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+64.60143208503723,-0.01,0.04,0.07 +64.61138606071472,-0.37,-0.15,0.0 From e4b66b3d75c7d43570c79137a9bfc4f9eaa6f86b Mon Sep 17 00:00:00 2001 From: Mahshad <70588620+mahshaddav81@users.noreply.github.com> Date: Tue, 1 Dec 2020 21:57:40 -0500 Subject: [PATCH 09/17] Add files via upload --- Group 6 data.csv | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 Group 6 data.csv diff --git a/Group 6 data.csv b/Group 6 data.csv new file mode 100644 index 0000000..457471c --- /dev/null +++ b/Group 6 data.csv @@ -0,0 +1,9 @@ +name,time,wx,wy,wz +Christina,76.58,1.89,3.15,2.46 +Jiani,33.11,0.61,0.61,0.3 +Yixiong,64.61,1.43,1.14,0.44 +Mahshad,29.51,0.03,0.01,0.08 +Yujie,33.41,0.1,0.9,0.5 +Zhixin,35.32,0.8,3.14,1.84 +XI GU ,31.68,0.31,3.15,1.71 +Jiaao,2.08,0.34,0.33,0.27 \ No newline at end of file From e812b9f3434f330bd7826954245e56d5b3317ac0 Mon Sep 17 00:00:00 2001 From: Mahshad <70588620+mahshaddav81@users.noreply.github.com> Date: Sun, 6 Dec 2020 20:57:33 -0500 Subject: [PATCH 10/17] Add files via upload --- distance.csv | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 distance.csv diff --git a/distance.csv b/distance.csv new file mode 100644 index 0000000..9e1679b --- /dev/null +++ b/distance.csv @@ -0,0 +1,7 @@ +name,distance +Christina,7.55 +Jiani,1.73 +Yixiong,5.89 +Mahshad,11.88 +Zhixin,7.19 +XI GU ,7.74 \ No newline at end of file From 29453b35b3a5b5c5152831fbd08d59ae9a38a0f0 Mon Sep 17 00:00:00 2001 From: jy2893 <47014077+jy2893@users.noreply.github.com> Date: Sun, 6 Dec 2020 21:29:50 -0500 Subject: [PATCH 11/17] Add files via upload --- survey.csv | 55 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 survey.csv diff --git a/survey.csv b/survey.csv new file mode 100644 index 0000000..ea85e54 --- /dev/null +++ b/survey.csv @@ -0,0 +1,55 @@ +,Christina,Yixiong,Zhixin,Yujie,Mahshad,Xi,Jiani +I would recommend this activity to others.,4,5,5,5,4,4,5 +This activity was very easy for me.,3,3,2,3,2,2,2 +It was too difficult to keep my balance.,2,3,4,3,4,3,1 +I felt a lot of pressure on my back when doing this exercise.,2,2,2,2,3,3,4 +I felt a lot of pressure on my stomach when doing this exercise.,4,4,4,2,4,4,3 +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, +,,,,,,, \ No newline at end of file From 31c1033c0548d3a1afa1a5e459d1c12c776aed52 Mon Sep 17 00:00:00 2001 From: Mahshad <70588620+mahshaddav81@users.noreply.github.com> Date: Sun, 6 Dec 2020 21:50:02 -0500 Subject: [PATCH 12/17] Add files via upload --- Book4.csv | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 Book4.csv diff --git a/Book4.csv b/Book4.csv new file mode 100644 index 0000000..9c07ef2 --- /dev/null +++ b/Book4.csv @@ -0,0 +1,6 @@ +,Christina,Yixiong,Zhixin,Yujie,Mahshad,Xi,Jiani, +I would recommend this activity to others.,4,5,5,5,4,4,5, +This activity was very easy for me.,3,3,2,3,2,2,2, +It was too difficult to keep my balance.,2,3,4,3,4,3,1, +I felt a lot of pressure on my back when doing this exercise.,2,2,2,2,3,3,4, +I felt a lot of pressure on my stomach when doing this exercise.,4,4,4,2,4,4,3, \ No newline at end of file From cba792416c2016238cfad2421a05e5e271d88020 Mon Sep 17 00:00:00 2001 From: Mahshad <70588620+mahshaddav81@users.noreply.github.com> Date: Sun, 6 Dec 2020 22:35:48 -0500 Subject: [PATCH 13/17] Add files via upload --- Group 6_Dec 6.Rmd | 66 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 Group 6_Dec 6.Rmd diff --git a/Group 6_Dec 6.Rmd b/Group 6_Dec 6.Rmd new file mode 100644 index 0000000..50fdf85 --- /dev/null +++ b/Group 6_Dec 6.Rmd @@ -0,0 +1,66 @@ +--- +title: "Assignment 6_Group 6" +author: "Mahshad D" +date: "12/6/2020" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +### Task I +* You will be working in groups on a quantified student project +* Download the *Sensor Kinetics Pro* app to your [iOS](https://itunes.apple.com/us/app/sensor-kinetics-pro/id623633248?mt=8) or [Android](https://play.google.com/store/apps/details?id=com.innoventions.sensorkineticspro&hl=en) device (or similar gyroscope measuring app) +* Split into subgroups with each subgroup investigating one measures available in the app +* Each subgroup shopuld report back aafter 20min with what they have learned + +##Please see https://docs.google.com/document/d/1bkCk6XsSL_e085Si-h1YvBbpkEhOxEwpKKUIGNYZ7VA/edit + + +### Task II +* In consultation with your group select a physical activity that: a) can be measured using the app, b) has a concrrete, sontinuous, countable outcome and c) some members of the group are experts and some are novices at - If you are looking for inspiration you can find previous student projects below. +* Collect measurements for all members of the group performing the task using the app +* Create a Github repo where you can store and share your data +* Using K-means, can you identify the novices from the experts using only the app data? +* Visualize your results using ggplot2 + +```{r} +library(tidyr) +library(dplyr) +library(ggplot2) +library(readr) + +D1 <- read_csv("Group 6 data.csv") +D2 <- read_csv("distance.csv") +D3 <- merge(D1, D2) +D3 <- D3[,-1] +D3 <- scale(D3) +fit <- kmeans(D3, 2) +D4 <- data.frame(D3, fit$cluster) +ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Distance") + ylab("Time") + +``` +#Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts. + + +### Task III +* Devise five questions on a five point Likert scale that ask members of your group about their experience with your chosen task +* Collect data from all members of your group +* Run a PCA analysis on the data +* What does PC1 represent in your analysis? +* Visualize the PC1 results for each member of your group + +```{r} + +``` + +### Task IV +* If you could only choose one thing to predict the score for each member of your group (raw data variable, a cluster or a PC) what would it be and why? +* Create a visualization that supports your conclusion +* Based on your conclusion devise an intervention that would help novice members of your group imporve their performance +```{r} + + +``` + From 24fe879105b164bdb00f6e5992683d5c940b40b4 Mon Sep 17 00:00:00 2001 From: jy2893 <47014077+jy2893@users.noreply.github.com> Date: Sun, 6 Dec 2020 22:49:11 -0500 Subject: [PATCH 14/17] version 2 --- Group 6_Dec 6.Rmd | 31 +++++++++++++++++++++++++++---- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/Group 6_Dec 6.Rmd b/Group 6_Dec 6.Rmd index 50fdf85..9b24e23 100644 --- a/Group 6_Dec 6.Rmd +++ b/Group 6_Dec 6.Rmd @@ -1,6 +1,6 @@ --- title: "Assignment 6_Group 6" -author: "Mahshad D" +author: "Christina, Jiani, Yixiong, Mahshad, Yujie, Zhixin, Xi Gu, Jiaao" date: "12/6/2020" output: html_document --- @@ -31,6 +31,7 @@ library(dplyr) library(ggplot2) library(readr) + D1 <- read_csv("Group 6 data.csv") D2 <- read_csv("distance.csv") D3 <- merge(D1, D2) @@ -49,18 +50,40 @@ ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Dis * Collect data from all members of your group * Run a PCA analysis on the data * What does PC1 represent in your analysis? + +#PC1 represents the question “I would recommend this activity to others”. + * Visualize the PC1 results for each member of your group ```{r} - +K1 <- read.csv("survey.csv") +K1 <- K1[1:5,] +ggpairs(K1, 2:8, progress = FALSE) +ggcorr(K1[,-1], method = c("everything", "pearson")) +K2 <- K1[,-1] +pca <- prcomp(K2, scale. = TRUE) +loadings <- abs(pca$rotation) +summary(pca) ``` ### Task IV * If you could only choose one thing to predict the score for each member of your group (raw data variable, a cluster or a PC) what would it be and why? * Create a visualization that supports your conclusion -* Based on your conclusion devise an intervention that would help novice members of your group imporve their performance -```{r} +#We choose raw distance and time variables to predict the score for each member of our group. We think that both time and distance as raw data variables can be good predictors of novice and expert groups in our study. Based on the data and the nature of the exercise we implemented, we think that if you are an expert, you can hold up the position for a longer time and vice versa. We defined the distance based on the following: + +#Distance Formula: The distance between two points is the length of the path connecting them. The shortest path distance is a straight line. In a 3 dimensional plane, the distance between points (X1, Y1, Z1) and (X2, Y2, Z2) please find the formula in the google doc. +#What it means in terms of correlation analysis that we did is that the longer a person holds up the position, they have a lower distance. In other words, there is negative correlation between time and distance. The following correlation table shows this negative correlation, supporting our argument. + +```{r} +D5 <- D3[, c(1,5)] +cor(D5) ``` + +* Based on your conclusion devise an intervention that would help novice members of your group imporve their performance + +#We will recommend novice members of our group to move less while performing the Hollow Body Hold. + + From e19d25c9b9c3f5a0690e74d7bc284a871e514f97 Mon Sep 17 00:00:00 2001 From: jy2893 <47014077+jy2893@users.noreply.github.com> Date: Sun, 6 Dec 2020 22:55:43 -0500 Subject: [PATCH 15/17] aaaa --- Group 6_Dec 6.