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# MAKE FRIENDS WITH R
# BASED ON MAKE FRIENDS WITH R BY JONATHAN TEMPLIN
# CREATED BY JIHONG ZHANG
# R comments begin with a # -- there are no multiline comments
# RStudio helps you build syntax
# GREEN: Comments and character values in single or double quotes
# You can use the tab key to complete object names, functions, and arugments
# R is case sensitive. That means R and r are two different things.# In R, every statement is a function
# The print function prints the contents of what is inside to the console
print(x = 10)[1] 10# The terms inside the function are called the arguments; here print takes x
# To find help with what the arguments are use:
?print
# Each function returns an object
print(x = 10)[1] 10# You can determine what type of object is returned by using the class function
class(print(x = 10))[1] 10[1] "numeric"# Each objects can be saved into the R environment (the workspace here)
# You can save the results of a function call to a variable of any name
MyObject = print(x = 10)[1] 10class(MyObject)[1] "numeric"# You can view the objects you have saved in the Environment tab in RStudio
# Or type their name
MyObject[1] 10# There are literally thousands of types of objects in R (you can create them),
# but for our course we will mostly be working with data frames (more later)
# The process of saving the results of a function to a variable is called
# assignment. There are several ways you can assign function results to
# variables:
# The equals sign takes the result from the right-hand side and assigns it to
# the variable name on the left-hand side:
MyObject = print(x = 10)[1] 10# The <- (Alt "-" in RStudio) functions like the equals (right to left)
MyObject2 <- print(x = 10)[1] 10identical(MyObject, MyObject2)[1] TRUE# The -> assigns from left to right:
print(x = 10) -> MyObject3[1] 10identical(MyObject, MyObject2, MyObject3)[1] TRUEThe data frame is an R object that is a rectangular array of data. The variables in the data frame can be any class (e.g., numeric, character) and go across the columns. The observations are across the rows.
We will start by importing data from a comma-separated values (csv) file.
We will use the read.csv() function. Here, the argument stringsAsFactors keeps R from creating data strings
We will use here::here() function to quickly point to the target data file.
# Note: The argument file is the path to the file. If you opened this script directly in RStudio, then the current directory is the directory that contains the script. If the data file is in that directory, you can omit the full path. To find the current directory used in the environment, use the getwd() function.
getwd()[1] "/Users/jihong/Documents/website-jihong/posts/Lectures/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01"# To show the files in that directory, use the dir() function. You can see if the file you are opening is or is not in the current directory.
dir()[1] "_extensions" "data"
[3] "figures" "Lecture01.qmd"
[5] "MakeFriendsWithR.qmd" "MakeFriendsWithR.rmarkdown"
[7] "quarto-uark-revealjs-theme.Rproj" "template_files"
[9] "template.html" # Method 1: The most convientent way of importing data file is using the here package
root_path <- getwd()
HeightsData = read.csv(file = here::here(root_path, "data", "heights.csv"), stringsAsFactors = FALSE)
head(HeightsData) ID HeightIN
1 1 72.76783
2 2 69.45293
3 3 69.70142
4 4 69.36786
5 5 70.55350
6 6 69.76497# You can also set the directory using setwd(). Here, I set my directory to
# my root folder:
setwd("~")
getwd()[1] "/Users/jihong"dir() [1] "Desktop" "Documents" "Downloads"
[4] "files" "files.txt" "Library"
[7] "Movies" "Music" "OneDrive"
[10] "package-lock.json" "package.json" "Pictures"
[13] "Public" "Rlibs" "Zotero" # If I tried to re-load the data, I would get an error:
HeightsData = read.csv(file = "heights.csv", stringsAsFactors = FALSE)Warning in file(file, "rt"): cannot open file 'heights.csv': No such file or
directoryError in file(file, "rt"): cannot open the connection# Method 2: I can use the full path to the file:
HeightsData =
read.csv(
file = "/Users/jihong/Documents/website-jihong/posts/Lectures/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01/data/heights.csv",
stringsAsFactors = FALSE)
# Or, I can reset the current directory and use the previous syntax:
setwd("/Users/jihong/Documents/website-jihong/posts/Lectures/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01/data/")
HeightsData = read.csv(file = "heights.csv", stringsAsFactors = FALSE)
HeightsData ID HeightIN
1 1 72.76783
2 2 69.45293
3 3 69.70142
4 4 69.36786
5 5 70.55350
6 6 69.76497
7 7 70.55302
8 8 69.02545
9 9 69.48786
10 10 69.29473# Note: Windows users will have to either change the direction of the slash
