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<h1 class="title toc-ignore">Additional Code Not Shown in Final Report</h1>
</div>
<p>Loading libraries:</p>
<pre><code>library(readr)
library(plyr)
library(tidyr)
library(lubridate)
library(tidyverse)
library(dplyr)
library(ggplot2)
</code></pre>
<p>#Cleaning NMSS Data</p>
<div id="konrad-ramya-cleaning-donations-data-for-2013-2017" class="section level2">
<h2>Konrad & Ramya: Cleaning donations data for 2013-2017:</h2>
<p>Reading in the data:</p>
<pre><code>donations2013 <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Donations\\2013 Bike Donations Date Fixed.csv")
donations2014 <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Donations\\2014 Bike Donations Date Fixed.csv")
donations2015 <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Donations\\2015 Bike Donations Date Fixed.csv")
donations2016 <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Donations\\2016 Bike Donations Date Fixed.csv")
donations2017 <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Donations\\2017 Bike Donations Date Fixed.csv")
</code></pre>
<p>Comparing column names to make sure the data is in the right order and all fields are the same:</p>
<pre><code>cols2013 <- colnames(donations2013)
cols2014 <- colnames(donations2014)
cols2015 <- colnames(donations2015)
cols2016 <- colnames(donations2016)
cols2017 <- colnames(donations2017)
identical(cols2013, cols2014)
identical(cols2013, cols2015)
identical(cols2013, cols2016)
identical(cols2013, cols2017)
remove(cols2013, cols2014, cols2015, cols2016, cols2017)</code></pre>
<p>Changing some columns to factors:</p>
<pre><code>cols <- c(1, 2, 3, 4, 6, 7, 10:19, 21:32, 35, 36, 38, 39, 44:49)
donations2013[,cols] <- lapply(donations2013[,cols], factor)
donations2014[,cols] <- lapply(donations2014[,cols], factor)
donations2015[,cols] <- lapply(donations2015[,cols], factor)
donations2016[,cols] <- lapply(donations2016[,cols], factor)
donations2017[,cols] <- lapply(donations2017[,cols], factor)</code></pre>
<div id="refactoring-certain-columns" class="section level3">
<h3>Refactoring certain columns:</h3>
<div id="donor-gender" class="section level4">
<h4>Donor Gender:</h4>
<pre><code>#summary(donations2013$`Donor Gender`)
levels(donations2013$`Donor Gender`) <- list(Female=c("Female", "F", "Feiale"), Male=c("Male", "M", "Iale"))
#summary(donations2013$`Donor Gender`)
#summary(donations2014$`Donor Gender`)
levels(donations2014$`Donor Gender`) <- list(Female=c("Female", "F", "Feiale"), Male=c("Male"))
#summary(donations2014$`Donor Gender`)
#summary(donations2015$`Donor Gender`)
levels(donations2015$`Donor Gender`) <- list(Female=c("Female", "F", "Femala"), Male=c("Male"))
#summary(donations2015$`Donor Gender`)
#summary(donations2016$`Donor Gender`)
levels(donations2016$`Donor Gender`) <- list(Female=c("Female", "F", "Femala"), Male=c("Male"))
#summary(donations2016$`Donor Gender`)
#summary(donations2017$`Donor Gender`)
levels(donations2017$`Donor Gender`) <- list(Female=c("Female", "F", "Femala", "Famale"), Male=c("Male", "M"))
#summary(donations2017$`Donor Gender`)
</code></pre>
</div>
</div>
<div id="prior-participants" class="section level3">
<h3>Prior participants:</h3>
<pre><code>#summary(donations2013$`Is Prior Participant`)
levels(donations2013$`Is Prior Participant`) <- list(Yes = c("Yes"))
#summary(donations2013$`Is Prior Participant`)
#summary(donations2014$`Is Prior Participant`)
levels(donations2014$`Is Prior Participant`) <- list(Yes = "Yes")
#summary(donations2014$`Is Prior Participant`)
#summary(donations2015$`Is Prior Participant`)
levels(donations2015$`Is Prior Participant`) <- list(Yes = "Yes")
#summary(donations2015$`Is Prior Participant`)
#summary(donations2016$`Is Prior Participant`)
levels(donations2016$`Is Prior Participant`) <- list(Yes = "Yes")
#summary(donations2016$`Is Prior Participant`)
#summary(donations2017$`Is Prior Participant`)
levels(donations2017$`Is Prior Participant`) <- list(Yes = "Yes")
#summary(donations2017$`Is Prior Participant`)
</code></pre>
</div>
<div id="is-team-captain" class="section level3">
<h3>Is Team Captain</h3>
<pre><code>#summary(donations2013$`Is Team Captain`)
levels(donations2013$`Is Team Captain`) <- list(Yes ="TRUE", No = "FALSE")
#summary(donations2013$`Is Team Captain`)
#summary(donations2014$`Is Team Captain`)
levels(donations2014$`Is Team Captain`) <- list(Yes = c("TRUE", "PRUE"), No = "FALSE")
#summary(donations2014$`Is Team Captain`)
#summary(donations2015$`Is Team Captain`)
levels(donations2015$`Is Team Captain`) <- list(Yes = "TRUE", No = c("FALSE", "BALSE"))
#summary(donations2015$`Is Team Captain`)
#summary(donations2016$`Is Team Captain`)
