This tidyselect interface doesn't support predicates yet

[yurasong]

Dear whom it may concern,
Hello!
During making some functions which I need, I faced this error.
Error: This tidyselect interface doesn't support predicates yet.
Contact the package author and suggest using eval_select().

I still do not get which is exactly problem on my function. The designed function is as below,

p_long <- pvals %>%
tidyr::gather(
-interacting_pair,
-id_cp_interaction,
-partner_a, -partner_b,
-gene_a, -gene_b, -source,
-secreted, -is_integrin,
key = "celltype_pair", value = "pval"
)

Thanks in advance,

Best regards,

Yura

[dromano]

Could you post the output of dput(pvals %>% slice(1:50)), like this?

```
[output here]
```

[yurasong]

Hello!

Here they are!
Thanks in advance

[nirgrahamuk]

That is an image. It is not the result of using the dput() function

[yurasong]

It had a body limit so I could not put in here. Do you know other ways?

[nirgrahamuk]

how many rows of data were you passing for dput to process ?
Dromano shared the slice function syntax which limits the rows, in his example, to 50.
Unless your data is extemely wide with many columns, I would be suprised if 50rows would hit a limit.

[yurasong]

I am working with the data composed of 592 obs of 20 variables.

[nirgrahamuk]

By 592 objects, you mean 592 rows in the table ?
That's ok, I guess thats more than we would need to answer the particulars of your question.
maybe try changing slice down to 5?

dput(pvals %>% slice(1:5))

[yurasong]

Hello again!

I tried both slice(1:5) and slice(1:3), however, neither of them was available to put on here.
Also I could not post the result as attached file on here because only image files are available.

If you don't mind, could I share the data via link?

yurasong/test/blob/master/result.rtf

I uploaded the results coming from dput(pvals %>% slice(1:5)) as rtf format.

Thanks a lot for your help!

[nirgrahamuk]

Hi, I see now what happened. Many of your variables are factors, and many of these factors have very high number of levels, so even with few rows, a lot of information was needing to be passed to dput()

I wonder, whether the choice of factor vs character string, was explicitly yours in every case? it might be worth thinking about ?
anyway, before I look at your posted issue, I will make some amendments to your data to make it copy pasteable for others here that want to see with less effort.

using tidyverse subpackage(forcats) I was able to reduce the data like so (I named the dataset you provided to me as naughty_df for obvious reasons

df_to_share <- mutate_if(naughty_df,is.factor,forcats::fct_drop)
pryr::object_size(naughty_df)
#161 kB
pryr::object_size(df_to_share)
#8.7 kB 

so the size was reduced by 20 times !

dput(df_to_share) is here:

df_to_share <- structure(list(id_cp_interaction = structure(c(2L, 1L, 5L, 4L, 
3L), .Label = c("CPI-SS008FB916C", "CPI-SS03722104E", "CPI-SS07A03C20F", 
"CPI-SS0D4CA5D92", "CPI-SS0D9366D11"), class = "factor"), interacting_pair = structure(c(5L, 
4L, 1L, 2L, 3L), .Label = c("FGF1_FGFR4", "FGF18_FGFR4", "FGF19_FGFR4", 
"FGF2_FGFR4", "HGF_CD44"), class = "factor"), partner_a = structure(c(5L, 
4L, 3L, 1L, 2L), .Label = c("simple:O76093", "simple:O95750", 
"simple:P05230", "simple:P09038", "simple:P14210"), class = "factor"), 
    partner_b = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("simple:P16070", 
    "simple:P22455"), class = "factor"), gene_a = structure(c(1L, 
    3L, 2L, 4L, 5L), .Label = c("ENSG00000019991", "ENSG00000113578", 
    "ENSG00000138685", "ENSG00000156427", "ENSG00000162344"), class = "factor"), 
    gene_b = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("ENSG00000026508", 
    "ENSG00000160867"), class = "factor"), secreted = structure(c(1L, 
    1L, 1L, 1L, 1L), .Label = "True", class = "factor"), receptor_a = structure(c(1L, 
    1L, 1L, 1L, 1L), .Label = "False", class = "factor"), receptor_b = structure(c(1L, 
    1L, 1L, 1L, 1L), .Label = "True", class = "factor"), annotation_strategy = structure(c(5L, 
    1L, 4L, 2L, 3L), .Label = c("curated", "I2D", "I2D,IMEx,InnateDB-All", 
    "I2D,IMEx,InnateDB-All,IntAct", "IMEx,InnateDB-All,IntAct"
    ), class = "factor"), is_integrin = structure(c(1L, 1L, 1L, 
    1L, 1L), .Label = "False", class = "factor"), BC.BC = c(0, 
    0, 0, 0, 0), BC.Erm = c(0, 0.4, 0.4, 0.4, 0.4), BC.Erp = c(1, 
    0, 0, 0.4, 0.2), Erm.BC = c(0, 0.6, 1, 0, 0), Erp.BC = c(0.4, 
    1, 0.4, 1, 1), Erm.Erm = c(0, 0.6, 1, 1, 0.6), Erm.Erp = c(1, 
    0.6, 0.6, 0.6, 0.6), Erp.Erm = c(0, 1, 0.4, 1, 1), Erp.Erp = c(1, 
    0.6, 0.4, 1, 1)), class = "data.frame", row.names = c(NA, 
-5L))

