Simplest Tutorial Ever!!
As I promised you to talk more about tidyverse and other R packages, this Tutorial is only a begging so don’t judge harshly I’m new at this. Initially I did not want to make tutorials on this blog because I believed that the internet is full of them, but one more won’t do no harm right ?
let’s get started, first we need to load the tidyverse library so we can have access to all the great functions, if you don’t have the tidyverse library yet you should install it first by running:
# install.packages(tidyverse)
then now we need to load (attach) the package so we can use it
library(tidyverse)
like I said before one of the great things in R is that it has many built in datasets we can choose one to play around with, I’ll use mtcars because it’s one of the most popular datasets with very clear variables
?mtcars
when you run this line you will get informations about The mtcars dataset (like where it came from, what each variable means, etc…). Now we will turn it into tibble which is according to tidyverse website : “A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not. Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does not exist). This forces you to confront problems earlier, typically leading to cleaner, more expressive code.” this is optinal however this Tutorial is so simple this stuff it wont matter but it’s just a habit for me.
data <- as_tibble(mtcars)
data
## # A tibble: 32 x 11
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rows
We can see that the data has 32 observations and 11 columns, now let’s make use of tidyverse
data %>% select(mpg, disp, wt, am)
## # A tibble: 32 x 4
## mpg disp wt am
## <dbl> <dbl> <dbl> <dbl>
## 1 21 160 2.62 1
## 2 21 160 2.88 1
## 3 22.8 108 2.32 1
## 4 21.4 258 3.22 0
## 5 18.7 360 3.44 0
## 6 18.1 225 3.46 0
## 7 14.3 360 3.57 0
## 8 24.4 147. 3.19 0
## 9 22.8 141. 3.15 0
## 10 19.2 168. 3.44 0
## # ... with 22 more rows
The select() function works similar to the SQL statement SELECT, as you can see it’s very easy to choose certin columns but this is not the true power of tidyverse the “pipe” %>% is very helpful because now we can chain commands together, we can select some columns and then select individual cases given a certain condition like for example “show the displacement and weight and miles per gallon for all the manual (am == 1) cars in the dataset”
data %>% select(mpg, disp, wt, am) %>% filter(am == 1)
## # A tibble: 13 x 4
## mpg disp wt am
## <dbl> <dbl> <dbl> <dbl>
## 1 21 160 2.62 1
## 2 21 160 2.88 1
## 3 22.8 108 2.32 1
## 4 32.4 78.7 2.2 1
## 5 30.4 75.7 1.62 1
## 6 33.9 71.1 1.84 1
## 7 27.3 79 1.94 1
## 8 26 120. 2.14 1
## 9 30.4 95.1 1.51 1
## 10 15.8 351 3.17 1
## 11 19.7 145 2.77 1
## 12 15 301 3.57 1
## 13 21.4 121 2.78 1
we can add even more conditions and more functions let assume for the sake of example that there’s something called weight displacement ratio which is obtained by dividing the displacement of a car by its weight in 1000 of pounds and we want to do this for the manual cars that has high miles per gallon (let’s assume that this means > 25) we can do all of this just by running :
data %>% mutate(Weight_displacemet_ratio = disp/wt*1000)
%>% select(mpg, disp, wt,Weight_displacemet_ratio, am)
%>% filter(mpg > 25, am == 1)
## # A tibble: 6 x 5
## mpg disp wt Weight_displacemet_ratio am
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 32.4 78.7 2.2 35773. 1
## 2 30.4 75.7 1.62 46873. 1
## 3 33.9 71.1 1.84 38747. 1
## 4 27.3 79 1.94 40827. 1
## 5 26 120. 2.14 56215. 1
## 6 30.4 95.1 1.51 62855. 1
An important thing to remember is that the original data is still as it is its not modified so if you check that
data
## # A tibble: 32 x 11
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rows
this is very helpful because you don’t want to create new instances of your dataframe to apply functions to it especially in the first stages of analysis.
Okay lets do something fun, hmm lets do a very simple analysis by plotting very simple boxplot to see if there is difference in mpg in the groups of cars that have a different number of cylinders
data = data %>% mutate(Cyl = as.factor(cyl))
box_plot = ggplot(data, aes(x = Cyl, y = mpg)) + geom_boxplot()
box_plot
Easy enough, but what an ugly graph lets make things more interesting
box_plot = ggplot(data, aes(x = Cyl, y = mpg, fill = Cyl))
+ geom_boxplot() + scale_fill_brewer(palette="Set3")
+ theme_classic()
+labs(
title = "Fuel Consumtion Categorized By The Number Of Cylinders",
subtitle = "(1973-74)",
caption = "Data from the 1974 Motor Trend US magazine.",
tag = "Figure 1",
x = "Number of Cylinders",
y = "Fuel economy (mpg)",
colour = "Cylinders")
box_plot
It seems that cars with fewer number of cylinders have more miles per gallon which means they are more efficient than cars with higher number of cylinders.
Now let see if there is a relationship between the weight of a car and its gas consumtion
sp<-ggplot(data, aes(x=wt, y=mpg, color=Cyl)) + geom_point()
+ scale_color_manual(values=c("#997777", "#E69F00", "#56B4E9"))
+ theme_minimal()
+labs(
title = "Fuel economy declines as weight increases",
subtitle = "(1973-74)",
caption = "Data from the 1974 Motor Trend US magazine.",
tag = "Figure 2",
x = "Weight (1000 lbs)",
y = "Fuel economy (mpg)",
colour = "Gears")
sp
Looks like heavier cars tends to consume more gallons than lighter ones. we’ve gain a good amount of overall informations with few steps.
See? that was very easy don’t worry if you don’t understand some of ggplot commands and options I won’t explain it here today I think it deserve its own tutorial(s). The whole point of this tutorial was to whet your appetite to go try this for yourself, in future Tuts we will go through more complicated stuff with more realistic data, for now I think this is enough for a first tutorial I’m just experimenting.
Hasta luego !