对R的一些新认识
2018-11-02 本文已影响99人
刘小泽
刘小泽写于18.11.2 这几天都是花花在写教程，我在旁边看着，被论文拖住了，我今天忍不住又学了点东西。唉，这年头，不学点知识充实充实，都感觉对不住自己的十几个小时
今天突然有兴趣学起了dplyr，确实很给力，看花花一直使用十分眼馋啊🤒
另外还学习了Rmd新技能，十分提高效率。全文均由Rmd导出
激动人心的时刻
第一次用Rmarkdown编辑，这个确实很效率，借此机会利用生物数据学习一下强大的dplyr、tydir:
- 第一部分 dplyr
- 第二部分 tidyr
- 第三部分 Rmd
第一部分 dplyr
其中的四个数据messy、tidy、dose、name2go.csv
公众号后台回复rmd获得哦。另外附送Rmd官方小抄
加载包
library(dplyr)
library(readr)
读取数据
ydat <- read_csv("tidy.csv")
#> Parsed with column specification:
#> cols(
#> symbol = col_character(),
#> systematic_name = col_character(),
#> nutrient = col_character(),
#> rate = col_double(),
#> expression = col_double(),
#> bp = col_character(),
#> mf = col_character()
#> )
先简单看下ydat数据
head(ydat$symbol)
#> [1] "SFB2" NA "QRI7" "CFT2" "SSO2" "PSP2"
mean(ydat$expression)
#> [1] 0.003367182
sd(ydat$expression)
#> [1] 0.6675023
range(ydat$rate)
#> [1] 0.05 0.30
看一下dplyr能干啥
#能看到包含的几个主要函数
ydat %>%
filter(bp == "leucine biosynthesis") %>%
group_by(nutrient,symbol) %>%
summarize(mean=mean(expression),
sd=sd(expression),
r=cor(rate,expression))
#> # A tibble: 24 x 5
#> # Groups: nutrient [?]
#> nutrient symbol mean sd r
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Ammonia LEU1 -0.818 0.387 0.659
#> 2 Ammonia LEU2 -0.535 0.379 -0.193
#> 3 Ammonia LEU4 -0.368 0.561 -0.670
#> 4 Ammonia LEU9 -1.01 0.641 0.870
#> 5 Glucose LEU1 -0.553 0.412 0.979
#> 6 Glucose LEU2 -0.393 0.327 0.899
#> 7 Glucose LEU4 1.09 1.01 -0.972
#> 8 Glucose LEU9 -0.165 0.346 0.354
#> 9 Leucine LEU1 2.70 1.08 -0.951
#> 10 Leucine LEU2 0.285 1.16 -0.974
#> # ... with 14 more rows
分步开始学习
1. filter()
1.1 找某个基因
filter(ydat, symbol == "LEU1") %>% head
#> # A tibble: 6 x 7
#> symbol systematic_name nutrient rate expression bp mf
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 LEU1 YGL009C Glucose 0.05 -1.12 leucine … 3-isopropylm…
#> 2 LEU1 YGL009C Glucose 0.1 -0.77 leucine … 3-isopropylm…
#> 3 LEU1 YGL009C Glucose 0.15 -0.67 leucine … 3-isopropylm…
#> 4 LEU1 YGL009C Glucose 0.2 -0.59 leucine … 3-isopropylm…
#> 5 LEU1 YGL009C Glucose 0.25 -0.2 leucine … 3-isopropylm…
#> 6 LEU1 YGL009C Glucose 0.3 0.03 leucine … 3-isopropylm…
1.2 找多个基因
filter(ydat, symbol == "LEU1" | symbol == "ADH2") %>% head
#> # A tibble: 6 x 7
#> symbol systematic_name nutrient rate expression bp mf
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 LEU1 YGL009C Glucose 0.05 -1.12 leucine … 3-isopropylm…
#> 2 ADH2 YMR303C Glucose 0.05 6.28 fermenta… alcohol dehy…
#> 3 LEU1 YGL009C Glucose 0.1 -0.77 leucine … 3-isopropylm…
#> 4 ADH2 YMR303C Glucose 0.1 5.81 fermenta… alcohol dehy…
#> 5 LEU1 YGL009C Glucose 0.15 -0.67 leucine … 3-isopropylm…
#> 6 ADH2 YMR303C Glucose 0.15 5.64 fermenta… alcohol dehy…
1.3 看下生长率最低时（0.05）LEU1基因表达量
# 注意LEU1和leucine的相关性
filter(ydat, symbol == "LEU1" & rate == .05)
#> # A tibble: 6 x 7