levels(donations2016$`Is Team Captain`) <- list(Yes = c("TRUE", "PRUE"), No = "FALSE")
#summary(donations2016$`Is Team Captain`)
#summary(donations2017$`Is Team Captain`)
levels(donations2017$`Is Team Captain`) <- list(Yes = c("TRUE"), No = c("FALSE", "BALSE", "FALSA"))
#summary(donations2017$`Is Team Captain`)
</code></pre>
</div>
<div id="gift-payment-method" class="section level3">
<h3>Gift Payment Method</h3>
<pre><code>#summary(donations2013$`Gift Payment Method`)
levels(donations2013$`Gift Payment Method`) <- list(Cash = "Cash", Check= "Check", `Credit Card` = "Credit Card")
#summary(donations2013$`Gift Payment Method`)
#summary(donations2014$`Gift Payment Method`)
levels(donations2014$`Gift Payment Method`) <- list(Cash = "Cash", Check= "Check", `Credit Card` = "Credit Card")
#summary(donations2014$`Gift Payment Method`)
#summary(donations2015$`Gift Payment Method`)
# No NAs or anything to refactor
#summary(donations2016$`Gift Payment Method`)
levels(donations2016$`Gift Payment Method`) <- list(Cash = "Cash", Check= "Check", `Credit Card` = c("Credit Card", "Credit Car"))
#summary(donations2016$`Gift Payment Method`)
#summary(donations2017$`Gift Payment Method`)
levels(donations2017$`Gift Payment Method`) <- list(Cash = "Cash", Check= "Check", `Credit Card` = c("Credit Card", "Credit Car"))
#summary(donations2017$`Gift Payment Method`)
</code></pre>
</div>
<div id="gift-type" class="section level3">
<h3>Gift Type</h3>
<pre><code>#summary(donations2013$`Gift Type`)
levels(donations2013$`Gift Type`) <- list(Offline=c("offline", "ofbline"), Online="online")
#summary(donations2013$`Gift Type`)
#summary(donations2014$`Gift Type`)
levels(donations2014$`Gift Type`) <- list(Offline="offline", Online=c("online", "ojline"))
#summary(donations2014$`Gift Type`)
#summary(donations2015$`Gift Type`)
levels(donations2015$`Gift Type`) <- list(Offline="offline", Online="online")
#summary(donations2015$`Gift Type`)
#summary(donations2016$`Gift Type`)
levels(donations2016$`Gift Type`) <- list(Offline="offline", Online=c("online", "onhine", "onlina"))
#summary(donations2016$`Gift Type`)
#summary(donations2017$`Gift Type`)
levels(donations2017$`Gift Type`) <- list(Offline="offline", Online=c("online", "ojline", "onlina"))
#summary(donations2017$`Gift Type`)
</code></pre>
</div>
<div id="offline-status" class="section level3">
<h3>Offline status</h3>
<pre><code>summary(donations2013$`Offline Status`)
summary(donations2014$`Offline Status`)
summary(donations2015$`Offline Status`)
summary(donations2016$`Offline Status`)
levels(donations2016$`Offline Status`)<- list(confirmed = c("confirmed", "confirme`"), `Teamraiser Participant Gift` = c("(Teamraiser Participant Gift", "Teamraiser Participant Gift"))
summary(donations2017$`Offline Status`)</code></pre>
</div>
<div id="dealing-with-dates" class="section level3">
<h3>Dealing with dates</h3>
<ul>
<li>Date format was changed in Excel * to eg 08/12/13 to enable easy conversion of dates.</li>
</ul>
<pre><code>#Since each dataset is the same, can identify all date fields from just 2013 data
date_cols <- which(names(donations2013)%in%c("Event Date", "Date Recorded", "Donor Opt-out Date", "Registration Date", "Team Creation Date"))
donations2013[, date_cols] <- lapply(donations2013[, date_cols], as.Date, "%m/%d/%y")
donations2014[, date_cols] <- lapply(donations2014[, date_cols], as.Date, "%m/%d/%y")
donations2015[, date_cols] <- lapply(donations2015[, date_cols], as.Date, "%m/%d/%y")
donations2016[, date_cols] <- lapply(donations2016[, date_cols], as.Date, "%m/%d/%y")
donations2017[, date_cols] <- lapply(donations2017[, date_cols], as.Date, "%m/%d/%y")</code></pre>
</div>
<div id="writing-out-formatted-donations-data-to-an-rds-file" class="section level3">
<h3>Writing out Formatted Donations Data to an RDS file</h3>
<pre><code>saveRDS(donations2013, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2013.rds")
saveRDS(donations2014, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2014.rds")
saveRDS(donations2015, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2015.rds")
saveRDS(donations2016, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2016.rds")
saveRDS(donations2017, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2017.rds")
</code></pre>
</div>
</div>
<div id="konrad-cleaning-bike-teams-data" class="section level2">
<h2>Konrad: Cleaning Bike Teams Data</h2>
<pre><code>teams <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Bike Teams\\2013-2017 Bike Teams Date Format Fixed.csv")</code></pre>
<pre><code>#summary(teams)
factor_cols <- c(1:5, 7, 8, 10, 11, 20:22, 26)
teams[ ,factor_cols] <- lapply(teams[ ,factor_cols], factor)
