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Intro to Statistics Using R > Week 1 > Flashcards

Week 1 Flashcards

1
Q

library()

A
  • List of all packages installed on your computer
  • displayed in R editor
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2
Q

search()

A

List of all packages currently active on your computer

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3
Q

install.packages(“< lib_name >”)

A
  • package is downloaded from CRAN and installed on your computer
  • also in Tools Menu (RStudio)
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4
Q

library(“< lib_name >”)

A
  • loads the library
  • used after install.packages(_)
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5
Q

library(help = “< lib_name >”)

A

package documentation listed in editor

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6
Q

update.packages ()

A
  • updates all packages
  • also in Tools Menu (RStudio)
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7
Q

detach(“ package:< lib_name> “, unload=TRUE)

A
  • Package is removed
  • Package can also be removed from the right hand side window
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8
Q

Task

Clear the console

A
  • Edit -> Clear Console
  • Ctrl + L
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9
Q

comments

A
  • start with #
  • single lines only
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10
Q

run single command from script

A
  • Ctrl + Enter
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11
Q

index number

A
  • first number displayed in [ ]
  • indexes start at 1
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12
Q

print(“string”)

A
  • prints string and quotes
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13
Q

assignment operator

A
  • <-
  • can also use =
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14
Q

display variable value

A

type variable name in editor and execute

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15
Q

concatenate vectors

A

c(x,y) = x values, y values

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16
Q

multiple vectors subtraction

A

performs operation on values of same index

  • x <- 5,5,5
  • y <- 1,2,3
  • x - y = 4,3,2
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17
Q

vector multiplication

A

y * 3 = each value in y vector x 3

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18
Q

table commands

dim(table_name)

A
  • gives dimensions of table
  • rows then columns
19
Q

summary(table_name)

A
  • gives summary stats for each column
  • min, 1st quart, median, mean, 3rd, quart, max
20
Q

head(table_name)

A

prints first 6 rows of table

21
Q

names(table_name)

A

prints column headers / names

22
Q

table( table_name $ column_name )

A
  • makes a new table with the counts for each value type (new headers) in specified column
  • eg. table(iris$species) = setosa 50 versicolor 50 virginica 50
23
Q

pie(table(table name $ table column)

A
  • creates pie chart for counts of each value in specified table column
24
Q

pie(table(iris$Species ), col= purple”,”red”,”green”

A

assigns pie chart colors to each iris species in same order species are listed

25
Q

variable names

rules

A
  • case-sensitive
  • can’t begin with number or symbol
  • no blank spaces
  • can use period (eg. abc.x)
26
Q

vector creation

methods (3)

A
  1. Concatenate (c)
  2. Sequence (seq)
  3. Repeat (rep)
27
Q

vector creation

concatenate

A
  • c( )
  • eg. y <- c(2,3,5,6)
  • x <- c(first = “alpha”, second=”beta”, third=”gamma”)
28
Q

concatenate

order of restriction for conversion
(Least to most restrictive)

A
  1. Strings
  2. Numerics
  3. True/False
29
Q

repeat function
rep(…)

A

used to create vector with repeating pattern

  • v = c(11,22,33)
  • x = rep(v,3)
    11 22 33 11 22 33 11 22 33
  • gender = c(“male”,”female”)
  • y = rep(gender,c(2,5))
    “male” “male” “female” “female” “female” “female” “female”
30
Q

vector value assignment

A

assigning nonexistent value to vector, matrix, array, or list expands structure to accomodate new value
- x < c(8,6,4)
- x[7] < 10
- x = 8 6 4 NA NA NA 10

31
Q

repeat function

array declaration

A
  • n <- 10
  • y < rep (0,n)
  • y = 0 0 0 0 0 0 0 0 0 0
32
Q

R Data Input

Sources (5)

A
  1. Text Files (eg. ASCII, XML, webscraping)
  2. Statistical Packages (eg. SPSS, SAS, Stata)
  3. Keyboard
  4. Database Management Systems (eg. MySql, Oracle, Access)
  5. Other (eg. Excel, NetCFD, HDF5)
33
Q

Import / Export

Flat Files

A

Import
- AHW < read.csv(“AHW_1.csv”, header=TRUE)
- weatherdata <-read.table (file=”C:/work/DM1/weather.csv”, header=TRUE, sep=”,”)

Export
write.table(z,”D t RDataFiles Output z.txt”)

34
Q

Import / Expoort

Databases

A

Import
connection < dbConnect (driver, user, password, host, dbname
AHW < dbSendQuery (connection, “SELECT * FROM AHW”)

Export
connnection <- dbConnect (driver, user, password, host,dbname
dbWriteTable (connnection , “AHW”, AHW)

35
Q

Import / Export

R objects

A
  • Import: > load(‘AHW.Rdata’)
  • Export: > save(AHW, file= “New_AHW.Rdata”)
36
Q

Import

Web

A

connection <- url (‘http://pace.sdsc.edu/sites/bootcamp/images/AHW_1.csv’)
AHW <- read.csv(connection, header=TRUE)

37
Q

How to enter data in R

5

A
  1. Sequential Data: assignment operator and vector definition (x = 1:5)
    2.** Non-sequential Data**: concatenation operator
  2. List Objects
  3. Read a CSV
  4. Structure: provides info about data frame
38
Q

ls()

A

displays list objects
eg. x = 5, y = 3
ls() = “x” “y”

39
Q

Read data from a spreadsheet

process

A
  1. convert spreadsheet to .csv file
  2. variable -< read.csv(“Path”)
  • need forward slash or 2 back slashes in path
  • example variable name: sn.csv
40
Q

str(csv_variable)

A
  • ## provides details of Data frame from csv file
41
Q

Data input

keyboard

example

A

mydata <- data.frame (age=numeric(0), gender=character(0),
weight=numeric(0))
mydata <- edit( mydata)

42
Q

Data Types

A
  1. Vector (1 dimensional)
  2. Matrix (2 dimensional)
  3. Array (3 dimensional)
  4. Data Frame
    - Column can be different modes
  5. List
    - Vectors, matrix, arrays, data frames, lists
43
Q

Data Frame

A
  • More general than a matrix
  • Different columns can contain different modes of data
  • concatenate values for individual columns

variable <- data.frame(column1, column2, column3, etc)

44
Q

Specifying Data Frame Elements

A
  • patientdata [1:2] = all rows of 1st 2 data columns
  • patientdata [ diabetes”,”status”]) = all rows of specified columns
  • patientdata$age = all values in age column (without header)

Intro to Statistics Using R (5 decks)

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