October 6, 2018
First things first
Some things you should know
- Typical uses for R
- Data analysis
- Data visualization
- Reshaping data
- Automation
- Mental models and programming
- Excel/SPSS/Stata –> R/Python/Julia/JavaScript
- What all R programmers do
- Google for answers
- Borrow code
- Ask friends and pros for help
- Useful references (see Reference Materials in the syllabus for more)
- help()
- ??
- R Bloggers
- stackoverflow
Quick Tour of R Studio
Quick Tour of R Studio
- Open and save (locally) an R Notebook
- Make sure you can install packages
- 2 ways to install packages
- Command line/Console
- R Studio GUI
- 2 ways to install packages
Quick Tour of R Studio
- Open and save (locally) an R Notebook
- Make sure you can install packages
- 2 ways to install packages
- Command line/Console
- R Studio GUI
- 2 ways to install packages
install.packages('devtools')
Quick Tour of R Studio
- Open and save (locally) an R Notebook
- Make sure you can install packages
- 2 ways to install packages
- Command line/Console
- R Studio GUI
- 2 ways to install packages
Libraries, functions, data
Libraries, functions, data
- Libraries (or packages) are collections of functions (and datasets)
- Over 10,000 libraries on CRAN
install.packages('tidyverse')
install.packages(c('ggthemes', 'rmarkdown'))
library(tidyverse)
Libraries, functions, data
- Functions perform operations in R
foo <- c(1,2,4) foo %>% min() foo %>% mean() foo %>% max() foo %>% sd()
Libraries, functions, data
- Functions perform operations in R
foo <- c(1,2,4) foo %>% min() ## [1] 1 foo %>% mean() ## [1] 2.333333 foo %>% max() ## [1] 4 foo %>% sd() ## [1] 1.527525
Libraries, functions, data
- Function arguments
- Let’s look at arguments in the
lmandminfunctions - Arguments are inputs
- Arguments are comma separated
- Let’s look at arguments in the
help(lm)
help(min)
cat_function <- function(love=TRUE){
if(love==TRUE){
print('I love cats!')
}
else {
print('I am not a cool person.')
}
}
Libraries, functions, data
- Function arguments
- Let’s look at arguments in the
lmandminfunctions - Arguments are inputs
- Arguments are comma separated
- Let’s look at arguments in the
foo <- c(1,2,NA, 4) foo %>% min() foo %>% mean() foo %>% max() foo %>% sd()
Libraries, functions, data
- Function arguments
- Let’s look at arguments in the
lmandminfunctions - Arguments are inputs
- Arguments are comma separated
- Let’s look at arguments in the
foo <- c(1,2,NA, 4) foo %>% min() ## [1] NA foo %>% mean() ## [1] NA foo %>% max() ## [1] NA foo %>% sd() ## [1] NA
Libraries, functions, data
- Function arguments
- Let’s look at arguments in the
lmandminfunctions - Arguments are inputs
- Arguments are comma separated
- Let’s look at arguments in the
foo <- c(1,2,NA, 4) min(foo, na.rm = TRUE) ## [1] 1 mean(foo, na.rm = TRUE) ## [1] 2.333333 max(foo, na.rm = TRUE) ## [1] 4 sd(foo, na.rm = TRUE) ## [1] 1.527525
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Integers, numbers, characters, factors
- Confirm data type
c('foo', 'moo', 'boo') %>% class()
c('foo', 'moo', 'boo') %>% is.character()
c('foo', 'moo', 'boo') %>% is.factor()
- Coerce data type
c('foo', 'moo', 'boo') %>% as.factor()
c('foo', 'moo', 'boo') %>% as.factor() %>% class()
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Integers, numbers, characters, factors
- Confirm data type
c('foo', 'moo', 'boo') %>% class()
## [1] "character"
c('foo', 'moo', 'boo') %>% is.character()
## [1] TRUE
c('foo', 'moo', 'boo') %>% is.factor()
## [1] FALSE
- Coerce data type
c('foo', 'moo', 'boo') %>% as.factor()
c('foo', 'moo', 'boo') %>% as.factor() %>% class()
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Integers, numbers, characters, factors
- Confirm data type
c('foo', 'moo', 'boo') %>% class()
## [1] "character"
c('foo', 'moo', 'boo') %>% is.character()
## [1] TRUE
c('foo', 'moo', 'boo') %>% is.factor()
## [1] FALSE
- Coerce data type
c('foo', 'moo', 'boo') %>% as.factor()
## [1] foo moo boo
## Levels: boo foo moo
c('foo', 'moo', 'boo') %>% as.factor() %>% class()
