Foundations of Statistics Using R: In Class Session
Kristen Sosulski, @sosulski, #rstats
05-16-2018
- DATA BASICS
- APPLICATION: Capital Bikeshare
- E. Exercise: Import the bike sharing data
- F. Exercise: Take a look at the data.
- G. Exercise: Understanding the variables
- H. Exercise: Renaming columns
- I. Exercise: Dealing with missing values
- J. Exercise: Understand - Calculate basic summary statistics
- K. Exercise: Understand - Visualize
- L. Exercise: Scatter plots
- M. Exercise: Interactive chart - Use ggvis to filter
- N. Communicate - Create an RMarkdown document
- ITERATION
- CONDITIONALS
- FUNCTIONS
- CODING IN OTHER LANGUAGES
- SQL
- SHINY
- FURTHER STUDY
Updated on Wed May 16 15:34:25 2018.
DATA BASICS
This section will guide you in the process of decoding your data into information and ultimately intelligible insights. In doing so, we will explore the use of tidyverse and R base packages.
When working with a new data what initial questions do you have?
Consider the following questions to guide your understanding.
- What does your data represent in the real world?
- How is this real world phenomena characterized by the data that you have?
- From what time period is the data?
Once you have this basic understanding of your data you can dig deeper. Then you can use visualization techniques to explore your data and derive some basic understandings of the phenomena you are studying, such as the largest and smallest values for each variable. In addition, calculating summary statistics translate data into information by revealing the shape of the data, the mean, median, minimum value, maximum value, and variability all with simple visualizations.
For any data science project there are few simple steps to follow. 
A. Exercise: Importing your data
Using the World internet usage data we will compare of read.csv to read_csv for importing data.
utils package using read.csv()
library(utils)
internet_utils <- read.csv("world_internet_usage.csv")
head(internet_utils)
## country X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007
## 1 China 1.78 2.64 4.60 6.20 7.30 8.52 10.52 16.00
## 2 Mexico 5.08 7.04 11.90 12.90 14.10 17.21 19.52 20.81
## 3 Panama 6.55 7.27 8.52 9.99 11.14 11.48 17.35 22.29
## 4 Senegal 0.40 0.98 1.01 2.10 4.39 4.79 5.61 7.70
## 5 Singapore 36.00 41.67 47.00 53.84 62.00 61.00 59.00 69.90
## 6 United Arab Emirates 23.63 26.27 28.32 29.48 30.13 40.00 52.00 61.00
## X2008 X2009 X2010 X2011 X2012
## 1 22.60 28.90 34.30 38.30 42.30
## 2 21.71 26.34 31.05 34.96 38.42
## 3 33.82 39.08 40.10 42.70 45.20
## 4 10.60 14.50 16.00 17.50 19.20
## 5 69.00 69.00 71.00 71.00 74.18
## 6 63.00 64.00 68.00 78.00 85.00Use readr to import the data
library(readr)
internet_readr <- read_csv("world_internet_usage.csv")
## Parsed with column specification:
## cols(
## country = col_character(),
## `2000` = col_double(),
## `2001` = col_double(),
## `2002` = col_double(),
## `2003` = col_double(),
## `2004` = col_double(),
## `2005` = col_double(),
## `2006` = col_double(),
## `2007` = col_double(),
## `2008` = col_double(),
## `2009` = col_double(),
## `2010` = col_double(),
## `2011` = col_double(),
## `2012` = col_double()
## )
head(internet_readr)
## # A tibble: 6 x 14
## country `2000` `2001` `2002` `2003` `2004` `2005` `2006` `2007` `2008`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 China 1.78 2.64 4.60 6.20 7.30 8.52 10.5 16.0 22.6
## 2 Mexico 5.08 7.04 11.9 12.9 14.1 17.2 19.5 20.8 21.7
## 3 Panama 6.55 7.27 8.52 9.99 11.1 11.5 17.4 22.3 33.8
## 4 Senegal 0.400 0.980 1.01 2.10 4.39 4.79 5.61 7.70 10.6
## 5 Singapore 36.0 41.7 47.0 53.8 62.0 61.0 59.0 69.9 69.0
## 6 United A… 23.6 26.3 28.3 29.5 30.1 40.0 52.0 61.0 63.0
## # ... with 4 more variables: `2009` <dbl>, `2010` <dbl>, `2011` <dbl>,
## # `2012` <dbl>Select the second row, first column.
internet_readr[[2,1]]
## [1] "Mexico"
internet_utils [2,1] # double [[ ]] works too
## [1] Mexico
## 7 Levels: China Mexico Panama Senegal Singapore ... United StatesExtract the variable “country”
internet_readr$country
## [1] "China" "Mexico" "Panama"
## [4] "Senegal" "Singapore" "United Arab Emirates"
## [7] "United States"
internet_utils$country
## [1] China Mexico Panama
## [4] Senegal Singapore United Arab Emirates
## [7] United States
## 7 Levels: China Mexico Panama Senegal Singapore ... United StatesAn alternative using the infix function
#to use with infix function add a .
internet_readr %>% .$country
## [1] "China" "Mexico" "Panama"
## [4] "Senegal" "Singapore" "United Arab Emirates"
## [7] "United States"B. Exercise: Tidy data - reshaping
Rename columns first to remove the X in front of each year.
names(internet_utils) <-c("country", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012")
names(internet_utils)
## [1] "country" "2000" "2001" "2002" "2003" "2004" "2005"
## [8] "2006" "2007" "2008" "2009" "2010" "2011" "2012"Reshape a data frame
library(reshape2)
internet_utils_reshaped <- melt(internet_utils,id.vars="country", variable.name="year", value.name="usage")
internet_utils_reshaped
## country year usage
## 1 China 2000 1.78
## 2 Mexico 2000 5.08
## 3 Panama 2000 6.55
## 4 Senegal 2000 0.40
## 5 Singapore 2000 36.00
## 6 United Arab Emirates 2000 23.63
## 7 United States 2000 43.08
## 8 China 2001 2.64
## 9 Mexico 2001 7.04
## 10 Panama 2001 7.27
## 11 Senegal 2001 0.98
## 12 Singapore 2001 41.67
## 13 United Arab Emirates 2001 26.27
## 14 United States 2001 49.08
## 15 China 2002 4.60
## 16 Mexico 2002 11.90
## 17 Panama 2002 8.52
## 18 Senegal 2002 1.01
## 19 Singapore 2002 47.00
## 20 United Arab Emirates 2002 28.32
## 21 United States 2002 58.79
## 22 China 2003 6.20
## 23 Mexico 2003 12.90
## 24 Panama 2003 9.99
## 25 Senegal 2003 2.10
## 26 Singapore 2003 53.84
## 27 United Arab Emirates 2003 29.48
## 28 United States 2003 61.70
## 29 China 2004 7.30
## 30 Mexico 2004 14.10
## 31 Panama 2004 11.14
## 32 Senegal 2004 4.39
## 33 Singapore 2004 62.00
## 34 United Arab Emirates 2004 30.13
## 35 United States 2004 64.76
## 36 China 2005 8.52
## 37 Mexico 2005 17.21
## 38 Panama 2005 11.48
## 39 Senegal 2005 4.79
## 40 Singapore 2005 61.00
## 41 United Arab Emirates 2005 40.00
## 42 United States 2005 67.97
## 43 China 2006 10.52
## 44 Mexico 2006 19.52
## 45 Panama 2006 17.35
## 46 Senegal 2006 5.61
## 47 Singapore 2006 59.00
## 48 United Arab Emirates 2006 52.00
## 49 United States 2006 68.93
## 50 China 2007 16.00
## 51 Mexico 2007 20.81
## 52 Panama 2007 22.29
## 53 Senegal 2007 7.70
## 54 Singapore 2007 69.90
## 55 United Arab Emirates 2007 61.00
## 56 United States 2007 75.00
## 57 China 2008 22.60
## 58 Mexico 2008 21.71
## 59 Panama 2008 33.82
## 60 Senegal 2008 10.60
## 61 Singapore 2008 69.00
## 62 United Arab Emirates 2008 63.00
## 63 United States 2008 74.00
## 64 China 2009 28.90
## 65 Mexico 2009 26.34
## 66 Panama 2009 39.08
## 67 Senegal 2009 14.50
## 68 Singapore 2009 69.00
## 69 United Arab Emirates 2009 64.00
## 70 United States 2009 71.00
## 71 China 2010 34.30
## 72 Mexico 2010 31.05
## 73 Panama 2010 40.10
## 74 Senegal 2010 16.00
## 75 Singapore 2010 71.00
## 76 United Arab Emirates 2010 68.00
## 77 United States 2010 74.00
## 78 China 2011 38.30
## 79 Mexico 2011 34.96
## 80 Panama 2011 42.70
## 81 Senegal 2011 17.50
## 82 Singapore 2011 71.00
## 83 United Arab Emirates 2011 78.00
## 84 United States 2011 77.86
## 85 China 2012 42.30
## 86 Mexico 2012 38.42
## 87 Panama 2012 45.20
## 88 Senegal 2012 19.20
## 89 Singapore 2012 74.18
## 90 United Arab Emirates 2012 85.00
## 91 United States 2012 81.03Reshape a tibble
internet_readr_reshaped <- melt(internet_readr,id.vars="country", variable.name="year", value.name="usage")
internet_readr_reshaped
## country year usage
## 1 China 2000 1.78
## 2 Mexico 2000 5.08
## 3 Panama 2000 6.55
## 4 Senegal 2000 0.40
## 5 Singapore 2000 36.00
## 6 United Arab Emirates 2000 23.63
## 7 United States 2000 43.08
## 8 China 2001 2.64
## 9 Mexico 2001 7.04
## 10 Panama 2001 7.27
## 11 Senegal 2001 0.98
## 12 Singapore 2001 41.67
## 13 United Arab Emirates 2001 26.27
## 14 United States 2001 49.08
## 15 China 2002 4.60
## 16 Mexico 2002 11.90
## 17 Panama 2002 8.52
## 18 Senegal 2002 1.01
## 19 Singapore 2002 47.00
## 20 United Arab Emirates 2002 28.32
## 21 United States 2002 58.79
## 22 China 2003 6.20
## 23 Mexico 2003 12.90
## 24 Panama 2003 9.99
## 25 Senegal 2003 2.10
## 26 Singapore 2003 53.84
## 27 United Arab Emirates 2003 29.48
## 28 United States 2003 61.70
## 29 China 2004 7.30
## 30 Mexico 2004 14.10
## 31 Panama 2004 11.14
## 32 Senegal 2004 4.39
## 33 Singapore 2004 62.00
## 34 United Arab Emirates 2004 30.13
## 35 United States 2004 64.76
## 36 China 2005 8.52
## 37 Mexico 2005 17.21
## 38 Panama 2005 11.48
## 39 Senegal 2005 4.79
## 40 Singapore 2005 61.00
## 41 United Arab Emirates 2005 40.00
## 42 United States 2005 67.97
## 43 China 2006 10.52
## 44 Mexico 2006 19.52
## 45 Panama 2006 17.35
## 46 Senegal 2006 5.61
## 47 Singapore 2006 59.00
## 48 United Arab Emirates 2006 52.00
## 49 United States 2006 68.93
## 50 China 2007 16.00
## 51 Mexico 2007 20.81
## 52 Panama 2007 22.29
## 53 Senegal 2007 7.70
## 54 Singapore 2007 69.90
## 55 United Arab Emirates 2007 61.00
## 56 United States 2007 75.00
## 57 China 2008 22.60
## 58 Mexico 2008 21.71
## 59 Panama 2008 33.82
## 60 Senegal 2008 10.60
## 61 Singapore 2008 69.00
## 62 United Arab Emirates 2008 63.00
## 63 United States 2008 74.00
## 64 China 2009 28.90
## 65 Mexico 2009 26.34
## 66 Panama 2009 39.08
## 67 Senegal 2009 14.50
## 68 Singapore 2009 69.00
## 69 United Arab Emirates 2009 64.00
## 70 United States 2009 71.00
## 71 China 2010 34.30
## 72 Mexico 2010 31.05
## 73 Panama 2010 40.10
## 74 Senegal 2010 16.00
## 75 Singapore 2010 71.00
## 76 United Arab Emirates 2010 68.00
## 77 United States 2010 74.00
## 78 China 2011 38.30
## 79 Mexico 2011 34.96
## 80 Panama 2011 42.70
## 81 Senegal 2011 17.50
## 82 Singapore 2011 71.00
## 83 United Arab Emirates 2011 78.00
## 84 United States 2011 77.86
## 85 China 2012 42.30
## 86 Mexico 2012 38.42
## 87 Panama 2012 45.20
## 88 Senegal 2012 19.20
## 89 Singapore 2012 74.18
## 90 United Arab Emirates 2012 85.00
## 91 United States 2012 81.03
class(internet_readr_reshaped) # turns into a data.frame!