#> symbol systematic_name nutrient rate expression bp mf
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 LEU1 YGL009C Glucose 0.05 -1.12 leucine… 3-isopropylm…
#> 2 LEU1 YGL009C Ammonia 0.05 -0.76 leucine… 3-isopropylm…
#> 3 LEU1 YGL009C Phosphate 0.05 -0.81 leucine… 3-isopropylm…
#> 4 LEU1 YGL009C Sulfate 0.05 -1.57 leucine… 3-isopropylm…
#> 5 LEU1 YGL009C Leucine 0.05 3.84 leucine… 3-isopropylm…
#> 6 LEU1 YGL009C Uracil 0.05 -2.07 leucine… 3-isopropylm…
1.4 只显示 LEU1和Leucine的数据
filter(ydat, ydat$symbol == "LEU1" & ydat$nutrient == "Leucine")
#> # A tibble: 6 x 7
#> symbol systematic_name nutrient rate expression bp mf
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 LEU1 YGL009C Leucine 0.05 3.84 leucine … 3-isopropylm…
#> 2 LEU1 YGL009C Leucine 0.1 3.36 leucine … 3-isopropylm…
#> 3 LEU1 YGL009C Leucine 0.15 3.24 leucine … 3-isopropylm…
#> 4 LEU1 YGL009C Leucine 0.2 2.84 leucine … 3-isopropylm…
#> 5 LEU1 YGL009C Leucine 0.25 2.04 leucine … 3-isopropylm…
#> 6 LEU1 YGL009C Leucine 0.3 0.87 leucine … 3-isopropylm…
1.5 filter + ggplot2
# NOTE LEU1 is highly expressed when starved of leucine!
library(ggplot2)
#> Need help getting started? Try the cookbook for R:
#> http://www.cookbook-r.com/Graphs/
filter(ydat, ydat$symbol == "LEU1") %>%
ggplot(aes(rate, expression, colour = nutrient))+
geom_line(lwd=1.5)
1.6 小练习
# display bp variable is "leucine biosynthesis" and nutrient was Leucine, then decide genes number
filter(ydat, ydat$bp == "leucine biosynthesis" & ydat$nutrient == "Leucine") %>% select(symbol) %>% unique()
# show expression value which is >99%
filter(ydat,
ydat$expression >quantile(ydat$expression, probs = .99))
2. select()
# just type colname is fine
select(ydat, symbol, systematic_name)
# remove columns
nogo <- select(ydat, -bp, -mf)
nogo
filter(nogo, symbol == "LEU1" & rate == .05)
3.mutate()
mutate(nogo, signal = 2^expression) %>% head
#> # A tibble: 6 x 6
#> symbol systematic_name nutrient rate expression signal
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 SFB2 YNL049C Glucose 0.05 -0.24 0.847
#> 2 <NA> YNL095C Glucose 0.05 0.28 1.21
#> 3 QRI7 YDL104C Glucose 0.05 -0.02 0.986
#> 4 CFT2 YLR115W Glucose 0.05 -0.33 0.796
#> 5 SSO2 YMR183C Glucose 0.05 0.05 1.04
#> 6 PSP2 YML017W Glucose 0.05 -0.69 0.620
# mutate(nogo, signal = 2^expression, sigsr = sqrt(signal)) %>% head
# 看看tidydata长什么样
library(tidyr)
mutate(nogo, signal=2^expression, sigsr=sqrt(signal)) %>%
gather(unit, value, expression:sigsr) %>%
ggplot(aes(value))+geom_histogram(bins = 100)+facet_wrap(~unit, scales = "free")
4.arrange()
arrange(ydat, symbol)
arrange(ydat, expression)
arrange(ydat, desc(expression)) # arrange by decending
4.1 exercise
filter(ydat, bp == "leucine biosynthesis" & nutrient == "Leucine") %>% arrange(symbol)
#> # A tibble: 24 x 7
#> symbol systematic_name nutrient rate expression bp mf