[yurasong]

Actually it was the output coming from the analysis, which will be used further downstream analysis Anyway it is nice to know how to post the datasets with smaller size! Thanks!

Also when I saw the vignette of dplyr::gather, it said that it is retired and moved to pivot_longer. However, when I apply it, still it gives the same issue.

[nirgrahamuk]

ok, we still have some issues to work out, but we are making progress. so the first two things to say about

@yurasong

p_long <- pvals %>%
tidyr::gather(
-interacting_pair,
-id_cp_interaction,
-partner_a, -partner_b,
-gene_a, -gene_b, -source,
-secreted, -is_integrin,
key = "celltype_pair", value = "pval"
)

are, that gathers parameters are like :

gather(
  data,
  key = "key",
  value = "value",
  ...,
  na.rm = FALSE,
  convert = FALSE,
  factor_key = FALSE
)

so I don't think it knows what to do with, or likes the minus symbol fronted list of variables you pass.
I assume that you intend them simply to be columns that are not selected, so I would just let the select function handle that first, before going to the next function.
Also, you try to drop a column called source, which doesnt exist in the version of the dataframe you shared with me, but I'm thinking it might be some strange nameclash, because rather than complaining about a variable being missing (so undroppable) it gives you the odd tidyselect error.
Anyway this works now :

step_1_towards_p_long <- pvals %>%
  select(  -interacting_pair,
           -id_cp_interaction,
           -partner_a, -partner_b,
           -gene_a, -gene_b,
           -secreted, -is_integrin)

Ok that still leaves, a fair number of columns, I'm thinking you might want to drop more, because I'm suspicious that the gather function you tried, will give good results. What is the intention here exactly...

anyway, here is the naive application of the function based on what you wrote.

  step_2_towards_p_long <- tidyr::gather(step_1_towards_p_long,
    key = "celltype_pair", value = "pval"
  )

this does give warnings about jamming incompatible data together, so I think it is worth going slow here, and really thinking about what you need. I don't want to start making assumptions for you, but I do need to warn you not to blindly use this...

[yurasong]

Hmmm... I tried on my side and I got this error,

Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘select’ for signature ‘"data.frame"’

Also when I check class(pvals), it returns data.frame

[nirgrahamuk]

Ok. Perhaps the problem is relying on tidyr only. Please consider installing and using the tidyverse which is a superlibrary of which tidyr is a member. Select is a function from the dplyr library of the tidyverse library. It's all good stuff.

Actually it's surprising because you used slice() successfully. And select() should work wherever slice () works - they are both dplyr

[yurasong]

Yes, it is a lot weird that I receive same error again...

[nirgrahamuk]

you could run sessionInfo() so we can see whats loaded.
Also you haven't said if you get that error when you run my code, or a version of your code adapated from mine....or both ... ?

[dromano]

I could be that select() is being overwritten by a package loaded after dplyr.

[nirgrahamuk]

Good point ! 'conflicts()' would show that...
@yurasong there's always dplyr::select( ) to be explicit.

[andresrcs]

The original code would work with a little modification, you have to use a vector to exclude several variables from the gathering.