## [1] "data.frame"Use the gather function to reshape
tidy_internet_readr <-
internet_readr %>%
gather(`2000`,`2001`,`2002`,`2003`,`2004`,`2005`,`2006`,`2007`,`2008`,`2009`,`2010`,`2011`,`2012`, key="year", value="usage")
tidy_internet_readr
## # A tibble: 91 x 3
## country year usage
## <chr> <chr> <dbl>
## 1 China 2000 1.78
## 2 Mexico 2000 5.08
## 3 Panama 2000 6.55
## 4 Senegal 2000 0.400
## 5 Singapore 2000 36.0
## 6 United Arab Emirates 2000 23.6
## 7 United States 2000 43.1
## 8 China 2001 2.64
## 9 Mexico 2001 7.04
## 10 Panama 2001 7.27
## # ... with 81 more rowsC. Exercise: Understand - Visualize
Create a few statistical visualizations to understand the makeup of your data.
Build a boxplot
boxplot(internet_readr$`2000`, main="Range of internet users in 2000", sub="Median of 6.55 users per 100 people", col="#999999", frame=FALSE, las=1)
Build multiple box plots
boxplot(internet_readr[,2:14], main="Range of internet users per 100 people", col="#999999", frame=FALSE, las=1)
Build a single histogram
hist(internet_readr$`2000`, main="Frequency of internet users in 2000 per 100 people", xlab="Year: 2000", col="#999999", border="#FFFFFF", label=TRUE, breaks=6, las=1
)
Build a percentage (rather than count) histogram
library(lattice)
histogram(internet_readr$`2000`, main="Frequency of internet users in 2000 per 100 people", xlab="Year: 2000", col="#999999", border="#FFFFFF")
***
Build a histogram matrix
histogram(~ usage | year, data=tidy_internet_readr, layout=c(4,4), main="Histogram matrix: 2000-2012", col="#999999", border="#FFFFFF", xlab="Usage")
Re-arrange the years
h <-histogram(~tidy_internet_readr$usage|tidy_internet_readr$year,col="#999999",breaks=5,layout=c(3,5), xlab="Usage", main="Histogram matrix: 2000-2011")
update (h, index.cond=list(c(10:12, 7:9, 4:6, 1:3)))
Rearrange the years and show all years
tidy_internet_readr$year<-as.character(tidy_internet_readr$year)
h <-histogram(~tidy_internet_readr$usage|tidy_internet_readr$year,col="#999999", xlab="Usage", main="Histogram matrix: 2000-2012", breaks=5,layout=c(4,4))
update(h, index.cond=list(c(10:13, 6:9, 2:5, 1)))
Build column chart using ggplot
library(ggplot2)
g <- ggplot(tidy_internet_readr,aes(tidy_internet_readr$year, tidy_internet_readr$usage))
g + geom_col() + theme_few() + labs(title = "Internet Usage per 100 people", x = "Year",y ="Usage")
D. Exercise: Communicate
Create charts and reports.
Create a presentation ready line chart using ggplot and apply a ggtheme.
library(ggthemes)
library(ggplot2)
ggplot(tidy_internet_readr,aes(x=year,y=usage,colour=country,group=country)) + geom_line() + labs(title = "Internet Usage per 100 people", subtitle = "Since 2011, the UAE has surpassed Singapore and the US in internet users", caption = "Source: World Bank, 2013",x = "Year",y ="Usage") + theme_few()
Create a new markdown document and publish it with the graph you created above.
Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents.
See the sample markdown here: http://rpubs.com/sosulski/msbaR
For more details on using R Markdown see http://rmarkdown.rstudio.com.
APPLICATION: Capital Bikeshare
Understand your data
The type of data you have will dictate the types of questions you use to guide your analysis. To begin, import the bike sharing data from the Capital Bikeshare system.
E. Exercise: Import the bike sharing data
This data spans the District of Columbia, Arlington County, Alexandria, Montgomery County and Fairfax County. The Capital Bikeshare system is owned by the participating jurisdictions and is operated by Motivate, a Brooklyn, NY-based company that operates several other bikesharing systems including Citibike in New York City, Hubway in Boston and Divvy Bikes in Chicago.
Import the data
library(readr)
bikeshare <- read_csv("bikesharedailydata.csv")
## Parsed with column specification:
## cols(
## instant = col_integer(),
## dteday = col_character(),
## season = col_integer(),
## yr = col_integer(),
## mnth = col_integer(),
## holiday = col_integer(),
## weekday = col_integer(),
## workingday = col_integer(),
## weathersit = col_integer(),
## temp = col_double(),
## atemp = col_double(),
## hum = col_double(),
## windspeed = col_double(),
## casual = col_integer(),
## registered = col_integer(),
## cnt = col_integer()
## )F. Exercise: Take a look at the data.
Preview the data
You can preview the data using the head function to show the first few observations.
head(bikeshare)
## # A tibble: 6 x 16
## instant dteday season yr mnth holiday weekday workingday weathersit
## <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 1 1/1/11 1 0 1 0 6 0 2
## 2 2 1/2/11 1 0 1 0 0 0 2
## 3 3 1/3/11 1 0 1 0 1 1 1
## 4 4 1/4/11 1 0 1 0 2 1 1
## 5 5 1/5/11 1 0 1 0 3 1 1
## 6 6 1/6/11 1 0 1 0 4 1 1
## # ... with 7 more variables: temp <dbl>, atemp <dbl>, hum <dbl>,
## # windspeed <dbl>, casual <int>, registered <int>, cnt <int>Next, you can view the variables and types by using the str function.
str(bikeshare)One of the first things you may notice is the data dimensions, the number of rows and columns. Specifically there are 731 rows (observations) and 16 columns (variables or attributes).
Rows are commonly referred to as observations or records and columns are described as attributes or variables.
However, the variable names listed at the first row of every column are not very descriptive.
G. Exercise: Understanding the variables
Take a look column named season. What is the meaning of season? What are the possible values for this variable?
bikeshare$seasonWhat type of variable is it?
It is an integer. You’ll notice that in the column seasons the values are integers that range between 1 and 4.
What do the numbers represent?
If we really think about it’s unlikely that the numbers represent quantities. Instead, they probably represent the seasons of the year because we know there are four seasons. The numbers (1 through 4) are probably a code for the each of the four seasons of the year. Without additional information, such as a data dictionary or readme file, it would be impossible for the user of the data to know what the possible values of 1 through 4 correspond to in the categorical variable named season.
This leads us to the next step, reviewing the data dictionary along with the data set to better understand the meaning behind the values.
Review the data dictionary
A data dictionary defines the characteristics of each of the data attributes. If your data comes from a reputable source, odds are that it is accompanied with a data dictionary or metadata. To know which season is represented by each number in the variable season we can review the data dictionary.
| Field | Definition |
|---|---|
| instant | record index |
| dteday | date |
| season | season (1:winter, 2:spring, 3:summer, 4:fall) |
| yr | year (0: 2011, 1:2012) |
| mnth | month ( 1 to 12) |
| hr | hour (0 to 23) |
| holiday | weather day is holiday or not |
| weekday | day of the week |
| workingday | if day is neither weekend nor holiday is 1, otherwise is 0. |
| weathersit | 1, 2, 3, 4 |
| – 1 | Clear, Few clouds, Partly cloudy, Partly cloudy |
| – 2 | Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist |
| – 3 | Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds |
| – 4 | Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog |
| temp | Normalized temperature in degrees F |
| atemp | Normalized feeling temperature in degrees F |
| hum | Normalized humidity. |
| windspeed | Normalized wind speed |
| casual | count of casual users |
| registered | count of registered users |
| cnt | count of total rental bikes including both casual and registered |
For example, season is a categorical variable defined by one of four values, each representing a season (1: winter, 2: spring, 3: summer, 4: fall).
You’ll notice that the variable year is coded with the value of 0 for 2011 and 1 for 2012, rather than actual year value of 2011 or 2012.
The variable weathersit is encoded with four possible values, 1 through 4. The values represent the daily weather situation as defined below.
- Clear, Few clouds, Partly cloudy, Partly cloudy
- Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
- Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
- Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
It is essential undergo this process of understanding to help inform the formulate questions for exploration and further analysis. Visualizing data without understanding the meaning of the variables will make it difficult for you to interpret the result. By approaching a data visualization task informed about the data and its attributes you can better formulate questions for visual exploration. The next step is to prepare the data for analytical and visualization tasks.
