Fall 2021 Data
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 3.6.2datasetsum<-read_csv("/Users/rebeccagibble/Downloads/Fall 2021 Data.csv")## Warning: Missing column names filled in: 'X8' [8], 'X9' [9], 'X10' [10],
## 'X11' [11], 'X12' [12], 'X13' [13]##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Timestamp = col_character(),
## Week = col_character(),
## Mentor = col_character(),
## VOH = col_character(),
## Student = col_double(),
## Status = col_character(),
## Concern = col_character(),
## X8 = col_logical(),
## X9 = col_logical(),
## X10 = col_logical(),
## X11 = col_logical(),
## X12 = col_logical(),
## X13 = col_logical()
## )Fall 2021 Virtual Office Hour Logs
In Fall of 2021, we had 554 logged office hours (in the response sheet), but it is important to note that some office hours get logged many times as a result of multiple mentors working the same office hour. Excluding duplicates and days where we had training/faculty lectures, we actually hosted 324 office hours.
Out of these office hours, we saw 195 students according to the log. This response sheet does not include the students supervisors met with.
This means that we see students in at least 60.18% of office hours.
Frequency Per Office Hour
newname1<-datasetsum%>%
select(VOH,Student)
name2<-aggregate(x = newname1$Student,
by = list(newname1$VOH),
FUN = sum)
name2[with(name2, order(-x)), ]## Group.1 x
## 24 Wednesday 3:00 - 4:00 15
## 12 Thursday 3:00 - 4:00 14
## 6 Monday 3:00 - 4:00 13
## 15 Tuesday 11:00 - 12:00 11
## 17 Tuesday 2:00 - 3:00 11
## 1 Monday 1:00 - 2:00 10
## 5 Monday 2:00 - 3:00 10
## 16 Tuesday 12:00 - 1:00 10
## 4 Monday 12:00 - 1:00 9
## 7 Thursday 1:00 - 2:00 9
## 13 Tuesday 1:00 - 2:00 9
## 10 Thursday 12:00 - 1:00 8
## 11 Thursday 2:00 - 3:00 8
## 3 Monday 11:00 - 12:00 7
## 18 Tuesday 3:00 - 4:00 7
## 21 Wednesday 11:00 - 12:00 7
## 2 Monday 10:00 - 11:00 6
## 14 Tuesday 10:00 - 11:00 6
## 19 Wednesday 1:00 - 2:00 5
## 20 Wednesday 10:00 - 11:00 5
## 22 Wednesday 12:00 - 1:00 5
## 9 Thursday 11:00 - 12:00 4
## 23 Wednesday 2:00 - 3:00 4
## 8 Thursday 10:00 - 11:00 2my_data <- as_tibble(name2)
my_data## # A tibble: 24 x 2
## Group.1 x
## <chr> <dbl>
## 1 Monday 1:00 - 2:00 10
## 2 Monday 10:00 - 11:00 6
## 3 Monday 11:00 - 12:00 7
## 4 Monday 12:00 - 1:00 9
## 5 Monday 2:00 - 3:00 10
## 6 Monday 3:00 - 4:00 13
## 7 Thursday 1:00 - 2:00 9
## 8 Thursday 10:00 - 11:00 2
## 9 Thursday 11:00 - 12:00 4
## 10 Thursday 12:00 - 1:00 8
## # … with 14 more rowscolnames(my_data)## [1] "Group.1" "x"names(my_data)[names(my_data) == "Group.1"] <- "VOH"
names(my_data)[names(my_data) == "x"] <- "Frequency"
my_data[with(my_data, order(-Frequency)), ]## # A tibble: 24 x 2
## VOH Frequency
## <chr> <dbl>
## 1 Wednesday 3:00 - 4:00 15
## 2 Thursday 3:00 - 4:00 14
## 3 Monday 3:00 - 4:00 13
## 4 Tuesday 11:00 - 12:00 11
## 5 Tuesday 2:00 - 3:00 11
## 6 Monday 1:00 - 2:00 10
## 7 Monday 2:00 - 3:00 10
## 8 Tuesday 12:00 - 1:00 10
## 9 Monday 12:00 - 1:00 9
## 10 Thursday 1:00 - 2:00 9
## # … with 14 more rowsBar Chart For Frequency
ggplot(my_data, aes(x = reorder(VOH,+Frequency), y = Frequency)) +
coord_flip()+
geom_bar(stat = "identity")