# Removed time from team creation date and Event Date (in Excel)!!!
teams$`Team Creation Date` <- as.Date(teams$`Team Creation Date`, format = '%m/%d/%y')
teams$`Event Date` <- as.Date(teams$`Event Date`, format = '%m/%d/%y')
summary(teams$`Team Division`)
levels(teams$`Team Division`) <- list(Association = "Association", Brewery="Beer/Brewery", `Bike Club`="Bike Club", `Bike Shop` = c("Bike Shop", "Bike Shops"), `Civic Team` = "Civic Team", `Club/Organization` = "Club/Organization", Corporate = c("Corporate", "Corporation"), `Family/Friends` = c("Family and Friends", "Family/Friends", "Frien`s and Family", "Friend and Family", "Friends and Family"), Ohana = "Ohana", `Ohana/Friends` = "Ohana and Friends", Open = "Open", `Open Team` = "Open Team", Organization = c("Organization", "Organization (Clubs, Civic Groups, etc.)", "Organization (Clubs; Civic Groups; etc.)", "Organization (Clubs; Civic Groups; Place of Worship; etc.)"), Other = "Other", `Place of Worship` = c("Place of Worship", "Place of worship", "Religious"), School = "School", `Small Business` = "Small Business", `Volunteer Group` = "Volunteer Group")
</code></pre>
<pre><code>#summary(teams$`Event Type`)
#summary(teams$`Internal Event Name`)
#summary(teams$`Event ID`)
#summary(teams$`Team Creation Date`)
#summary(teams$`Captain Email Domain`)
#summary(teams$Company)
#summary(teams$`Team Name`)
#summary(teams$`Number of Participants`)</code></pre>
<pre><code>saveRDS(teams, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Bike Teams/teams.rds")</code></pre>
</div>
<div id="ramya-cleaning-bike-events" class="section level2">
<h2>Ramya: Cleaning Bike Events</h2>
<pre><code>BikeEvents<-read.csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Events\\2013-2017 Bike Events.csv")</code></pre>
<pre><code>str(BikeEvents)</code></pre>
<pre><code>levels(BikeEvents$City)</code></pre>
<pre><code>#Cleaning cities
BikeEvents$City[BikeEvents$City == 'Athens, AL ']<- "Athens"
BikeEvents$City[BikeEvents$City == 'Cherry Hill, NJ with alternate start locations']<-"Cherry Hill"
#as.factor(BikeEvents$City[BikeEvents$City == 'Ridgeland, MS']<- "Ridgeland")
BikeEvents$City[BikeEvents$City == 'St. Augustine ']<-"St. Augustine"
BikeEvents$City[BikeEvents$City == 'Sioux Falls area']<-"Sioux Falls"
BikeEvents$City[BikeEvents$City == 'Schodack ']<-"Schodack"
BikeEvents$City[BikeEvents$City == 'San Antonio ']<-"San Antonio"
BikeEvents$City[BikeEvents$City == 'New York City']<-"New York"
BikeEvents$City[BikeEvents$City == 'New Albany ']<-"New Albany"
BikeEvents$City[BikeEvents$City == 'Keuka Park ']<-"Keuka Park"
BikeEvents$City[BikeEvents$City == 'Hollidaysburg, PA to State College']<-"Hollidaysburg"
BikeEvents$City[BikeEvents$City == 'Duluth to White Bear Lake']<-"Duluth"
BikeEvents$City[BikeEvents$City == 'Duluth to the Twin Cities']<-"Duluth"
BikeEvents$City[BikeEvents$City == 'Fargo area']<-"Fargo"
BikeEvents$City[BikeEvents$City == 'Dallas ']<-"Dallas"
BikeEvents$City[BikeEvents$City == 'DeKalb']<-"Dekalb"
BikeEvents$City[BikeEvents$City == 'DeKalb ']<-"Dekalb"
</code></pre>
<pre><code>summary(BikeEvents$Event.Date)</code></pre>
<pre><code>#Date Conversion in R
datecols_bikeevents<-which(names(BikeEvents)%in%c("Event.Created.Date", "Event.Date"))
for (i in datecols_bikeevents) {
BikeEvents[,i] = as.Date(BikeEvents[,i], '%m/%d/%y')
}
</code></pre>
</div>
<div id="saving-the-work" class="section level2">
<h2>Saving the work</h2>
<pre><code>saveRDS(BikeEvents, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Bike Events/Bike Events.rds")</code></pre>
</div>
<div id="jordan-cleaning-bike-participants" class="section level2">
<h2>Jordan: Cleaning Bike Participants</h2>
<div id="import-data" class="section level3">
<h3>Import data</h3>
<pre><code>participants <- read_csv("C:\\Users\\Konrad\\Desktop\\ETM 527\\Participants\\2013-2017 Bike MS Participants.csv")</code></pre>
</div>
<div id="basic-data-exploration" class="section level3">
<h3>Basic data exploration</h3>
<pre><code>class(participants)
dim(participants)
glimpse(participants)
head(participants, n = 5)
tail(participants, n = 5)
summary(participants)</code></pre>
<p>Able to see that some fields need to be converted from integers to characters. Also, easily able to identify which fields will need to be rid of NA values.</p>
<pre><code># need to change fields from int to char
participants$`Team ID` <- as.character(participants$`Team ID`)
class(participants$`Team ID`)
participants$`Contact ID` <- as.character(participants$`Contact ID`)
participants$`Address - Participant ZIP/Postal Code` <- as.character(participants$`Address - Participant ZIP/Postal Code`)
participants$`Event ID` <- as.character(participants$`Event ID`)