# or put two slashes between folder levels.
# To show my data in RStudio, I can either double click it in the
# Environment tab or use the View() function
View(HeightsData)
# You can see the variable names and contents by using the $:
HeightsData$ID [1] 1 2 3 4 5 6 7 8 9 10# To read in SPSS files, we will need the foreign library. The foreign
# library comes installed with R (so no need to use install.packages()).
library(foreign)
# The read.spss() function imports the SPSS file to an R data frame if the
# argument to.data.frame is TRUE
WideData = read.spss(file = here::here(root_path, "data", "wide.sav"), to.data.frame = TRUE)
WideData ID Gender DVTime1 DVTime2 DVTime3 DVTime4 AgeTime1 AgeTime2 AgeTime3
1 1 1 21.0 20.0 21.5 23.0 8.0 10.0 12.0
2 2 1 21.0 21.5 24.0 25.5 8.1 10.1 12.1
3 3 1 20.5 24.0 24.5 26.0 8.5 10.4 12.2
4 4 1 23.5 24.5 25.0 26.5 8.7 10.6 12.5
5 5 1 21.5 23.0 22.5 23.5 7.9 10.0 12.1
6 6 1 20.0 21.0 21.0 22.5 8.0 10.0 11.9
7 7 1 21.5 22.5 23.0 25.0 8.2 10.2 12.0
8 8 1 23.0 23.0 23.5 24.0 7.9 9.9 12.1
9 9 1 20.0 21.0 22.0 21.5 8.0 10.1 12.4
10 10 1 16.5 19.0 19.0 19.5 8.3 10.2 12.1
AgeTime4
1 14.0
2 14.2
3 14.1
4 14.4
5 13.9
6 13.8
7 14.1
8 14.0
9 14.3
10 14.2# The WideData and HeightsData have the same set of ID numbers. We can use the merge() function to merge them into a single data frame. Here, x is the name of the left-side data frame and y is the name of the right-side data frame. The arguments by.x and by.y are the name of the variable(s) by which we will merge:
AllData = merge(x = WideData, y = HeightsData, by.x = "ID", by.y = "ID")
AllData ID Gender DVTime1 DVTime2 DVTime3 DVTime4 AgeTime1 AgeTime2 AgeTime3
1 1 1 21.0 20.0 21.5 23.0 8.0 10.0 12.0
2 2 1 21.0 21.5 24.0 25.5 8.1 10.1 12.1
3 3 1 20.5 24.0 24.5 26.0 8.5 10.4 12.2
4 4 1 23.5 24.5 25.0 26.5 8.7 10.6 12.5
5 5 1 21.5 23.0 22.5 23.5 7.9 10.0 12.1
6 6 1 20.0 21.0 21.0 22.5 8.0 10.0 11.9
7 7 1 21.5 22.5 23.0 25.0 8.2 10.2 12.0
8 8 1 23.0 23.0 23.5 24.0 7.9 9.9 12.1
9 9 1 20.0 21.0 22.0 21.5 8.0 10.1 12.4
10 10 1 16.5 19.0 19.0 19.5 8.3 10.2 12.1
AgeTime4 HeightIN
1 14.0 72.76783