## [1] "factor"
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Data frames look a little like spreadsheets
data_frame(x = c(1:3), y = c(4:6), z = c('foo', 'boo', 'moo'))
## # A tibble: 3 x 3
## x y z
## <int> <int> <chr>
## 1 1 4 foo
## 2 2 5 boo
## 3 3 6 moo
Libraries, functions, data
- Call a specific variable by name or location in the data frame
- Use the
$between the data frame name and the variable name - Call the column (or row) by index number
- Use the
cars$speed %>% head(7) ## [1] 4 4 7 7 8 9 10
cars[,1] %>% head(7) ## [1] 4 4 7 7 8 9 10
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Matrices and data frames have similar shapes
- Traditionally matrices contained data of a single type
- When it comes down to using a matrix or data frame in our class, data frames are the way to go
matrix(data = 1:6, nrow = 3, ncol = 2) ## [,1] [,2] ## [1,] 1 4 ## [2,] 2 5 ## [3,] 3 6
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Vectors contain 1 or more values in a string
c('foo', 'moo', 'boo')
## [1] "foo" "moo" "boo"
1:10
## [1] 1 2 3 4 5 6 7 8 9 10
rep(1:2, times = 2)
## [1] 1 2 1 2
rep(c(1,2), times = 2)
## [1] 1 2 1 2
seq(from = 0, to = 100, by = 10)
## [1] 0 10 20 30 40 50 60 70 80 90 100
seq(0, 100, 10)
## [1] 0 10 20 30 40 50 60 70 80 90 100
cars$speed
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14
## [24] 15 15 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24
## [47] 24 24 24 25
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Call a specific element by its location in a vector
c('foo', 'moo', 'boo')[2]
seq(from = 0, to = 100, by = 10)[6]
Libraries, functions, data
- Oh my, so many types of data!
- Data frames, matrices, vectors
- Call a specific element by its location in a vector
c('foo', 'moo', 'boo')[2]
## [1] "moo"
seq(from = 0, to = 100, by = 10)[6]
## [1] 50
Let’s apply what we learned to real data
Let’s apply what we learned to real data
Exercise - 5 minutes
- What type of data object is candi?
- What are the min() and mean() for amount?
- What is the max() for election_year?
To answer the questions, import the political contributions dataset
candi <- read_csv('https://goo.gl/GTRqZs') %>% as.data.frame()
Let’s apply what we learned to real data
Exercise - 5 minutes
- What type of data object is candi?
- What are the min() and mean() for amount?
- What is the max() for election_year?
candi %>% class() ## [1] "data.frame"
Let’s apply what we learned to real data
Exercise - 5 minutes
- What type of data object is candi?
- What are the min() and mean() for amount?
- What is the max() for election_year?
candi %>% class() ## [1] "data.frame" candi$amount %>% min(na.rm = TRUE) ## [1] -225000 candi$amount %>% mean(na.rm = TRUE) ## [1] 358.1753
Let’s apply what we learned to real data
Exercise - 5 minutes
- What type of data object is candi?
- What are the min() and mean() for amount?
- What is the max() for election_year?
candi %>% class() ## [1] "data.frame" candi$amount %>% min(na.rm = TRUE) ## [1] -225000 candi$amount %>% mean(na.rm = TRUE) ## [1] 358.1753 candi$election_year %>% max(na.rm = TRUE) ## [1] 2023
Base R functions to explore your data
Base R functions to explore your data
- Get in the habit of running these functions when you open a new dataset
head()shows you the top subset of a data in a data frame- Number of rows is a
head()argument and defaults at 5 tail()shows the bottom subset of data in a data frame
- Number of rows is a
summary()shows summary statistics on all variabes in a dataset- Reported summary statistics depend on the variable type
ls()shows all variables in a data frame- You can also use
ls()without calling an object between the parentheses to see all objects in your workspace
- You can also use
str()tells the variable type and selected variable values in a data frame for all variablesdim()tells you the dimensions of your datasetnrow()reports the number of rows onlyncol()reports the number of columns only
Base R functions to explore your data
Exercise - 5 minutes
- How many observations (or rows) are in candi?