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 LEU1 YGL009C Leucine 0.05 3.84 leucine … 3-isopropyl…
#> 2 LEU1 YGL009C Leucine 0.1 3.36 leucine … 3-isopropyl…
#> 3 LEU1 YGL009C Leucine 0.15 3.24 leucine … 3-isopropyl…
#> 4 LEU1 YGL009C Leucine 0.2 2.84 leucine … 3-isopropyl…
#> 5 LEU1 YGL009C Leucine 0.25 2.04 leucine … 3-isopropyl…
#> 6 LEU1 YGL009C Leucine 0.3 0.87 leucine … 3-isopropyl…
#> 7 LEU2 YCL018W Leucine 0.05 1.54 leucine … 3-isopropyl…
#> 8 LEU2 YCL018W Leucine 0.1 1.23 leucine … 3-isopropyl…
#> 9 LEU2 YCL018W Leucine 0.15 0.69 leucine … 3-isopropyl…
#> 10 LEU2 YCL018W Leucine 0.2 0.39 leucine … 3-isopropyl…
#> # ... with 14 more rows
5.summarize()
# get mean value
summarize(ydat, meanexp=mean(expression))
#> # A tibble: 1 x 1
#> meanexp
#> <dbl>
#> 1 0.00337
# measure correlation
summarize(ydat, r=cor(rate, expression))
#> # A tibble: 1 x 1
#> r
#> <dbl>
#> 1 -0.0220
# total num of observation
summarize(ydat,total=n())
#> # A tibble: 1 x 1
#> total
#> <int>
#> 1 198430
# total distinct num
summarize(ydat, distinct=n_distinct(symbol))
#> # A tibble: 1 x 1
#> distinct
#> <int>
#> 1 4211
6.group_by()
ydat
group_by(ydat, nutrient)
group_by(ydat, nutrient, expression)
# real power comes with summarize
summarize(group_by(ydat, symbol), meanexp=mean(expression))
#> # A tibble: 4,211 x 2
#> symbol meanexp
#> <chr> <dbl>
#> 1 AAC1 0.529
#> 2 AAC3 -0.216
#> 3 AAD10 0.438
#> 4 AAD14 -0.0717
#> 5 AAD16 0.242
#> 6 AAD4 -0.792
#> 7 AAD6 0.290
#> 8 AAH1 0.0461
#> 9 AAP1 -0.00361
#> 10 AAP1' -0.421
#> # ... with 4,201 more rows
summarize(group_by(ydat, nutrient), r=cor(rate, expression))
#> # A tibble: 6 x 2
#> nutrient r
#> <chr> <dbl>
#> 1 Ammonia -0.0175
#> 2 Glucose -0.0112
#> 3 Leucine -0.0384
#> 4 Phosphate -0.0194
#> 5 Sulfate -0.0166
#> 6 Uracil -0.0353
6.1 exercise
filter(ydat, rate ==.05 & symbol == "ADH2") %>%
select(nutrient, expression)
#> # A tibble: 6 x 2
#> nutrient expression
#> <chr> <dbl>
#> 1 Glucose 6.28
#> 2 Ammonia 0.55
#> 3 Phosphate -4.6
#> 4 Sulfate -1.18
#> 5 Leucine 4.15
#> 6 Uracil 0.63
filter(ydat, rate == .05 & nutrient == "Leucine") %>%
arrange(desc(expression)) %>% head %>%
select(symbol, expression, bp)
#> # A tibble: 6 x 3
#> symbol expression bp
#> <chr> <dbl> <chr>
#> 1 HXT3 5.16 hexose transport
#> 2 HXT5 4.9 hexose transport
#> 3 HSP26 4.86 response to stress*
#> 4 QDR2 4.61 multidrug transport
#> 5 YRO2 4.4 biological process unknown
#> 6 BAP3 4.29 amino acid transport
filter(ydat, rate == .05 & bp == "response to stress") %>%
group_by(nutrient)%>% summarize( meanexp=mean(expression) )
#> # A tibble: 6 x 2
#> nutrient meanexp
#> <chr> <dbl>
#> 1 Ammonia 0.943
#> 2 Glucose 0.743
#> 3 Leucine 0.811
#> 4 Phosphate 0.981
#> 5 Sulfate 0.743
#> 6 Uracil 0.731
第二部分 tidyr
加载包和数据
library(readr)