2 14.2 69.45293
3 14.1 69.70142
4 14.4 69.36786
5 13.9 70.55350
6 13.8 69.76497
7 14.1 70.55302
8 14.0 69.02545
9 14.3 69.48786
10 14.2 69.29473## Method 2: Use dplyr method, |> can be typed using `command + shift + M`
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionWideData |>
left_join(HeightsData, by = "ID") ID Gender DVTime1 DVTime2 DVTime3 DVTime4 AgeTime1 AgeTime2 AgeTime3
1 1 1 21.0 20.0 21.5 23.0 8.0 10.0 12.0
2 2 1 21.0 21.5 24.0 25.5 8.1 10.1 12.1
3 3 1 20.5 24.0 24.5 26.0 8.5 10.4 12.2
4 4 1 23.5 24.5 25.0 26.5 8.7 10.6 12.5
5 5 1 21.5 23.0 22.5 23.5 7.9 10.0 12.1
6 6 1 20.0 21.0 21.0 22.5 8.0 10.0 11.9
7 7 1 21.5 22.5 23.0 25.0 8.2 10.2 12.0
8 8 1 23.0 23.0 23.5 24.0 7.9 9.9 12.1
9 9 1 20.0 21.0 22.0 21.5 8.0 10.1 12.4
10 10 1 16.5 19.0 19.0 19.5 8.3 10.2 12.1
AgeTime4 HeightIN
1 14.0 72.76783
2 14.2 69.45293
3 14.1 69.70142
4 14.4 69.36786
5 13.9 70.55350
6 13.8 69.76497
7 14.1 70.55302
8 14.0 69.02545
9 14.3 69.48786
10 14.2 69.29473# Sometimes, certain packages require repeated measures data to be in a long
# format.
library(dplyr) # contains variable selection
## Wrong Way
AllData |>
tidyr::pivot_longer(starts_with("DVTime"), names_to = "DV", values_to = "DV_Value") |>
tidyr::pivot_longer(starts_with("AgeTime"), names_to = "Age", values_to = "Age_Value") # A tibble: 160 × 7
ID Gender HeightIN DV DV_Value Age Age_Value
<dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl>
1 1 1 72.8 DVTime1 21 AgeTime1 8
2 1 1 72.8 DVTime1 21 AgeTime2 10
3 1 1 72.8 DVTime1 21 AgeTime3 12
4 1 1 72.8 DVTime1 21 AgeTime4 14
5 1 1 72.8 DVTime2 20 AgeTime1 8
6 1 1 72.8 DVTime2 20 AgeTime2 10
7 1 1 72.8 DVTime2 20 AgeTime3 12
8 1 1 72.8 DVTime2 20 AgeTime4 14
9 1 1 72.8 DVTime3 21.5 AgeTime1 8
10 1 1 72.8 DVTime3 21.5 AgeTime2 10
# ℹ 150 more rows## Correct Way
AllData |>
tidyr::pivot_longer(c(starts_with("DVTime"), starts_with("AgeTime"))) |>
tidyr::separate(name, into = c("Variable", "Time"), sep = "Time") |>
tidyr::pivot_wider(names_from = "Variable", values_from = "value") -> AllDataLong# The psych package makes getting descriptive statistics very easy.
