##          group value
## 1          New    20
## 2       Novice    20
## 3 Intermediate    11
## 4     Advanced     3

• A new way of thinking, not just a new skill

• A new language for speaking and reading (vectors, data frames, functions, objects, etc.)

• A new syntax for writing c(), print(), cat(), sort(), require(), subset()

• Identify two major takeaways from the pre-module.

• Describe what you do when things go wrong for you in R.

• Review what you’ve learned in lessons 1 through 6.

• Introduce you to techniques and approaches to programming in R.

• Give you an opportunity to practice and solve problems.

• Download rmsba.zip file and unzip
• Rename the folder rmsba that contains the files in the zip folder. Move it to a location you can find.
• Create a new project in RStudio named rmsba
• Set your working directory to the rmsba folder you created above.

• 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?
• What's the source?

• Once you have this basic understanding of your data you can dig deeper.

• 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.

• Calculating summary statistics can translate the data into information by revealing the shape of the data, the mean, median, minimum value, maximum value, and variability.

For any data science project there are few simple steps to follow.

• Begin by creating a new folder that contains your data.
• Then create a new project in RStudio.
• Set your working directory to the folder you created above.
  
• Create a new RMarkdown document.
• Save it in your working directory.

• Go to file > New file > RMarkdown
• Save the file in you working directory
• Name it internet.Rmd

Planning your programs, presenting your code, and sharing your work.

• Create .Rmd
• Write text
• Embed code
• Render output

Resource: https://bookdown.org/yihui/rmarkdown/

---
title: "Hello R Markdown"
author: "Awesome Me"
date: "2018-02-14"
output: html_document
---

This is a paragraph in an R Markdown document.
Below is a sample code chunk:

#{r chunkname, echo=TRUE, eval=TRUE}

myformula <- (2+2)

• metadata - YAML header
• text - Text outside of code chunks
• code - R code chunks

YAML Ain't markup language

#title: "Hello R Markdown"
#author: "Awesome Me"
#date: "2019-05-20"
#output: html_document

• # Header 1
• ## Header 2
• ### Header 3
• ** Bold **
• _Italics_

echo=TRUE

2+2

## [1] 4

echo=FALSE

## [1] 4

plot(attitude)

https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf

We have a couple different options.

• read_csv() function from the readr library
• read.csv() function from the utils library.

• Typically faster (~10x)
• Produce tibbles, they don’t convert character vectors to factors, use row names, or munge the column names.
• Base R functions inherit some behavior from your OS and environment variables, so import code that works on your computer might not work on someone else’s.
• Column names that are numbers convert from 2002 to 2002 vs. X2002.
• Column names that contain spaces (bad practice) convert from Country Name to "Country Name" vs. Country.Name

internet_baser <- read.csv("world_internet_usage.csv")
head(internet_baser, 5)

##     country X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009
## 1     China  1.78  2.64  4.60  6.20  7.30  8.52 10.52 16.00 22.60 28.90
## 2    Mexico  5.08  7.04 11.90 12.90 14.10 17.21 19.52 20.81 21.71 26.34
## 3    Panama  6.55  7.27  8.52  9.99 11.14 11.48 17.35 22.29 33.82 39.08
## 4   Senegal  0.40  0.98  1.01  2.10  4.39  4.79  5.61  7.70 10.60 14.50
## 5 Singapore 36.00 41.67 47.00 53.84 62.00 61.00 59.00 69.90 69.00 69.00
##   X2010 X2011 X2012
## 1 34.30 38.30 42.30
## 2 31.05 34.96 38.42
## 3 40.10 42.70 45.20
## 4 16.00 17.50 19.20
## 5 71.00 71.00 74.18

library(readr)
internet_readr <- read_csv("world_internet_usage.csv")
head(internet_readr, 5)

## # A tibble: 5 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   4.6    6.2    7.3    8.52  10.5    16     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   0.4    0.98   1.01   2.1    4.39   4.79   5.61    7.7   10.6
## 5 Singap~  36     41.7   47     53.8   62     61     59      69.9   69  
## # ... with 4 more variables: `2009` <dbl>, `2010` <dbl>, `2011` <dbl>,
## #   `2012` <dbl>

