# Part 3 Week 1

Getting Started with Data Visualization in R > Week 1

## 3.2 R basics

### 3.2.1 Basic R

``````####Do Basic Math

2 + 2``````
``#> [1] 4``
``2 - 2``
``#> [1] 0``
``2 / 2``
``#> [1] 1``
``2 * 2``
``#> [1] 4``
``````a <- 2 + 2

####Run each line by either (1) setting the cursor at the end of the line and hitting control+enter on a PC or cmd+enter on Mac or click in the run button in the menu bar above or (2) selecting the line(s) of code you want to execute and then using the keyboard short cut or clicking run

##### You can use pound signs/hashtags to tell R to ignore lines of code

#2+2
2 + 2``````
``#> [1] 4``
``````#############

#START FOLLOWING ALONG WITH THE VIDEO HERE

#############

##### If you tell R to do something it doesn't understand, it will throw an error. R is very picky about punctuation, spelling etc.

######these commands will not do anything
#2234r*S&F(SD&F234)
#c(c))

#### R can also act do logical tests using logical operators.

####Is equal?
2 == 2``````
``#> [1] TRUE``
``2 == 3``
``#> [1] FALSE``
``````#####Is not equal?
2 != 2``````
``#> [1] FALSE``
``2 != 3``
``#> [1] TRUE``
``````####Greater than and less than
2 > 1``````
``#> [1] TRUE``
``2 < 1``
``#> [1] FALSE``
``````####Greater than or equal to, less than or equal to
3 >= 1``````
``#> [1] TRUE``
``3 <= 1``
``#> [1] FALSE``
``````#### R can work with character strings
"apple"``````
``#> [1] "apple"``
``````#### It is ok to use spaces in character strings.
"an apple"``````
``#> [1] "an apple"``
``````###You can use logical operators to see whether character strings exactly match each other

"apple" == "apple"``````
``#> [1] TRUE``
``"apple" == "appla"``
``#> [1] FALSE``
``"apple" == "orange"``
``#> [1] FALSE``
``"apple" != "orange"``
``#> [1] TRUE``
``````#### If you try to use inequalities with characters, R will compare how long the character string is

"apple" < "apples"``````
``#> [1] TRUE``
``"apple" > "apples"``
``#> [1] FALSE``
``````####save the output of a command to an object
a <- 2 + 2
my_a <- 2 + 2
my.a <- 2 + 2

######## Don't do this. It won't work
#9a <- 2+2
#my object <- 2+2

#### See what is in the object by "running" the object
a``````
``#> [1] 4``
``````####you can save series of numbers or strings and put them into vectors using the combine function, c().

numbers <- c(1, 2, 3)
numbers``````
``#> [1] 1 2 3``
``````fruits <- c("apple", "orange")
fruits``````
``#> [1] "apple"  "orange"``
``````numbers2 <- c(4:6)
numbers2``````
``#> [1] 4 5 6``
``````true_false <- c(TRUE, FALSE, TRUE)
true_false``````
``#> [1]  TRUE FALSE  TRUE``
``````#### You can combine vectors together

numbers3 <- c(7:9)

all_numbers <- c(numbers, numbers2, numbers3)

all_numbers``````
``#> [1] 1 2 3 4 5 6 7 8 9``
``````#####You can select certain elements of a vector

x <- c(-1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)

### By position in the vector
x[4] #The fourth element.``````
``#> [1] 12``
``x[-4] #All but the fourth.``
``#>  [1] -1 10 11 13 14 15 16 17 18 19``
``x[2:4] #Elements two to four.``
``#> [1] 10 11 12``
``x[-(2:4)] #All elements except two to four.``
``#> [1] -1 13 14 15 16 17 18 19``
``x[c(1, 5)] #Elements one and five.``
``#> [1] -1 13``
``````### By Value
x[x == 10] # Elements which are equal to 10.``````
``#> [1] 10``
``x[x < 0] #all elements less than zero.``
``#> [1] -1``
``x[x %in% c(10, 12, 15)] #Elements in the set 2, 4, 7.``
``#> [1] 10 12 15``
``````#### When you save different kinds of data, that data is given a "class" that describes what kind of data are in the vector

class(numbers)``````
``#> [1] "numeric"``
``class(fruits)``
``#> [1] "character"``
``class(true_false)``
``#> [1] "logical"``
``````#### If you combine numbers and character vectors together, the numbers will convert to characters

