class: center, middle, inverse, title-slide # R Basics <br> <br> <br> ### Omni Analytics Group --- ## Loading CryptoPunks Data set This data set displays the sales of CryptoPunks since June 23rd, 2017 to December 30th, 2020. ```r Punks <- read.csv("punks.csv") names(Punks) # See all column names ``` ``` ## [1] "Transaction" "From" "To" "Crypto" "USD" ## [6] "Txn" "ID" "Sex" "Type" "Skin" ## [11] "Slots" "Rank" ``` --- ## First 6 Rows Recall that `head()` displays the first 6 rows of our data. ```r head(Punks) ``` ``` ## Transaction From To Crypto USD Txn ID Sex Type Skin ## 1 Sold 0xf5099e 14715954 25.00 2822 2018-11-30 0 Girl Female Mid ## 2 Sold 0x00d7c9 10528156 1.60 386 2017-07-07 0 Girl Female Mid ## 3 Sold 0xc352b5 55241 0.98 320 2017-06-23 0 Girl Female Mid ## 4 Claimed <NA> 12800693 NA NA 2017-06-23 0 Girl Female Mid ## 5 Sold EliteCat… 0xcf6165 60.00 36305 2020-11-30 1 Guy Male Dark ## 6 Sold 0xf5099e GoWest23 31.00 5155 2019-04-06 1 Guy Male Dark ## Slots Rank ## 1 3 3682560000% ## 2 3 3682560000% ## 3 3 3682560000% ## 4 3 3682560000% ## 5 2 2050240500% ## 6 2 2050240500% ``` --- ## Some Computations ### Addition and Subtraction How much more did Punk 3830 last sell for compared to Punk 1 in ETH? ```r 99.99-60.00 ``` ``` ## [1] 39.99 ``` -- ### Multiplication/Division What is the average sale price in ETH for Punk 0? ```r (0.98+1.6+25)/3 ``` ``` ## [1] 9.193333 ``` --- ## More Calculator Operations ```r # Integer division 82 %/% 10 ``` ``` ## [1] 8 ``` ```r # Modulo operator (Remainder) 82 %% 10 ``` ``` ## [1] 2 ``` ```r # Powers 8^3 ``` ``` ## [1] 512 ``` --- ## Even More Functions - Exponentiation * `exp(x)` - Logarithms * `log(x)` * `log(x, base = 10)` - Trigonometric functions * `sin(x)` * `asin(x)` * `cos(x)` * `tan(x)` --- ## Creating Variables We can create variables using the assignment operator `<-`: ```r alien.punk <- 13 ``` We can then perform any of the functions on the variables: ```r # Logarithm log(alien.punk) ``` ``` ## [1] 2.564949 ``` ```r # Square root sqrt(alien.punk) ``` ``` ## [1] 3.605551 ``` ```r # Square alien.punk^2 ``` ``` ## [1] 169 ``` --- ## Rules for Variable Creation - Variable names can't start with a number. - Variables in R are case-sensitive. - Some common letters are used internally by R and should be avoided as variable names: * c, q, t, C, D, F, T, I - There are reserved words that R won't let you use for variable names: * for, in, while, if, else, repeat, break, next - R will let you use the name of a predefined function. Try not to overwrite those though! <br> <br> <p align="center"> <img src="images/stickers/book2.png" width="200px" height="150px"> <img src="images/stickers/pen1.png" width="200px" height="150px"> </p> --- ## Vectors A variable does not need to be a single value. We can create a **vector** using the `c()` (combine) function: What are the top 5 highest sales in ETH? ```r y <- c(189.99, 185, 150, 140, 100) # Creates a vector of top 5 highest sales ``` Operations will then be done element-wise. For example, we can divide the vector by 5: ```r y / 5 ``` ``` ## [1] 37.998 37.000 30.000 28.000 20.000 ``` --- ## Getting Help We will talk MUCH more about vectors later, but for now, let's talk about a couple ways to get help. The primary function to use is the `help` function. Just pass in the name of the function you need help with: ```r help(head) ``` The `?` function also works: ```r ?head ``` Googling for help is a bit difficult. You may need to search for R + CRAN + (your query) to get good results. --- ## R Reference Card You can download and an R reference card from: http://cran.r-project.org/doc/contrib/Short-refcard.pdf Having this open or printed off and near you while working is helpful until you master the basics. --- ## Your Turn Using the R Reference Card (and the Help pages, if needed), do the following: 1. Find out how many rows and columns the CryptoPunks data set has using at least 2 methods. 