IPE Survey Analysis
2025-03-26
Assessing Interprofessional Education (IPE) Facilitation Skills in Students
This analysis evaluates students’ facilitation skills in Interprofessional Education (IPE) using survey data collected before and after participating in the program. The pre-survey captures students’ competency levels prior to attending, while the post-survey assesses their skills after completing the program, providing insights into the program’s impact.
To begin, we first examine the demographics of students who participated in the pre-survey.
library(gtools)## Warning: package 'gtools' was built under R version 4.3.3library(ggplot2)
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(readxl)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutpre_survey <- read_excel('/Users/ramyaamudapakula/Desktop/School/SEM3/CEA/Julie Survey/Pre-Expand Interprofessional Facilitation Scale (IPFS) 24-25_March 4, 2025_14.47.xlsx', sheet = 1)
pre_survey <- pre_survey[-1, ] #ignoring the first row(just a heading column)
pre_survey## # A tibble: 43 × 35
## StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 45548.3879… 45548.… IP Ad… 134.68.2… 100 341 True
## 2 45549.4761… 45549.… IP Ad… 68.58.14… 100 8927 True
## 3 45550.6757… 45550.… IP Ad… 66.244.8… 100 52 True
## 4 45551.8686… 45551.… IP Ad… 68.38.14… 100 114 True
## 5 45551.9647… 45551.… IP Ad… 96.63.22… 100 122 True
## 6 45552.4622… 45552.… IP Ad… 149.163.… 100 46 True
## 7 45553.4262… 45553.… IP Ad… 68.50.72… 100 82 True
## 8 45553.8411… 45553.… IP Ad… 66.244.6… 100 40 True
## 9 45554.5264… 45554.… IP Ad… 38.124.1… 100 72 True
## 10 45558.5225… 45558.… IP Ad… 134.68.1… 100 155 True
## # ℹ 33 more rows
## # ℹ abbreviated name: ¹`Duration (in seconds)`
## # ℹ 28 more variables: RecordedDate <chr>, ResponseId <chr>,
## # LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
## # UserLanguage <chr>, IPFS_1 <chr>, IPFS_2 <chr>, IPFS_3 <chr>, IPFS_4 <chr>,
## # IPFS_5 <chr>, IPFS_6 <chr>, IPFS_7 <chr>, IPFS_8 <chr>, IPFS_9 <chr>,
## # IPFS_10 <chr>, IPFS_11 <chr>, IPFS_12 <chr>, IPFS_13 <chr>, …In the pre-survey, we received 43 responses. Since the only demographic-related question asked was about the students’ academic programs, let’s take a closer look at their distribution across different programs.
pre_survey_programs <- pre_survey %>%
group_by(Program) %>%
summarise(Count = n())
pre_survey_programs## # A tibble: 4 × 2
## Program Count
## <chr> <int>
## 1 Medicine 1
## 2 Nursing (Graduate) 7
## 3 Nursing (Traditional BSN) 34
## 4 Occupational Therapy 1Let’s visualize our results for better interpretation
pre_survey_programs_plot <- ggplot(pre_survey_programs, aes(x = Program, y = Count, fill = Program))+
geom_col()+
labs(title = "Distribution of Pre-Survey Responses Across Programs",
x = 'Programs',
y = 'Number of Students')+
theme_light()+
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
pre_survey_programs_plot
Next, let’s examine the responses provided by students in the pre-survey for each of the IPE competencies, which are denoted by ‘IPFS’.
competency_data <- pre_survey %>%
select(starts_with("IPFS")) %>% #selecting only the columns starting with "IPFS" from the dataset
#changing wide fromat to long format
pivot_longer(
cols = everything(),
names_to = "Competency",
values_to = "Response"
)
print(competency_data)## # A tibble: 774 × 2
## Competency Response
## <chr> <chr>
## 1 IPFS_1 Excellent
## 2 IPFS_2 Excellent
## 3 IPFS_3 Good
## 4 IPFS_4 Good
## 5 IPFS_5 Excellent
## 6 IPFS_6 Fair
## 7 IPFS_7 Good
## 8 IPFS_8 Excellent
## 9 IPFS_9 Excellent
## 10 IPFS_10 Good
## # ℹ 764 more rowsNow, let’s group and count the responses for each competency.
#sorting the Competency column alphabetically and making sure that the competencies are ordered correctly(or else 'IPFS_10' occurs before 'IPFS_2'.)