fruits_numbers <- c(numbers, fruits)
fruits_numbers``````
``#> [1] "1"      "2"      "3"      "apple"  "orange"``
``````#### Generally for data visualization purposes, it is good to not mix characters and numbers in the same vector.

##### You can change the class of a vector using as.logical,as.numeric,as.character, and as.factor

####here's an example with 1s and 0s

my_vector <- c(1, 0, 1, 0)

my_vector_character <- as.character(my_vector)
my_vector_character``````
``#> [1] "1" "0" "1" "0"``
``class(my_vector_character)``
``#> [1] "character"``
``````my_vector_logical <- as.logical(my_vector)
my_vector_logical``````
``#> [1]  TRUE FALSE  TRUE FALSE``
``class(my_vector_logical)``
``#> [1] "logical"``
``````my_vector_factor <- as.factor(my_vector)
my_vector_factor``````
``````#> [1] 1 0 1 0
#> Levels: 0 1``````
``class(my_vector_factor)``
``#> [1] "factor"``
``````my_vector_numeric_again <- as.numeric(my_vector_character)
my_vector_numeric_again``````
``#> [1] 1 0 1 0``
``class(my_vector_numeric_again)``
``#> [1] "numeric"``

### 3.2.2 Functions in R

``````####Functions

x + y
}

``#> [1] 5``
``````####The last expression is returned

x + y
x * y
}

``#> [1] 6``
``````####you can force R to return a specific object

total <- x + y
product <- x * y
total_and_product <- c(total, product)

subtract <- x - y

return(total_and_product)
}

``#> [1] 5 6``
``````##### R has many basic mathematical functions already built in that can be applied to numbers and vectors of numbers

sum(c(2, 3, 5))``````
``#> [1] 10``
``````####add all the numbers in two vectors
sum(c(1, 2, 3), c(4, 5))``````
``#> [1] 15``
``````#####this does the same thing, just saving the vector to an object
my_vector <- c(1, 2, 3, 4, 5)
sum(my_vector)``````
``#> [1] 15``
``````##### There are many other functions for doing basic math and descriptive statistics

max(my_vector)``````
``#> [1] 5``
``min(my_vector)``
``#> [1] 1``
``median(my_vector)``
``#> [1] 3``
``mean(my_vector)``
``#> [1] 3``
``sd(my_vector) ###standard deviation``
``#> [1] 1.581``
``summary(my_vector) ### a five number summary``
``````#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#>       1       2       3       3       4       5``````
``````##### Other functions will sort vectors or tell you information about the vector

my_vector <- c(2, 2, 1, 3, 5)

sort(my_vector)``````
``#> [1] 1 2 2 3 5``
``rev(my_vector)``
``#> [1] 5 3 1 2 2``
``table(my_vector)``
``````#> my_vector
#> 1 2 3 5
#> 1 2 1 1``````
``unique(my_vector)``
``#> [1] 2 1 3 5``
``length(my_vector)``
``#> [1] 5``
``````####create data fast
seq(1, 5)``````
``#> [1] 1 2 3 4 5``
``seq(1, 9, by = 2)``
``#> [1] 1 3 5 7 9``
``rep("a", 5)``
``#> [1] "a" "a" "a" "a" "a"``
``rep(10, 5)``
``#> [1] 10 10 10 10 10``

## 3.3 Dataframe

``````#####creating dataframes

variable1 <- c(1, 2, 3, 4, 5)
variable2 <- c(6, 7, 8, 9, 10)

data.frame(variable1, variable2)``````
``````#>   variable1 variable2
#> 1         1         6
#> 2         2         7
#> 3         3         8
#> 4         4         9
#> 5         5        10``````
``````my_dat <- data.frame("height" = variable1, "weight" = variable2)

my_dat``````
``````#>   height weight
#> 1      1      6
#> 2      2      7
#> 3      3      8
#> 4      4      9
#> 5      5     10``````
``````#####get a single column of a dataframe
my_dat\$weight``````
``#> [1]  6  7  8  9 10``
``````####use a column in a function
mean(my_dat\$weight)``````
``#> [1] 8``
``````####save a column to another object
my_weights <- my_dat\$weight
my_weights``````
``#> [1]  6  7  8  9 10``
``````####selecting data from dataframes