At this point, you may want to rename the columns in your data set to make the data more usable when you begin the analysis. Renaming columns is a manual process that literally involves change the each column name. It is best practice to use lower case lettering and avoid spaces or hyphenation.
Preparing your data
H. Exercise: Renaming columns
There are many ways to rename columns. Two approaches are presented below
Renaming columns with the rename function from the dplyr library.
library(dplyr)
bikeshare <- rename(bikeshare, humidity = hum)
names(bikeshare)
## [1] "instant" "dteday" "season" "yr" "mnth"
## [6] "holiday" "weekday" "workingday" "weathersit" "temp"
## [11] "atemp" "humidity" "windspeed" "casual" "registered"
## [16] "cnt"Renaming columns with R base functions.
# Rename column where names is "yr"
names(bikeshare)[names(bikeshare) == "yr"] <- "year"
names(bikeshare)
## [1] "instant" "dteday" "season" "year" "mnth"
## [6] "holiday" "weekday" "workingday" "weathersit" "temp"
## [11] "atemp" "humidity" "windspeed" "casual" "registered"
## [16] "cnt"I. Exercise: Dealing with missing values
Even before you define the questions you seek to have answered from the data, it needs to be formatted appropriately. The rows should correspond to observations and the columns correspond the observed variables. This makes it easier to map the data to visual properties such as position, color, size, or shape. A preprocessing step is necessary to verify the dataset for correctness and consistency. Incomplete information has a high potential for incorrect results.
Tactics
There are several ways you tackle working with data that are incomplete. Each has its pros and cons.
- Ignore any record with missing values
- Replace empty fields with a pre-defined value
- Replace empty fields with the most frequently appeared value
- Use the mean value
- Manual approach
Problem
- Row 7, column 3: The season variable has no value
- Row 10, column 5: The month has no value.
Solution
In these two cases it’s easy to replace the value with a pre-known value. We wouldn’t want to ignore the record because the values can be easily determined.
Update the values
bikeshare$season[7]
## [1] NA
1->bikeshare$season[7]
bikeshare$season[7]
## [1] 1bikeshare$mnth[10]
## [1] NA
1->bikeshare$mnth[10]
bikeshare$mnth[10]
## [1] 1J. Exercise: Understand - Calculate basic summary statistics
It is helpful to calculate some summary statistics about your data to learn more about the distribution, the median, minimum, maximum values, variance, standard deviation, number of observations and attributes.
summary(bikeshare)
## instant dteday season year
## Min. : 1.0 Length:731 Min. :1.000 Min. :0.0000
## 1st Qu.:183.5 Class :character 1st Qu.:2.000 1st Qu.:0.0000
## Median :366.0 Mode :character Median :3.000 Median :1.0000
## Mean :366.0 Mean :2.497 Mean :0.5007
## 3rd Qu.:548.5 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :731.0 Max. :4.000 Max. :1.0000
## mnth holiday weekday workingday
## Min. : 1.00 Min. :0.00000 Min. :0.000 Min. :0.000
## 1st Qu.: 4.00 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.000
## Median : 7.00 Median :0.00000 Median :3.000 Median :1.000
## Mean : 6.52 Mean :0.02873 Mean :2.997 Mean :0.684
## 3rd Qu.:10.00 3rd Qu.:0.00000 3rd Qu.:5.000 3rd Qu.:1.000
## Max. :12.00 Max. :1.00000 Max. :6.000 Max. :1.000
## weathersit temp atemp humidity
## Min. :1.000 Min. :0.05913 Min. :0.07907 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:0.33708 1st Qu.:0.33784 1st Qu.:0.5200
## Median :1.000 Median :0.49833 Median :0.48673 Median :0.6267
## Mean :1.395 Mean :0.49538 Mean :0.47435 Mean :0.6279
## 3rd Qu.:2.000 3rd Qu.:0.65542 3rd Qu.:0.60860 3rd Qu.:0.7302
## Max. :3.000 Max. :0.86167 Max. :0.84090 Max. :0.9725
## windspeed casual registered cnt
## Min. :0.02239 Min. : 2.0 Min. : 20 Min. : 22
## 1st Qu.:0.13495 1st Qu.: 315.5 1st Qu.:2497 1st Qu.:3152
## Median :0.18097 Median : 713.0 Median :3662 Median :4548
## Mean :0.19049 Mean : 848.2 Mean :3656 Mean :4504
## 3rd Qu.:0.23321 3rd Qu.:1096.0 3rd Qu.:4776 3rd Qu.:5956
## Max. :0.50746 Max. :3410.0 Max. :6946 Max. :8714The summary function shows the mean, median, minimum, and maximum values for each variable in the data set. This is particular useful for continuous variables such as temp, cnt, casual, and registered. For example, you can easily see the average number of customers (casual and registered) per day.
K. Exercise: Understand - Visualize
Explore the data visually. As a first step, consider scatterplots to show relationships between variables, histograms for frequencies, density plots to show distributions, and box plots to show the range of values.
Kernal density plot
Let’s say you wanted to see know the distribution of the ridership.
Kernal density plots are an effective way to view the distribution of a variable. Create the plot using plot(density(x)) where x is a numeric vector.
Build a a density plot that shows the shape of the data for the number of riders per day.
density_riders = density(bikeshare$cnt)
plot(density_riders, main= "Daily ridership",sub= round(mean(bikeshare$cnt), 2),"Mean =", frame=FALSE, las=1)
polygon(density_riders, col="gray", border="gray")
How would we interpret the density plot?
Histogram
Build a histogram that shows the frequency of the weather situation by day.
hist(bikeshare$weathersit, col="gray",border="gray", las=1, xlab="Weather", main="Frequency of weather situations")
| Value | Meaning |
|---|---|
| 1 | Clear, Few clouds, Partly cloudy, Partly cloudy |
| 2 | Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist |
| 3 | Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds |
| 4 | Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog |
How would we interpret the histogram?
You can check to see if your histogram makes is clear by reviewing the sum of each value for weathersit.
weathersituation <- table(bikeshare$weathersit)
barplot(weathersituation, las=1)
L. Exercise: Scatter plots
To see relationships, scatter plots are useful. In this case, we are looking for positive or negative correlations.
Scatter plot
Build a simple scatter plot that shows the relationship between the rentals and temperature
plot(bikeshare$cnt, bikeshare$atemp, main= "Relationship between bike rentals and average daily temperature", frame=FALSE, xlab="Number of rentals per day", col="#999999", cex=2, ylab="Average daily temperature in degrees fahrenheit", las=1)
Scatter plot with fit lines
To aid in the interpretation, it is helpful to add a linear regression line if there is a linear relationship or a lowess line. A lowess line will more accurate fit the line to the data.
plot(bikeshare$cnt, bikeshare$atemp, main= "Relationship between bike rentals and average daily temperature", frame=FALSE, xlab="Number of rentals per day", ylab="Average daily temperature in degrees fahrenheit", cex=2, las=1)
# Add fit lines
abline(lm(bikeshare$atemp~bikeshare$cnt), col="blue") # regression line (y~x)
lines(lowess(bikeshare$cnt, bikeshare$atemp), col="orange") # lowess line (x,y)
How would we interpret this scatter plot? Use this to inform the title of your plot.
Scatter plot with grouped categorical data (season)
Consider using color to group categorical data. In this example, we are grouping the points by season. We’re using the ggvis package.
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp) %>%
layer_points(fill = ~season) %>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit")Rework the ggvis chart to categorize season as discrete values
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp) %>%
layer_points(fill = ~as.factor(season)) %>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit")Scatter plot with grouped categorical data (year)
Let’s look at the data by year.
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp) %>%
layer_points(fill = ~year) %>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit")Change year to a factor
bikeshare$year1<-ordered(factor(bikeshare$year, levels =c(0,1),
labels = c("2011", "2012")))
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp) %>%
layer_points(fill = ~year1) %>%
add_legend("fill",
title = "Year",
orient = "right")%>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit")%>%
scale_ordinal("fill", range = c("#000000", "#4cbea3"))M. Exercise: Interactive chart - Use ggvis to filter
Build on the example above and add a filter to hide and reveal different seasons.
#This code is set to eval=FALSE because it cannot be knit
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp) %>%
filter(bikeshare$season %in% eval(input_checkboxgroup(choices=unique(bikeshare$season),
selected = "1")))%>%
layer_points(fill = ~factor(season)) %>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit")
%>%
add_legend(title ="Seasons", size = 200)Refined example
bikeshare$season<-ordered(factor(bikeshare$season, levels =c(1,2,3,4),labels = c("Winter", "Spring", "Summer", "Fall")))
library(ggvis)
bikeshare %>%
ggvis(x=~cnt, y=~atemp, fill = ~factor(season)) %>%
filter(bikeshare$season %in% eval(input_checkboxgroup(choices=unique(bikeshare$season),
selected = "Spring")))%>%
layer_points() %>%
add_legend("fill",
title = "Season",
orient = "left")%>%
add_axis("x", title = "Number of rentals per day") %>%
add_axis("y", title = "Average daily temperature in degrees fahrenheit") %>%
scale_ordinal("fill", range = c("#000000", "#999999", "#CCCCCC", "#4cbea3"))
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.N. Communicate - Create an RMarkdown document
Complete on your own.
Create an RMarkdown document named Bike_Sharing.Rmd. Include the code and markup for exercises E-L.
ITERATION
This section will introduce control structures such as the while loop, for loop, if/else conditional statements, and functions.
