How do I present this data

General
1

I have some example data below. Two different brands with 3 different types of clothing. forget about what's in the value column it just compares how each brand performs with different clothing but how do I check

  1. What simple Statistical test to run on them. I thought of checking whether the data was normal distribution or not using a histogram and then the shapiro test but do I do that for each brand and clothing type

  2. How can easily show a comparison between the sets visually ?

Sorry for the simple questions

https://ibb.co/gtzNk8B

2

It really depends on why you have the data and who you want to see the results. Can you tell us something adout this?

BTW, a screenshot of the data is not a lot of use. A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.

3

Sorry about that. My daughter was asked the question at Uni...and doesnt know where to start. I know a bit of R but I'm certainly not a statistician !

She's given me the actual data which is to compare how two different dyes affect 3 different sets of clothing

{Redacted 3 verbatim Homework Q's}

structure(list(Dyes = c("Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1",
"Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Jeans Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1",
"Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "Shirt Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1", "T-Shirts Dye1",
"T-Shirts Dye1", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2", "Jeans Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2", "Shirt Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2", "T-Shirts Dye2",
"T-Shirts Dye2", "T-Shirts Dye2"), Value = c(193.3, 204.3, 129.3,
109.7, 62.9, 108.1, 153.1, 261.2, 126.8, 141.5, 2.1, 129.5, 131.8,
66.5, 164.5, 120.7, 82.2, 55.3, 63.1, 123.8, 89.7, 53.4, 25,
108.7, 99.8, 128.9, 156, 34.9, 119.7, 175.8, 0.3, 116.3, 113.6,
175.2, 60.4, 88.5, 115.7, 214.1, 114.1, 192.2, 94.6, 177.1, 152,
112.9, 160, 100.1, 46.9, 77.9, 109.6, 93, 80.5, 115.9, 96.1,
70.8, 24.3, 77.1, 189.6, 71.4, 101.6, 71.3, 113.3, 101.6, 103.3,
150.3, 171.3, 132.6, 104, 120.4, 142.4, 122.8, 128.6, 121, 136.9,
137.1, 196.8, 166.2, 109.8, 116.6, 200.2, 107, 115.2, 97.1, 152.2,
159.7, 134.2, 128.3, 110.4, 125.2, 105.3, 103.9, 98.6, 106.1,
153.7, 100.9, 100.1, 78.3, 131, 95.7, 38.7, 101.9, 56.4, 181.3,
39.7, 101.4, 42.1, 54.2, 76.8, 129.4, 122.2, 106.8, 159.8, 116.8,
53.3, 138, 33, 104.9, 177.6, 138.2, 136.6, 130.7, 172.6, 178.5,
42.3, 197.2, 259.1, 180.3, 172, 162.9, 161.9, 206.3, 213.4, 134.1,
184.7, 166.6, 169.5, 134.7, 125.9, 28.4, 175.7, 131.8, 200.3,
114.9, 253.7, 93.4, 118.6, 18.1, 90.6, 141.8, 192.2, 156.9, 162.4,
196.9, 228.4, 166.7, 186.5, 176.5, 172.4, 170.3, 147.6, 153.7,
166.8, 180.5, 204.5, 214.2, 135.5, 177.1, 168.2, 186.3, 174.5,
141.6, 156.6, 154.4, 232.4, 225, 209.1, 266.4, 157.5, 162.3,
122.1, 176.6, 146.7, 205.5, 141.6, 145, 155.8, 142.2, 159.2,
187.7, 212.3, 158.2, 170.3, 194, 240.7, 187.3, 200.8, 206.7,
974.3, 1362.8, 1258.6, 2947.3, 5003.3, 2020.8, 2145.1, 2015.6,
2361.4, 6122, 1442, 2296.4, 2399.4, 1971.3, 3528.9, 346, 98.2,
1969.8, 1673.6, 1326.7, 1323.8, 2253.9, 1752.4, 1381.5, 1493.6,
892.9, 456.1, 134.6, 451.2, 98.4, 783.5, 6566.3, 1200.4, 1250.2,
1541.6, 4934.5, 2343.7, 1866.5, 2178.1, 1662.2, 3981.2, 654,
131.2, 893.4, 85.6, 1446.7, 3966.8, 3345.1, 7497.9, 6369.4, 381.6,
386.5, 358.1, 495.7, 478.9, 352.7, 592.4, 495.8, 325.8, 455.9,
314.2, 333, 464.2, 491.4, 351.7, 576, 369.7, 333.6, 314.5, 361.8,
120.3, 321.8, 285.8, 350.7, 285.7, 510.7, 529.8, 596.6, 315.2,
358.6, 348.5, 376.1, 368.7, 216.9, 399.3, 507.2, 356.6, 469.2,
493.9, 252.8, 233.6, 342.7, 286.1, 399, 188.9, 358.8, 382.2,
249.3, 362.2, 246.9)), class = "data.frame", row.names = c(NA,
-296L))

4

Hi, welcome!

Please have a look to our homework policy, homework inspired questions are welcome but they should not include verbatim instructions from your course.

5

Its not verbatim..I have changed the questions and the data

6

It doesnt make sense to me that you would have intially changed the questions and the data to facilitate your initial post and then unprompted changed them yet again. On the balance of probability , at least one set of questions and data was likely verbatim; but mistakes can happen and I concede I am not infallible, just attempting to moderate.

7

Here is a thought, which may or may not be what you are looking for. I suggest you group by Dyes, and calculate each mean. The output should look like this:

Dyes MeanValue

1 Jeans Dye1 116.
2 Jeans Dye2 178.
3 Shirt Dye1 117.
4 Shirt Dye2 2124.
5 T-Shirts Dye1 128.
6 T-Shirts Dye2 375.

Then you can do an Analysis of Variance test to whether all means are equal.
Possibly you should split the Dyes variable at the space to create two variables.

The output could be boxplots, before summarizing into 6 groups.

2 Likes
8

I see nirgrahamuk has pointed out our homework policy. I do not think I am violating it by suggesting graph it. Fire up ggplot2 and run a few different plots on it. geom_point() & geom_boxplot() are likely candidates and I would do some summary stats, broken down by Dyes.

It might be a good idea to refer your daughter to the Anscombe Quartet to illustrate why graphing is a good idea.
R has the Anscombe data set anscombe

1 Like
9

Hi @ply, here some plots that you could use for better understand the data:

df is the data:


ggplot(df,aes(x=Dyes, y=Value, fill=Dyes))+
  geom_col()

ggplot(df,aes(x=Dyes, y=Value, fill=Dyes))+
  geom_boxplot()+
  geom_jitter()
10

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