In this blog post, I am gonna talk about the ggplot2. ggplot2 is based on the idea “grammer of graphics” which means a graphs can be build from same basic components like : Data, Geoms (Marks or points that represents the data point) and a coordinate system (x, y, z etc).

And with ggplot2 you can also play with aesthetics of geoms like size, color, location/position of your data points on coordinate system using “aes” argument.

So, lets make some plots and while going through the plottings, I will try to explain the details of arguments and codes for plotting particular plot.

I am going to use inbuilt dataset “mtcars” and “mpg” for plotting.

mtcars
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Before starting you need to install the package and if you already have the packages, you can load the same using “library” function or if you dont have then use “install.packages” and after that use “library” to load. Along with ggplot2, I also installed dplyr package as it gives you more power to mutate or filter your data and other interesting twitch and tweaks.

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
  1. Lets draw a density plot. Density plot is generally used to study the distribution of variables. So, lets say I want to check the distribution of ‘mpg’ in our dataset, how you are going to do it. So, for that I used the dataset “mtcars” with pipe (%>%) function to plot. pipe funciton (package = dplyr) lets you join several arguments together rather than writing each of them separate or in repeated mode. And to add function in ggplot you need to use “+” sign. So here we use the ggplot (which will draw the coordinate system) and added to the geom, as we are drawing density plot, the geom is density. Lets say if want to draw line plot, or bar plot you can change the geom accordingly ie. geom_point, geom_line, geom_bar etc. and As I am going to plot mpg as variable for the plot. So, insidse aes, whatever you wrote first is considered as x and second argument witll be considered as y.

so, if you write something like ggplot(mtcars, aes(cyl,mpg)); it will mean mtcars is your data and for geom x= cyl and y= mpg.

mtcars %>% ggplot() + geom_density(aes(mpg))

1. (i) Lets say you want to fill the plot area with red color. Okay, before going to that, I want point out something important, so if you want to modify your geom or data points you need to use your arguments within aesthetic function, however if it is to modify the background of overal visual of the plot, you can use arguments outside of aes or aesthetic function.

so, for filling the density plot with red, we can modify our codes like:

mtcars %>% ggplot(aes()) + geom_density(aes(mpg), fill = "red")

  1. Lets say you want to draw a bar chart. For this lets use mpg dataset.
mpg
## # A tibble: 234 x 11
##    manufacturer model displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4      1.8  1999     4 auto~ f        18    29 p     comp~
##  2 audi         a4      1.8  1999     4 manu~ f        21    29 p     comp~
##  3 audi         a4      2    2008     4 manu~ f        20    31 p     comp~
##  4 audi         a4      2    2008     4 auto~ f        21    30 p     comp~
##  5 audi         a4      2.8  1999     6 auto~ f        16    26 p     comp~
##  6 audi         a4      2.8  1999     6 manu~ f        18    26 p     comp~
##  7 audi         a4      3.1  2008     6 auto~ f        18    27 p     comp~
##  8 audi         a4 q~   1.8  1999     4 manu~ 4        18    26 p     comp~
##  9 audi         a4 q~   1.8  1999     4 auto~ 4        16    25 p     comp~
## 10 audi         a4 q~   2    2008     4 manu~ 4        20    28 p     comp~
## # ... with 224 more rows
mpg %>% ggplot(aes(class)) + geom_bar()

    1. Position: bar plots automatically plotted into stacked position when multiple bars are plotted on single point.Like the plot below which was plotted for dirve (like front wheel, 4 wheel or rear wheel drive) for different classes.
mpg %>% ggplot(aes(class)) + geom_bar(aes(fill = drv))

    1. But lets say you want them in dodge position, what to do now, you just need to add one position function with dodge argument.
mpg %>% ggplot(aes(class)) + geom_bar(aes(fill = drv), position = "dodge")

2(iii) how about flipping the coordinat? So here one thing to remember when you keep adding the functions in ggplot it should be added with “+” sign. so, here we are adding “coord_flip” with previous code.

mpg %>% ggplot(aes(class)) + geom_bar(aes(fill = drv), position = "dodge") + coord_flip()

Before wrapping up the post, how about we make a dataframe from scratch using some codes: So, here we made one student table with their name marks.

std.data <- data.frame(std_id = c(1:5), 
                       std_name=c("Sam", "Rick", "Dan", "Ryan", "Rex"),
                       Marks = c(80, 85, 93, 95, 88))
print(std.data)
##   std_id std_name Marks
## 1      1      Sam    80
## 2      2     Rick    85
## 3      3      Dan    93
## 4      4     Ryan    95
## 5      5      Rex    88