2. Create a vector with the top 5 sales in ETH. <br> <br> <br> <p align="center"> <img src="images/Cut_outs/Cut_out_02.png" width="200px" height="150px"> </p> --- ## Answers ### 1. ```r dim(Punks) # Finds dimension of data frame ``` ``` ## [1] 17554 12 ``` ```r str(Punks) # Finds structure of data ``` ``` ## 'data.frame': 17554 obs. of 12 variables: ## $ Transaction: chr "Sold" "Sold" "Sold" "Claimed" ... ## $ From : chr "0xf5099e" "0x00d7c9" "0xc352b5" NA ... ## $ To : chr "14715954" "10528156" "55241" "12800693" ... ## $ Crypto : num 25 1.6 0.98 NA 60 31 0.42 NA NA NA ... ## $ USD : num 2822 386 320 NA 36305 ... ## $ Txn : chr "2018-11-30" "2017-07-07" "2017-06-23" "2017-06-23" ... ## $ ID : int 0 0 0 0 1 1 1 1 2 3 ... ## $ Sex : chr "Girl" "Girl" "Girl" "Girl" ... ## $ Type : chr "Female" "Female" "Female" "Female" ... ## $ Skin : chr "Mid" "Mid" "Mid" "Mid" ... ## $ Slots : int 3 3 3 3 2 2 2 2 1 3 ... ## $ Rank : chr "3682560000%" "3682560000%" "3682560000%" "3682560000%" ... ``` --- ### 2. ```r top5 <- c(3,3,4,2,0) # Vector of top5 ``` --- ## Some Useful Functions There are a whole variety of useful functions to operate on vectors. A couple of the more common ones are `length()`, which returns the length (number of elements) of a vector, and `sum()`, which adds up all the elements of a vector. ```r length(top5) # calculates the length of this vector ``` ``` ## [1] 5 ``` ```r sum(top5) # Calculates the sum of the vector elements ``` ``` ## [1] 12 ``` --- ## Data Frames Introduction - `Punks` is a data frame. - Data frames hold data sets. - Not every column needs be the same type - like an Excel spreadsheet. - Each column in a data frame is a vector - so each column needs to have values that are all the same type. - We can access different columns using the `$` operator. ```r type <- Punks$Type # Creates column named type skin <- Punks$Skin # Creates column named skin slots <- Punks$Slots # Creates column named slots ``` --- ## More about Vectors A vector is a list of values that are all the same type. We have seen that we can create them using the `c()` or the `rep()` function. We can also use the `:` operator if we wish to create consecutive values: ```r a <- 10:15 a ``` ``` ## [1] 10 11 12 13 14 15 ``` We can extract the specific elements of the vector as follows: ```r type[3] # Selects the 3rd type in the type column ``` ``` ## [1] "Female" ``` --- ## Indexing Vectors We saw that we can access individual elements of the vector. But **indexing** is a lot more powerful than that: ```r head(type) ``` ``` ## [1] "Female" "Female" "Female" "Female" "Male" "Male" ``` ```r type[c(1, 3, 5)] # Selects the 1st, 3rd, and 5th type ``` ``` ## [1] "Female" "Female" "Male" ``` ```r type[1:6] # Selects the 1st through 6th type ``` ``` ## [1] "Female" "Female" "Female" "Female" "Male" "Male" ``` --- ## Logical Statements - R has built in support for logical statements. - TRUE and FALSE are built in. T (for TRUE) and F (for FALSE) are supported but can be modified. - Logical statements can result from a comparison using: - `\(<\)` - `\(>\)` - `\(<=\)` - `\(>=\)` - `\(==\)` - `\(!=\)` --- ## Indexing with Logical Statements We can index vectors using logical statements as well: ```r x <- slots[1:5] #Pulls the first 5 slots x > 3 # Returns which of the first 5 slots are greater than 3 ``` ``` ## [1] FALSE FALSE FALSE FALSE FALSE ``` ```r x[x < 3] # returns which is less than 3 ``` ``` ## [1] 2 ``` --- ## Logical Examples In this example, we gather the ID of Alien Type Punks. ```r alien_ID <- (Punks$ID[Punks$Type == "Alien"]) # creates variable, alien_ID, the ID of Punks that are Alien Type str(alien_ID) ``` ``` ## int [1:15] 635 2890 3100 3443 3443 5822 5822 5905 5905 6089 ... ``` We see which IDs are less than 5000 to find certain punks which are labeled `little`. ```r