O.Exercise: Iteration using the while loop
Create a while loop
x <- 10
while (x > 0) {
print(x)
x <- x - 1
}
## [1] 10
## [1] 9
## [1] 8
## [1] 7
## [1] 6
## [1] 5
## [1] 4
## [1] 3
## [1] 2
## [1] 1Using a variable as a counter
counter <- 0
while (counter < 9) {
print(counter)
counter <- counter + 1 }
## [1] 0
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8P. Exercise: Iteration using a for loop
Iterate through an array of numbers using a for loop
for (i in c(1,2,3,4)){
print(i)
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4Iterate through a column in the bikeshare data
for (i in bikeshare$atemp){
print(i)
}
## [1] 0.363625
## [1] 0.353739
## [1] 0.189405
## [1] 0.212122
## [1] 0.22927
## [1] 0.233209
## [1] 0.208839
## [1] 0.162254
## [1] 0.116175
## [1] 0.150888
## [1] 0.191464
## [1] 0.160473
## [1] 0.150883
## [1] 0.188413
## [1] 0.248112
## [1] 0.234217
## [1] 0.176771
## [1] 0.232333
## [1] 0.298422
## [1] 0.25505
## [1] 0.157833
## [1] 0.0790696
## [1] 0.0988391
## [1] 0.11793
## [1] 0.234526
## [1] 0.2036
## [1] 0.2197
## [1] 0.223317
## [1] 0.212126
## [1] 0.250322
## [1] 0.18625
## [1] 0.23453
## [1] 0.254417
## [1] 0.177878
## [1] 0.228587
## [1] 0.243058
## [1] 0.291671
## [1] 0.303658
## [1] 0.198246
## [1] 0.144283
## [1] 0.149548
## [1] 0.213509
## [1] 0.232954
## [1] 0.324113
## [1] 0.39835
## [1] 0.254274
## [1] 0.3162
## [1] 0.428658
## [1] 0.511983
## [1] 0.391404
## [1] 0.27733
## [1] 0.284075
## [1] 0.186033
## [1] 0.245717
## [1] 0.289191
## [1] 0.350461
## [1] 0.282192
## [1] 0.351109
## [1] 0.400118
## [1] 0.263879
## [1] 0.320071
## [1] 0.200133
## [1] 0.255679
## [1] 0.378779
## [1] 0.366252
## [1] 0.238461
## [1] 0.3024
## [1] 0.286608
## [1] 0.385668
## [1] 0.305
## [1] 0.32575
## [1] 0.380091
## [1] 0.332
## [1] 0.318178
## [1] 0.36693
## [1] 0.410333
## [1] 0.527009
## [1] 0.466525
## [1] 0.32575
## [1] 0.409735
## [1] 0.440642
## [1] 0.337939
## [1] 0.270833
## [1] 0.256312
## [1] 0.257571
## [1] 0.250339
## [1] 0.257574
## [1] 0.292908
## [1] 0.29735
## [1] 0.257575
## [1] 0.283454
## [1] 0.315637
## [1] 0.378767
## [1] 0.542929
## [1] 0.39835
## [1] 0.387608
## [1] 0.433696
## [1] 0.324479
## [1] 0.341529
## [1] 0.426737
## [1] 0.565217
## [1] 0.493054
## [1] 0.417283
## [1] 0.462742
## [1] 0.441913
## [1] 0.425492
## [1] 0.445696
## [1] 0.503146
## [1] 0.489258
## [1] 0.564392
## [1] 0.453892
## [1] 0.321954
## [1] 0.450121
## [1] 0.551763
## [1] 0.5745
## [1] 0.594083
## [1] 0.575142
## [1] 0.578929
## [1] 0.497463
## [1] 0.464021
## [1] 0.448204
## [1] 0.532833
## [1] 0.582079
## [1] 0.40465
## [1] 0.441917
## [1] 0.474117
## [1] 0.512621
## [1] 0.518933
## [1] 0.525246
## [1] 0.522721
## [1] 0.5284
## [1] 0.523363
## [1] 0.4943
## [1] 0.500629
## [1] 0.536
## [1] 0.550512
## [1] 0.538529
## [1] 0.527158
## [1] 0.510742
## [1] 0.529042
## [1] 0.571975
## [1] 0.5745
## [1] 0.590296
## [1] 0.604813
## [1] 0.615542
## [1] 0.654688
## [1] 0.637008
## [1] 0.612379
## [1] 0.61555
## [1] 0.671092
## [1] 0.725383
## [1] 0.720967
## [1] 0.643942
## [1] 0.587133
## [1] 0.594696
## [1] 0.616804
## [1] 0.621858
## [1] 0.65595
## [1] 0.727279
## [1] 0.757579
## [1] 0.703292
## [1] 0.678038
## [1] 0.643325
## [1] 0.601654
## [1] 0.591546
## [1] 0.587754
## [1] 0.595346
## [1] 0.600383
## [1] 0.643954
## [1] 0.645846
## [1] 0.595346
## [1] 0.637646
## [1] 0.693829
## [1] 0.693833
## [1] 0.656583
## [1] 0.643313
## [1] 0.637629
## [1] 0.637004
## [1] 0.692558
## [1] 0.654688
## [1] 0.637008
## [1] 0.652162
## [1] 0.667308
## [1] 0.668575
## [1] 0.665417
## [1] 0.696338
## [1] 0.685633
## [1] 0.686871
## [1] 0.670483
## [1] 0.664158
## [1] 0.690025
## [1] 0.729804
## [1] 0.739275
## [1] 0.689404
## [1] 0.635104
## [1] 0.624371
## [1] 0.638263
## [1] 0.669833
## [1] 0.703925
## [1] 0.747479
## [1] 0.74685
## [1] 0.826371
## [1] 0.840896
## [1] 0.804287
## [1] 0.794829
## [1] 0.720958
## [1] 0.696979
## [1] 0.690667
## [1] 0.7399
## [1] 0.785967
## [1] 0.728537
## [1] 0.729796
## [1] 0.703292
## [1] 0.707071
## [1] 0.679937
## [1] 0.664788
## [1] 0.656567
## [1] 0.676154
## [1] 0.715292
## [1] 0.703283
## [1] 0.724121
## [1] 0.684983
## [1] 0.651521
## [1] 0.654042
## [1] 0.645858
## [1] 0.624388
## [1] 0.616167
## [1] 0.645837
## [1] 0.666671
## [1] 0.662258
## [1] 0.633221
## [1] 0.648996
## [1] 0.675525
## [1] 0.638254
## [1] 0.606067
## [1] 0.630692
## [1] 0.645854
## [1] 0.659733
## [1] 0.635556
## [1] 0.647959
## [1] 0.607958
## [1] 0.594704
## [1] 0.611121
## [1] 0.614921
## [1] 0.604808
## [1] 0.633213
## [1] 0.665429
## [1] 0.625646
## [1] 0.5152
## [1] 0.544229
## [1] 0.555361
## [1] 0.578946
## [1] 0.607962
## [1] 0.609229
## [1] 0.60213
## [1] 0.603554
## [1] 0.6269
## [1] 0.553671
## [1] 0.461475
## [1] 0.478512
## [1] 0.490537
## [1] 0.529675
## [1] 0.532217
## [1] 0.550533
## [1] 0.554963
## [1] 0.522125
## [1] 0.564412
## [1] 0.572637
## [1] 0.589042
## [1] 0.574525
## [1] 0.575158
## [1] 0.574512
## [1] 0.544829
## [1] 0.412863
## [1] 0.345317
## [1] 0.392046
## [1] 0.472858
## [1] 0.527138
## [1] 0.480425
## [1] 0.504404
## [1] 0.513242
## [1] 0.523983
## [1] 0.542925
## [1] 0.546096
## [1] 0.517717
## [1] 0.551804
## [1] 0.529675
## [1] 0.498725
## [1] 0.503154
## [1] 0.510725
## [1] 0.522721
## [1] 0.513848
## [1] 0.466525
## [1] 0.423596
## [1] 0.425492
## [1] 0.422333
## [1] 0.457067
## [1] 0.463375
## [1] 0.472846
## [1] 0.457046
## [1] 0.318812
## [1] 0.227913
## [1] 0.321329
## [1] 0.356063
## [1] 0.397088
## [1] 0.390133
## [1] 0.405921
## [1] 0.403392
## [1] 0.323854
## [1] 0.362358
## [1] 0.400871
## [1] 0.412246
## [1] 0.409079
## [1] 0.373721
## [1] 0.306817
## [1] 0.357942
## [1] 0.43055
## [1] 0.524612
## [1] 0.507579
## [1] 0.451988
## [1] 0.323221
## [1] 0.272721
## [1] 0.324483
## [1] 0.457058
## [1] 0.445062
## [1] 0.421696
## [1] 0.430537
## [1] 0.372471
## [1] 0.380671
## [1] 0.385087
## [1] 0.4558
## [1] 0.490122
## [1] 0.451375
## [1] 0.311221
## [1] 0.305554
## [1] 0.331433
## [1] 0.310604
## [1] 0.3491
## [1] 0.393925
## [1] 0.4564
## [1] 0.400246
## [1] 0.256938
## [1] 0.317542
## [1] 0.266412
## [1] 0.253154
## [1] 0.270196
## [1] 0.301138
## [1] 0.338362
## [1] 0.412237
## [1] 0.359825
## [1] 0.249371
## [1] 0.245579
## [1] 0.280933
## [1] 0.396454
## [1] 0.428017
## [1] 0.426121
## [1] 0.377513
## [1] 0.299242
## [1] 0.279961
## [1] 0.315535
## [1] 0.327633
## [1] 0.279974
## [1] 0.263892
## [1] 0.318812
## [1] 0.414121
## [1] 0.375621
## [1] 0.252304
## [1] 0.126275
## [1] 0.119337
## [1] 