summary(participants)
#change event date from character to date-time
participants$`Event Date` <- mdy_hm(participants$`Event Date`)
class(participants$`Event Date`)
head(participants$`Event Date`, n = 5)</code></pre>
<pre><code># acknowledge NA's
map(participants, ~sum(is.na(.)))</code></pre>
</div>
</div>
<div id="writing-out-jordans-work" class="section level2">
<h2>Writing out Jordan’s work</h2>
<pre><code>saveRDS(participants, "C:/Users/Konrad/Desktop/ETM 527/RDS Data/Participants/Participants.rds")</code></pre>
<pre><code>library(readr)
library(dplyr)
library(ggplot2)
donations2013 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2013.rds")
donations2014 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2014.rds")
donations2015 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2015.rds")
donations2016 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2016.rds")
donations2017 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2017.rds")
participants <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Participants/Participants.rds")
str(participants)
</code></pre>
</div>
<div id="creating-data-aggregations" class="section level1">
<h1>Creating Data Aggregations</h1>
<div id="from-donations-data" class="section level2">
<h2>From donations data</h2>
<pre><code>gen_2013 <- summary(donations2013$`Donor Gender`)
gen_2014 <- summary(donations2014$`Donor Gender`)
gen_2015 <- summary(donations2015$`Donor Gender`)
gen_2016 <- summary(donations2016$`Donor Gender`)
gen_2017 <- summary(donations2017$`Donor Gender`)
gender_year <- tibble(c(2013, 2014, 2015, 2016, 2017),
c(gen_2013[1], gen_2014[1], gen_2015[1], gen_2016[1], gen_2017[1]),
c(gen_2013[2], gen_2014[2], gen_2015[2], gen_2016[2], gen_2017[2]),
c(gen_2013[3], gen_2014[3], gen_2015[3], gen_2016[3], gen_2017[3]))
colnames(gender_year) <- c("Year", "Female", "Male", "NA")
year_num <- tibble(2013:2017, c(nrow(donations2013), nrow(donations2014), nrow(donations2015), nrow(donations2016), nrow(donations2017)))
colnames(year_num) <- c("Year", "Number of Participants")
tot_gifts2013 <- sum(donations2013$`Gift Amount($)`, na.rm = TRUE)
tot_gifts2014 <- sum(donations2014$`Gift Amount($)`, na.rm = TRUE)
tot_gifts2015 <- sum(donations2015$`Gift Amount($)`, na.rm = TRUE)
tot_gifts2016 <- sum(donations2016$`Gift Amount($)`, na.rm = TRUE)
tot_gifts2017 <- sum(donations2017$`Gift Amount($)`, na.rm = TRUE)
med_gifts2013 <- median(donations2013$`Gift Amount($)`, na.rm = TRUE)
med_gifts2014 <- median(donations2014$`Gift Amount($)`, na.rm = TRUE)
med_gifts2015 <- median(donations2015$`Gift Amount($)`, na.rm = TRUE)
med_gifts2016 <- median(donations2016$`Gift Amount($)`, na.rm = TRUE)
med_gifts2017 <- median(donations2017$`Gift Amount($)`, na.rm = TRUE)
tot_donations <- tibble(2013:2017,
c(tot_gifts2013, tot_gifts2014, tot_gifts2015, tot_gifts2016, tot_gifts2017),
c(med_gifts2013, med_gifts2014, med_gifts2015, med_gifts2016, med_gifts2017))
colnames(tot_donations) <- c("Year", "Total Donations", "Median")
add_tot_gifts2013 <- sum(donations2013$`Additional Gift Amount($)`, na.rm = TRUE)
add_tot_gifts2014 <- sum(donations2014$`Additional Gift Amount($)`, na.rm = TRUE)
add_tot_gifts2015 <- sum(donations2015$`Additional Gift Amount($)`, na.rm = TRUE)
add_tot_gifts2016 <- sum(donations2016$`Additional Gift Amount($)`, na.rm = TRUE)
add_tot_gifts2017 <- sum(donations2017$`Additional Gift Amount($)`, na.rm = TRUE)
add_med_gifts2013 <- median(donations2013$`Additional Gift Amount($)`, na.rm = TRUE)
add_med_gifts2014 <- median(donations2014$`Additional Gift Amount($)`, na.rm = TRUE)
add_med_gifts2015 <- median(donations2015$`Additional Gift Amount($)`, na.rm = TRUE)
add_med_gifts2016 <- median(donations2016$`Additional Gift Amount($)`, na.rm = TRUE)
add_med_gifts2017 <- median(donations2017$`Additional Gift Amount($)`, na.rm = TRUE)
add_donations <- tibble(2013:2017,
c(add_tot_gifts2013, add_tot_gifts2014, add_tot_gifts2015, add_tot_gifts2016, add_tot_gifts2017),
c(add_med_gifts2013, add_med_gifts2014, add_med_gifts2015, add_med_gifts2016, add_med_gifts2017))
colnames(add_donations) <- c("Year", "Additional Donations", "Median")</code></pre>
</div>
<div id="plotting-donations-data" class="section level2">
<h2>Plotting donations data</h2>
<pre><code>ggplot(data = subset(donations2013, !is.na(`Donor Gender`))) +
geom_bar(aes(`Donor Gender`, fill = `Donor Gender`), na.rm = TRUE) +
xlab("Donor Gender") +
ylab("Count") +
ggtitle("2013 NMS Participants by gender")
ggplot(data = subset(donations2013, !is.na(`Gift Payment Method`))) +
geom_bar(aes(`Gift Payment Method`, fill = `Donor Gender`)) +
xlab("Gift Payment Method") +