## install.packages("psych")
library(psych)
Attaching package: 'psych'The following object is masked from 'package:rstan':
lookup# We can use describe() to get descriptive statistics across all cases:
DescriptivesWide = describe(AllData)
DescriptivesWide vars n mean sd median trimmed mad min max range skew
ID 1 10 5.50 3.03 5.50 5.50 3.71 1.00 10.00 9.00 0.00
Gender 2 10 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00 NaN
DVTime1 3 10 20.85 1.92 21.00 21.06 1.11 16.50 23.50 7.00 -0.78
DVTime2 4 10 21.95 1.76 22.00 22.00 1.48 19.00 24.50 5.50 -0.13
DVTime3 5 10 22.60 1.81 22.75 22.75 1.85 19.00 25.00 6.00 -0.48
DVTime4 6 10 23.70 2.18 23.75 23.88 2.22 19.50 26.50 7.00 -0.43
AgeTime1 7 10 8.16 0.27 8.05 8.12 0.22 7.90 8.70 0.80 0.79
AgeTime2 8 10 10.15 0.21 10.10 10.12 0.15 9.90 10.60 0.70 0.85
AgeTime3 9 10 12.14 0.18 12.10 12.12 0.15 11.90 12.50 0.60 0.72
AgeTime4 10 10 14.10 0.18 14.10 14.10 0.15 13.80 14.40 0.60 0.00
HeightIN 11 10 70.00 1.10 69.59 69.77 0.39 69.03 72.77 3.74 1.50
kurtosis se
ID -1.56 0.96
Gender NaN 0.00
DVTime1 0.19 0.61
DVTime2 -1.37 0.56
DVTime3 -0.87 0.57
DVTime4 -1.04 0.69
AgeTime1 -0.85 0.08
AgeTime2 -0.51 0.07
AgeTime3 -0.77 0.06
AgeTime4 -1.22 0.06
HeightIN 1.19 0.35DescriptivesLong = describe(AllDataLong)
DescriptivesLong vars n mean sd median trimmed mad min max range skew
ID 1 40 5.50 2.91 5.50 5.50 3.71 1.00 10.00 9.00 0.00
Gender 2 40 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00 NaN
HeightIN 3 40 70.00 1.05 69.59 69.77 0.39 69.03 72.77 3.74 1.69
Time* 4 40 2.50 1.13 2.50 2.50 1.48 1.00 4.00 3.00 0.00
DV 5 40 22.27 2.12 22.50 22.31 2.22 16.50 26.50 10.00 -0.24
Age 6 40 11.14 2.25 11.25 11.14 2.89 7.90 14.40 6.50 -0.01
kurtosis se
ID -1.31 0.46
Gender NaN 0.00
HeightIN 1.92 0.17
Time* -1.44 0.18
DV -0.13 0.34
Age -1.42 0.36# We can use describeBy() to get descriptive statistics by groups:
DescriptivesLongID = describeBy(AllDataLong, group = AllDataLong$ID)
DescriptivesLongID
Descriptive statistics by group
group: 1
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0 NaN NaN
HeightIN 3 4 72.77 0.00 72.77 72.77 0.00 72.77 72.77 0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3 0.00 -2.08
DV 5 4 21.38 1.25 21.25 21.38 1.11 20.00 23.00 3 0.21 -1.92
Age 6 4 11.00 2.58 11.00 11.00 2.97 8.00 14.00 6 0.00 -2.08
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.62
Age 1.29
------------------------------------------------------------
group: 2
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 2.00 0.00 2.00 2.00 0.00 2.00 2.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.45 0.00 69.45 69.45 0.00 69.45 69.45 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 23.00 2.12 22.75 23.00 2.22 21.00 25.50 4.5 0.14 -2.25
Age 6 4 11.12 2.62 11.10 11.12 2.97 8.10 14.20 6.1 0.02 -2.07
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 1.06
Age 1.31
------------------------------------------------------------
group: 3
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 3.00 0.00 3.00 3.00 0.00 3.0 3.0 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.0 1.0 0.0 NaN NaN
HeightIN 3 4 69.70 0.00 69.70 69.70 0.00 69.7 69.7 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.0 4.0 3.0 0.00 -2.08
DV 5 4 23.75 2.33 24.25 23.75 1.48 20.5 26.0 5.5 -0.45 -1.83
Age 6 4 11.30 2.40 11.30 11.30 2.74 8.5 14.1 5.6 0.00 -2.07
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 1.16
Age 1.20
------------------------------------------------------------
group: 4
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 4.00 0.00 4.00 4.00 0.00 4.00 4.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.37 0.00 69.37 69.37 0.00 69.37 69.37 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 24.88 1.25 24.75 24.88 1.11 23.50 26.50 3.0 0.21 -1.92
Age 6 4 11.55 2.45 11.55 11.55 2.82 8.70 14.40 5.7 0.00 -2.08