- What is the median value for amount?
- How many variables are in candi?
Hint: There are multiple ways to answers these questions with the functions you know
Base R functions to explore your data
Exercise - 5 minutes
- How many observations (or rows) are in candi?
- What is the median value for amount?
- How many variables are in candi?
candi %>% dim() ## [1] 97756 22
Base R functions to explore your data
Exercise - 5 minutes
- How many observations (or rows) are in candi?
- What is the median value for amount?
- How many variables are in candi?
candi %>% dim() ## [1] 97756 22 candi$amount %>% median(na.rm = TRUE) ## [1] 35
Base R functions to explore your data
Exercise - 5 minutes
- How many observations (or rows) are in candi?
- What is the median value for amount?
- How many variables are in candi?
candi %>% dim() ## [1] 97756 22 candi$amount %>% median(na.rm = TRUE) ## [1] 35 candi %>% ncol() ## [1] 22
Base R functions to explore your data
Exercise - 5 minutes
- How many observations (or rows) are in candi?
- What is the median value for amount?
- How many variables are in candi?
candi %>% dim() ## [1] 97756 22 candi$amount %>% median(na.rm = TRUE) ## [1] 35 candi %>% ncol() ## [1] 22
Other methods to answer questions
candi %>% summary() candi %>% ls()
Base R functions to explore vectors
table()shows you the distribution of values in a vectorlength()tells you in the number of elements in a vectorunique()shows you the unique values in a vectorsummary()shows you descriptive statistics for a vectorsummary()can run on a vector or data frame
Base R functions to explore vectors
Exercise - 7 minutes
- How many ‘DEMOCRAT’ values are there in party?
- Which value in contributor_state is most frequent?
- Is ‘Mayoral Race’ a value you from the type variable?
- How many distinct contributor_zip values are there?
Hint: There are multiple ways to answers these questions with the functions you know
Base R functions to explore vectors
Exercise - 7 minutes
- How many ‘DEMOCRAT’ values are there in party?
- Which value in contributor_state is most frequent?
- Is ‘Mayoral Race’ a value you from the type variable?
- How many distinct contributor_zip values are there?
table(candi$party)[1] ## DEMOCRAT ## 13961 table(candi$contributor_state) %>% tail() ## ## VT WA WI WV WY ZZ ## 18 84691 43 54 9 3 candi$type %>% unique() ## [1] "Candidate" "Political Committee" NA candi$contributor_zip %>% unique() %>% length() ## [1] 3450
Troubleshooting your R code
Make sure…
- You loaded your libraries
- Arguments in your functions are comma-separated
- Your functions have a closing paren
- You have an end quotation mark where relevant
- The variable type is correct to perform the function you call
- For example you probably don’t want to run
min()on a character string
- For example you probably don’t want to run
- Your definition operator looks like this
<-
- You pipe operator looks like this
%>%
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
Begin the exercise by importing the crime dataset in R Studio
crime <- read_csv('https://goo.gl/FHW2Ni') %>% as.data.frame()
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
crime %>% dim() ## [1] 23459 13
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
crime %>% dim() ## [1] 23459 13 crime$occurred_date %>% max(na.rm = TRUE) ## [1] "2018-09-28"
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
table(crime$neighborhood)[13:15] ## ## COMMERCIAL DUWAMISH COMMERCIAL HARBOR ISLAND DOWNTOWN COMMERCIAL ## 15 9 1938
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
crime$crime_subcategory %>% table() %>% tail(5) ## . ## THEFT-BICYCLE THEFT-BUILDING THEFT-SHOPLIFT TRESPASS WEAPON ## 470 907 2037 680 190
Practice with crime data
Exercise - 10 minutes
- How many rows and columns are there in crime?
- What is the most recent occurred_date?
- Which neighborhood sees the most incident activity?
- How many ‘THEFT-BICYCLE’ incidences are there in crime_subcategory?
- What are the earliest and latest reported_time values?
crime$crime_subcategory %>% table() %>% tail(5) ## . ## THEFT-BICYCLE THEFT-BUILDING THEFT-SHOPLIFT TRESPASS WEAPON ## 470 907 2037 680 190 crime$reported_time %>% min(na.rm = TRUE) ## [1] 0 crime$reported_time %>% max(na.rm = TRUE) ## [1] 2359