Gaston Sanchez
- Get started with
dplyr- Understand basic verbs
- Understand the pipe
%>%operator- How to install packages
The most common format/structure for a data set is in a tabular format, with row and columns. When your data is in this shape, most of the times you will work with R data frames (or similar structures).
You need to learn how to manipulate data tables. In the past (before 2014), R users manipulated data frames using bracket notation, e.g. dat[ , ], in order to select specific rows, columns, or cells. This is still possible and it is a good skill to learn. But nowadays there is an interesting alternative for data wrangling provided with the functionality of the package "dplyr".
The data for this lab is in the file nba2017-players.csv, which as you may guess, contains 15 variables measured on 441 players.
To import the data in R you can use the function read.csv():
dat <- read.csv('../data/nba2017-players.csv', stringsAsFactors = FALSE)Notice that I'm spcifying the argument stringsAsFactors = FALSE to avoid the conversion of characters into R factors. Why do you think this is a good practice?
All the default reading table functions generate a data frame. Typically, everytime I read a new data set which I'm not familiar with, or a data set that I haven't worked on in a long time, I always like to call a couple of functions to inspect its contents:
dim()head()tail()str()summary()
A first check-up is to examine the dimensions of the data frame with dim():
# dimensions (# of rows, # of columns)
dim(dat)## [1] 441 15
If you know in advanced how many rows and columns are in the data table, this is a good way to make sure that R was able to read all the records.
Then, depending on the size of the data, you may want to take a peek at its contents with head() or tail(), just to get an idea of what the data looks like:
# display first few rows
head(dat)## player team position height weight age experience
## 1 Al Horford BOS C 82 245 30 9
## 2 Amir Johnson BOS PF 81 240 29 11
## 3 Avery Bradley BOS SG 74 180 26 6
## 4 Demetrius Jackson BOS PG 73 201 22 0
## 5 Gerald Green BOS SF 79 205 31 9
## 6 Isaiah Thomas BOS PG 69 185 27 5
## college salary games minutes points points3
## 1 University of Florida 26540100 68 2193 952 86
## 2 12000000 80 1608 520 27
## 3 University of Texas at Austin 8269663 55 1835 894 108
## 4 University of Notre Dame 1450000 5 17 10 1
## 5 1410598 47 538 262 39
## 6 University of Washington 6587132 76 2569 2199 245
## points2 points1
## 1 293 108
## 2 186 67
## 3 251 68
## 4 2 3
## 5 56 33
## 6 437 590
For a more detailed description of how R is treating the data type in each column, you should use the structure function str().
# check the structure
str(dat, vec.len = 1)## 'data.frame': 441 obs. of 15 variables:
## $ player : chr "Al Horford" ...
## $ team : chr "BOS" ...
## $ position : chr "C" ...
## $ height : int 82 81 ...
## $ weight : int 245 240 ...
## $ age : int 30 29 ...
## $ experience: int 9 11 ...
## $ college : chr "University of Florida" ...
## $ salary : num 26540100 ...
## $ games : int 68 80 ...
## $ minutes : int 2193 1608 ...
## $ points : int 952 520 ...
## $ points3 : int 86 27 ...
## $ points2 : int 293 186 ...
## $ points1 : int 108 67 ...
This function str() displays the dimensions of the data frame, and then a list with the name of all the variables, and their data types (e.g. chr character, num real, etc). The argument vec.len = 1 indicates that just the first element in each column should be displayed.
When working with data frames, remember to always take some time inspecting the contents, and checking how R is handling the data types. It is in these early stages of data exploration that you can catch potential issues in order to avoid disastrous consequences or bugs in subsequent stages.
In order to use the functions in "dplyr", you must load the package first. Here I'm assuming that you already installed the package. If that's not the case then run:
install.packages("dplyr")Remember that you only need to install a package once! After a package has been installed in your machine, there is no need to call install.packages() again on the same package. What you should always invoke in order to use the functions in a package is the library() function:
library(dplyr)About loading packages: Another rule to keep in mind is to always load any required packages at the very top of your script files (.R or .Rmd or .Rnw files). Avoid calling the library() function in the middle of a script. Instead, load all the packages before anything else.
The first verbs to learn about "dplyr" are filter(), slice(), and select(). Why? Because these are the functions to select a subset of rows and columns, which are the most essential type of operations when wrangling data frames.
Select rows by position with slice()
slice(dat, 1:3)## player team position height weight age experience
## 1 Al Horford BOS C 82 245 30 9
## 2 Amir Johnson BOS PF 81 240 29 11
## 3 Avery Bradley BOS SG 74 180 26 6
## college salary games minutes points points3
## 1 University of Florida 26540100 68 2193 952 86
## 2 12000000 80 1608 520 27
## 3 University of Texas at Austin 8269663 55 1835 894 108
## points2 points1
## 1 293 108
## 2 186 67
## 3 251 68
Select rows by condition with filter()
filter(dat, height > 85)## player team position height weight age experience
## 1 Edy Tavares CLE C 87 260 24 1
## 2 Boban Marjanovic DET C 87 290 28 1
## 3 Kristaps Porzingis NYK PF 87 240 21 1
## 4 Roy Hibbert DEN C 86 270 30 8
## 5 Alexis Ajinca NOP C 86 248 28 6
## college salary games minutes points points3 points2
## 1 5145 1 24 6 0 3
## 2 7000000 35 293 191 0 72
## 3 4317720 66 2164 1196 112 331
## 4 Georgetown University 5000000 6 11 4 0 2
## 5 4600000 39 584 207 0 89
## points1
## 1 0
## 2 47
## 3 198
## 4 0
## 5 29
Select columns by name with select()
player_height <- select(dat, player, height)- subset the data by selecting the last 5 rows.
- select those players with height less than 70 inches tall.
- of those players that are centers (position
C), select their names and salaries. - display the player names of the lakers (
'LAL').
Use functions in "dplyr" to answer the following questions:
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What's the largest height value?
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What's the minimum height value?
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What's the overall average height?
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Who is the tallest player?
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Who is the shortest player?
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Which are the unique teams?
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How many different teams?
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Who is the oldest player?
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What is the median salary?
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Are there any players from "University of California, Berkeley"? If so how many and who are they?
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Are there any players from "University of California, Los Angeles"? If so how many and who are they?
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Are there any players with weight greater than 260 pounds? If so how many and who are they?
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Obtain height values in increasing order
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Obtain weight values in decreasing order
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Sort data by height in increasing order
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Sort data by weight in decreasing order
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Sort data by gender in increasing order
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Sort data by species in increasing order
# =====================================================
# Subsetting operations
# =====================================================
# create a data frame `durant` with Kevin Durant's information (i.e. row)
# create a data frame `ucla` with the data of players from college UCLA
# create a data frame `rookies`
# with those players with 0 years of experience
# create a data frame `rookie_centers` with the data of Center rookie players
# create a data frame for players with more than 50 games
# and more than 100 minutes
## =====================================================
# More Wrangling
# =====================================================
# get the data of those above the average weight?
# how many unarmed humans?
# standard deviation of weight in non-humans
# which humans use a lightsaber
# median height of unarmed non-humans
# add a column 'weight_kgm' for weight to kilograms
# get the log of height for players
# with age less than or equal to 25 years
#