As best practice, we're going to use read_csv() function from the readr library to import the world_internet_usage.csv data as tibble data frame.

class(internet_readr)

## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

Take a look at your data.

knitr::kable(head(internet_readr, 10))

• What function can we use?

hist(internet_readr$`2000`)

hist(internet_readr$`2000`, breaks=8, 
     main="Internet usage for 2000", 
     col="magenta", xlab=" ", labels=TRUE)

par(mfrow=c(8,2))
hist(internet_readr$`2000`, col= "blue", labels=TRUE, breaks = 4)
hist(internet_readr$`2001`)
hist(internet_readr$`2002`)
hist(internet_readr$`2003`)
hist(internet_readr$`2004`)
hist(internet_readr$`2005`)
hist(internet_readr$`2006`)
hist(internet_readr$`2007`)
hist(internet_readr$`2008`)
hist(internet_readr$`2009`)
hist(internet_readr$`2010`)
hist(internet_readr$`2011`)
hist(internet_readr$`2012`)

#Redo the histogram matrix 
#and add aesthetics

boxplot(internet_readr$`2000`, 
        main="Internet usage for 2000", 
        col="magenta", 
        xlab=median(internet_readr$`2000`),
        frame.plot=FALSE, horizontal=TRUE, 
        border="dark blue")

#Build box plots for 2000 -2012 
#based on the example above.  
#Include aesthetics.

• Technically, we would use geom_col(), geom_bar() or geom_line().

• However, what variable would you map for the x-axis and the y-axis?

ggplot(internet_readr,aes(XVALUE, YVALUE)) 
  + geom_col()

ggplot(internet_readr,aes(X,Y)) 
  + geom_col()

## [1] "Wide format"

## [1] "Long format"

• The gather() function from the tidyr package to reshape a tibble from wide to long form.
• Note the use of the pipe %>% that passes the left hand side of the operator to the first argument of the right hand side of the operator.

tidy_internet_readr <- 
internet_readr %>%
gather(`2000`:`2012`, key="year", 
       value="usage")

• Let's create a time series line graph.
• Use the ggplot2() package

ggplot(data, aes(x,y,color, group)) + geom_line() + ... + ...

library(ggplot2)
#assignment ggplot call to a variable
line01 <- ggplot(tidy_internet_readr,
        aes(x=year,y=usage,color=country,
            group=country)) + geom_line()

line01

• Labels: +labs(title="", subtitle="",x="", y="", caption="")

library(ggthemes)
library(ggplot2)
line02<-ggplot(tidy_internet_readr,
               aes(x=year,y=usage,color=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 = " ",y ="Usage")

library(ggplot2)
bar01 <- ggplot(tidy_internet_readr,
        aes(tidy_internet_readr$year, tidy_internet_readr$usage))

bar01 <- bar01 + geom_col() + theme_few() +
  labs(title = "Internet Usage per 100 people", 
       x = "Year",y ="Usage", 
       caption="World Bank (2013)")

bar01

• fill="#4cbea3", color="#4cbea3"

*+ theme(text=element_text(family="Avenir"))

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x,
## x$y, : font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

theme(panel.border = element_blank(), panel.grid.major = element_blank(),panel.grid.minor = element_blank(), axis.line = element_line(color = "gray"), axis.ticks.x=element_blank(), axis.ticks.y=element_blank())

bar01c <- bar01 + geom_col(fill="#4cbea3", color="#4cbea3") + theme_few() +
  labs(title = "Internet Usage per 100 people", 
       x = " ",y ="Usage", 
       caption="World Bank (2013)") +
  theme(text=element_text(family="Avenir"), 
        panel.border = element_blank(), panel.grid.major =
          element_blank(),panel.grid.minor =
          element_blank(), 
        axis.line = element_line(color= "gray"),
        axis.ticks.x=element_blank(),
        axis.ticks.y=element_blank())

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x,
## x$y, : font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