little <- alien_ID < 5000 # Finds punks with an ID of less than 5000 alien_ID[little] ``` ``` ## [1] 635 2890 3100 3443 3443 ``` --- This code locates the Punks ID that correspond to those slots. ```r (Punks$ID[little][Punks$Type == "Alien"]) ``` ``` ## [1] 1648 7504 8244 9479 9479 NA NA NA NA NA NA NA NA NA NA ``` --- ## Element-wise Logical Operators - `&` (elementwise AND) - `|` (elementwise OR) ```r c(T, T, F, F) & c(T, F, T, F) ``` ``` ## [1] TRUE FALSE FALSE FALSE ``` ```r c(T, T, F, F) | c(T, F, T, F) ``` ``` ## [1] TRUE TRUE TRUE FALSE ``` --- ## Your Turn 1. When was **Punk 9976** last sold? Note: There are many ways to answer this. Some are faster than others. 2. Find out the number of sales in the Punks data. **Challenge**: Among all the sales, how many of the sales are more than or equal to 1.00 ETH? Hint(You will need to use <b> 2. </b>.) <br> <br> <br> <p align="right"> <img src="images/Cut_outs/Cut_out_06.png" width="200px" height="150px"> </p> --- ## Answers ### 1 ```r Punks$Txn[Punks$ID == 9976 & Punks$Transaction == "Sold"][1] ``` ``` ## [1] "2020-01-10" ``` ### 2. ```r sold <- Punks$Transaction == "Sold" sum(sold) ``` ``` ## [1] 7554 ``` --- ### **Challenge** ```r # Finds the transactions that are more than 1 ETH sales_1_ETH <- Punks$Crypto[sold] >= 1.00 sum(sales_1_ETH) ``` ``` ## [1] 3846 ``` --- ## Modifying Vectors We can modify vectors using indexing as well. Here we create a new data frame that consists of the first 5 columns of the Punks data set. ```r x <- Punks[1:5] head(x) ``` ``` ## Transaction From To Crypto USD ## 1 Sold 0xf5099e 14715954 25.00 2822 ## 2 Sold 0x00d7c9 10528156 1.60 386 ## 3 Sold 0xc352b5 55241 0.98 320 ## 4 Claimed <NA> 12800693 NA NA ## 5 Sold EliteCat… 0xcf6165 60.00 36305 ## 6 Sold 0xf5099e GoWest23 31.00 5155 ``` --- We can replace all the `Claimed` with `Free` by the following: ```r x[1][x[1] == "Claimed"] <- "Free" head(x) ``` ``` ## Transaction From To Crypto USD ## 1 Sold 0xf5099e 14715954 25.00 2822 ## 2 Sold 0x00d7c9 10528156 1.60 386 ## 3 Sold 0xc352b5 55241 0.98 320 ## 4 Free <NA> 12800693 NA NA ## 5 Sold EliteCat… 0xcf6165 60.00 36305 ## 6 Sold 0xf5099e GoWest23 31.00 5155 ``` --- ## Data Types in R - You can use `mode()` or `class()` to find information about variables. - `str()` is useful to find information about the structure of your data. - There are many data types but numeric, integer, character, date, and factor are most common. ```r str(Punks) ``` ``` ## 'data.frame': 17554 obs. of 12 variables: ## $ Transaction: chr "Sold" "Sold" "Sold" "Claimed" ... ## $ From : chr "0xf5099e" "0x00d7c9" "0xc352b5" NA ... ## $ To : chr "14715954" "10528156" "55241" "12800693" ... ## $ Crypto : num 25 1.6 0.98 NA 60 31 0.42 NA NA NA ... ## $ USD : num 2822 386 320 NA 36305 ... ## $ Txn : chr "2018-11-30" "2017-07-07" "2017-06-23" "2017-06-23" ... ## $ ID : int 0 0 0 0 1 1 1 1 2 3 ... ## $ Sex : chr "Girl" "Girl" "Girl" "Girl" ... ## $ Type : chr "Female" "Female" "Female" "Female" ... ## $ Skin : chr "Mid" "Mid" "Mid" "Mid" ... ## $ Slots : int 3 3 3 3 2 2 2 2 1 3 ... ## $ Rank : chr "3682560000%" "3682560000%" "3682560000%" "3682560000%" ... ``` --- ## Vector Elements Elements of a vector must all be the same type. ```r claims <- Punks$Transaction == "Claimed" head(claims) ``` ``` ## [1] FALSE FALSE FALSE TRUE FALSE FALSE ``` ```r str(claims) ``` ``` ## logi [1:17554] FALSE FALSE FALSE TRUE FALSE FALSE ... ``` ```r claims[claims == "FALSE"] <- ":-(" #Replacing FALSE with a frownie face. head(claims) ``` ``` ## [1] ":-(" ":-(" ":-(" "TRUE" ":-(" ":-(" ``` ```r str(claims) ``` ``` ## chr [1:17554] ":-(" ":-(" ":-(" "TRUE" ":-(" ":-(" ":-(" "TRUE" "TRUE" ... ``` By changing one value to character, all values are now character types instead of logical types. --- ## Converting Between Types We can convert between different types using the `as` series of