ylab("Dollars") +
ggtitle("2013 NMS Gift Payment Method Types by Gender")
library(scales)
ggplot(tot_donations, aes(Year, `Total Donations`/1000000)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
ylab("Gift Amount ($M)") +
ggtitle("Gift amounts have fallen every year since 2014") +
theme(plot.title = element_text(hjust = 0.5)) +
ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Gift amounts have fallen.png")
ggplot(add_donations, aes(Year, `Additional Donations`/1000000)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
ylab("Additional gift Amount ($M)") +
ggtitle("Additional gift amounts were increasing but fell in 2017") +
theme(plot.title = element_text(hjust = 0.5)) +
ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Additional gift amounts were increasing.png")
colnames(add_donations)
ggplot() +
geom_bar(data = tot_donations, aes(Year, `Total Donations` / 1000000), stat = "identity") +
geom_bar(data = add_donations, aes(Year, `Additional Donations` /1000000), stat = "identity", fill = "red", alpha = 0.6)
participants <- tibble(2013:2017, c(nrow(donations2013), nrow(donations2014), nrow(donations2015), nrow(donations2016), nrow(donations2017)))
colnames(participants) <- c("Year", "Participants")
ggplot(participants) +
geom_bar(aes(x = Year, y = Participants), stat = "identity") +
ylim(0, 1000000) +
scale_y_continuous(labels= comma) +
ggtitle("Number of participants has fallen every year since 2013") +
theme(plot.title = element_text(hjust = 0.5)) +
ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Number of participants has fallen.png")
don_per_part = tibble(2013:2017, tot_donations$`Total Donations` / participants$Participants)
colnames(don_per_part) <- c("Year", "Average Donation")
ggplot(don_per_part, aes(Year, `Average Donation`)) +
geom_bar(stat = "identity") +
ylab("Averate Gift Amount") +
ggtitle("Average gift per participant has increased every year since 2013") +
theme(plot.title = element_text(hjust = 0.5)) +
ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Average gift per participant has increased.png")
</code></pre>
</div>
<div id="ramyas-bike-events-data-aggregation" class="section level2">
<h2>Ramya’s Bike Events Data Aggregation</h2>
<pre><code>#Average amount by total participants in all the states
Amount_by_States<-aggregate(BikeEvents$Total.From.Participant..., by=list(State = BikeEvents$State), FUN = mean)
head(Amount_by_States)
</code></pre>
<pre><code>#Amount raised by participants from 2013-2017
Amount_by_year <-aggregate(BikeEvents$Total.From.Participant..., by = list(year = BikeEvents$Fiscal.Year), sum)
Amount_by_year</code></pre>
<pre><code>#selft donars over
Donors_by_year <-aggregate(BikeEvents$Total.From.Participant..., by = list(year = BikeEvents$Fiscal.Year), sum)
Donors_by_year
</code></pre>
<pre><code>#Active registrations overs the years
Active_registrations <-aggregate(BikeEvents$Active.Registrations, by = list(year = BikeEvents$Fiscal.Year), sum)
Active_registrations
</code></pre>
</div>
<div id="ramyas-plots" class="section level2">
<h2>Ramya’s Plots</h2>
<pre><code>library('ggplot2')
Amounts_ggplot<-ggplot(Amount_by_States, aes(x=Amount_by_States$State,y=Amount_by_States$x))+
theme(axis.text.x = element_text(face = "bold", size = 8, angle = 90))+
geom_bar(stat = 'identity', fill = "blue")+
ggtitle("Average amount raised by Bike Events participants across all the states") +
labs(x ='States', y = 'Average amount')</code></pre>
<pre><code>Amounts_ggplot
ggsave(filename="D:/527- Data Mining/TUN Data Challenge/Data Visualizations/Amounts_ggplot.pdf", plot=Amounts_ggplot)
</code></pre>
<pre><code>ggplot(Amount_by_year, aes(x = Amount_by_year$year, y = Amount_by_year$x)) +
geom_bar(stat = 'identity', fill = "blue")+ggtitle("Participants amount to Bike events slightly decreasing since 2014") +
labs(x ='Year', y = 'Total amount')
</code></pre>
<pre><code>#Donars by year
ggplot(Donors_by_year, aes(x = Donors_by_year$year, y = Donors_by_year$x))+
geom_bar(stat = 'identity', fill = "blue")+ggtitle("Self donors gradually decreasing since 2014 ") +
labs(x ='Year', y = 'Total amount')
</code></pre>
<pre><code>#Donars by year
ggplot(Active_registrations, aes(x = Active_registrations$year, y = Active_registrations$x)) +
geom_bar(stat = 'identity', fill = "blue")+ggtitle("Active registrations have fallen since 2013 ") +
labs(x ='Year', y = 'No of registrations')
</code></pre>
</div>
<div id="participants-eda" class="section level2">
<h2>Participants EDA</h2>
<pre><code># some basic data viz
barplot(table(participants$`Participant Occupation`), ylab = "Count", main = "Occupations of our Participants",
ylim = c(0,2500), las = 2, col=rgb(0.2,0.4,0.6,0.6))