• ggthemes() package with additional themes
• theme_classic(). White background no grid lines

t1 <- bar01 + theme_classic()
t2 <- bar01 + theme_bw()
t3 <- bar01 + theme_minimal()
t4 <- bar01 + theme_economist()
t5 <- bar01 + theme_fivethirtyeight()
t6 <- bar01 + theme_hc()

• ggsave("plot.png",width=5, height=5, units="in"")
• Saves in your working directory

• 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.

library(readr)
bikeshare <- read_csv("bikesharedailydata.csv")

## # A tibble: 6 x 16
##   instant dteday season    yr  mnth holiday weekday workingday weathersit
##     <dbl> <chr>   <dbl> <dbl> <dbl>   <dbl>   <dbl>      <dbl>      <dbl>
## 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 <dbl>, registered <dbl>, cnt <dbl>

• 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).

• However, the variable names listed at the first row of every column are not very descriptive.

• Take a look column named season. What is the meaning of season?

• What are the possible values for this variable?

bikeshare$season

##   [1]  1  1  1  1  1  1 NA  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [24]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [47]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [70]  1  1  1  1  1  1  1  1  1  1  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [93]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [116]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [139]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [162]  2  2  2  2  2  2  2  2  2  2  3  3  3  3  3  3  3  3  3  3  3  3  3
## [185]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [208]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [231]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [254]  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4  4  4  4
## [277]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [300]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [323]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [346]  4  4  4  4  4  4  4  4  4  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [369]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [392]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [415]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
## [438]  1  1  1  1  1  1  1  1  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [461]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [484]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [507]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
## [530]  2  2  2  2  2  2  2  2  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [553]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [576]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [599]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
## [622]  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4  4  4  4  4  4
## [645]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [668]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [691]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
## [714]  4  4  4  4  4  4  4  1  1  1  1  1  1  1  1  1  1  1

• It is an integer or data of type numeric.

• You’ll notice that in the column seasons the values are integers that range between 1 and 4.

• 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.

• 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.

• season is a categorical variable defined by one of four values, each representing a season (1: winter, 2: spring, 3: summer, 4: fall).

• year is coded with the value of 0 for 2011 and 1 for 2012, rather than actual year value of 2011 or 2012.

• 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 and hyphenation.

There two key ways to rename columns.

• with rename() from dplyr
• with names() from base

library(dplyr)
bikeshare <- rename(bikeshare, humidity = hum, month=mnth)
names(bikeshare)

##  [1] "instant"    "dteday"     "season"     "yr"         "month"     
##  [6] "holiday"    "weekday"    "workingday" "weathersit" "temp"      
## [11] "atemp"      "humidity"   "windspeed"  "casual"     "registered"
## [16] "cnt"

# Rename column where names is equal to "yr"
names(bikeshare)[names(bikeshare) == "yr"] <- "year"
names(bikeshare)

##  [1] "instant"    "dteday"     "season"     "year"       "month"     
##  [6] "holiday"    "weekday"    "workingday" "weathersit" "temp"      
## [11] "atemp"      "humidity"   "windspeed"  "casual"     "registered"
## [16] "cnt"

• We can use a function called is.na()counting the number NA values.
• Let's look for missing values in the seasons column.

sum(is.na(bikeshare$season)==TRUE)

## [1] 1

• In this case it seems necessary to "loop" through the entire data set and to identify which fields need closer inspection.
• We can build a simple for loop to do this.

Let's iterate through a vector of numbers using a for loop

myvector <- c(1,2,3,4)
for (i in myvector){
    print(paste ("Loop", i))
}

## [1] "Loop 1"
## [1] "Loop 2"
## [1] "Loop 3"
## [1] "Loop 4"

myvector <- c(1,2,3,4,9,11)
mycounter <- 1
for (i in myvector){
    print(paste ("The value for loop number", mycounter,"is:", i))
    mycounter <- mycounter +1 # update 
}