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.62
Age 1.23
------------------------------------------------------------
group: 5
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 5.00 0.00 5.00 5.00 0.00 5.00 5.00 0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0 NaN NaN
HeightIN 3 4 70.55 0.00 70.55 70.55 0.00 70.55 70.55 0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3 0.00 -2.08
DV 5 4 22.62 0.85 22.75 22.62 0.74 21.50 23.50 2 -0.28 -1.96
Age 6 4 10.97 2.60 11.05 10.97 2.89 7.90 13.90 6 -0.05 -2.09
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.43
Age 1.30
------------------------------------------------------------
group: 6
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 6.00 0.00 6.00 6.00 0.00 6.00 6.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.76 0.00 69.76 69.76 0.00 69.76 69.76 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 21.12 1.03 21.00 21.12 0.74 20.00 22.50 2.5 0.27 -1.85
Age 6 4 10.93 2.49 10.95 10.93 2.82 8.00 13.80 5.8 -0.02 -2.07
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.52
Age 1.25
------------------------------------------------------------
group: 7
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 7.00 0.00 7.00 7.00 0.00 7.00 7.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 70.55 0.00 70.55 70.55 0.00 70.55 70.55 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 23.00 1.47 22.75 23.00 1.11 21.50 25.00 3.5 0.35 -1.87
Age 6 4 11.12 2.52 11.10 11.12 2.82 8.20 14.10 5.9 0.02 -2.05
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.74
Age 1.26
------------------------------------------------------------
group: 8
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 8.00 0.00 8.00 8.00 0.00 8.00 8.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.03 0.00 69.03 69.03 0.00 69.03 69.03 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 23.38 0.48 23.25 23.38 0.37 23.00 24.00 1.0 0.32 -2.08
Age 6 4 10.97 2.65 11.00 10.97 3.04 7.90 14.00 6.1 -0.02 -2.10
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.24
Age 1.32
------------------------------------------------------------
group: 9
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 9.00 0.00 9.00 9.00 0.00 9.00 9.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.49 0.00 69.49 69.49 0.00 69.49 69.49 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 21.12 0.85 21.25 21.12 0.74 20.00 22.00 2.0 -0.28 -1.96
Age 6 4 11.20 2.74 11.25 11.20 3.11 8.00 14.30 6.3 -0.03 -2.11
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.43
Age 1.37
------------------------------------------------------------
group: 10
vars n mean sd median trimmed mad min max range skew kurtosis
ID 1 4 10.00 0.00 10.00 10.00 0.00 10.00 10.00 0.0 NaN NaN
Gender 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.0 NaN NaN
HeightIN 3 4 69.29 0.00 69.29 69.29 0.00 69.29 69.29 0.0 NaN NaN
Time 4 4 2.50 1.29 2.50 2.50 1.48 1.00 4.00 3.0 0.00 -2.08
DV 5 4 18.50 1.35 19.00 18.50 0.37 16.50 19.50 3.0 -0.68 -1.73
Age 6 4 11.20 2.53 11.15 11.20 2.82 8.30 14.20 5.9 0.04 -2.07
se
ID 0.00
Gender 0.00
HeightIN 0.00
Time 0.65
DV 0.68
Age 1.27# Transforming data is accomplished by the creation of new variables.
AllDataLong$AgeC = AllDataLong$Age - mean(AllDataLong$Age)
# You can also use functions to create new variables. Here we create new terms
# using the function for significant digits:
AllDataLong$AgeYear = signif(x = AllDataLong$Age, digits = 2)
AllDataLong$AgeDecade = signif(x = AllDataLong$Age, digits = 1)
head(AllDataLong)# A tibble: 6 × 9
ID Gender HeightIN Time DV Age AgeC AgeYear AgeDecade
<dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 72.8 1 21 8 -3.14 8 8
2 1 1 72.8 2 20 10 -1.14 10 10
3 1 1 72.8 3 21.5 12 0.863 12 10
4 1 1 72.8 4 23 14 2.86 14 10
5 2 1 69.5 1 21 8.1 -3.04 8.1 8
6 2 1 69.5 2 21.5 10.1 -1.04 10 10