0.278412
## [1] 0.340267
## [1] 0.390779
## [1] 0.340258
## [1] 0.247479
## [1] 0.318826
## [1] 0.282821
## [1] 0.381938
## [1] 0.249362
## [1] 0.183087
## [1] 0.161625
## [1] 0.190663
## [1] 0.364278
## [1] 0.275254
## [1] 0.190038
## [1] 0.220958
## [1] 0.174875
## [1] 0.16225
## [1] 0.243058
## [1] 0.349108
## [1] 0.294821
## [1] 0.35605
## [1] 0.415383
## [1] 0.326379
## [1] 0.272721
## [1] 0.262625
## [1] 0.381317
## [1] 0.466538
## [1] 0.398971
## [1] 0.309346
## [1] 0.272725
## [1] 0.264521
## [1] 0.296426
## [1] 0.361104
## [1] 0.266421
## [1] 0.261988
## [1] 0.293558
## [1] 0.210867
## [1] 0.101658
## [1] 0.227913
## [1] 0.333946
## [1] 0.351629
## [1] 0.330162
## [1] 0.351629
## [1] 0.355425
## [1] 0.265788
## [1] 0.273391
## [1] 0.295113
## [1] 0.392667
## [1] 0.444446
## [1] 0.410971
## [1] 0.255675
## [1] 0.268308
## [1] 0.357954
## [1] 0.353525
## [1] 0.34847
## [1] 0.475371
## [1] 0.359842
## [1] 0.413492
## [1] 0.303021
## [1] 0.241171
## [1] 0.255042
## [1] 0.3851
## [1] 0.524604
## [1] 0.397083
## [1] 0.277767
## [1] 0.35967
## [1] 0.459592
## [1] 0.542929
## [1] 0.548617
## [1] 0.532825
## [1] 0.436229
## [1] 0.505046
## [1] 0.464
## [1] 0.532821
## [1] 0.538533
## [1] 0.513258
## [1] 0.531567
## [1] 0.570067
## [1] 0.486733
## [1] 0.437488
## [1] 0.43875
## [1] 0.315654
## [1] 0.47095
## [1] 0.482304
## [1] 0.375621
## [1] 0.421708
## [1] 0.417287
## [1] 0.427513
## [1] 0.461483
## [1] 0.53345
## [1] 0.431163
## [1] 0.390767
## [1] 0.426129
## [1] 0.492425
## [1] 0.476638
## [1] 0.436233
## [1] 0.337274
## [1] 0.387604
## [1] 0.431808
## [1] 0.487996
## [1] 0.573875
## [1] 0.614925
## [1] 0.598487
## [1] 0.457038
## [1] 0.493046
## [1] 0.515775
## [1] 0.542921
## [1] 0.389504
## [1] 0.301125
## [1] 0.405283
## [1] 0.470317
## [1] 0.483583
## [1] 0.452637
## [1] 0.377504
## [1] 0.450121
## [1] 0.457696
## [1] 0.577021
## [1] 0.537896
## [1] 0.537242
## [1] 0.590917
## [1] 0.584608
## [1] 0.546737
## [1] 0.527142
## [1] 0.557471
## [1] 0.553025
## [1] 0.491783
## [1] 0.520833
## [1] 0.544817
## [1] 0.585238
## [1] 0.5499
## [1] 0.576404
## [1] 0.595975
## [1] 0.572613
## [1] 0.551121
## [1] 0.566908
## [1] 0.583967
## [1] 0.565667
## [1] 0.580825
## [1] 0.584612
## [1] 0.6067
## [1] 0.627529
## [1] 0.642696
## [1] 0.641425
## [1] 0.6793
## [1] 0.672992
## [1] 0.611129
## [1] 0.631329
## [1] 0.607962
## [1] 0.566288
## [1] 0.575133
## [1] 0.578283
## [1] 0.525892
## [1] 0.542292
## [1] 0.569442
## [1] 0.597862
## [1] 0.648367
## [1] 0.663517
## [1] 0.659721
## [1] 0.597875
## [1] 0.611117
## [1] 0.624383
## [1] 0.599754
## [1] 0.594708
## [1] 0.571975
## [1] 0.544842
## [1] 0.654692
## [1] 0.720975
## [1] 0.752542
## [1] 0.724121
## [1] 0.652792
## [1] 0.674254
## [1] 0.654042
## [1] 0.594704
## [1] 0.640792
## [1] 0.675512
## [1] 0.786613
## [1] 0.687508
## [1] 0.750629
## [1] 0.702038
## [1] 0.70265
## [1] 0.732337
## [1] 0.761367
## [1] 0.752533
## [1] 0.804913
## [1] 0.790396
## [1] 0.654054
## [1] 0.664796
## [1] 0.650271
## [1] 0.654683
## [1] 0.667933
## [1] 0.666042
## [1] 0.705196
## [1] 0.724125
## [1] 0.755683
## [1] 0.745583
## [1] 0.714642
## [1] 0.613025
## [1] 0.549912
## [1] 0.623125
## [1] 0.690017
## [1] 0.70645
## [1] 0.654054
## [1] 0.739263
## [1] 0.734217
## [1] 0.697604
## [1] 0.667933
## [1] 0.684987
## [1] 0.662896
## [1] 0.667308
## [1] 0.707088
## [1] 0.722867
## [1] 0.751267
## [1] 0.731079
## [1] 0.710246
## [1] 0.697621
## [1] 0.707717
## [1] 0.699508
## [1] 0.667942
## [1] 0.638267
## [1] 0.644579
## [1] 0.662254
## [1] 0.676779
## [1] 0.654037
## [1] 0.654688
## [1] 0.2424
## [1] 0.618071
## [1] 0.603554
## [1] 0.595967
## [1] 0.601025
## [1] 0.621854
## [1] 0.637008
## [1] 0.6471
## [1] 0.618696
## [1] 0.595996
## [1] 0.654688
## [1] 0.66605
## [1] 0.635733
## [1] 0.652779
## [1] 0.6894
## [1] 0.702654
## [1] 0.649
## [1] 0.661629
## [1] 0.686888
## [1] 0.708983
## [1] 0.655329
## [1] 0.657204
## [1] 0.611121
## [1] 0.578925
## [1] 0.565654
## [1] 0.554292
## [1] 0.570075
## [1] 0.579558
## [1] 0.594083
## [1] 0.585867
## [1] 0.563125
## [1] 0.55305
## [1] 0.565067
## [1] 0.540404
## [1] 0.532192
## [1] 0.571971
## [1] 0.610488
## [1] 0.518933
## [1] 0.502513
## [1] 0.544179
## [1] 0.596613
## [1] 0.607975
## [1] 0.585863
## [1] 0.530296
## [1] 0.517663
## [1] 0.512
## [1] 0.542333
## [1] 0.599133
## [1] 0.607975
## [1] 0.580187
## [1] 0.538521
## [1] 0.419813
## [1] 0.387608
## [1] 0.438112
## [1] 0.503142
## [1] 0.431167
## [1] 0.433071
## [1] 0.391396
## [1] 0.508204
## [1] 0.53915
## [1] 0.460846
## [1] 0.450108
## [1] 0.512625
## [1] 0.537896
## [1] 0.472842
## [1] 0.456429
## [1] 0.482942
## [1] 0.530304
## [1] 0.558721
## [1] 0.529688
## [1] 0.52275
## [1] 0.515133
## [1] 0.467771
## [1] 0.4394
## [1] 0.309909
## [1] 0.3611
## [1] 0.369942
## [1] 0.356042
## [1] 0.323846
## [1] 0.329538
## [1] 0.308075
## [1] 0.281567
## [1] 0.274621
## [1] 0.341891
## [1] 0.355413
## [1] 0.393937
## [1] 0.421713
## [1] 0.475383
## [1] 0.323225
## [1] 0.281563
## [1] 0.324492
## [1] 0.347204
## [1] 0.326383
## [1] 0.337746
## [1] 0.375621
## [1] 0.380667
## [1] 0.364892
## [1] 0.350371
## [1] 0.378779
## [1] 0.248742
## [1] 0.257583
## [1] 0.339004
## [1] 0.281558
## [1] 0.289762
## [1] 0.298422
## [1] 0.323867
## [1] 0.316904
## [1] 0.359208
## [1] 0.455796
## [1] 0.469054
## [1] 0.428012
## [1] 0.258204
## [1] 0.321958
## [1] 0.389508
## [1] 0.390146
## [1] 0.435575
## [1] 0.338363
## [1] 0.297338
## [1] 0.294188
## [1] 0.294192
## [1] 0.338383
## [1] 0.369938
## [1] 0.4015
## [1] 0.409708
## [1] 0.342162
## [1] 0.335217
## [1] 0.301767
## [1] 0.236113
## [1] 0.259471
## [1] 0.2589
## [1] 0.294465
## [1] 0.220333
## [1] 0.226642
## [1] 0.255046
## [1] 0.2424
## [1] 0.2317
## [1] 0.223487Next, us a for loop to round each number in bikshare$atemp
output <- vector("double", nrow(bikeshare)) #1.output
for (i in seq_along(bikeshare$atemp)) { #2. sequence
output[[i]] <- round(bikeshare$atemp[[i]], 2) #3. body
}
output