occ.count <- table(participants$`Participant Occupation`)
occ.count
occupation.viz <- ggplot(data = participants, aes(x = participants$`Participant Occupation`)) +
geom_bar(aes(y = (..count..), fill = `Participant Gender`)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
occupation.viz + labs(x = "Occupations") +
labs(title = "Frequency of Participant Occupation") +
labs(y = "Count")</code></pre>
<pre><code>library(readr)
library(dplyr)
library(ggplot2)
library(scales)
donations2013 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2013.rds")
donations2014 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2014.rds")
donations2015 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2015.rds")
donations2016 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2016.rds")
donations2017 <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Donations/Donations2017.rds")
participants <- readRDS("C:/Users/Konrad/Desktop/ETM 527/RDS Data/Participants/Participants.rds")
#summary(donations2017$`Donor Gender`)
#levels(donations2017$`Donor Gender`) <- list(Male = "Male", Female = "Female", None = "")</code></pre>
</div>
</div>
<div id="creating-data-aggregations-1" class="section level1">
<h1>Creating Data Aggregations</h1>
<div id="from-donations-data-1" class="section level2">
<h2>From donations data</h2>
<pre><code># gen_2013 <- summary(donations2013$`Donor Gender`)
# gen_2014 <- summary(donations2014$`Donor Gender`)
# gen_2015 <- summary(donations2015$`Donor Gender`)
# gen_2016 <- summary(donations2016$`Donor Gender`)
# gen_2017 <- summary(donations2017$`Donor Gender`)
#
#
# gender_year <- tibble(c(2013, 2014, 2015, 2016, 2017),
# c(gen_2013[1], gen_2014[1], gen_2015[1], gen_2016[1], gen_2017[1]),
# c(gen_2013[2], gen_2014[2], gen_2015[2], gen_2016[2], gen_2017[2]),
# c(gen_2013[3], gen_2014[3], gen_2015[3], gen_2016[3], gen_2017[3]),
# c(gen_2013[4], gen_2014[4], gen_2015[4], gen_2016[4], gen_2017[4]))
#
# colnames(gender_year) <- c("Year", "Female", "Male", "None", "NA")
#
#
# year_num <- tibble(2013:2017, c(nrow(donations2013), nrow(donations2014), nrow(donations2015), nrow(donations2016), nrow(donations2017)))
# colnames(year_num) <- c("Year", "Number of Participants")
#
#
# tot_gifts2013 <- sum(donations2013$`Gift Amount($)`, na.rm = TRUE)
# tot_gifts2014 <- sum(donations2014$`Gift Amount($)`, na.rm = TRUE)
# tot_gifts2015 <- sum(donations2015$`Gift Amount($)`, na.rm = TRUE)
# tot_gifts2016 <- sum(donations2016$`Gift Amount($)`, na.rm = TRUE)
# tot_gifts2017 <- sum(donations2017$`Gift Amount($)`, na.rm = TRUE)
#
# med_gifts2013 <- median(donations2013$`Gift Amount($)`, na.rm = TRUE)
# med_gifts2014 <- median(donations2014$`Gift Amount($)`, na.rm = TRUE)
# med_gifts2015 <- median(donations2015$`Gift Amount($)`, na.rm = TRUE)
# med_gifts2016 <- median(donations2016$`Gift Amount($)`, na.rm = TRUE)
# med_gifts2017 <- median(donations2017$`Gift Amount($)`, na.rm = TRUE)
#
# mean_gifts2013 <- mean(donations2013$`Gift Amount($)`, na.rm = TRUE)
# mean_gifts2014 <- mean(donations2014$`Gift Amount($)`, na.rm = TRUE)
# mean_gifts2015 <- mean(donations2015$`Gift Amount($)`, na.rm = TRUE)
# mean_gifts2016 <- mean(donations2016$`Gift Amount($)`, na.rm = TRUE)
# mean_gifts2017 <- mean(donations2017$`Gift Amount($)`, na.rm = TRUE)
#
#
# tot_donations <- tibble(2013:2017,
# c(tot_gifts2013, tot_gifts2014, tot_gifts2015, tot_gifts2016, tot_gifts2017),
# c(med_gifts2013, med_gifts2014, med_gifts2015, med_gifts2016, med_gifts2017),
# c(mean_gifts2013, mean_gifts2014, mean_gifts2015, mean_gifts2016, mean_gifts2017))
#
# colnames(tot_donations) <- c("Year", "Total Donations", "Median", "Mean")
#
# add_tot_gifts2013 <- sum(donations2013$`Additional Gift Amount($)`, na.rm = TRUE)
# add_tot_gifts2014 <- sum(donations2014$`Additional Gift Amount($)`, na.rm = TRUE)
# add_tot_gifts2015 <- sum(donations2015$`Additional Gift Amount($)`, na.rm = TRUE)
# add_tot_gifts2016 <- sum(donations2016$`Additional Gift Amount($)`, na.rm = TRUE)
# add_tot_gifts2017 <- sum(donations2017$`Additional Gift Amount($)`, na.rm = TRUE)
#
# add_med_gifts2013 <- median(donations2013$`Additional Gift Amount($)`, na.rm = TRUE)
# add_med_gifts2014 <- median(donations2014$`Additional Gift Amount($)`, na.rm = TRUE)
# add_med_gifts2015 <- median(donations2015$`Additional Gift Amount($)`, na.rm = TRUE)
# add_med_gifts2016 <- median(donations2016$`Additional Gift Amount($)`, na.rm = TRUE)
# add_med_gifts2017 <- median(donations2017$`Additional Gift Amount($)`, na.rm = TRUE)
#
# add_mean_gifts2013 <- mean(donations2013$`Additional Gift Amount($)`, na.rm = TRUE)
# add_mean_gifts2014 <- mean(donations2014$`Additional Gift