I have the following process which uses group_split of dplyr:
library(tidyverse)
set.seed(1)
iris %>% sample_n(size = 5) %>%
group_by(Species) %>%
group_split()
The result is:
[[1]]
# A tibble: 2 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5 3.5 1.6 0.6 setosa
2 5.1 3.8 1.5 0.3 setosa
[[2]]
# A tibble: 2 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.9 3 4.2 1.5 versicolor
2 6.2 2.2 4.5 1.5 versicolor
[[3]]
# A tibble: 1 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 6.2 3.4 5.4 2.3 virginica
What I want to achieve is to name this list by grouped name (i.e. Species). Yielding this (done by hand):
$setosa
# A tibble: 2 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5 3.5 1.6 0.6 setosa
2 5.1 3.8 1.5 0.3 setosa
$versicolor
# A tibble: 2 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.9 3 4.2 1.5 versicolor
2 6.2 2.2 4.5 1.5 versicolor
$virginica
# A tibble: 1 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 6.2 3.4 5.4 2.3 virginica
How can I achieve that?
Update
I tried this new data, where the naming now is called Cluster :
df <- structure(list(Cluster = c("Cluster9", "Cluster11", "Cluster1",
"Cluster9", "Cluster6", "Cluster12", "Cluster9", "Cluster11",
"Cluster8", "Cluster8"), gene_name = c("Tbc1d8", "Vimp", "Grhpr",
"H1f0", "Zfp398", "Pikfyve", "Ankrd13a", "Fgfr1op2", "Golga7",
"Lars2"), p_value = c(3.46629097620496e-47, 3.16837338947245e-62,
1.55108439059684e-06, 9.46078511685542e-131, 0.000354049720507017,
0.0146807415917158, 1.42799750295289e-38, 2.0697825959399e-08,
4.13777221466668e-06, 3.92889640704683e-184), morans_test_statistic = c(14.3797687352223,
16.6057085487911, 4.66393667525872, 24.301453902967, 3.38642377758137,
2.17859882998961, 12.9350063459509, 5.48479186018979, 4.4579286289179,
28.9144540271157), morans_I = c(0.0814728893885783, 0.0947505609609695,
0.0260671534007409, 0.138921824574569, 0.018764800166045, 0.0119813199210325,
0.0736554862590782, 0.0309849638728409, 0.0250591347318986, 0.165310420808725
), q_value = c(1.57917584337356e-46, 1.62106594498462e-61, 3.43312171446844e-06,
6.99503520654745e-130, 0.000683559649593623, 0.0245476826213791,
5.96116678335584e-38, 4.97603701391971e-08, 8.9649490080526e-06,
3.48152096326702e-183)), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame"))
With Ronak Shah's approach I get inconsistent result:
df %>% group_split(Cluster) %>% setNames(unique(df$Cluster))
$Cluster9
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster1 Grhpr 0.00000155 4.66 0.0261 0.00000343
$Cluster11
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster11 Vimp 3.17e-62 16.6 0.0948 1.62e-61
2 Cluster11 Fgfr1op2 2.07e- 8 5.48 0.0310 4.98e- 8
$Cluster1
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster12 Pikfyve 0.0147 2.18 0.0120 0.0245
$Cluster6
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster6 Zfp398 0.000354 3.39 0.0188 0.000684
$Cluster12
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster8 Golga7 4.14e- 6 4.46 0.0251 8.96e- 6
2 Cluster8 Lars2 3.93e-184 28.9 0.165 3.48e-183
$Cluster8
# A tibble: 3 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster9 Tbc1d8 3.47e- 47 14.4 0.0815 1.58e- 46
2 Cluster9 H1f0 9.46e-131 24.3 0.139 7.00e-130
3 Cluster9 Ankrd13a 1.43e- 38 12.9 0.0737 5.96e- 38
Note that $Cluster9 has Cluster1 in it.
Please advice how to go about this?
Lots of good answers. You can also just do:
iris %>% sample_n(size = 5) %>%
split(f = as.factor(.$Species))
Which will give you:
$setosa
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
4 5.5 3.5 1.3 0.2 setosa
5 5.3 3.7 1.5 0.2 setosa
$versicolor
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
3 5 2.3 3.3 1 versicolor
$virginica
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 2.6 6.9 2.3 virginica
2 7.2 3.0 5.8 1.6 virginica
Also works with your dataframe above:
df %>%
split(f = as.factor(.$Cluster))
Gives you:
$Cluster1
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster1 Grhpr 0.00000155 4.66 0.0261 0.00000343
$Cluster11
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster11 Vimp 3.17e-62 16.6 0.0948 1.62e-61
2 Cluster11 Fgfr1op2 2.07e- 8 5.48 0.0310 4.98e- 8
$Cluster12
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster12 Pikfyve 0.0147 2.18 0.0120 0.0245
$Cluster6
# A tibble: 1 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster6 Zfp398 0.000354 3.39 0.0188 0.000684
$Cluster8
# A tibble: 2 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster8 Golga7 4.14e- 6 4.46 0.0251 8.96e- 6
2 Cluster8 Lars2 3.93e-184 28.9 0.165 3.48e-183
$Cluster9
# A tibble: 3 x 6
Cluster gene_name p_value morans_test_statistic morans_I q_value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Cluster9 Tbc1d8 3.47e- 47 14.4 0.0815 1.58e- 46
2 Cluster9 H1f0 9.46e-131 24.3 0.139 7.00e-130
3 Cluster9 Ankrd13a 1.43e- 38 12.9 0.0737 5.96e- 38