## [1] 0.36 0.35 0.19 0.21 0.23 0.23 0.21 0.16 0.12 0.15 0.19 0.16 0.15 0.19
## [15] 0.25 0.23 0.18 0.23 0.30 0.26 0.16 0.08 0.10 0.12 0.23 0.20 0.22 0.22
## [29] 0.21 0.25 0.19 0.23 0.25 0.18 0.23 0.24 0.29 0.30 0.20 0.14 0.15 0.21
## [43] 0.23 0.32 0.40 0.25 0.32 0.43 0.51 0.39 0.28 0.28 0.19 0.25 0.29 0.35
## [57] 0.28 0.35 0.40 0.26 0.32 0.20 0.26 0.38 0.37 0.24 0.30 0.29 0.39 0.30
## [71] 0.33 0.38 0.33 0.32 0.37 0.41 0.53 0.47 0.33 0.41 0.44 0.34 0.27 0.26
## [85] 0.26 0.25 0.26 0.29 0.30 0.26 0.28 0.32 0.38 0.54 0.40 0.39 0.43 0.32
## [99] 0.34 0.43 0.57 0.49 0.42 0.46 0.44 0.43 0.45 0.50 0.49 0.56 0.45 0.32
## [113] 0.45 0.55 0.57 0.59 0.58 0.58 0.50 0.46 0.45 0.53 0.58 0.40 0.44 0.47
## [127] 0.51 0.52 0.53 0.52 0.53 0.52 0.49 0.50 0.54 0.55 0.54 0.53 0.51 0.53
## [141] 0.57 0.57 0.59 0.60 0.62 0.65 0.64 0.61 0.62 0.67 0.73 0.72 0.64 0.59
## [155] 0.59 0.62 0.62 0.66 0.73 0.76 0.70 0.68 0.64 0.60 0.59 0.59 0.60 0.60
## [169] 0.64 0.65 0.60 0.64 0.69 0.69 0.66 0.64 0.64 0.64 0.69 0.65 0.64 0.65
## [183] 0.67 0.67 0.67 0.70 0.69 0.69 0.67 0.66 0.69 0.73 0.74 0.69 0.64 0.62
## [197] 0.64 0.67 0.70 0.75 0.75 0.83 0.84 0.80 0.79 0.72 0.70 0.69 0.74 0.79
## [211] 0.73 0.73 0.70 0.71 0.68 0.66 0.66 0.68 0.72 0.70 0.72 0.68 0.65 0.65
## [225] 0.65 0.62 0.62 0.65 0.67 0.66 0.63 0.65 0.68 0.64 0.61 0.63 0.65 0.66
## [239] 0.64 0.65 0.61 0.59 0.61 0.61 0.60 0.63 0.67 0.63 0.52 0.54 0.56 0.58
## [253] 0.61 0.61 0.60 0.60 0.63 0.55 0.46 0.48 0.49 0.53 0.53 0.55 0.55 0.52
## [267] 0.56 0.57 0.59 0.57 0.58 0.57 0.54 0.41 0.35 0.39 0.47 0.53 0.48 0.50
## [281] 0.51 0.52 0.54 0.55 0.52 0.55 0.53 0.50 0.50 0.51 0.52 0.51 0.47 0.42
## [295] 0.43 0.42 0.46 0.46 0.47 0.46 0.32 0.23 0.32 0.36 0.40 0.39 0.41 0.40
## [309] 0.32 0.36 0.40 0.41 0.41 0.37 0.31 0.36 0.43 0.52 0.51 0.45 0.32 0.27
## [323] 0.32 0.46 0.45 0.42 0.43 0.37 0.38 0.39 0.46 0.49 0.45 0.31 0.31 0.33
## [337] 0.31 0.35 0.39 0.46 0.40 0.26 0.32 0.27 0.25 0.27 0.30 0.34 0.41 0.36
## [351] 0.25 0.25 0.28 0.40 0.43 0.43 0.38 0.30 0.28 0.32 0.33 0.28 0.26 0.32
## [365] 0.41 0.38 0.25 0.13 0.12 0.28 0.34 0.39 0.34 0.25 0.32 0.28 0.38 0.25
## [379] 0.18 0.16 0.19 0.36 0.28 0.19 0.22 0.17 0.16 0.24 0.35 0.29 0.36 0.42
## [393] 0.33 0.27 0.26 0.38 0.47 0.40 0.31 0.27 0.26 0.30 0.36 0.27 0.26 0.29
## [407] 0.21 0.10 0.23 0.33 0.35 0.33 0.35 0.36 0.27 0.27 0.30 0.39 0.44 0.41
## [421] 0.26 0.27 0.36 0.35 0.35 0.48 0.36 0.41 0.30 0.24 0.26 0.39 0.52 0.40
## [435] 0.28 0.36 0.46 0.54 0.55 0.53 0.44 0.51 0.46 0.53 0.54 0.51 0.53 0.57
## [449] 0.49 0.44 0.44 0.32 0.47 0.48 0.38 0.42 0.42 0.43 0.46 0.53 0.43 0.39
## [463] 0.43 0.49 0.48 0.44 0.34 0.39 0.43 0.49 0.57 0.61 0.60 0.46 0.49 0.52
## [477] 0.54 0.39 0.30 0.41 0.47 0.48 0.45 0.38 0.45 0.46 0.58 0.54 0.54 0.59
## [491] 0.58 0.55 0.53 0.56 0.55 0.49 0.52 0.54 0.59 0.55 0.58 0.60 0.57 0.55
## [505] 0.57 0.58 0.57 0.58 0.58 0.61 0.63 0.64 0.64 0.68 0.67 0.61 0.63 0.61
## [519] 0.57 0.58 0.58 0.53 0.54 0.57 0.60 0.65 0.66 0.66 0.60 0.61 0.62 0.60
## [533] 0.59 0.57 0.54 0.65 0.72 0.75 0.72 0.65 0.67 0.65 0.59 0.64 0.68 0.79
## [547] 0.69 0.75 0.70 0.70 0.73 0.76 0.75 0.80 0.79 0.65 0.66 0.65 0.65 0.67
## [561] 0.67 0.71 0.72 0.76 0.75 0.71 0.61 0.55 0.62 0.69 0.71 0.65 0.74 0.73
## [575] 0.70 0.67 0.68 0.66 0.67 0.71 0.72 0.75 0.73 0.71 0.70 0.71 0.70 0.67
## [589] 0.64 0.64 0.66 0.68 0.65 0.65 0.24 0.62 0.60 0.60 0.60 0.62 0.64 0.65
## [603] 0.62 0.60 0.65 0.67 0.64 0.65 0.69 0.70 0.65 0.66 0.69 0.71 0.66 0.66
## [617] 0.61 0.58 0.57 0.55 0.57 0.58 0.59 0.59 0.56 0.55 0.57 0.54 0.53 0.57
## [631] 0.61 0.52 0.50 0.54 0.60 0.61 0.59 0.53 0.52 0.51 0.54 0.60 0.61 0.58
## [645] 0.54 0.42 0.39 0.44 0.50 0.43 0.43 0.39 0.51 0.54 0.46 0.45 0.51 0.54
## [659] 0.47 0.46 0.48 0.53 0.56 0.53 0.52 0.52 0.47 0.44 0.31 0.36 0.37 0.36
## [673] 0.32 0.33 0.31 0.28 0.27 0.34 0.36 0.39 0.42 0.48 0.32 0.28 0.32 0.35
## [687] 0.33 0.34 0.38 0.38 0.36 0.35 0.38 0.25 0.26 0.34 0.28 0.29 0.30 0.32
## [701] 0.32 0.36 0.46 0.47 0.43 0.26 0.32 0.39 0.39 0.44 0.34 0.30 0.29 0.29
## [715] 0.34 0.37 0.40 0.41 0.34 0.34 0.30 0.24 0.26 0.26 0.29 0.22 0.23 0.26
## [729] 0.24 0.23 0.22
#simple way to round without a loop
#atemp_rounded<- round(bikeshare$atemp, 2)Return to 4.2
How would you compute the individual measures of central tendancy and variability for the attitude data set?
You will need to think through this problem. First, you need a place to store the output, then you need to use the seq_along function to iterate through the dataset. Finally, you need to update the output.
Try it.
attitudestats <- vector("double", ncol (attitude)) #1 store output
for (i in seq_along(attitude)) { #2 sequence
attitudestats[[i]] <- median(attitude[[i]])
}
attitudestats## [1] 65.5 65.0 51.5 56.5 63.5 77.5 41.0Refined example
attitudestats <- vector("double", ncol (attitude)) #1 store output
for (i in seq_along(attitude)) { #2 sequence
attitudestats[[i]] <- median(attitude[[i]])
print (paste(i, colnames(attitude[i]),":", attitudestats[[i]]))
}
## [1] "1 rating : 65.5"
## [1] "2 complaints : 65"
## [1] "3 privileges : 51.5"
## [1] "4 learning : 56.5"
## [1] "5 raises : 63.5"
## [1] "6 critical : 77.5"
## [1] "7 advance : 41"Try to code the sample problem using a while loop for fun
i<- 1
attitudestats_while <-vector("double", ncol(attitude))
while (i <= ncol(attitude)){
attitudestats_while[[i]] <- median(attitude[[i]])
print(paste(i, colnames(attitude[i]), ":",median(attitude[[i]])))
i <-i+1
}
## [1] "1 rating : 65.5"
## [1] "2 complaints : 65"
## [1] "3 privileges : 51.5"
## [1] "4 learning : 56.5"
## [1] "5 raises : 63.5"
## [1] "6 critical : 77.5"
## [1] "7 advance : 41"CONDITIONALS
Q. Exercise - Conditionals
Let’s review of Boolean variables and logical operators
3 > 4
## [1] FALSE
c(1, 2, 3, 4, 5) > 4
## [1] FALSE FALSE FALSE FALSE TRUE
c(1, 2, 3, 4, 6) == 3
## [1] FALSE FALSE TRUE FALSE FALSEConditional statements using if/else logic
Build a program that checks to seet which prices are considered “cheap”
prices <- c(12.43, 9.99, 18.22, 7.25, 0.50)
numCheap <- 0
for (p in prices){
if (p < 10){
numCheap <- numCheap + 1
}
}
print(numCheap)
## [1] 3Alternative approach
prices <- c(12.43, 9.99, 18.22, 7.25, 0.50, 11)
sum(prices < 10)
## [1] 3FUNCTIONS
Some funcions are built in such as:
sqrt(25)
## [1] 5
mean(c(1,2,3,4,5))
## [1] 3
toupper("hello world")
## [1] "HELLO WORLD"R. Exercise: Functions
Write your own function. Here’s a an example of the form… with one minor error…
f <- function(x) x + 2
#f("hello world") # causes an error because we need the parameter as a numeric.
f(3)
## [1] 5Pass in mulitple arguments
addTogether <- function(x, y) x + y
addTogether(5, 10)
## [1] 15
addTogether(x = 5, y = 10) #alternative
## [1] 15Create multi-line functions
f <- function(x){
y <- x^2
z <- y/2
z
}
f(2)
## [1] 2You try it: Write a function that averages two numbers
avg <- function(x,y){
(x + y)/2
}
avg(1,2)
## [1] 1.5Apply a function over a vector
f <- function(x) x^2
sapply(c(1,2,3,4,5),f)
## [1] 1 4 9 16 25
sapply(attitude,f)