competency_data <- competency_data %>%
mutate(Competency = factor(Competency, levels = mixedsort(unique(Competency))))#grouping and counting the responses for each competency
summary <- competency_data %>%
group_by(Competency, Response) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Percentage = (Count / sum(Count)) * 100)
summary## # A tibble: 61 × 4
## Competency Response Count Percentage
## <fct> <chr> <int> <dbl>
## 1 IPFS_1 Excellent 16 2.07
## 2 IPFS_1 Fair 2 0.258
## 3 IPFS_1 Good 25 3.23
## 4 IPFS_2 Excellent 18 2.33
## 5 IPFS_2 Fair 2 0.258
## 6 IPFS_2 Good 23 2.97
## 7 IPFS_3 Excellent 14 1.81
## 8 IPFS_3 Fair 8 1.03
## 9 IPFS_3 Good 21 2.71
## 10 IPFS_4 Excellent 9 1.16
## # ℹ 51 more rowsNow, let’s visualize the responses for each competency provided by students in the pre-survey.
pre_competency_data_plot <- ggplot(summary, aes(x = Competency, y = Count, fill = Response)) +
geom_bar(stat = "identity", position = "dodge") +
scale_x_discrete(limits = sort(unique(summary$Competency))) +
labs(title = "Student Responses for Each Competency in Pre-Survey",
x = "Competency",
y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#Using plotly for a more interactive plot(to include hover data)
pre_competency_data_plot <- ggplotly(pre_competency_data_plot, tooltip = c("Competency", "Response", "Count", "Percentage"))
pre_competency_data_plotFrom the graph, we can see that the need for the program arises from the fact that most students rated their competencies as “Good,” with some even selecting “Poor.” This indicates a clear opportunity for improvement, making the program crucial in enhancing students’ skills and proficiency in IPE.
Next, let’s compare the pre-survey and post-survey data.
We will only include the responses from students who completed both the pre and post surveys to eliminate any potential bias in our analysis. This will allow us to accurately evaluate whether the program had an impact on students’ performance.
Loading the Datasets
(Only considering the students who have responded to both pre and post surveys to exclude any biased results in our analysis)
Pre-Survey Data
pre <- read_excel('/Users/ramyaamudapakula/Desktop/School/SEM3/CEA/Julie Survey/Pre-Expand Interprofessional Facilitation Scale (IPFS) 24-25_March 4, 2025_14.47.xlsx', sheet = 2)
#ignoring the first row(just a heading column)
pre <- pre[-1, ]
pre## # A tibble: 16 × 35
## StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 45551.9647… 45551.… IP Ad… 96.63.22… 100 122 True
## 2 45552.4622… 45552.… IP Ad… 149.163.… 100 46 True
## 3 45554.5264… 45554.… IP Ad… 38.124.1… 100 72 True
## 4 45563.9086… 45563.… IP Ad… 73.146.1… 100 92 True
## 5 45572.8801… 45572.… IP Ad… 96.64.13… 100 175 True
## 6 45581.4943… 45581.… IP Ad… 104.28.1… 100 108 True
## 7 45587.6090… 45587.… IP Ad… 68.38.14… 100 143 True
## 8 45589.5094… 45589.… IP Ad… 38.124.1… 100 7968 True
## 9 45593.7648… 45593.