Fall 2020 VOH
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)## Warning: package 'readr' was built under R version 3.6.2library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.6.2dataset<-read_csv("/Users/rebeccagibble/Downloads/Fall 2020 VOH Log (Responses) csv.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Name = col_character(),
## Hour = col_character(),
## Student = col_character(),
## ID = col_character(),
## Type = col_character(),
## Concern = col_character(),
## Outcome = col_character()
## )name<-read_csv("/Users/rebeccagibble/Downloads/1,0 Name.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Name = col_character(),
## Hour = col_character(),
## Student = col_double(),
## ID = col_character(),
## Type = col_character(),
## Concern = col_character(),
## Outcome = col_character()
## )updated<-read_csv("/Users/rebeccagibble/Downloads/VOH CSV.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Name = col_character(),
## Hour = col_character(),
## Student = col_character(),
## ID = col_character(),
## Grade = col_character(),
## Concerns = col_character(),
## Outcome = col_character()
## )student0<-read_csv("/Users/rebeccagibble/Downloads/student0.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Name = col_character(),
## Hour = col_character(),
## Student0 = col_double()
## )week<-read_csv("/Users/rebeccagibble/Downloads/weeksupdated.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Name = col_character(),
## Hour = col_character(),
## Student0 = col_double(),
## Number = col_double()
## )type<-read_csv("/Users/rebeccagibble/Downloads/Concern.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## `Your Name` = col_character(),
## `Your Office Hour` = col_character(),
## `Student Name` = col_character(),
## `CUNY ID#` = col_character(),
## `Student Type` = col_character(),
## `Student Concerns` = col_character()
## )Fall 2020 Virtual Office Hour Logs
In Fall of 2020, we had 381 logged office hours (in the response sheet).
Out of these logs, we saw 115 students and 1 professor, however, 122 office hours were logged as having a student visit (in total).
There is a discrepancy between 115 and 122 because those who stayed for more than one office hour were only counted once in student count.
Frequency per office hour
newname1<-name%>%
select(Hour,Student)
name2<-aggregate(x = newname1$Student,
by = list(newname1$Hour),
FUN = sum)
name2[with(name2, order(-x)), ]## Group.1 x
## 3 Friday 11:00-12:00 13
## 18 Thursday 3:00-4:00 13
## 30 Wednesday 3:00-4:00 11
## 9 Monday 11:00-12:00 6
## 2 Friday 10:00-11:00 5
## 13 Thursday 1:00-2:00 5
## 15 Thursday 11:00-12:00 5
## 21 Tuesday 11:00-12:00 5
## 25 Wednesday 1:00-2:00 5
## 10 Monday 12:00-1:00 4
## 16 Thursday 12:00-1:00 4
## 17 Thursday 2:00-3:00 4
## 4 Friday 12:00-1:00 3
## 7 Monday 1:00-2:00 3
## 8 Monday 10:00-11:00 3
## 23 Tuesday 2:00-3:00 3
## 24 Tuesday 3:00-4:00 3
## 27 Wednesday 11:00-12:00 3
## 11 Monday 2:00-3:00 2
## 12 Monday 3:00-4:00 2
## 14 Thursday 10:00-11:00 2
## 19 Tuesday 1:00-2:00 2
## 20 Tuesday 10:00-11:00 2
## 26 Wednesday 10:00-11:00 2
## 28 Wednesday 12:00-1:00 2
## 1 Friday 1:00-2:00 1
## 5 Friday 2:00-3:00 1
## 22 Tuesday 12:00-1:00 1
## 6 Friday 3:00-4:00 0
## 29 Wednesday 2:00-3:00 0my_data <- as_tibble(name2)
my_data## # A tibble: 30 x 2
## Group.1 x
## <chr> <dbl>
## 1 Friday 1:00-2:00 1
## 2 Friday 10:00-11:00 5
## 3 Friday 11:00-12:00 13
## 4 Friday 12:00-1:00 3
## 5 Friday 2:00-3:00 1
## 6 Friday 3:00-4:00 0
## 7 Monday 1:00-2:00 3
## 8 Monday 10:00-11:00 3
## 9 Monday 11:00-12:00 6
## 10 Monday 12:00-1:00 4
## # … with 20 more rowscolnames(my_data)## [1] "Group.1" "x"names(my_data)[names(my_data) == "Group.1"] <- "Hour"
names(my_data)[names(my_data) == "x"] <- "Frequency"
my_data[with(my_data, order(-Frequency)), ]## # A tibble: 30 x 2
## Hour Frequency
## <chr> <dbl>
## 1 Friday 11:00-12:00 13
## 2 Thursday 3:00-4:00 13
## 3 Wednesday 3:00-4:00 11
## 4 Monday 11:00-12:00 6
## 5 Friday 10:00-11:00 5
## 6 Thursday 1:00-2:00 5
## 7 Thursday 11:00-12:00 5
## 8 Tuesday 11:00-12:00 5
## 9 Wednesday 1:00-2:00 5
## 10 Monday 12:00-1:00 4
## # … with 20 more rowsBar Chart
ggplot(my_data, aes(x = reorder(Hour,+Frequency), y = Frequency)) +