Note: There is no column named source on the sample data so I have excluded it from the code and also, you are mixing data types in your gathering command, I don't know if this is intentional but be aware of that.

df_to_share <- structure(list(id_cp_interaction = structure(c(2L, 1L, 5L, 4L, 
                                                              3L), .Label = c("CPI-SS008FB916C", "CPI-SS03722104E", "CPI-SS07A03C20F", 
                                                                              "CPI-SS0D4CA5D92", "CPI-SS0D9366D11"), class = "factor"), interacting_pair = structure(c(5L, 
                                                                                                                                                                       4L, 1L, 2L, 3L), .Label = c("FGF1_FGFR4", "FGF18_FGFR4", "FGF19_FGFR4", 
                                                                                                                                                                                                   "FGF2_FGFR4", "HGF_CD44"), class = "factor"), partner_a = structure(c(5L, 
                                                                                                                                                                                                                                                                         4L, 3L, 1L, 2L), .Label = c("simple:O76093", "simple:O95750", 
                                                                                                                                                                                                                                                                                                     "simple:P05230", "simple:P09038", "simple:P14210"), class = "factor"), 
                              partner_b = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("simple:P16070", 
                                                                                      "simple:P22455"), class = "factor"), gene_a = structure(c(1L, 
                                                                                                                                                3L, 2L, 4L, 5L), .Label = c("ENSG00000019991", "ENSG00000113578", 
                                                                                                                                                                            "ENSG00000138685", "ENSG00000156427", "ENSG00000162344"), class = "factor"), 
                              gene_b = structure(c(1L, 2L, 2L, 2L, 2L), .Label = c("ENSG00000026508", 
                                                                                   "ENSG00000160867"), class = "factor"), secreted = structure(c(1L, 
                                                                                                                                                 1L, 1L, 1L, 1L), .Label = "True", class = "factor"), receptor_a = structure(c(1L, 
                                                                                                                                                                                                                               1L, 1L, 1L, 1L), .Label = "False", class = "factor"), receptor_b = structure(c(1L, 
                                                                                                                                                                                                                                                                                                              1L, 1L, 1L, 1L), .Label = "True", class = "factor"), annotation_strategy = structure(c(5L, 
                                                                                                                                                                                                                                                                                                                                                                                                     1L, 4L, 2L, 3L), .Label = c("curated", "I2D", "I2D,IMEx,InnateDB-All", 
                                                                                                                                                                                                                                                                                                                                                                                                                                 "I2D,IMEx,InnateDB-All,IntAct", "IMEx,InnateDB-All,IntAct"
                                                                                                                                                                                                                                                                                                                                                                                                     ), class = "factor"), is_integrin = structure(c(1L, 1L, 1L, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                     1L, 1L), .Label = "False", class = "factor"), BC.BC = c(0, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             0, 0, 0, 0), BC.Erm = c(0, 0.4, 0.4, 0.4, 0.4), BC.Erp = c(1, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        0, 0, 0.4, 0.2), Erm.BC = c(0, 0.6, 1, 0, 0), Erp.BC = c(0.4, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 1, 0.4, 1, 1), Erm.Erm = c(0, 0.6, 1, 1, 0.6), Erm.Erp = c(1, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            0.6, 0.6, 0.6, 0.6), Erp.Erm = c(0, 1, 0.4, 1, 1), Erp.Erp = c(1, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           0.6, 0.4, 1, 1)), class = "data.frame", row.names = c(NA, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 -5L))
library(dplyr)