#####remember you specify row, then column in the brackets

###first row, all columns
my_dat[1, ]``````
``````#>   height weight
#> 1      1      6``````
``````###first column, all rows
my_dat[, 1]``````
``#> [1] 1 2 3 4 5``
``````###first column, first row,
my_dat[1, 1]``````
``#> [1] 1``
``````###first column, first three rows
my_dat[1:3, 1]``````
``#> [1] 1 2 3``
``````#####creating new columns
####single value repeated
my_dat\$variable3 <- 100
my_dat``````
``````#>   height weight variable3
#> 1      1      6       100
#> 2      2      7       100
#> 3      3      8       100
#> 4      4      9       100
#> 5      5     10       100``````
``````####add vector with same length
my_dat\$variable4 <- c("apple", "orange", "grape", "cherry", "melon")
my_dat``````
``````#>   height weight variable3 variable4
#> 1      1      6       100     apple
#> 2      2      7       100    orange
#> 3      3      8       100     grape
#> 4      4      9       100    cherry
#> 5      5     10       100     melon``````
``````####this won't work because the vector is too short - needs to be same length as other vectors/columns in the dataframe
#my_dat\$variable5 <- c("banana","mango")

####get the dimensions of a dataframe (rows and columns)
dim(my_dat)``````
``#> [1] 5 4``
``````####get more information about the structure of a data frame
str(my_dat)``````
``````#> 'data.frame':    5 obs. of  4 variables:
#>  \$ height   : num  1 2 3 4 5
#>  \$ weight   : num  6 7 8 9 10
#>  \$ variable3: num  100 100 100 100 100
#>  \$ variable4: chr  "apple" "orange" "grape" "cherry" ...``````
``nrow(my_dat)``
``#> [1] 5``
``ncol(my_dat)``
``#> [1] 4``
``````###get the column headers of the data frame
names(my_dat)``````
``#> [1] "height"    "weight"    "variable3" "variable4"``
``````###if you have a big dataframe, use head() to see the first few rows or tail to see the last few
``````#>   height weight variable3 variable4
#> 1      1      6       100     apple
#> 2      2      7       100    orange
#> 3      3      8       100     grape
#> 4      4      9       100    cherry
#> 5      5     10       100     melon``````
``tail(my_dat)``
``````#>   height weight variable3 variable4
#> 1      1      6       100     apple
#> 2      2      7       100    orange
#> 3      3      8       100     grape
#> 4      4      9       100    cherry
#> 5      5     10       100     melon``````
``````####Spreadsheet View
# View(my_dat)

#####Missing data causes lots of problems. Some functions "break" and throw errors if you include missing data
with_missing <- c(1, 2, 3, NA)
sum(with_missing)``````
``#> [1] NA``
``````#### You should look at the documentation for a function to understand how it handles missing data. Sometimes you can use an argument with a function to tell it how to deal with the missing data, often telling the function to ignore the missing cells.

# ?sum

sum(with_missing, na.rm = TRUE)``````
``#> [1] 6``
``````#####combining two dataframes with cbind

my_dat2 <- data.frame("variable4" = 400:499, "variable5" = 500:599)

all_dat <- cbind(my_dat, my_dat2)

``````#>   height weight variable3 variable4 variable4 variable5
#> 1      1      6       100     apple       400       500
#> 2      2      7       100    orange       401       501
#> 3      3      8       100     grape       402       502
#> 4      4      9       100    cherry       403       503
#> 5      5     10       100     melon       404       504
#> 6      1      6       100     apple       405       505``````
``````#### adding a row

new_row <- c(1000, 2000, 3000, 4000)

all_dat_plus_new_row <- rbind(all_dat, new_row)

tail(all_dat_plus_new_row)``````
``````#>     height weight variable3 variable4 variable4 variable5
#> 96       1      6       100     apple       495       595
#> 97       2      7       100    orange       496       596
#> 98       3      8       100     grape       497       597
#> 99       4      9       100    cherry       498       598
#> 100      5     10       100     melon       499       599
#> 101   1000   2000      3000      4000      1000      2000``````
``````#### combining two dataframes with rbind

#####you have to make sure the two dataframes have the same column names and order

#######this won't work
# my_dat <- data.frame("variable1"=1:100,"variable2"=200:299)