## [1] "The value for loop number 1 is: 1"
## [1] "The value for loop number 2 is: 2"
## [1] "The value for loop number 3 is: 3"
## [1] "The value for loop number 4 is: 4"
## [1] "The value for loop number 5 is: 9"
## [1] "The value for loop number 6 is: 11"

counter <-0
for (i in bikeshare$season){
      counter <- counter +1
       if(is.na(i)==TRUE){
         print(paste("It's true. 
                     There's an NA value 
                     on row",counter))
         print(bikeshare[counter,])
    }
}

## [1] "It's true. \n                     There's an NA value \n                     on row 7"
## # A tibble: 1 x 16
##   instant dteday season  year month holiday weekday workingday weathersit
##     <dbl> <chr>   <dbl> <dbl> <dbl>   <dbl>   <dbl>      <dbl>      <dbl>
## 1       7 1/7/11     NA     0     1       0       5          1          2
## # ... with 7 more variables: temp <dbl>, atemp <dbl>, humidity <dbl>,
## #   windspeed <dbl>, casual <dbl>, registered <dbl>, cnt <dbl>

There are several ways you tackle working with data that are incomplete. Each has its pros and cons.

1. Ignore any record with missing values
2. Replace empty fields with a pre-defined value
3. Replace empty fields with the most frequently appeared value
4. Use the mean value
5. Manual approach

• In this case it's easy to replace the value with a pre-defined value.

• We wouldn't want to ignore the record because the values can be easily determined.

bikeshare$season[7]

## [1] NA

1->bikeshare$season[7]
bikeshare$season[7]

## [1] 1

bikesall <- ggplot(bikeshare, aes(atemp,cnt)) + geom_point(color="#4cbea3")

bikesall

bikes2011 <- ggplot(bikeshare[bikeshare$year < 1,],
                    aes(atemp,cnt)) +
  geom_point(color="#4cbea3") + 
  theme_few() + labs(title = "Rentals in 2011", 
                     x = "Average temp",y=" ")

bikes2011

bikes2012 <- ggplot(bikeshare[bikeshare$year > 0,], aes(atemp,cnt)) + geom_point(color="#4cbea3") + theme_few() + 
  labs(title = "Rentals in 2012", x = "Average temp",y=" ")

bikes2012

We can do this by using the plot_grid function from the cowplot package

cowplot::plot_grid

-Pass in the two variables hist_age and hist_salary into the plot_grid function to see the graphs plotted side by side.

cowplot::plot_grid(bikes2011, bikes2012, labels =c(" ", " "))

This section will introduce control structures for iteration know as loops. We will cover two types of loops:

• while loop
• for 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] 1

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] 8

#pseudo code

column <- ncol(attitude)

while (column > 0)
{
 print(paste(names(attitude[column]), ":", mean(attitude[,column]))) #take the mean
  column <- column -1
  #move to the next ncol
} 


mean <- NULL

for (i in names(attitude)){
  mean[i] <- mean(attitude[,i])
}

mean

#

#pseudo code

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] 4

#pseudo code

for (a in bikeshare$dteday){
  print (a)
}

#
for (i in names(bikeshare)){ 
  print }

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 FALSE

Build a program that checks to see which prices are considered "cheap".

prices <- c(12.43, 9.99, 18.22, 7.25, 0.50)
 
v <- c()
for (p in prices){
  v <- prices[p<10]
  }
    
v

## [1] 12.43  9.99 18.22  7.25  0.50

##CODE HERE

#

#

• Infix operators are very similar to functions.
• You've been introduced to one infix operator, the %% remainder operator.

print(paste("The remainder from 5 / 3 is", 5%%3))

## [1] "The remainder from 5 / 3 is 2"

We use the `%/% for integer division. Helpful for situtations where non-integer values are not possible. Examples include: products such as tickets, furniture, cars, etc.

print(paste("Using integer division 5 / 3 is", 5%/%3))

## [1] "Using integer division 5 / 3 is 1"

Unlike the * operator, we use the %*% to multiply matrices. Using the operator, results in a matrix data structure.

mmatrix <- 2%*%5
mmatrix

##      [,1]
## [1,]   10

class(mmatrix)