… IP Ad… 134.6.13… 100 146 True
## 10 45594.5773… 45594.… IP Ad… 153.33.2… 100 433 True
## 11 45599.7229… 45599.… IP Ad… 174.226.… 100 145 True
## 12 45602.5363… 45602.… IP Ad… 96.63.22… 100 751 True
## 13 45677.3681… 45677.… IP Ad… 162.142.… 100 2747 True
## 14 45677.9225… 45677.… IP Ad… 68.51.11… 100 417 True
## 15 45682.4986… 45682.… IP Ad… 131.187.… 100 142 True
## 16 45688.4014… 45688.… IP Ad… 68.44.17… 100 125 True
## # ℹ abbreviated name: ¹`Duration (in seconds)`
## # ℹ 28 more variables: RecordedDate <chr>, ResponseId <chr>,
## # LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
## # UserLanguage <chr>, IPFS_1 <chr>, IPFS_2 <chr>, IPFS_3 <chr>, IPFS_4 <chr>,
## # IPFS_5 <chr>, IPFS_6 <chr>, IPFS_7 <chr>, IPFS_8 <chr>, IPFS_9 <chr>,
## # IPFS_10 <chr>, IPFS_11 <chr>, IPFS_12 <chr>, IPFS_13 <chr>, IPFS_14 <chr>,
## # IPFS_15 <chr>, IPFS_16 <chr>, IPFS_17 <chr>, IPFS_18 <chr>, First <chr>, …Post-Survey Data
post <- read_excel('/Users/ramyaamudapakula/Desktop/School/SEM3/CEA/Julie Survey/Post-Expand Interprofessional Facilitation Scale (IPFS) & CQI 24-25_March 4, 2025_14.44.xlsx', sheet = 2)
post <- post[-1, ]
post## # A tibble: 16 × 46
## StartDate EndDate Status IPAddress Progress Duration (in seconds…¹ Finished
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 45554.5664… 45554.… IP Ad… 38.124.1… 100 53 True
## 2 45563.9407… 45563.… IP Ad… 73.146.1… 100 50 True
## 3 45568.5139… 45568.… IP Ad… 74.136.1… 100 81 True
## 4 45580.6218… 45580.… IP Ad… 149.163.… 100 103 True
## 5 45587.5550… 45587.… IP Ad… 174.218.… 100 113 True
## 6 45587.8244… 45587.… IP Ad… 96.64.13… 100 133 True
## 7 45588.5359… 45588.… IP Ad… 68.57.21… 100 88 True
## 8 45589.6170… 45589.… IP Ad… 38.124.1… 100 47 True
## 9 45594.4703… 45594.… IP Ad… 134.6.13… 100 133 True
## 10 45594.7355… 45594.… IP Ad… 129.79.1… 100 131 True
## 11 45602.5754… 45602.… IP Ad… 96.63.22… 100 116 True
## 12 45645.96 45645.… IP Ad… 47.227.1… 100 279 True
## 13 45677.6521… 45677.… IP Ad… 162.142.… 100 800 True
## 14 45681.8213… 45681.… IP Ad… 68.51.11… 100 174 True
## 15 45683.7993… 45683.… IP Ad… 174.219.… 100 147 True
## 16 45690.9058… 45690.… IP Ad… 68.44.17… 100 168 True
## # ℹ abbreviated name: ¹`Duration (in seconds)`
## # ℹ 39 more variables: RecordedDate <chr>, ResponseId <chr>,
## # LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
## # UserLanguage <chr>, Q8_1 <chr>, Q8_2 <chr>, Q8_3 <chr>, Q8_4 <chr>,
## # Q8_5 <chr>, Q8_6 <chr>, Q8_7 <chr>, Q8_8 <chr>, Q8_9 <chr>, Q9 <chr>,
## # Q10 <chr>, First <chr>, Last <chr>, Email <chr>, Program <chr>,
## # IPFS_1 <chr>, IPFS_2 <chr>, IPFS_3 <chr>, IPFS_4 <chr>, IPFS_5 <chr>, …Next, let’s compare the responses for each competency between the pre and post surveys using the new data (i.e., only considering the students who took both the pre and post surveys and excluding those who completed only one, to ensure a fair comparison).