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Group 6_Dec 6.Rmd b/Group 6_Dec 6.Rmd index 9b24e23..6334d10 100644 --- a/Group 6_Dec 6.Rmd +++ b/Group 6_Dec 6.Rmd @@ -59,7 +59,7 @@ ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Dis K1 <- read.csv("survey.csv") K1 <- K1[1:5,] ggpairs(K1, 2:8, progress = FALSE) -ggcorr(K1[,-1], method = c("everything", "pearson")) +ggcorr(K1[,-1], method = c("everything", "spearman")) K2 <- K1[,-1] pca <- prcomp(K2, scale. = TRUE) loadings <- abs(pca$rotation) From db6c1fa0f5c4441171a4cb6afe58833fbfa4d061 Mon Sep 17 00:00:00 2001 From: jy2893 <47014077+jy2893@users.noreply.github.com> Date: Sun, 6 Dec 2020 23:17:25 -0500 Subject: [PATCH 16/17] Groupwork --- Group 6_Dec 6.Rmd | 26 ++- Group-6_Dec-6.html | 545 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 562 insertions(+), 9 deletions(-) create mode 100644 Group-6_Dec-6.html diff --git a/Group 6_Dec 6.Rmd b/Group 6_Dec 6.Rmd index 6334d10..9f2ff87 100644 --- a/Group 6_Dec 6.Rmd +++ b/Group 6_Dec 6.Rmd @@ -1,6 +1,6 @@ --- title: "Assignment 6_Group 6" -author: "Christina, Jiani, Yixiong, Mahshad, Yujie, Zhixin, Xi Gu, Jiaao" +author: "Christina Huang, Jiani Yang, Yixiong Xu, Mahshad Davoodifard, Yujie Yang, Zhixin Zheng, Xi Gu, Jiaao" date: "12/6/2020" output: html_document --- @@ -23,6 +23,9 @@ knitr::opts_chunk$set(echo = TRUE) * Collect measurements for all members of the group performing the task using the app * Create a Github repo where you can store and share your data * Using K-means, can you identify the novices from the experts using only the app data? + +### Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts. + * Visualize your results using ggplot2 ```{r} @@ -30,6 +33,7 @@ library(tidyr) library(dplyr) library(ggplot2) library(readr) +library(GGally) D1 <- read_csv("Group 6 data.csv") @@ -42,7 +46,7 @@ D4 <- data.frame(D3, fit$cluster) ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Distance") + ylab("Time") ``` -#Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts. +### Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts. ### Task III @@ -51,7 +55,7 @@ ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Dis * Run a PCA analysis on the data * What does PC1 represent in your analysis? -#PC1 represents the question “I would recommend this activity to others”. +### PC1 represents the question “I would recommend this activity to others”. * Visualize the PC1 results for each member of your group @@ -63,18 +67,22 @@ ggcorr(K1[,-1], method = c("everything", "spearman")) K2 <- K1[,-1] pca <- prcomp(K2, scale. = TRUE) loadings <- abs(pca$rotation) -summary(pca) +loadings_2 <- data.frame(loadings) +#Visualize the PC1 results for each member of your group +plot(loadings_2$PC1, type = "points") + ``` ### Task IV * If you could only choose one thing to predict the score for each member of your group (raw data variable, a cluster or a PC) what would it be and why? -* Create a visualization that supports your conclusion -#We choose raw distance and time variables to predict the score for each member of our group. We think that both time and distance as raw data variables can be good predictors of novice and expert groups in our study. Based on the data and the nature of the exercise we implemented, we think that if you are an expert, you can hold up the position for a longer time and vice versa. We defined the distance based on the following: +### We choose raw distance and time variables to predict the score for each member of our group. We think that both time and distance as raw data variables can be good predictors of novice and expert groups in our study. Based on the data and the nature of the exercise we implemented, we think that if you are an expert, you can hold up the position for a longer time and vice versa. We defined the distance based on the following: + +### Distance Formula: The distance between two points is the length of the path connecting them. The shortest path distance is a straight line. In a 3 dimensional plane, the distance between points (X1, Y1, Z1) and (X2, Y2, Z2) please find the formula in the google doc. -#Distance Formula: The distance between two points is the length of the path connecting them. The shortest path distance is a straight line. In a 3 dimensional plane, the distance between points (X1, Y1, Z1) and (X2, Y2, Z2) please find the formula in the google doc. +### What it means in terms of correlation analysis that we did is that the longer a person holds up the position, they have a lower distance. In other words, there is negative correlation between time and distance. The following correlation table shows this negative correlation, supporting our argument. -#What it means in terms of correlation analysis that we did is that the longer a person holds up the position, they have a lower distance. In other words, there is negative correlation between time and distance. The following correlation table shows this negative correlation, supporting our argument. +* Create a visualization that supports your conclusion ```{r} D5 <- D3[, c(1,5)] @@ -84,6 +92,6 @@ cor(D5) * Based on your conclusion devise an intervention that would help novice members of your group imporve their performance -#We will recommend novice members of our group to move less while performing the Hollow Body Hold. +### We will recommend novice members of our group to move less while performing the Hollow Body Hold. diff --git a/Group-6_Dec-6.html b/Group-6_Dec-6.html new file mode 100644 index 0000000..d02d5ce --- /dev/null +++ b/Group-6_Dec-6.html @@ -0,0 +1,545 @@ + + + + + + + + + + + + + + +Assignment 6_Group 6 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