functions: ```r ids <- head(Punks$ID) # Creates vector of first 6 rows ids ``` ``` ## [1] 0 0 0 0 1 1 ``` ```r as.character(ids) # Converts to character ``` ``` ## [1] "0" "0" "0" "0" "1" "1" ``` ```r as.numeric("2") ``` ``` ## [1] 2 ``` Notice that in one instance there are quotation marks and in the other there is not. ids is being converted from a numeric type to a character, while "2" is a character being converted to a numeric type. --- ## Statistical Functions Using the basic functions we have learned, it would not be difficult to compute some basic statistics. ```r (n <- length(slots)) # Assigns n to be the number of elements in slots ``` ``` ## [1] 17554 ``` ```r (meanslots <- sum(slots)/n) # Calculates mean by usual formula ``` ``` ## [1] 2.778227 ``` ```r (standdev <- sqrt(sum((slots - meanslots)^2) / (n - 1))) # Calculates standard deviation by usual formula ``` ``` ## [1] 0.7992775 ``` This is fairly easy, that is, if you know the formulae! --- ## Built-in Statistical Functions We don't need to memorize every formula. R does the work for us! ```r mean(slots) # Calculates mean ``` ``` ## [1] 2.778227 ``` ```r sd(slots) # Calculates standard deviation ``` ``` ## [1] 0.7992775 ``` ```r summary(slots) # calculates number summary ``` ``` ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.000 2.000 3.000 2.778 3.000 7.000 ``` ```r quantile(slots, c(.025, .975)) # 2.5% and 97.5% quartiles ``` ``` ## 2.5% 97.5% ## 1 4 ``` --- Which Punk has sold more than 20.00 ETH and has 1 slot? ```r condition <- which(Punks$Crypto > 20.00 & Punks$Slots == 1) Punks[condition,] ``` ``` ## Transaction From To Crypto USD Txn ID Sex Type ## 2588 Sold sov DANNY 23 8458 2020-09-15 1963 Girl Female ## 4815 Sold 0x88bd20 GoWest23 35 12797 2020-10-01 3105 Girl Female ## 4816 Sold 0x5aaeb9 0x88bd20 58 20636 2020-09-30 3105 Girl Female ## 4817 Sold GoWest23 0x5aaeb9 55 20080 2020-09-28 3105 Girl Female ## 9240 Sold 0xa7eba7 DANNY 29 9979 2020-09-22 5426 Girl Female ## 10197 Sold Snowfro DANNY 35 12534 2020-09-06 5944 Guy Zombie ## 17383 Sold 0x832179 trill 50 19870 2020-10-28 9909 Guy Zombie ## Skin Slots Rank ## 2588 Mid 1 0.672000000% ## 4815 Dark 1 0.330240000% ## 4816 Dark 1 0.330240000% ## 4817 Dark 1 0.330240000% ## 9240 Mid 1 1021440000% ## 10197 Zombie 1 0.038808000% ## 17383 Zombie 1 0.030888000% ``` Just Punk 1963, 3105, 5426, 5944, 9909! --- ## Your Turn 1. Determine which Punk has sold for more than 100 ETH or has 7 slots. 2. Which Punk was sold first? Punk 1111 or Punk 3773? <br> <br> <br> <p align="left"> <img src="images/Cut_outs/Cut_out_01.png" width="200px" height="150px"> </p> --- ## Answers ### 1. ```r condition <- which(Punks$Crypto > 100 | Punks$Slots == 7) Punks[condition,][!duplicated(Punks[condition,]$ID),] #Note !duplicated means NOT duplicated ``` ``` ## Transaction From To Crypto USD Txn ID Sex Type ## 4420 Sold 0x4dcaf3 DANNY 150.00 71403 2020-11-13 2923 Guy Male ## 5235 Sold jmg 0x7224a1 189.99 137522 2020-12-30 3306 Guy Male ## 7600 Sold DANNY tycoon.e… 185.00 63788 2020-10-03 4512 Girl Female ## 9026 Sold EliteCat… friskynf… 140.00 54978 2020-09-17 5314 Guy Ape ## 14602 Sold EliteCat… DANNY 85.00 18102 2020-05-20 8348 Guy Male ## Skin Slots Rank ## 4420 Light 4 2406541500% ## 5235 Mid 3 6437574000% ## 7600 Mid 3 1995520000% ## 9026 Ape 2 0.010020000% ## 14602 Mid 7 0.000423161% ``` --- ### 2. ```r Punk1111 <- Punks$Txn[which(Punks$ID == 1111 & Punks$Transaction == "Sold")] Punk3773 <- Punks$Txn[which(Punks$ID == 3773 & Punks$Transaction == "Sold")] Punk1111[length(Punk1111)] #Get the last value in the vector using the length() function ``` ``` ## [1] "2020-09-03" ``` ```r Punk3773[length(Punk3773)] ``` ``` ## [1] "2017-07-11" ``` ```r #We can also compare the dates using logic Punk1111[length(Punk1111)] < Punk3773[length(Punk3773)] ``` ``` ## [1] FALSE ``` Thus, Punk 3773 was first sold before Punk 1111.