Responses for pre:
pre_competency_data <- pre %>%
select(starts_with("IPFS")) %>% #selecting only the columns starting with "IPFS"
pivot_longer(
cols = everything(), #applying pivot_longer to all selected columns
names_to = "Competency",
values_to = "Response"
)
print(pre_competency_data)## # A tibble: 288 × 2
## Competency Response
## <chr> <chr>
## 1 IPFS_1 Excellent
## 2 IPFS_2 Excellent
## 3 IPFS_3 Excellent
## 4 IPFS_4 Good
## 5 IPFS_5 Excellent
## 6 IPFS_6 Excellent
## 7 IPFS_7 Excellent
## 8 IPFS_8 Good
## 9 IPFS_9 Excellent
## 10 IPFS_10 Excellent
## # ℹ 278 more rows#sorting the Competency column alphabetically and making sure that the competencies are ordered correctly(or else 'IPFS_10' occurs before 'IPFS_2'.)
pre_competency_data <- pre_competency_data %>%
mutate(Competency = factor(Competency, levels = mixedsort(unique(Competency))))Grouping and counting the responses for each competency
pre_summary <- pre_competency_data %>%
group_by(Competency, Response) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Percentage = (Count / sum(Count)) * 100)
pre_summary## # A tibble: 57 × 4
## Competency Response Count Percentage
## <fct> <chr> <int> <dbl>
## 1 IPFS_1 Excellent 5 1.74
## 2 IPFS_1 Good 11 3.82
## 3 IPFS_2 Excellent 6 2.08
## 4 IPFS_2 Good 10 3.47
## 5 IPFS_3 Excellent 6 2.08
## 6 IPFS_3 Fair 4 1.39
## 7 IPFS_3 Good 6 2.08
## 8 IPFS_4 Excellent 2 0.694
## 9 IPFS_4 Fair 5 1.74
## 10 IPFS_4 Good 9 3.12
## # ℹ 47 more rowsNow, let’s visualize the responses for each competency provided by students in the pre-survey.
new_pre_competency_data_plot <- ggplot(pre_summary, aes(x = Competency, y = Count, fill = Response)) +
geom_bar(stat = "identity", position = "dodge") +
scale_x_discrete(limits = sort(unique(pre_summary$Competency))) +
labs(title = "Student Competency Levels",
x = "Competency",
y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#Using plotly for a more interactive plot(to include hover data)
new_pre_competency_data_plot1 <- ggplotly(new_pre_competency_data_plot, tooltip = c("Competency", "Response", "Count", "Percentage"))
new_pre_competency_data_plot1Responses for post:
post_competency_data <- post %>%
select(starts_with("IPFS")) %>%
pivot_longer(
cols = everything(),
names_to = "Competency",
values_to = "Response"
)
print(post_competency_data)## # A tibble: 288 × 2
## Competency Response
## <chr> <chr>
## 1 IPFS_1 Excellent
## 2 IPFS_2 Excellent
## 3 IPFS_3 Excellent
## 4 IPFS_4 Excellent
## 5 IPFS_5 Excellent
## 6 IPFS_6 Excellent
## 7 IPFS_7 Excellent
## 8 IPFS_8 Excellent
## 9 IPFS_9 Excellent
## 10 IPFS_10 Excellent
## # ℹ 278 more rowspost_competency_data <- post_competency_data %>%
mutate(Competency = factor(Competency, levels = mixedsort(unique(Competency))))post_summary <- post_competency_data %>%
group_by(Competency, Response) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Percentage = (Count / sum(Count)) * 100)
post_summary## # A tibble: 46 × 4
## Competency Response Count Percentage
## <fct> <chr> <int> <dbl>
## 1 IPFS_1 Excellent 13 4.51
## 2 IPFS_1 Good 3 1.04
## 3 IPFS_2 Excellent 12 4.17
## 4 IPFS_2 Good 4 1.39
## 5 IPFS_3 Excellent 9 3.12
## 6 IPFS_3 Good 6 2.08
## 7 IPFS_3 Poor 1 0.347
## 8 IPFS_4 Excellent 9 3.12
## 9 IPFS_4 Good 6 2.08
## 10 IPFS_4 Poor 1 0.347
## # ℹ 36 more rowspost_competency_data_plot <- ggplot(post_summary, aes(x = Competency, y = Count, fill = Response)) +
geom_bar(stat = "identity", position = "dodge") +
scale_x_discrete(limits = sort(unique(post_summary$Competency))) + # Enforce order
labs(title = "Student Competency Levels",
x = "Competency",
y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#Using plotly for a more interactive plot(to include hover data)
post_competency_data_plot1 <- ggplotly(post_competency_data_plot, tooltip = c("Competency", "Response", "Count", "Percentage"))
post_competency_data_plot1From both the plots, we can observe that the responses have improved in the post-survey, suggesting that the program was helpful and effective. Specifically, we see a higher number of “Excellent” responses compared to the pre-survey.