Amount($)`, na.rm = TRUE)
# add_mean_gifts2015 <- mean(donations2015$`Additional Gift Amount($)`, na.rm = TRUE)
# add_mean_gifts2016 <- mean(donations2016$`Additional Gift Amount($)`, na.rm = TRUE)
# add_mean_gifts2017 <- mean(donations2017$`Additional Gift Amount($)`, na.rm = TRUE)
#
#
# add_donations <- tibble(2013:2017,
# c(add_tot_gifts2013, add_tot_gifts2014, add_tot_gifts2015, add_tot_gifts2016, add_tot_gifts2017),
# c(add_med_gifts2013, add_med_gifts2014, add_med_gifts2015, add_med_gifts2016, add_med_gifts2017),
# c(add_mean_gifts2013, add_mean_gifts2014, add_mean_gifts2015, add_mean_gifts2016, add_mean_gifts2017))
#
# colnames(add_donations) <- c("Year", "Additional Donations", "Median", "Mean")
#
#
# write_rds(tot_donations, "C:/Users/Konrad/Desktop/ETM 527/Calculated/Gift Amounts.rds")
# write_rds(add_donations, "C:/Users/Konrad/Desktop/ETM 527/Calculated/Additional Amounts.rds")
# write_rds(gender_year, "C:/Users/Konrad/Desktop/ETM 527/Calculated/Yearly Gender Breakdown.rds")
#
# gen_tot_2013_gifts <- aggregate(donations2013$`Gift Amount($)`, by = list(donations2013$`Donor Gender`), FUN = sum)
# gen_med_2013_gifts <- aggregate(donations2013$`Gift Amount($)`, by = list(donations2013$`Donor Gender`), FUN = median)
# gen_mean_2013_gifts <- aggregate(donations2013$`Gift Amount($)`, by = list(donations2013$`Donor Gender`), FUN = mean)
#
# gen_tot_2014_gifts <- aggregate(donations2014$`Gift Amount($)`, by = list(donations2014$`Donor Gender`), FUN = sum)
# gen_med_2014_gifts <- aggregate(donations2014$`Gift Amount($)`, by = list(donations2014$`Donor Gender`), FUN = median)
# gen_mean_2014_gifts <- aggregate(donations2014$`Gift Amount($)`, by = list(donations2014$`Donor Gender`), FUN = mean)
#
# gen_tot_2015_gifts <- aggregate(donations2015$`Gift Amount($)`, by = list(donations2015$`Donor Gender`), FUN = sum)
# gen_med_2015_gifts <- aggregate(donations2015$`Gift Amount($)`, by = list(donations2015$`Donor Gender`), FUN = median)
# gen_mean_2015_gifts <- aggregate(donations2015$`Gift Amount($)`, by = list(donations2015$`Donor Gender`), FUN = mean)
#
# gen_tot_2016_gifts <- aggregate(donations2016$`Gift Amount($)`, by = list(donations2016$`Donor Gender`), FUN = sum)
# gen_med_2016_gifts <- aggregate(donations2016$`Gift Amount($)`, by = list(donations2016$`Donor Gender`), FUN = median)
# gen_mean_2016_gifts <- aggregate(donations2016$`Gift Amount($)`, by = list(donations2016$`Donor Gender`), FUN = mean)
#
# gen_tot_2017_gifts <- aggregate(donations2017$`Gift Amount($)`, by = list(donations2017$`Donor Gender`), FUN = sum)
# gen_med_2017_gifts <- aggregate(donations2017$`Gift Amount($)`, by = list(donations2017$`Donor Gender`), FUN = median)
# gen_mean_2017_gifts <- aggregate(donations2017$`Gift Amount($)`, by = list(donations2017$`Donor Gender`), FUN = mean)
#
# gender_year_totals <- rbind(gen_tot_2013_gifts, gen_tot_2014_gifts, gen_tot_2015_gifts, gen_tot_2016_gifts, gen_tot_2017_gifts)
#
# gender_year_med <- rbind(gen_med_2013_gifts, gen_med_2014_gifts, gen_med_2015_gifts, gen_med_2016_gifts, gen_med_2017_gifts)
#
# gender_year_mean <- rbind(gen_mean_2013_gifts, gen_mean_2014_gifts, gen_mean_2015_gifts, gen_mean_2016_gifts, gen_mean_2017_gifts)
#
# gender_totals <- tibble(rep(2013:2017, each = 3),
# rep(c("Female", "Male", "None"), times = 5),
# gender_year_totals$x,
# gender_year_med$x,
# gender_year_mean$x)
#
# colnames(gender_totals) <- c("Year", "Gender", "Total Gifts", "Median", "Mean")
#
# write_rds(gender_totals, "C:/Users/Konrad/Desktop/ETM 527/Calculated/Gender Year Totals Breakdown.rds")
</code></pre>
<pre><code>tot_donations <- read_rds("C:/Users/Konrad/Desktop/ETM 527/Calculated/Gift Amounts.rds")
add_donations <- read_rds("C:/Users/Konrad/Desktop/ETM 527/Calculated/Additional Amounts.rds")
gender_year <- read_rds("C:/Users/Konrad/Desktop/ETM 527/Calculated/Yearly Gender Breakdown.rds")
gender_totals <- read_rds("C:/Users/Konrad/Desktop/ETM 527/Calculated/Gender Year Totals Breakdown.rds")
</code></pre>
<div id="total-donations" class="section level5">
<h5>Total Donations</h5>
<pre><code>ggplot(tot_donations, aes(Year, `Total Donations`)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = comma) +
ylab("Participants") +
ggtitle("The number of participants has fallen every year since 2014") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.title = element_text(size = 18, face = "bold")) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Participant numbers fallen.png")
ggplot(participants, aes(`Fiscal Year`)) +
geom_bar() +
scale_y_continuous(labels = comma) +
ylab("Participants") +
xlab("Fiscal Year") +
