)) +
geom_point(aes(color=gender)) +
scale_color_manual(values=c("blue","red"))

14. Learn more…
• Explore more with ggplot2
• Excellent tutorials from the University of Edinburgh Coding club (look
under “data visualization”)
• https://ourcodingclub.github.io/tutorials/
• Many other tutorials online:
https://www.google.com/search?q=ggplot2+tutorial