Now, let’s test our hypothesis!
We will perform statistical tests to determine if the program had a significant effect on the students’ competency levels.
#converting the responses to numeric values
response_levels <- c("Poor" = 1, "Fair" = 2, "Good" = 3, "Excellent" = 4)
#again chnaging data to long format
final_data <- bind_rows(
pre %>% mutate(Time = "Pre"),
post %>% mutate(Time = "Post")
) %>%
pivot_longer(cols = starts_with("IPFS_"), names_to = "Competency", values_to = "Response") %>%
mutate(Response = as.numeric(recode(Response, !!!response_levels))) #converting to numeric datatypeWe will now compute the medians of the responses from both the pre and post surveys to compare the overall change in competency levels. This will help us understand if there has been a shift in the students’ competency levels after participating in the program.
#medians for Pre and Post
median_scores <- final_data %>%
group_by(Competency, Time) %>%
summarise(Median_Response = median(Response), .groups = "drop") %>%
pivot_wider(names_from = Time, values_from = Median_Response, names_prefix = "Median_")
#another column to indicate whether performance improved or declined
median_scores <- median_scores %>%
mutate(Change = case_when(
Median_Post > Median_Pre ~ "Improved",
Median_Post < Median_Pre ~ "Declined",
TRUE ~ "No Change"
))
print(median_scores)## # A tibble: 18 × 4
## Competency Median_Post Median_Pre Change
## <chr> <dbl> <dbl> <chr>
## 1 IPFS_1 4 3 Improved
## 2 IPFS_10 4 3 Improved
## 3 IPFS_11 4 3 Improved
## 4 IPFS_12 4 3 Improved
## 5 IPFS_13 4 3 Improved
## 6 IPFS_14 4 4 No Change
## 7 IPFS_15 4 3 Improved
## 8 IPFS_16 4 3 Improved
## 9 IPFS_17 4 3 Improved
## 10 IPFS_18 3 2.5 Improved
## 11 IPFS_2 4 3 Improved
## 12 IPFS_3 4 3 Improved
## 13 IPFS_4 4 3 Improved
## 14 IPFS_5 4 3 Improved
## 15 IPFS_6 3.5 3 Improved
## 16 IPFS_7 4 3 Improved
## 17 IPFS_8 3.5 3 Improved
## 18 IPFS_9 4 4 No ChangePlotting the ‘Change’ - Difference between the median scores of Pre and Post
median_scores_plot <- ggplot(median_scores, aes(x = Change, fill = Change)) +
geom_bar() +
labs(title = "Overall Change in Competency Levels",
x = "Change",
y = "Number of Competencies",
fill = "Change Type") +
scale_fill_manual(values = c("Improved" = "lightpink2","No Change" = "palegreen4")) +
theme_minimal()
median_scores_plot
We can observe that there was no change in only 2 of the competencies, while the rest show an improvement. This suggests that the program helped in enhancing students’ competency levels.
However, to confirm this, we will perform statistical tests to determine if there was a significant difference between the pre and post survey responses. We will also analyze whether the change is positive (indicating improvement) or negative (indicating a decline).
Using Wilcoxon signed-rank test since we are comparing ordinal paired data
#performing wilcoxon test for each competency
wilcoxon_results <- final_data %>%
group_by(Competency) %>%
summarise(
p_value = wilcox.test(Response[Time == "Pre"], Response[Time == "Post"], paired = TRUE)$p.value
)## Warning: There were 36 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `p_value = `$`(...)`.