ggtitle("Number of participants has decreased every year") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.title = element_text(size =18, face = "bold")) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Number of participants decreased every year.png", width = 10)</code></pre>
</div>
</div>
<div id="sum-of-donations" class="section level2">
<h2>Sum of Donations</h2>
<pre><code>ggplot(tot_donations, aes(Year, `Total Donations`/1000000)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
ylab("Gift Amount ($M)") +
ggtitle("Total gift amounts have fallen every year since 2014") +
theme(plot.title = element_text(hjust = 0.5)) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Gift amounts have fallen.png")</code></pre>
<div id="mean-donations" class="section level3">
<h3>Mean Donations</h3>
<pre><code>ggplot(tot_donations, aes(Year, Mean)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
ylab("Gift Amount") +
ggtitle("Mean gift amount continues to rise") +
theme(plot.title = element_text(hjust = 0.5)) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Gift amounts have fallen.png")</code></pre>
</div>
<div id="median-donations" class="section level3">
<h3>Median Donations</h3>
<pre><code>ggplot(tot_donations, aes(Year, Median)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
ylab("Gift Amount") +
ggtitle("Median gift amount remains unchanged") +
theme(plot.title = element_text(hjust = 0.5)) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Gift amounts have fallen.png")</code></pre>
<div id="gender-totals" class="section level5">
<h5>Gender Totals</h5>
<pre><code>ggplot(gender_totals[order(gender_totals$Gender),], aes(Year, `Total Gifts`/1000000, fill = Gender)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels=dollar_format(prefix="$", suffix = "M")) +
scale_fill_manual(values=c("#A92A2A", "#205A8F", "#B4BFC3")) +
ylab("Gift Amount (M)") +
ggtitle("Total gift amount has decreased") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.title = element_text(size =18, face = "bold")) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Total gift amounts by gender.png", width = 12)
ggplot(gender_totals, aes(Year, Median, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
scale_fill_manual(values=c("#A92A2A", "#205A8F", "#B4BFC3")) +
ylab("Gift Amount ($)") +
ggtitle("Median gift amount remains unchanged") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.title = element_text(size =18, face = "bold")) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Median gift amounts by gender.png", width = 12)
ggplot(gender_totals, aes(Year, Mean, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(labels=dollar_format(prefix="$")) +
scale_fill_manual(values = c("#A92A2A", "#205A8F", "#B4BFC3")) +
ylab("Gift Amount ($)") +
ggtitle("Mean gift amount has fluctuated") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(plot.title = element_text(size =18, face = "bold")) #+
#ggsave("C:\\Users\\Konrad\\Desktop\\ETM 527\\Mean gift amounts by gender.png", width = 12)
</code></pre>
<pre><code>library(dplyr)
library(magrittr)
library(tidyr)
library(knitr)
library(tibble)
library(readxl)
library(graphics)
library(ggplot2)
library(scales)
library(RColorBrewer)</code></pre>
<pre><code>nt <-
read_excel("C:/Users/eva/Documents/ETM 527/Data Challenge 2018/National Teams/2013-2017 National Team Activity.xlsx", skip = 1, na = c("NA", " ", "-999"),
col_types = c("text", "text", "text",
"text", "text", "text", "date", "text",
"numeric", "numeric", "numeric",
"numeric", "numeric", "text", "text",
"text", "text", "text", "text", "text",
"text", "numeric", "text", "text",
"text", "text", "text"))
dim(nt)</code></pre>
<pre><code>#Removing the "Corporate Name" field as it was blank for all recornt
nt$'Corporate Name' <- NULL
str(nt)</code></pre>
<pre><code>nt$`Event Chapter` <- as.factor(nt$`Event Chapter`)
nt$`Event Category` <- as.factor(nt$`Event Category`)
nt$`Event Type` <- as.factor(nt$`Event Type`)
nt$`Local Team Name` <- as.factor(nt$`Local Team Name`)
nt$`Event Name` <- as.factor(nt$`Event Name`)
nt$Location <- as.factor(nt$Location)
cols_to_factor <- 12:ncol(nt)
nt[, cols_to_factor] <- lapply(nt[, cols_to_factor], as.factor)
</code></pre>
<pre><code># Fill down team names
for (i in 2:nrow(nt))
{
if (is.na(nt$`National Team Name`[i]) & !is.na(nt$`National Team Name`[i-1]))
{
nt$`National Team Name`[i] <- nt$`National Team Name`[i-1]
}
}
View(nt)
</code></pre>
<pre><code># add NA in the Team Name where applicable
nt$`National Team Name` <- as.factor(nt$`National Team Name`)
summary(nt$`National Team Name`)
nt$`National Team Name`[nt$`National Team Name` == "N/A"] <- NA #selects all matching rows and changes to NA
summary(nt$`National Team Name`)
</code></pre>