## rating complaints privileges learning raises critical advance
## [1,] 1849 2601 900 1521 3721 8464 2025
## [2,] 3969 4096 2601 2916 3969 5329 2209
## [3,] 5041 4900 4624 4761 5776 7396 2304
## [4,] 3721 3969 2025 2209 2916 7056 1225
## [5,] 6561 6084 3136 4356 5041 6889 2209
## [6,] 1849 3025 2401 1936 2916 2401 1156
## [7,] 3364 4489 1764 3136 4356 4624 1225
## [8,] 5041 5625 2500 3025 4900 4356 1681
## [9,] 5184 6724 5184 4489 5041 6889 961
## [10,] 4489 3721 2025 2209 3844 6400 1681
## [11,] 4096 2809 2809 3364 3364 4489 1156
## [12,] 4489 3600 2209 1521 3481 5476 1681
## [13,] 4761 3844 3249 1764 3025 3969 625
## [14,] 4624 6889 6889 2025 3481 5929 1225
## [15,] 5929 5929 2916 5184 6241 5929 2116
## [16,] 6561 8100 2500 5184 3600 2916 1296
## [17,] 5476 7225 4096 4761 6241 6241 3969
## [18,] 4225 3600 4225 5625 3025 6400 3600
## [19,] 4225 4900 2116 3249 5625 7225 2116
## [20,] 2500 3364 4624 2916 4096 6084 2704
## [21,] 2500 1600 1089 1156 1849 4096 1089
## [22,] 4096 3721 2704 3844 4356 6400 1681
## [23,] 2809 4356 2704 2500 3969 6400 1369
## [24,] 1600 1369 1764 3364 2500 3249 2401
## [25,] 3969 2916 1764 2304 4356 5625 1089
## [26,] 4356 5929 4356 3969 7744 5776 5184
## [27,] 6084 5625 3364 5476 6400 6084 2401
## [28,] 2304 3249 1936 2025 2601 6889 1444
## [29,] 7225 7225 5041 5041 5929 5476 3025
## [30,] 6724 6724 1521 3481 4096 6084 1521Try it using the bikeshare data.
f <- function(x) x^2
sapply(bikeshare$atemp,f)
## [1] 0.132223141 0.125131280 0.035874254 0.044995743 0.052564733
## [6] 0.054386438 0.043613728 0.026326361 0.013496631 0.022767189
## [11] 0.036658463 0.025751584 0.022765680 0.035499459 0.061559565
## [16] 0.054857603 0.031247986 0.053978623 0.089055690 0.065050502
## [21] 0.024911256 0.006252002 0.009769168 0.013907485 0.055002445
## [26] 0.041452960 0.048268090 0.049870482 0.044997440 0.062661104
## [31] 0.034689062 0.055004321 0.064728010 0.031640583 0.052252017
## [36] 0.059077191 0.085071972 0.092208181 0.039301477 0.020817584
## [41] 0.022364604 0.045586093 0.054267566 0.105049237 0.158682722
## [46] 0.064655267 0.099982440 0.183747681 0.262126592 0.153197091
## [51] 0.076911929 0.080698606 0.034608277 0.060376844 0.083631434
## [56] 0.122822913 0.079632325 0.123277530 0.160094414 0.069632127
## [61] 0.102445445 0.040053218 0.065371751 0.143473531 0.134140528
## [66] 0.056863649 0.091445760 0.082144146 0.148739806 0.093025000
## [71] 0.106113062 0.144469168 0.110224000 0.101237240 0.134637625
## [76] 0.168373171 0.277738486 0.217645576 0.106113062 0.167882770
## [81] 0.194165372 0.114202768 0.073350514 0.065695841 0.066342820
## [86] 0.062669615 0.066344365 0.085795096 0.088417022 0.066344881
## [91] 0.080346170 0.099626716 0.143464440 0.294771899 0.158682722
## [96] 0.150239962 0.188092220 0.105286621 0.116642058 0.182104467
## [101] 0.319470257 0.243102247 0.174125102 0.214130159 0.195287100
## [106] 0.181043442 0.198644924 0.253155897 0.239373391 0.318538330
## [111] 0.206017948 0.103654378 0.202608915 0.304442408 0.330050250
## [116] 0.352934611 0.330788320 0.335158787 0.247469436 0.215315488
## [121] 0.200886826 0.283911006 0.338815962 0.163741623 0.195290635
## [126] 0.224786930 0.262780290 0.269291458 0.275883361 0.273237244
## [131] 0.279206560 0.273908830 0.244332490 0.250629396 0.287296000
## [136] 0.303063462 0.290013484 0.277895557 0.260857391 0.279885438
## [141] 0.327155401 0.330050250 0.348449368 0.365798765 0.378891954
## [146] 0.428616377 0.405779192 0.375008040 0.378901803 0.450364472
## [151] 0.526180497 0.519793415 0.414661299 0.344725160 0.353663332
## [156] 0.380447174 0.386707372 0.430270403 0.528934744 0.573925941
## [161] 0.494619637 0.459735529 0.413867056 0.361987536 0.349926670
## [166] 0.345454765 0.354436860 0.360459747 0.414676754 0.417117056
## [171] 0.354436860 0.406592421 0.481398681 0.481404232 0.431101236
## [176] 0.413851616 0.406570742 0.405774096 0.479636583 0.428616377
## [181] 0.405779192 0.425315274 0.445299967 0.446992531 0.442779784
## [186] 0.484886610 0.470092611 0.471791771 0.449547453 0.441105849
## [191] 0.476134501 0.532613878 0.546527526 0.475277875 0.403357091
## [196] 0.389839146 0.407379657 0.448676248 0.495510406 0.558724855
## [201] 0.557784923 0.682889030 0.707106083 0.646877578 0.631753139
## [206] 0.519780438 0.485779726 0.477020905 0.547452010 0.617744125
## [211] 0.530766160 0.532602202 0.494619637 0.499949399 0.462314324
## [216] 0.441943085 0.431080225 0.457184232 0.511642645 0.494606978
## [221] 0.524351223 0.469201710 0.424479613 0.427770938 0.417132556
## [226] 0.389860375 0.379661772 0.417105431 0.444450222 0.438585659
## [231] 0.400968835 0.421195808 0.456334026 0.407368169 0.367317208
## [236] 0.397772399 0.417127389 0.435247631 0.403931429 0.419850866
## [241] 0.369612930 0.353672848 0.373468877 0.378127836 0.365792717
## [246] 0.400958703 0.442795754 0.391432917 0.265431040 0.296185204
## [251] 0.308425840 0.335178471 0.369617793 0.371159974 0.362560537
## [256] 0.364277431 0.393003610 0.306551576 0.212959176 0.228973734
## [261] 0.240626548 0.280555606 0.283254935 0.303086584 0.307983931
## [266] 0.272614516 0.318560906 0.327913134 0.346970478 0.330078976
## [271] 0.330806725 0.330064038 0.296838639 0.170455857 0.119243830
## [276] 0.153700066 0.223594688 0.277874471 0.230808181 0.254423395
## [281] 0.263417351 0.274558184 0.294767556 0.298220841 0.268030892
## [286] 0.304487654 0.280555606 0.248726626 0.253163948 0.260840026
## [291] 0.273237244 0.264039767 0.217645576 0.179433571 0.181043442
## [296] 0.178365163 0.208910242 0.214716391 0.223583340 0.208891046
## [301] 0.101641091 0.051944336 0.103252326 0.126780860 0.157678880
## [306] 0.152203758 0.164771858 0.162725106 0.104881413 0.131303320
## [311] 0.160697559 0.169946765 0.167345628 0.139667386 0.094136671
## [316] 0.128122475 0.185373303 0.275217751 0.257636441 0.204293152
## [321] 0.104471815 0.074376744 0.105289217 0.208902015 0.198080184
## [326] 0.177827516 0.185362108 0.138734646 0.144910410 0.148291998
## [331] 0.207753640 0.240219575 0.203739391 0.096858511 0.093363247
## [336] 0.109847833 0.096474845 0.121870810 0.155176906 0.208300960
## [341] 0.160196861 0.066017136 0.100832922 0.070975354 0.064086948
## [346] 0.073005878 0.090684095 0.114488843 0.169939344 0.129474031
## [351] 0.062185896 0.060309045 0.078923350 0.157175774 0.183198552
## [356] 0.181579107 0.142516065 0.089545775 0.078378162 0.099562336
## [361] 0.107343383 0.078385441 0.069638988 0.101641091 0.171496203
## [366] 0.141091136 0.063657308 0.015945376 0.014241320 0.077513242
## [371] 0.115781631 0.152708227 0.115775507 0.061245855 0.101650018
## [376] 0.079987718 0.145876636 0.062181407 0.033520850 0.026122641
## [381] 0.036352380 0.132698461 0.075764765 0.036114441 0.048822438
## [386] 0.030581266 0.026325063 0.059077191 0.121876396 0.086919422
## [391] 0.126771602 0.172543037 0.106523252 0.074376744 0.068971891
## [396] 0.145402654 0.217657705 0.159177859 0.095694948 0.074378926
## [401] 0.069971359 0.087868373 0.130396099 0.070980149 0.068637712
## [406] 0.086176299 0.044464892 0.010334349 0.051944336 0.111519931
## [411] 0.123642954 0.109006946 0.123642954 0.126326931 0.070643261
## [416] 0.074742639 0.087091683 0.154187373 0.197532247 0.168897163
## [421] 0.065369706 0.071989183 0.128131066 0.124979926 0.121431341
## [426] 0.225977588 0.129486265 0.170975634 0.091821726 0.058163451
## [431] 0.065046422 0.148302010 0.275209357 0.157674909 0.077154506
## [436] 0.129362509 0.211224806 0.294771899 0.300980613 0.283902481
## [441] 0.190295740 0.255071462 0.215296000 0.283898218 0.290017792
## [446] 0.263433775 0.282563475 0.324976384 0.236909013 0.191395750
## [451] 0.192501562 0.099637448 0.221793902 0.232617148 0.141091136
## [456] 0.177837637 0.174128440 0.182767365 0.212966559 0.284568902
## [461] 0.185901533 0.152698848 0.181585925 0.242482381 0.227183783
## [466] 0.190299230 0.113753751 0.150236861 0.186458149 0.238140096
## [471] 0.329332516 0.378132756 0.358186689 0.208883733 0.243094358
## [476] 0.266023851 0.294763212 0.151713366 0.090676266 0.164254310
## [481] 0.221198080 0.233852518 0.204880254 0.142509270 0.202608915
## [486] 0.209485628 0.332953234 0.289332107 0.288628967 0.349182901
## [491] 0.341766514 0.298921347 0.277878688 0.310773916 0.305836651
## [496] 0.241850519 0.271267014 0.296825563 0.342503517 0.302390010
## [501] 0.332241571 0.355186201 0.327885648 0.303734357 0.321384680
## [506] 0.341017457 0.319979155 0.337357681 0.341771191 0.368084890
## [511] 0.393792646 0.413058148 0.411426031 0.461448490 0.452918232
## [516] 0.373478655 0.398576306 0.369617793 0.320682099 0.330777968
## [521] 0.334411228 0.276562396 0.294080613 0.324264191 0.357438971
## [526] 0.420379767 0.440254809 0.435231798 0.357454516 0.373463988
## [531] 0.389854131 0.359704861 0.353677605 0.327155401 0.296852805
## [536] 0.428621615 0.519804951 0.566319462 0.524351223 0.426137395
## [541] 0.454618457 0.427770938 0.353672848 0.410614387 0.456316462
## [546] 0.618760012 0.472667250 0.563443896 0.492857353 0.493717022
## [551] 0.536317482 0.579679709 0.566305916 0.647884938 0.624725837
## [556] 0.427786635 0.441953722 0.422852373 0.428609830 0.446134492
## [561] 0.443611946 0.497301398 0.524357016 0.571056796 0.555894010
## [566] 0.510713188 0.375799651 0.302403208 0.388284766 0.476123460
## [571] 0.499071603 0.427786635 0.546509783 0.539074603 0.486651341
## [576] 0.446134492 0.469207190 0.439431107 0.445299967 0.499973440
## [581] 0.522536700 0.564402105 0.534476504 0.504449381 0.486675060
## [586] 0.500863352 0.489311442 0.446146515 0.407384763 0.415482087
## [591] 0.438580361 0.458029815 0.427764397 0.428616377 0.058757760
## [596] 0.382011761 0.364277431 0.355176665 0.361231051 0.386702397
## [601] 0.405779192 0.418738410 0.382784740 0.355211232 0.428616377
## [606] 0.443622603 0.404156447 0.426120423 0.475272360 0.493722644
## [611] 0.421201000 0.437752934 0.471815125 0.502656894 0.429456098
## [616] 0.431917098 0.373468877 0.335154156 0.319964448 0.307239621
## [621] 0.324985506 0.335887475 0.352934611 0.343240142 0.317109766
## [626] 0.305864303 0.319300714 0.292036483 0.283228325 0.327150825
## [631] 0.372695598 0.269291458 0.252519315 0.296130784 0.355947072
## [636] 0.369633601 0.343235455 0.281213848 0.267974982 0.262144000
## [641] 0.294125083 0.358960352 0.369633601 0.336616955 0.290004867
## [646] 0.176242955 0.150239962 0.191942125 0.253151872 0.185904982
## [651] 0.187550491 0.153190829 0.258271306 0.290682723 0.212379036
## [656] 0.202597212 0.262784391 0.289332107 0.223579557 0.208327432
## [661] 0.233232975 0.281222332 0.312169156 0.280569377 0.273267563
## [666] 0.265362008 0.218809708 0.193072360 0.096043588 0.130393210
## [671] 0.136857083 0.126765906 0.104876232 0.108595293 0.094910206
## [676] 0.079279975 0.075416694 0.116889456 0.126318401 0.155186360
## [681] 0.177841854 0.225988997 0.104474401 0.079277723 0.105295058
## [686] 0.120550618 0.106525863 0.114072361 0.141091136 0.144907365
## [691] 0.133146172 0.122759838 0.143473531 0.061872583 0.066349002
## [696] 0.114923712 0.079274907 0.083962017 0.089055690 0.104889834
## [701] 0.100428145 0.129030387 0.207749994 0.220011655 0.183194272
## [706] 0.066669306 0.103656954 0.151716482 0.152213901 0.189725581
## [711] 0.114489520 0.088409886 0.086546579 0.086548933 0.114503055
## [716] 0.136854124 0.161202250 0.167860645 0.117074834 0.112370437
## [721] 0.091063322 0.055749349 0.067325200 0.067029210 0.086709636
## [726] 0.048546631 0.051366596 0.065048462 0.058757760 0.053684890
## [731] 0.049946439S. Exercise: Functions and for loops
Create a function that computes the basic summary statistics (max, min, median, and mean) for the attitude data set.