## [1] "matrix"

##      [,1] [,2]
## [1,]    1    2
## [2,]    2    4

##      [,1] [,2]
## [1,]    0    0
## [2,]    2    3

##      [,1] [,2]
## [1,]    4    6
## [2,]    8   12

The outer product %o% is a handy function for linear algebra and linear programming.

c(1:3)%o%c(0,1,2)

##      [,1] [,2] [,3]
## [1,]    0    1    2
## [2,]    0    2    4
## [3,]    0    3    6

vector01 <-c(1:3)
vector02 <-c(0,1,2)
opvector <- vector01%o%vector02
opvector

##      [,1] [,2] [,3]
## [1,]    0    1    2
## [2,]    0    2    4
## [3,]    0    3    6

• Useful for identifying if some set of values are identical.
• %in% tells you which items from the left hand side are also in the right hand side.

Let's look at an example.

v1 <- 3
v2 <- 101
t <- c(1,2,3,4,5,6,7,8)

Next, we use the %in% operator

v1 %in% t

## [1] TRUE

You try it with v2

v2 %in% t

## [1] FALSE

 c(1:5) %in% c(3:8)

## [1] FALSE FALSE  TRUE  TRUE  TRUE

We can use the %in% to determine which values in one vector are identical to those in another vector.

Let's return to the example, the Country Name variable in the gdp data. ##Read in the data

library(readr)
gdp <- read_csv("gdp.csv")

##  [1] "Country Name" "Country Code" "1960"         "1961"        
##  [5] "1962"         "1963"         "1964"         "1965"        
##  [9] "1966"         "1967"         "1968"         "1969"        
## [13] "1970"         "1971"         "1972"         "1973"        
## [17] "1974"         "1975"         "1976"         "1977"        
## [21] "1978"         "1979"         "1980"         "1981"        
## [25] "1982"         "1983"         "1984"         "1985"        
## [29] "1986"         "1987"         "1988"         "1989"        
## [33] "1990"         "1991"         "1992"         "1993"        
## [37] "1994"         "1995"         "1996"         "1997"        
## [41] "1998"         "1999"         "2000"         "2001"        
## [45] "2002"         "2003"         "2004"         "2005"        
## [49] "2006"         "2007"         "2008"         "2009"        
## [53] "2010"         "2011"         "2012"         "2013"        
## [57] "2014"         "2015"         "2016"         "2017"

Luckily, your professor has done the work of identifying which of these country codes represent aggregate values.

You've been provided with a aggregateclcodes.csv

agg <- read_csv("aggregatecodes.csv")
summary(agg)

##  aggregate_code    
##  Length:46         
##  Class :character  
##  Mode  :character

Let's use the %in% infix function.

gdpagg<-gdp[!gdp$`Country Code` %in% 
              agg$aggregate_code, ,drop= FALSE]
knitr::kable(head(gdpagg,10))

Some functions are built in such as:

toupper("hello world")

## [1] "HELLO WORLD"

mean(c(1,2,3,4,5))

## [1] 3

is.numeric(4)

## [1] TRUE

is.na(NA)

## [1] TRUE

sqrt(25)

## [1] 5

• Functions are useful for executing repetitive commands.
• Planning is key to writing effective functions.

functionname <- function(x){
  return(print(paste("The value", x,   "is returned")))
}

functionname(34)

## [1] "The value 34 is returned"

• We write our own functions for repetitive tasks or for algorithms.
• For example, suppose we want to have a function to act on our data that adds 2 to every value.

myfunction <- function(x){ 
  x = x + 2
   print(x)
  #add two to x
}

#apply the function

myfunction(5)

#x+2 is returned from the function

##CODE HERE

f <- function(x) {
  if (is.numeric(x)==FALSE)
  {
    print("Sorry. This function needs requires a value of type numeric.")
  }    else{
        x + 2
    }
}
f(37)

## [1] 39

f("Hi class of 2020!")