coord_flip()+
geom_bar(stat = "identity")
Frequency per mentor
newname2<-student0%>%
select(Name,Student0)
name4<-aggregate(x = newname2$Student0,
by = list(newname2$Name),
FUN = sum)
name4[with(name4, order(-x)), ]## Group.1 x
## 2 Ashley Simons 8
## 5 Chloe Sweeney 7
## 14 Joseph Horowitz 6
## 7 Emily Scarpati 5
## 22 Reubena Kaidanian 5
## 3 Atika Fariha 4
## 12 Jia Ming Li 4
## 19 Marissa Soomdat 4
## 20 Nava Moskowitz 4
## 27 Sarah Persaud 4
## 1 Anlisa Outar 3
## 6 Clementina Jose 3
## 10 Geraldine Giraldo 3
## 11 Jared Willner 3
## 15 Joya Pariyal 3
## 17 Kelly Herrera 3
## 4 Brianna Sesson 2
## 9 Faith Oyebola 2
## 13 Jonathan Rosenfeld 2
## 18 Lynn Fortune 2
## 24 Sabah Ahsan 2
## 26 Samantha Mohamed 2
## 28 Sneha Sara Vinod 2
## 29 Trystan Gardner 2
## 30 Uzaiza Khan 2
## 8 Etan Ohevshalom 1
## 16 Katherine Ramnarine 1
## 21 Rebecca Spilky 1
## 23 Riyahauna Headley 1
## 25 Salma Razak 1my_data1 <- as_tibble(name4)
colnames(my_data1)## [1] "Group.1" "x"names(my_data1)[names(my_data1) == "Group.1"] <- "Mentor"
names(my_data1)[names(my_data1) == "x"] <- "Frequency"
my_data1[with(my_data1, order(-Frequency)), ]## # A tibble: 30 x 2
## Mentor Frequency
## <chr> <dbl>
## 1 Ashley Simons 8
## 2 Chloe Sweeney 7
## 3 Joseph Horowitz 6
## 4 Emily Scarpati 5
## 5 Reubena Kaidanian 5
## 6 Atika Fariha 4
## 7 Jia Ming Li 4
## 8 Marissa Soomdat 4
## 9 Nava Moskowitz 4
## 10 Sarah Persaud 4
## # … with 20 more rowsBar chart
ggplot(my_data1, aes(x = reorder(Mentor,+Frequency), y = Frequency)) +
coord_flip()+
geom_bar(stat = "identity")
### Frequency per week
weeks<-week%>%
select(Number,Student0)
weeks1<-aggregate(x = weeks$Student0,
by = list(weeks$Number),
FUN = sum)
weeks1[with(weeks1, order(-x)), ]## Group.1 x
## 11 11 24
## 4 4 17
## 7 7 16
## 12 12 10
## 13 13 8
## 10 10 7
## 16 16 7
## 5 5 5
## 2 2 4
## 6 6 4
## 8 8 4
## 9 9 4
## 15 15 4
## 14 14 3
## 1 1 2
## 3 3 2my_data3 <- as_tibble(weeks1)
my_data3## # A tibble: 16 x 2
## Group.1 x
## <dbl> <dbl>
## 1 1 2
## 2 2 4
## 3 3 2
## 4 4 17
## 5 5 5
## 6 6 4
## 7 7 16
## 8 8 4
## 9 9 4
## 10 10 7
## 11 11 24
## 12 12 10
## 13 13 8
## 14 14 3
## 15 15 4
## 16 16 7colnames(my_data3)## [1] "Group.1" "x"names(my_data3)[names(my_data3) == "Group.1"] <- "Week"
names(my_data3)[names(my_data3) == "x"] <- "Frequency"
sort(my_data3$Week, decreasing=TRUE)## [1] 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1my_data3## # A tibble: 16 x 2
## Week Frequency
## <dbl> <dbl>
## 1 1 2
## 2 2 4
## 3 3 2
## 4 4 17
## 5 5 5
## 6 6 4
## 7 7 16
## 8 8 4
## 9 9 4
## 10 10 7
## 11 11 24
## 12 12 10
## 13 13 8
## 14 14 3
## 15 15 4
## 16 16 7Bar chart
ggplot(my_data3, aes(x = Week, y = Frequency)) +
geom_bar(stat = "identity")
### Mean students per week #### 115 students throughout the semester, 16 weeks of VOH
115/16## [1] 7.1875Mean students per office hour
7.1875 students per week on average, 28 office hours per week
7.1875/28## [1] 0.2566964Breakdown of student types in numbers
table(type$`Student Type`)##
## Freshman High school High School Professor Senior Transfer
## 108 1 1 1 1 3
## Unknown
## 1Breakdown of student types in percentages
table(type$`Student Type`)%>%
prop.table()%>%
round(2)##
## Freshman High school High School Professor Senior Transfer
## 0.93 0.01 0.01 0.01 0.01 0.03
## Unknown
## 0.01Breakdown of what student concerns in numbers
concern1<-table(type$`Student Concerns`)
concern1##
## Academic Planning Assignment help
## 60 2
## Asynchronous Learning DegreeWorks
## 1 2
## Finals Campaign Financial Aid/Tuition Payment
## 1 6
## General Advisement Internships
## 22 4
## Major Info Other (Wrong Department)
## 4 4
## Resume Schedule
## 3 13
## Time Management Campaign
## 2Breakdown of student concerns in percentages
table(type$`Student Concerns`)%>%
prop.table()%>%
round(2)##
## Academic Planning Assignment help
## 0.48 0.02
## Asynchronous Learning DegreeWorks
## 0.01 0.02
## Finals Campaign Financial Aid/Tuition Payment
## 0.01 0.05
## General Advisement Internships
## 0.18 0.03
## Major Info Other (Wrong Department)
## 0.03 0.03
## Resume Schedule
## 0.02 0.10
## Time Management Campaign
## 0.02Bar chart
barplot(concern1)