col_max <- function (df){
output <- vector("double", ncol (df)) #1 store output
for (i in seq_along(df)) { #2 sequence
output[[i]] <- max(df[[i]])
print (paste(i, colnames(df[i]),":", output[[i]]))
}
}Let’s call the function col_max
col_max(attitude)
## [1] "1 rating : 85"
## [1] "2 complaints : 90"
## [1] "3 privileges : 83"
## [1] "4 learning : 75"
## [1] "5 raises : 88"
## [1] "6 critical : 92"
## [1] "7 advance : 72"Let’s improve on this function. We’re going to write a function that takes a parameter of another function. This way we can pass in our data and function for the mean(), median(), etc.
col_summary <- function(df, fun){
output <- vector("double", length(df)) #1 store output
for (i in seq_along(df)) { #2 sequence
output[i] <- fun(df[[i]])
print (paste(i, colnames(df[i]),":", output[[i]]))
}
}Now, let’s call the col_summary function
col_summary(attitude, mean)
## [1] "1 rating : 64.6333333333333"
## [1] "2 complaints : 66.6"
## [1] "3 privileges : 53.1333333333333"
## [1] "4 learning : 56.3666666666667"
## [1] "5 raises : 64.6333333333333"
## [1] "6 critical : 74.7666666666667"
## [1] "7 advance : 42.9333333333333"
col_summary(attitude[,2:4],median)
## [1] "1 complaints : 65"
## [1] "2 privileges : 51.5"
## [1] "3 learning : 56.5"
col_summary(attitude, max)
## [1] "1 rating : 85"
## [1] "2 complaints : 90"
## [1] "3 privileges : 83"
## [1] "4 learning : 75"
## [1] "5 raises : 88"
## [1] "6 critical : 92"
## [1] "7 advance : 72"
col_summary(attitude, min)
## [1] "1 rating : 40"
## [1] "2 complaints : 37"
## [1] "3 privileges : 30"
## [1] "4 learning : 34"
## [1] "5 raises : 43"
## [1] "6 critical : 49"
## [1] "7 advance : 25"CODING IN OTHER LANGUAGES
T. Exercise - Python and Unix
Python
execute = True
if execute:
print("Of course!")
print("This will execute as well")
## Of course!
## This will execute as wellUnix
pwd
ls -l
## /Users/ksosulsk/Dropbox/_becomingvisual_manuscript_2017/becomingvisual_R
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## drwxr-xr-x@ 11 ksosulsk staff 374 Aug 29 2017 Bike-Sharing-Dataset
## -rw-r--r--@ 1 ksosulsk staff 27512 Aug 14 2017 Bike_Sharing_Carlos_Arias.Rmd
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## drwxr-xr-x@ 4 ksosulsk staff 136 Aug 15 2017 bikeshareapp
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## -rwxrwxrwx@ 1 ksosulsk staff 308 Jul 30 2015 pakistan.childHIV.csv
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## drwxr-xr-x@ 3 ksosulsk staff 102 Aug 15 2017 rsconnect
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## drwxr-xr-x@ 3 ksosulsk staff 102 Aug 30 2017 sosulski_visualization_02_cheat_sheet_files
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## drwxr-xr-x@ 3 ksosulsk staff 102 Sep 5 2017 sosulski_visualization_cheat_sheet_version02_files
## -rwxrwxrwx@ 1 ksosulsk staff 794 Jul 30 2015 southasia.csv
## -rw-r--r--@ 1 ksosulsk staff 1782 Jan 8 23:02 sql_script.R
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Review session 7 from R Fundamentals (Sosulski, 2018): SQL & R http://becomingvisual.com/rfundamentals/sql-r.html
U. Exercise: SQL & R
SELECT all applicable data
The players on the San Antonio Spurs in 2014
Top 5 blockers in 2010
Top 10 combination power-forwards with the most defensive rebounds
Top 20 Player-seasons in the NBA 50-40-90 Club (players who have hit over 50% for FG%, 40% for 3P%, 90% for FT%, 300 field goals, 55 3-pointers, and 125 free throws) ordered by their amount of points
Top 10 oldest Milwaukee Bucks players with over 1000 points.
Exercise available at: http://becomingvisual.com/rfundamentals/sql-r.html#exercise-7.1
SHINY
Review session 8 from R Fundamentals (Sosulski, 2018): RShiny http://becomingvisual.com/rfundamentals/rshiny.html
V. Exercise: Build a RShiny APP
Modify your shiny app.
Use ggplot to create an interactive scatterplot of the same data
Exercise available at: http://becomingvisual.com/rfundamentals/rshiny.html#exercise-8.1 ***
W. Homework
Submit all the files for exercises U & V (with your name on it) to NYU Classes > Assignments > R in class session. Due date: Official end date of module 1.
X1. Optional Homework: Determine the average ridership
Write a script to determine the average ridership on weekends versus weekdays.
Let’s imagine it costs $10 per day to rent a bike on a weekday and $12 on a weekend. What is the annual weekday rental revenue in 2011 and 2012? What is the annual weekend revenue in 2011 and 2012?
Hint: Use a for loop and if/else logic.
X2. Optional Homework: Further analysis of Bikesharing data set
At this point in the process, you should have gained enough insight to frame a question to guide the rest of your analysis. Sometimes you don’t know what to ask of the data and other times the questions you have cannot be answered by the data that you have. In most visual analytical explorations there will be a back and forth between defining the questions and identifying the data sources that have contain the information you need to extract.
Often your question will fall into one of three categories: Past, present, or future.
Some questions that can guide an historical analysis of past events are:
- Do weather conditions affect rental behaviors?
- Does the precipitation, day of week, season, hour of the day, etc. affect rental behavior?
- Which weather conditions affect behavior the most? Do they differ by season?
These questions serve a purpose of guiding reports, where the analyst is reporting on past events.
A question based on the present is:
How many bikes were rented in the past hour or today?
This type of question is reserved for producing a current state of an event.
Can we answer this question?
The data we are using cannot answer this question since it is historical data from 2011 and 2012.
A question about the future could be framed as the following:
Will bike rentals be higher in the summer rather than the winter due to weather?
Questions about the future using involve analysis that requires prediction or forecasting methods. The analyst in this case is trying to predict the future from past data.
Try to answer the following questions. Show your work as a data visualization.
- Do weather conditions affect rental behaviors?
- Does the precipitation, day of week, season, hour of the day, etc. affect rental behavior?
- Which weather conditions affect behavior the most? Do they differ by season
X3. Optional Homework: Use temp and humidity to calculate the heat index for temperatures >=80
Use the data from: https://www.weather.gov/media/unr/heatindex.pdf
FURTHER STUDY
As a next step, I encourage you to select a data set from one of the resources provided below and explore it using the process we applied in class.
General Datasets
UCI Machine Learning Repository: Consists of diverse field of datasets (360 datasets currently and still growing) for the purpose of performing analytics and machine learning algorithms. http://archive.ics.uci.edu/ml/
Kaggle datasets: Perfect for exploring data through visualization. https://www.kaggle.com/datasets
Amazon Public Dataset: These are large dataset which deals with dataset with memory in Gbs or Tbs. https://aws.amazon.com/public-datasets/
Google Public Data: A set of dataset provided by Google, including Book corpus, US names, Genome dataset, BIgQuery dataset, and many more. https://cloud.google.com/public-datasets/
Open Data by Socrata: Thousands of free dataset for exploration. https://opendata.socrata.com/
Data.gov: A website dedicated to supply datasets of different domains, eg. Education, Nutrient, Sports. https://catalog.data.gov/dataset?res_format=CSV
Datahub: Just as its tagline, “The easy way to get, share data”. https://datahub.io/dataset?tags=weather
Harvard Dataverse: Find most of the datasets used for research purpose, and cited in different publications. https://dataverse.harvard.edu/
Challenges based dataset
KDD Data Center: Have a problem coming up with a problem statement? No worries, KDD provides you with the dataset and problem statements through its challenges. http://www.kdd.org/kdd-cup
CrowdAnalytics: More challenges to solve with dataset. https://www.crowdanalytix.com/community
DataDriven: Problem for data scientist to solve. https://www.drivendata.org/competitions/
Big Data Innovation Challenge: Tackle real problem with these analytics, and also win a challenge. https://bigdatainnovationchallenge.org/challenges/food-security-nutrition/
Census Dataset
Open Census Data: Details of population in different cities of countries is just a click away with this open data. http://census.okfn.org/en/latest/
Census.gov: Census data of United States. http://www.census.gov/data.html
Weather/Climate dataset
Wunderground: Want to work with weather data? Use Wunderground’s API to get your own dataset. https://www.wunderground.com/weather/api/
National Center for Environmental Information: Climate datasets available for analytics. https://www.ncdc.noaa.gov/cdo-web/datasets
News Dataset
BBC Dataset: It consists of documents from the BBC news website corresponding to stories in five topical areas. http://mlg.ucd.ie/datasets/bbc.html
The Guardian: A collection of news datasets from the guardian, which is updated regularly. https://www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index
Food, and Nutrition Datasets
United States Department of Agriculture: The dataset are provided by the Center of Nutritional Policy and Promotion giving details about food prices dataset, health eating index. https://www.cnpp.usda.gov/data
Nutritional Science Blog: A blog listing some of dataset relating to the domain of nutrition. http://nutsci.org/open-nutrition-food-data/