library(dplyr)
library(tidyr)
hr <- read_csv("dose.csv")
#> Parsed with column specification:
#> cols(
#> name = col_character(),
#> a_10 = col_integer(),
#> a_20 = col_integer(),
#> b_10 = col_integer(),
#> b_20 = col_integer(),
#> c_10 = col_integer(),
#> c_20 = col_integer()
#> )
分步来认识一下
1. gather()
hr %>% gather(key = drugdose, value = hr, a_10:c_20) %>% head
#> # A tibble: 6 x 3
#> name drugdose hr
#> <chr> <chr> <int>
#> 1 jon a_10 60
#> 2 ann a_10 65
#> 3 bill a_10 70
#> 4 kate a_10 75
#> 5 joe a_10 80
#> 6 jon a_20 55
hr %>% gather(key = drugdose, value = hr, -name) %>% head
#> # A tibble: 6 x 3
#> name drugdose hr
#> <chr> <chr> <int>
#> 1 jon a_10 60
#> 2 ann a_10 65
#> 3 bill a_10 70
#> 4 kate a_10 75
#> 5 joe a_10 80
#> 6 jon a_20 55
2.seperate()
hr %>% gather(key = drugdose, value = hr, -name) %>%
separate(drugdose, into=c("drug", "dose"), sep="_") %>% head
3.gather %>% separate %>% filter %>% group_by %>% summerize
3.1 create a new date.frame
hrtidy <- hr %>%
gather(key = drugdose, value = hr,-name) %>%
separate(drugdose, into=c("drug", "dose"), sep = "_")
# View(hrtidy)
3.2 filter
hrtidy %>% filter(drug == "a")
hrtidy %>% filter(dose == 20)
hrtidy %>% filter(hr>=80)
3.3 analyze
hrtidy %>%
filter(name!="joe") %>%
group_by(drug, dose) %>%
summarize(meanhr=mean(hr))
那么如何将messy data变tidy呢？
先看看什么叫messy data
yorig <- read_csv("messy.csv")
#> Parsed with column specification:
#> cols(
#> .default = col_double(),
#> GID = col_character(),
#> YORF = col_character(),
#> NAME = col_character(),
#> GWEIGHT = col_integer()
#> )
#> See spec(...) for full column specifications.
yorig[1:6,1:10] %>% knitr::kable()
| GID | YORF | NAME | GWEIGHT | G0.05 | G0.1 | G0.15 | G0.2 | G0.25 | G0.3 |
|---|---|---|---|---|---|---|---|---|---|
| GENE1331X | A_06_P5820 | SFB2::YNL049C::1082129 | 1 | -0.24 | -0.13 | -0.21 | -0.15 | -0.05 | -0.05 |
| GENE4924X | A_06_P5866 | NA::YNL095C::1086222 | 1 | 0.28 | 0.13 | -0.40 | -0.48 | -0.11 | 0.17 |
| GENE4690X | A_06_P1834 | QRI7::YDL104C::1085955 | 1 | -0.02 | -0.27 | -0.27 | -0.02 | 0.24 | 0.25 |
| GENE1177X | A_06_P4928 | CFT2::YLR115W::1081958 | 1 | -0.33 | -0.41 | -0.24 | -0.03 | -0.03 | 0.00 |
| GENE511X | A_06_P5620 | SSO2::YMR183C::1081214 | 1 | 0.05 | 0.02 | 0.40 | 0.34 | -0.13 | -0.14 |
| GENE2133X | A_06_P5307 | PSP2::YML017W::1083036 | 1 | -0.69 | -0.03 | 0.23 | 0.20 | 0.00 | -0.27 |
step1: seperate the NAME and remove unwanted stuff
yorig %>%
separate(NAME, into = c("symbol", "systematic_name", "somenum"),
sep = "::") %>%
select(-GID, -YORF, -somenum, -GWEIGHT) %>% head
step2: gather the data then separate
ynogo <- yorig %>%
separate(NAME, into = c("symbol", "systematic_name", "somenum"),
sep = "::") %>%
select(-GID, -YORF, -somenum, -GWEIGHT) %>%
gather(key = nutrientrate, value = expression, G0.05:U0.3) %>%