 + + + + + + + + + + + + + + + From 82ce4f8883bd10caae8757b0c73a4d3d7040741f Mon Sep 17 00:00:00 2001 From: Mahshad <70588620+mahshaddav81@users.noreply.github.com> Date: Wed, 9 Dec 2020 10:22:24 -0500 Subject: [PATCH 17/17] Add files via upload --- Group 6_Dec 6.Rmd | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/Group 6_Dec 6.Rmd b/Group 6_Dec 6.Rmd index 9f2ff87..0598cda 100644 --- a/Group 6_Dec 6.Rmd +++ b/Group 6_Dec 6.Rmd @@ -55,7 +55,12 @@ ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Dis * Run a PCA analysis on the data * What does PC1 represent in your analysis? +<<<<<<< HEAD +### PC1 represents the ratings for first and the second questions. +###For PC1, Yixiong and Yujie both rated high for question 1 and question 2. +======= ### PC1 represents the question “I would recommend this activity to others”. +>>>>>>> db6c1fa0f5c4441171a4cb6afe58833fbfa4d061 * Visualize the PC1 results for each member of your group @@ -65,11 +70,23 @@ K1 <- K1[1:5,] ggpairs(K1, 2:8, progress = FALSE) ggcorr(K1[,-1], method = c("everything", "spearman")) K2 <- K1[,-1] +<<<<<<< HEAD +K3 <-data.frame(t(K2)) +pca <- prcomp(K3, scale. = TRUE) +pca +pca$x +X1 <- data.frame(pca$x) +#loadings <- abs(pca$rotation) +#loadings_2 <- data.frame(loadings) +#Visualize the PC1 results for each member of your group +plot(X1$PC1, type = "points") +======= pca <- prcomp(K2, scale. = TRUE) loadings <- abs(pca$rotation) loadings_2 <- data.frame(loadings) #Visualize the PC1 results for each member of your group plot(loadings_2$PC1, type = "points") +>>>>>>> db6c1fa0f5c4441171a4cb6afe58833fbfa4d061 ```
+ + + + + + + +
+