## ℹ In group 1: `Competency = "IPFS_1"`.
## Caused by warning in `wilcox.test.default()`:
## ! cannot compute exact p-value with ties
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 35 remaining warnings.print(wilcoxon_results)## # A tibble: 18 × 2
## Competency p_value
## <chr> <dbl>
## 1 IPFS_1 0.00596
## 2 IPFS_10 0.0401
## 3 IPFS_11 0.0650
## 4 IPFS_12 0.0246
## 5 IPFS_13 0.0232
## 6 IPFS_14 0.203
## 7 IPFS_15 0.0232
## 8 IPFS_16 0.155
## 9 IPFS_17 0.341
## 10 IPFS_18 0.0439
## 11 IPFS_2 0.0411
## 12 IPFS_3 0.245
## 13 IPFS_4 0.0290
## 14 IPFS_5 0.0753
## 15 IPFS_6 0.155
## 16 IPFS_7 0.0150
## 17 IPFS_8 0.0616
## 18 IPFS_9 0.374Visualizing the Results for Better Interpretation
To better understand the impact of the program, we will visualize the results. If the p-value is less than 0.05, it indicates a significant difference between pre- and post-survey responses. If the p-value is greater than 0.05, it means there is no significant difference.
wilcoxon_results <- wilcoxon_results %>%
mutate(Competency = factor(Competency, levels = mixedsort(unique(Competency)))) #maintaing order
wilcoxon_results_plot <- ggplot(wilcoxon_results, aes(x = Competency, y = p_value, fill = p_value < 0.05)) +
geom_col() +
geom_hline(yintercept = 0.05, linetype = "dashed", color = "red") +
labs(title = "Wilcoxon Test Results for Competency Levels",
x = "Competency",
y = "P-Value",
fill = "Significant Difference (p < 0.05)") +
scale_fill_manual(values = c("TRUE" = "palegreen4", "FALSE" = "lightpink2")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
wilcoxon_results_plot
We can see from the results and the plot that 9 out of 18 competencies showed a significant difference based on the p-values from our test.
Now, let’s determine whether this difference indicates an improvement or a decline by analyzing the direction of change in competency levels(checking the median scores against the p-values).
#medians for Pre and Post
median_scores <- final_data %>%
group_by(Competency, Time) %>%
summarise(Median_Response = median(Response), .groups = "drop") %>%
pivot_wider(names_from = Time, values_from = Median_Response, names_prefix = "Median_")
#another column to indicate whether performance improved or declined
median_scores <- median_scores %>%
mutate(Change = case_when(
Median_Post > Median_Pre ~ "Improved",
Median_Post < Median_Pre ~ "Declined",
TRUE ~ "No Change"
))
new_wilcoxon_results <- wilcoxon_results %>%
left_join(median_scores, by = "Competency")
print(new_wilcoxon_results)## # A tibble: 18 × 5
## Competency p_value Median_Post Median_Pre Change
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 IPFS_1 0.00596 4 3 Improved
## 2 IPFS_10 0.0401 4 3 Improved
## 3 IPFS_11 0.0650 4 3 Improved
## 4 IPFS_12 0.0246 4 3 Improved
## 5 IPFS_13 0.0232 4 3 Improved
## 6 IPFS_14 0.203 4 4 No Change
## 7 IPFS_15 0.0232 4 3 Improved
## 8 IPFS_16 0.155 4 3 Improved
## 9 IPFS_17 0.341 4 3 Improved
## 10 IPFS_18 0.0439 3 2.5 Improved
## 11 IPFS_2 0.0411 4 3 Improved
## 12 IPFS_3 0.245 4 3 Improved
## 13 IPFS_4 0.0290 4 3 Improved
## 14 IPFS_5 0.0753 4 3 Improved
## 15 IPFS_6 0.155 3.5 3 Improved
## 16 IPFS_7 0.0150 4 3 Improved
## 17 IPFS_8 0.0616 3.5 3 Improved
## 18 IPFS_9 0.374 4 4 No Change