separate(nutrientrate, into = c("nutrient", "rate"),sep = 1)
step3: inner_join to GO
# import the data
sn2go <- read_csv("name2go.csv")
#> Parsed with column specification:
#> cols(
#> systematic_name = col_character(),
#> bp = col_character(),
#> mf = col_character()
#> )
head(sn2go)
# inner join
yjoined <- inner_join(ynogo, sn2go, by="systematic_name")
yjoined
# left join (use NA to replace no corresponding entry)
yljoined <- left_join(ynogo, sn2go, by="systematic_name")
yljoined
# see a little bit
glimpse(yjoined)
glimpse(yljoined)
step4: change nutrient
nutrientlookup <-
data_frame(nutrient = c("G", "L", "N", "P", "S", "U"), nutrientname = c("Glucose", "Leucine", "Ammonia","Phosphate", "Sulfate","Uracil"))
yjoined<- yjoined %>%
mutate(rate = as.numeric(rate)) %>%
mutate(symbol = ifelse(symbol == "NA", NA, symbol)) %>%
left_join(nutrientlookup) %>%
select(-nutrient) %>%
select(symbol:systematic_name, nutrient = nutrientname, rate:mf)
#> Joining, by = "nutrient"
yjoined %>% head %>% knitr::kable()
最后的tidy data，可以和一开始的那个对比一下
| symbol | systematic_name | nutrient | rate | expression | bp | mf |
|---|---|---|---|---|---|---|
| SFB2 | YNL049C | Glucose | 0.05 | -0.24 | ER to Golgi transport | molecular function unknown |
| NA | YNL095C | Glucose | 0.05 | 0.28 | biological process unknown | molecular function unknown |
| QRI7 | YDL104C | Glucose | 0.05 | -0.02 | proteolysis and peptidolysis | metalloendopeptidase activity |
| CFT2 | YLR115W | Glucose | 0.05 | -0.33 | mRNA polyadenylylation* | RNA binding |
| SSO2 | YMR183C | Glucose | 0.05 | 0.05 | vesicle fusion* | t-SNARE activity |
| PSP2 | YML017W | Glucose | 0.05 | -0.69 | biological process unknown | molecular function unknown |
第三部分 Rmd的学习
还是非常偶然的，之前一直用typora编辑，效率也比较高，但存在一个问题，就是当笔记中代码比较多时，就需要不断从R和typpora切换，十分麻烦。今天本来只是想看看dplyr和tidyr，然后想到为什么不能把所学的代码直接用r里的代码块导出呢？于是看了下Rmd，很简单，三五分钟就可以学会，豆豆和花花都已经体验过了，你也可以的！
介绍一下Rmd
它的应用主要是方便人们重复代码、教程。一般来说，好的重复流程有10个亮点：
- 代码写在一个一个小块中 【code in small chunks】
- 有版本迭代 【make incrementla changes (version control)】
- 代码中要有实例数据【add tests in code】
- 不要重复造轮子，多搜索别人已经写好的函数【don't reinvent wheel】
- 不要在原始数据上修改【make data read-only rather than modifying original data】
- 创建project，数据放在一个文件夹下【use project into one dir】
- 代码开源，毕竟是共享社会嘛，自己的代码需要流转【release code (GitHub, RPubs, Figshare)】
- 使用相对路径，不要用绝对路径【(e.g. data/huahua.csv NOT /Users/Download/Data/huahua.csv)】
- 有随机数需要设置seed【set.seed() 】
- 注释信息要详细【when and how to download data; data version;software version】
如何使用Rmd【四步走】
其实就是加强版的markdown
- 第一步：新建project=》file=》R Markdown =》 From template=》选HTML
- 第二步：进入Rmd的模版，熟悉md的小伙伴一定对其中的代码很熟悉了，包括标题大小、加粗、引用、代码块等
- 第三步：在模版基础上修改，另外记住两个快捷键
第一个：插入代码块 ctrl/cmd + alt/option + i
第二个：快速knit，修饰成html格式 ctrl/cmd + shift + k
- 第四步：导出为md
library(knitr)
knit("Rmd-homework.Rmd")
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