df_to_share %>%
    tidyr::gather(key = celltype_pair,
           value = pval,
           -c(interacting_pair, id_cp_interaction, partner_a,
              partner_b, gene_a, gene_b, secreted, is_integrin)
    )
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
#>    id_cp_interaction interacting_pair     partner_a     partner_b
#> 1    CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 2    CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 3    CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 4    CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 5    CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 6    CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 7    CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 8    CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 9    CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 10   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 11   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 12   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 13   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 14   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 15   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 16   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 17   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 18   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 19   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 20   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 21   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 22   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 23   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 24   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 25   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 26   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 27   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 28   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 29   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 30   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 31   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 32   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 33   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 34   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 35   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 36   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 37   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 38   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 39   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 40   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 41   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 42   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 43   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 44   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 45   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 46   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 47   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 48   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 49   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 50   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 51   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 52   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 53   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 54   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 55   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#> 56   CPI-SS03722104E         HGF_CD44 simple:P14210 simple:P16070
#> 57   CPI-SS008FB916C       FGF2_FGFR4 simple:P09038 simple:P22455
#> 58   CPI-SS0D9366D11       FGF1_FGFR4 simple:P05230 simple:P22455
#> 59   CPI-SS0D4CA5D92      FGF18_FGFR4 simple:O76093 simple:P22455
#> 60   CPI-SS07A03C20F      FGF19_FGFR4 simple:O95750 simple:P22455
#>             gene_a          gene_b secreted is_integrin       celltype_pair
#> 1  ENSG00000019991 ENSG00000026508     True       False          receptor_a
#> 2  ENSG00000138685 ENSG00000160867     True       False          receptor_a
#> 3  ENSG00000113578 ENSG00000160867     True       False          receptor_a
#> 4  ENSG00000156427 ENSG00000160867     True       False          receptor_a
#> 5  ENSG00000162344 ENSG00000160867     True       False          receptor_a
#> 6  ENSG00000019991 ENSG00000026508     True       False          receptor_b
#> 7  ENSG00000138685 ENSG00000160867     True       False          receptor_b
#> 8  ENSG00000113578 ENSG00000160867     True       False          receptor_b
#> 9  ENSG00000156427 ENSG00000160867     True       False          receptor_b
#> 10 ENSG00000162344 ENSG00000160867     True       False          receptor_b
#> 11 ENSG00000019991 ENSG00000026508     True       False annotation_strategy
#> 12 ENSG00000138685 ENSG00000160867     True       False annotation_strategy
#> 13 ENSG00000113578 ENSG00000160867     True       False annotation_strategy
#> 14 ENSG00000156427 ENSG00000160867     True       False annotation_strategy
#> 15 ENSG00000162344 ENSG00000160867     True       False annotation_strategy
#> 16 ENSG00000019991 ENSG00000026508     True       False               BC.BC
#> 17 ENSG00000138685 ENSG00000160867     True       False               BC.BC
#> 18 ENSG00000113578 ENSG00000160867     True       False               BC.BC
#> 19 ENSG00000156427 ENSG00000160867     True       False               BC.BC
#> 20 ENSG00000162344 ENSG00000160867     True       False               BC.BC
#> 21 ENSG00000019991 ENSG00000026508     True       False              BC.Erm
#> 22 ENSG00000138685 ENSG00000160867     True       False              BC.Erm
#> 23 ENSG00000113578 ENSG00000160867     True       False              BC.Erm
#> 24 ENSG00000156427 ENSG00000160867     True       False              BC.Erm
#> 25 ENSG00000162344 ENSG00000160867     True       False              BC.Erm
#> 26 ENSG00000019991 ENSG00000026508     True       False              BC.Erp
#> 27 ENSG00000138685 ENSG00000160867     True       False              BC.Erp
#> 28 ENSG00000113578 ENSG00000160867     True       False              BC.Erp
#> 29 ENSG00000156427 ENSG00000160867     True       False              BC.Erp
#> 30 ENSG00000162344 ENSG00000160867     True       False              BC.Erp
#> 31 ENSG00000019991 ENSG00000026508     True       False              Erm.BC
#> 32 ENSG00000138685 ENSG00000160867     True       False              Erm.BC
#> 33 ENSG00000113578 ENSG00000160867     True       False              Erm.BC
#> 34 ENSG00000156427 ENSG00000160867     True       False              Erm.BC
#> 35 ENSG00000162344 ENSG00000160867     True       False              Erm.BC
#> 36 ENSG00000019991 ENSG00000026508     True       False              Erp.BC
#> 37 ENSG00000138685 ENSG00000160867     True       False              Erp.BC
#> 38 ENSG00000113578 ENSG00000160867     True       False              Erp.BC
#> 39 ENSG00000156427 ENSG00000160867     True       False              Erp.BC
#> 40 ENSG00000162344 ENSG00000160867     True       False              Erp.BC
#> 41 ENSG00000019991 ENSG00000026508     True       False             Erm.Erm
#> 42 ENSG00000138685 ENSG00000160867     True       False             Erm.Erm
#> 43 ENSG00000113578 ENSG00000160867     True       False             Erm.Erm
#> 44 ENSG00000156427 ENSG00000160867     True       False             Erm.Erm
#> 45 ENSG00000162344 ENSG00000160867     True       False             Erm.Erm
#> 46 ENSG00000019991 ENSG00000026508     True       False             Erm.Erp
#> 47 ENSG00000138685 ENSG00000160867     True       False             Erm.Erp
#> 48 ENSG00000113578 ENSG00000160867     True       False             Erm.Erp
#> 49 ENSG00000156427 ENSG00000160867     True       False             Erm.Erp
#> 50 ENSG00000162344 ENSG00000160867     True       False             Erm.Erp
#> 51 ENSG00000019991 ENSG00000026508     True       False             Erp.Erm
#> 52 ENSG00000138685 ENSG00000160867     True       False             Erp.Erm
#> 53 ENSG00000113578 ENSG00000160867     True       False             Erp.Erm
#> 54 ENSG00000156427 ENSG00000160867     True       False             Erp.Erm
#> 55 ENSG00000162344 ENSG00000160867     True       False             Erp.Erm
#> 56 ENSG00000019991 ENSG00000026508     True       False             Erp.Erp
#> 57 ENSG00000138685 ENSG00000160867     True       False             Erp.Erp
#> 58 ENSG00000113578 ENSG00000160867     True       False             Erp.Erp
#> 59 ENSG00000156427 ENSG00000160867     True       False             Erp.Erp
#> 60 ENSG00000162344 ENSG00000160867     True       False             Erp.Erp
#>                            pval
#> 1                         False
#> 2                         False
#> 3                         False
#> 4                         False
#> 5                         False
#> 6                          True
#> 7                          True
#> 8                          True
#> 9                          True
#> 10                         True
#> 11     IMEx,InnateDB-All,IntAct
#> 12                      curated
#> 13 I2D,IMEx,InnateDB-All,IntAct
#> 14                          I2D
#> 15        I2D,IMEx,InnateDB-All
#> 16                            0
#> 17                            0
#> 18                            0
#> 19                            0
#> 20                            0
#> 21                            0
#> 22                          0.4
#> 23                          0.4
#> 24                          0.4
#> 25                          0.4
#> 26                            1
#> 27                            0
#> 28                            0
#> 29                          0.4
#> 30                          0.2
#> 31                            0
#> 32                          0.6
#> 33                            1
#> 34                            0
#> 35                            0
#> 36                          0.4
#> 37                            1
#> 38                          0.4
#> 39                            1
#> 40                            1
#> 41                            0
#> 42                          0.6
#> 43                            1
#> 44                            1
#> 45                          0.6
#> 46                            1
#> 47                          0.6
#> 48                          0.6
#> 49                          0.6
#> 50                          0.6
#> 51                            0
#> 52                            1
#> 53                          0.4
#> 54                            1
#> 55                            1
#> 56                            1
#> 57                          0.6
#> 58                          0.4
#> 59                            1
#> 60                            1