Task I

+ +

##Please see https://docs.google.com/document/d/1bkCk6XsSL_e085Si-h1YvBbpkEhOxEwpKKUIGNYZ7VA/edit

+
+
+

Task II

+
    +
  • In consultation with your group select a physical activity that: a) can be measured using the app, b) has a concrrete, sontinuous, countable outcome and c) some members of the group are experts and some are novices at - If you are looking for inspiration you can find previous student projects below.
    • +
  • Collect measurements for all members of the group performing the task using the app
    • +
  • Create a Github repo where you can store and share your data
    • +
  • Using K-means, can you identify the novices from the experts using only the app data?
    • +
+
+
+

Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts.

+
    +
  • Visualize your results using ggplot2
    • +
+
library(tidyr)
+
## Warning: package 'tidyr' was built under R version 3.6.2
+
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, union
+
library(ggplot2)
+
## Warning: package 'ggplot2' was built under R version 3.6.2
+
library(readr)
+library(GGally)
+
## Warning: package 'GGally' was built under R version 3.6.2
+
## Registered S3 method overwritten by 'GGally':
+##   method from   
+##   +.gg   ggplot2
+
D1 <- read_csv("Group 6  data.csv")
+
## Parsed with column specification:
+## cols(
+##   name = col_character(),
+##   time = col_double(),
+##   wx = col_double(),
+##   wy = col_double(),
+##   wz = col_double()
+## )
+
D2 <- read_csv("distance.csv")
+
## Parsed with column specification:
+## cols(
+##   name = col_character(),
+##   distance = col_double()
+## )
+
D3 <- merge(D1, D2)
+D3 <- D3[,-1]
+D3 <- scale(D3)
+fit <- kmeans(D3, 2)
+D4 <- data.frame(D3, fit$cluster)
+ggplot(D4, aes(distance, time, colour = fit.cluster)) + geom_point() + xlab("Distance") + ylab("Time")
+

### Yes, the experts are the data points with the lowest distance (highest stability) and longest time; all in all, the four data points in the middle of the graph are the experts.

+
+
+

Task III

+
    +
  • Devise five questions on a five point Likert scale that ask members of your group about their experience with your chosen task
    • +
  • Collect data from all members of your group
    • +
  • Run a PCA analysis on the data
    • +
  • What does PC1 represent in your analysis?
    • +
+
+
+

PC1 represents the question “I would recommend this activity to others”.

+
    +
  • Visualize the PC1 results for each member of your group
    • +
+
K1 <- read.csv("survey.csv")
+K1 <- K1[1:5,]
+ggpairs(K1, 2:8, progress = FALSE)
+

+
ggcorr(K1[,-1], method = c("everything", "spearman"))
+

+
K2 <- K1[,-1]
+pca <- prcomp(K2, scale. = TRUE)
+loadings <- abs(pca$rotation)
+loadings_2 <- data.frame(loadings)
+#Visualize the PC1 results for each member of your group
+plot(loadings_2$PC1, type = "points")
+
## Warning in plot.xy(xy, type, ...): plot type 'points' will be truncated to
+## first character
+

+
+
+

Task IV

+
    +
  • If you could only choose one thing to predict the score for each member of your group (raw data variable, a cluster or a PC) what would it be and why?
    • +
+
+
+

We choose raw distance and time variables to predict the score for each member of our group. We think that both time and distance as raw data variables can be good predictors of novice and expert groups in our study. Based on the data and the nature of the exercise we implemented, we think that if you are an expert, you can hold up the position for a longer time and vice versa. We defined the distance based on the following:

+
+
+

Distance Formula: The distance between two points is the length of the path connecting them. The shortest path distance is a straight line. In a 3 dimensional plane, the distance between points (X1, Y1, Z1) and (X2, Y2, Z2) please find the formula in the google doc.

+
+
+

What it means in terms of correlation analysis that we did is that the longer a person holds up the position, they have a lower distance. In other words, there is negative correlation between time and distance. The following correlation table shows this negative correlation, supporting our argument.

+
    +
  • Create a visualization that supports your conclusion
    • +
+
D5 <- D3[, c(1,5)]
+cor(D5)
+
##                 time    distance
+## time      1.00000000 -0.08780678
+## distance -0.08780678  1.00000000
+
    +
  • Based on your conclusion devise an intervention that would help novice members of your group imporve their performance
    • +
+
+
+

We will recommend novice members of our group to move less while performing the Hollow Body Hold.

+
+ + + + +

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