# my_dat2 <- data.frame("variable4"=400:499,"variable5"=500:599)

# rbind(my_dat,my_dat2)

######this will work
my_dat <- data.frame("variable1" = 1:100, "variable2" = 200:299)

my_dat2 <- data.frame("variable1" = 400:499, "variable2" = 500:599)

rbind(my_dat, my_dat2)``````
``````#>     variable1 variable2
#> 1           1       200
#> 2           2       201
#> 3           3       202
#> 4           4       203
#> 5           5       204
#> 6           6       205
#> 7           7       206
#> 8           8       207
#> 9           9       208
#> 10         10       209
#> 11         11       210
#> 12         12       211
#> 13         13       212
#> 14         14       213
#> 15         15       214
#> 16         16       215
#> 17         17       216
#> 18         18       217
#> 19         19       218
#> 20         20       219
#> 21         21       220
#> 22         22       221
#> 23         23       222
#> 24         24       223
#> 25         25       224
#> 26         26       225
#> 27         27       226
#> 28         28       227
#> 29         29       228
#> 30         30       229
#> 31         31       230
#> 32         32       231
#> 33         33       232
#> 34         34       233
#> 35         35       234
#> 36         36       235
#> 37         37       236
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#> 39         39       238
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#> 41         41       240
#> 42         42       241
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#> 98         98       297
#> 99         99       298
#> 100       100       299
#> 101       400       500
#> 102       401       501
#> 103       402       502
#> 104       403       503
#> 105       404       504
#> 106       405       505
#> 107       406       506
#> 108       407       507
#> 109       408       508
#> 110       409       509
#> 111       410       510
#> 112       411       511
#> 113       412       512
#> 114       413       513
#> 115       414       514
#> 116       415       515
#> 117       416       516
#> 118       417       517
#> 119       418       518
#> 120       419       519
#> 121       420       520
#> 122       421       521
#> 123       422       522
#> 124       423       523
#> 125       424       524
#> 126       425       525
#> 127       426       526
#> 128       427       527
#> 129       428       528
#> 130       429       529
#> 131       430       530
#> 132       431       531
#> 133       432       532
#> 134       433       533
#> 135       434       534
#> 136       435       535
#> 137       436       536
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#> 140       439       539
#> 141       440       540
#> 142       441       541
#> 143       442       542
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#> 149       448       548
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#> 167       466       566
#> 168       467       567
#> 169       468       568
#> 170       469       569
#> 171       470       570
#> 172       471       571
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#> 174       473       573
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#> 177       476       576
#> 178       477       577
#> 179       478       578
#> 180       479       579
#> 181       480       580
#> 182       481       581
#> 183       482       582
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#> 186       485       585
#> 187       486       586
#> 188       487       587
#> 189       488       588
#> 190       489       589
#> 191       490       590
#> 192       491       591
#> 193       492       592
#> 194       493       593
#> 195       494       594
#> 196       495       595
#> 197       496       596
#> 198       497       597
#> 199       498       598
#> 200       499       599``````

## 3.4 Basics of Importing Data into R

``````####Importing Data in R

####Import CSV
cces_sample <-

####Write CSV
write.csv(cces_sample,
"/Users/haoqiwang/Desktop/2021REUDataScience/week1/test.csv")

####type in your directory path in setwd() or use the Session-->Set Working Directory menu options

getwd()``````
``#> [1] "/Users/haoqiwang/Desktop/2021REUDataScience"``
``````setwd("/Users/haoqiwang/Desktop/2021REUDataScience/week1")

#### Don't need the whole file path now

class(cces_sample)``````
``#> [1] "data.frame"``
``median(cces_sample\$pew_religimp, na.rm = T)``
``#> [1] 2``
``table(cces_sample\$race)``
``````#>
#>   1   2   3   4   5   6   7   8
#> 794  81  67  27   6  14  10   1``````

## 3.5 Base R Visualizations

``````#####Visualizations with Base R

####Univariate Statistics

numbers1 <- c(1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6)

hist(numbers1)``````

``boxplot(numbers1)``

``````numbers2 <- c(5, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9)

boxplot(numbers1, numbers2)``````

``````######Bivariate Statistics

#####make some fake data to create a scatterplot

#rnorm - normal distribution
#rpois - poisson
#rbinom - binomial
#runif - uniform

variable1 <- runif(50, 0, 100)
variable2 <- runif(50, 0, 100)
my_dat <- data.frame(variable1, variable2)

#####this does the same thing as the previous three lines
my_dat <-
data.frame("variable1" = runif(50, 0, 100),
"variable2" = runif(50, 0, 100))

#### Scatterplot
plot(my_dat\$variable1, my_dat\$variable2)

#####this does the same thing
plot(x = my_dat\$variable1, y = my_dat\$variable2)``````

``````###Base R has some limited graphics customization ability
plot(
my_dat\$variable1,
my_dat\$variable2,
main = "My First Plot",
``# ? plot``