^{Created on 2020-03-03 by the reprex package (v0.3.0.9001)}

[yurasong]

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] biomaRt_2.40.5 viridis_0.5.1 viridisLite_0.3.0 cowplot_1.0.0 xlsx_0.6.3 forcats_0.5.0
[7] stringr_1.4.0 dplyr_0.8.4 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_2.1.3
[13] ggplot2_3.2.1 tidyverse_1.3.0

loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 lubridate_1.7.4 lattice_0.20-40 prettyunits_1.1.1 xlsxjars_0.6.1
[6] digest_0.6.25 assertthat_0.2.1 R6_2.4.1 cellranger_1.1.0 backports_1.1.5
[11] reprex_0.3.0 stats4_3.6.1 RSQLite_2.2.0 httr_1.4.1 pillar_1.4.3
[16] progress_1.2.2 rlang_0.4.4 lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.11
[21] blob_1.2.1 S4Vectors_0.22.1 RCurl_1.98-1.1 bit_1.1-15.2 munsell_0.5.0
[26] broom_0.5.5 compiler_3.6.1 modelr_0.1.6 xfun_0.12 BiocGenerics_0.30.0
[31] pkgconfig_2.0.3 tidyselect_1.0.0 gridExtra_2.3 IRanges_2.18.3 XML_3.99-0.3
[36] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2 bitops_1.0-6
[41] grid_3.6.1 nlme_3.1-144 jsonlite_1.6.1 gtable_0.3.0 lifecycle_0.1.0
[46] DBI_1.1.0 magrittr_1.5 scales_1.1.0 cli_2.0.2 stringi_1.4.6
[51] fs_1.3.1 xml2_1.2.2 generics_0.0.2 vctrs_0.2.3 tools_3.6.1
[56] bit64_0.9-7 Biobase_2.44.0 glue_1.3.1 hms_0.5.3 parallel_3.6.1
[61] AnnotationDbi_1.46.1 colorspace_1.4-1 rvest_0.3.5 memoise_1.1.0 rJava_0.9-11
[66] knitr_1.28 haven_2.2.0

Here they are!