## [1] "Sorry. This function needs requires a value of type numeric."

addTogether <- function(x, y) {
  if (is.numeric(x) & is.numeric(y)==TRUE)
  {
  x + y
  } else {
    print("Sorry, please enter two numbers")
  }
}

#Call the function
addTogether(5, 10)

## [1] 15

addTogether(x = 5, y = 10) 

## [1] 15

#Passing in non-numeric data
addTogether(x=4, y="Hey what's up?")

## [1] "Sorry, please enter two numbers"

##CODE HERE
f <- function(x, y) {
  if (is.numeric(x) & is.numeric(y)==TRUE)
  {
  mean(c(x,y))
  } else {
    print("Sorry, please enter two numbers")
  }
}

f(2,3)

## [1] 2.5

#
#
#
#

What if we could apply a function to all the elements of the input?

• Input is the List, vector or data frame
• Output is a vector (or matrix)

sapply(X, FUN)

Arguments:

• X: A vector or an object
• FUN: Function applied to each element of x

df1 <-as.data.frame(c(1,2,3,4,5,6,7))

sapply(df1, max)

## c(1, 2, 3, 4, 5, 6, 7) 
##                      7

library(tibble)
df1 <-as_tibble(c(1,2,3,4,5,6,7))

## Warning: Calling `as_tibble()` on a vector is discouraged, because the behavior is likely to change in the future. Use `tibble::enframe(name = NULL)` instead.
## This warning is displayed once per session.

sapply(df1, max)

## value 
##     7

df3 <-c(1,2,3,4,5,6,7)
sapply(df3, max)

## [1] 1 2 3 4 5 6 7

avg <-function(x){
  mean(x, na.rm=TRUE)
}

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    1521

##CODE HERE

#
#

• Import data set
• Prepare - remove values that are not countries.
• Understand - summary stats & visualize
• Communciate

Let's take the average of select column.

gdpagg_avg<-sapply(gdpagg[3:50],avg)

gdpagg_avg <-as.data.frame(gdpagg_avg)
kable(head(gdpagg_avg, 10))

summaryfun <- function(x){
    xmean <- mean(x, na.rm=TRUE)
    xmedian <- median(x, na.rm=TRUE)
    print(paste("The mean is", prettyNum(xmean, big.mark=",", scientific=FALSE)))
   print(paste("The median is",prettyNum(xmedian, big.mark=",", scientific=FALSE)))
  }

• After we write the function, we can see that it is available for us to use at any point (just like a variable) in our work space).
• X is the data and FUN is the function. In our case, the data is gdpagg$2017 and the function is `summaryfun

summaryfun(gdpagg$`2017`)

## [1] "The mean is 421,352,224,909"
## [1] "The median is 35,052,862,071"

• It’s a web application framework for R
• Makes it incredibly easy to build interactive web applications with R.
• Learn more at: https://cran.r-project.org/web/packages/shiny/shiny.pdf

• The components of a shiny app To build a Shiny app in R, start with a template.
• In RStudio, go to File -> New File -> Shiny Web App. Choose a single file web application.
• app.R will be the file you modify. It is saved in a new directory
• Directory name is your app name

library (shiny)

#Defines the user interface through nested R functions
ui<-fluidPage()

#Specifies how to build and rebuild #R objects in the ui
server <- function(input,output){}

#Combines ui and server into an app call with runApp()
shinyApp(ui=ui, server=server)

runExample("02_text")
runExample("03_reactivity")
runExample("04_mpg")
runExample("05_sliders")
runExample("06_tabsets")
runExample("07_widgets")
runExample("08_html")
runExample("09_upload")
runExample("11_timer")

• Revise app to provide a default view of most recent distributions by most recent assignment due date

• Revise app to include a selector by one or more students

• Revise app to include doughnut charts to show completion, late or incompleted assessments by assignment

• Revise app to include a student list

Review session 8 from R Fundamentals (Sosulski, 2019): RShiny http://becomingvisual.com/rfundamentals/rshiny.html

• Submit 2 files from today
• .Rmd and app.R via NYU Classes > Assignments > In Class Worksheet.
• Name your files lastname_first_inclass.Rmd & lastname_first_app.R