### Frequency Per Mentor #### This is not as telling as some Mentors did see students, but another Mentor logged it.
newname2<-datasetsum%>%
select(Mentor,Student)
name4<-aggregate(x = newname2$Student,
by = list(newname2$Mentor),
FUN = sum)
name4[with(name4, order(-x)), ]## Group.1 x
## 3 Ashley 26
## 7 Brianna 19
## 5 Bria 16
## 4 Atika 13
## 11 Nirvana 13
## 1 Amaya 12
## 15 Reveena 12
## 20 Stephanie 12
## 19 Sneha 11
## 17 Sabah 10
## 2 Anlisa 9
## 12 Rachel 9
## 16 Riyahauna 8
## 18 Sheyla 8
## 8 Chloe 4
## 21 Uzaiza 4
## 10 Miss Prity 3
## 6 Bria 2
## 13 Reubena 2
## 9 Jorge 1
## 14 Reubena 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: 21 x 2
## Mentor Frequency
## <chr> <dbl>
## 1 Ashley 26
## 2 Brianna 19
## 3 Bria 16
## 4 Atika 13
## 5 Nirvana 13
## 6 Amaya 12
## 7 Reveena 12
## 8 Stephanie 12
## 9 Sneha 11
## 10 Sabah 10
## # … with 11 more rowsBar Chart For Frequency Per Mentor
ggplot(my_data1, aes(x = reorder(Mentor,+Frequency), y = Frequency)) +
coord_flip()+
geom_bar(stat = "identity")
### Frequency Per Week (Week 1, Week 2, Week 3, etc.)
weeks<-datasetsum%>%
select(Week,Student)
weeks1<-aggregate(x = weeks$Student,
by = list(weeks$Week),
FUN = sum)
weeks1[with(weeks1, order(-x)), ]## Group.1 x
## 1 Wk 1 27
## 10 Wk 2 24
## 12 Wk 4 19
## 3 Wk 11 17
## 2 Wk 10 16
## 13 Wk 5 14
## 17 Wk 9 13
## 16 Wk 8 12
## 4 Wk 12 11
## 5 Wk 13 11
## 8 Wk 16 8
## 7 Wk 15 6
## 11 Wk 3 5
## 9 Wk 17 4
## 14 Wk 6 4
## 15 Wk 7 3
## 6 Wk 14 1my_data3 <- as_tibble(weeks1)
my_data3## # A tibble: 17 x 2
## Group.1 x
## <chr> <dbl>
## 1 Wk 1 27
## 2 Wk 10 16
## 3 Wk 11 17
## 4 Wk 12 11
## 5 Wk 13 11
## 6 Wk 14 1
## 7 Wk 15 6
## 8 Wk 16 8
## 9 Wk 17 4
## 10 Wk 2 24
## 11 Wk 3 5
## 12 Wk 4 19
## 13 Wk 5 14
## 14 Wk 6 4
## 15 Wk 7 3
## 16 Wk 8 12
## 17 Wk 9 13colnames(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$Frequency, decreasing=TRUE)## [1] 27 24 19 17 16 14 13 12 11 11 8 6 5 4 4 3 1my_data3## # A tibble: 17 x 2
## Week Frequency
## <chr> <dbl>
## 1 Wk 1 27
## 2 Wk 10 16
## 3 Wk 11 17
## 4 Wk 12 11
## 5 Wk 13 11
## 6 Wk 14 1
## 7 Wk 15 6
## 8 Wk 16 8
## 9 Wk 17 4
## 10 Wk 2 24
## 11 Wk 3 5
## 12 Wk 4 19
## 13 Wk 5 14
## 14 Wk 6 4
## 15 Wk 7 3
## 16 Wk 8 12
## 17 Wk 9 13Bar Chart (Per Week)
ggplot(my_data3, aes(x = Week, y = Frequency)) +
geom_bar(stat = "identity")
### Mean Students Per Week #### 195 students throughout the semester, ~17 weeks of VOH. This number is a little off because many weeks were shortened (for example, week 17 only had 1 day of VOH).
195/17## [1] 11.47059Breakdown of student types in numbers
table(datasetsum$Status)##
## Freshman Parent Revisiting Student Senior
## 175 1 1 1
## Sophomore Transfer
## 1 11Breakdown of student types in percentages
There are no percentage signs, but the numbers presented are percentages of the total population
table(datasetsum$Status)%>%
prop.table()%>%
round(2)*100##
## Freshman Parent Revisiting Student Senior
## 92 1 1 1
## Sophomore Transfer
## 1 6Breakdown of Student Concerns in Numbers
concern1<-table(datasetsum$Concern)
concern1##
## Academic Planning Admissions Process
## 69 2
## Campaign Services Campus Access
## 26 7
## Financial Aid/Tuition Payment General Advisement
## 9 34
## Help with paper Schedule Issue
## 2 15
## Technology Transfer Process
## 22 5Breakdown of Student Concerns in Percentages
There are no percentage signs, but the numbers presented are percentages of the total population
table(datasetsum$Concern)%>%
prop.table()%>%
round(2)*100##
## Academic Planning Admissions Process
## 36 1
## Campaign Services Campus Access
## 14 4
## Financial Aid/Tuition Payment General Advisement
## 5 18
## Help with paper Schedule Issue
## 1 8
## Technology Transfer Process
## 12 3