20181220_kidney_phospho

library(rsconnect)
library(Biobase)

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, basename, cbind,
##     colMeans, colnames, colSums, dirname, do.call, duplicated,
##     eval, evalq, Filter, Find, get, grep, grepl, intersect,
##     is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
##     paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
##     Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which, which.max,
##     which.min

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

library(SafeQuant)
library(Ringo)

## Loading required package: RColorBrewer

## Warning: package 'RColorBrewer' was built under R version 3.5.2

## Loading required package: limma

## 
## Attaching package: 'limma'

## The following object is masked from 'package:BiocGenerics':
## 
##     plotMA

## Loading required package: Matrix

## Loading required package: grid

## Loading required package: lattice

## Warning: package 'lattice' was built under R version 3.5.2

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following objects are masked from 'package:BiocGenerics':
## 
##     combine, intersect, setdiff, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(gridExtra)

## Warning: package 'gridExtra' was built under R version 3.5.2

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following object is masked from 'package:BiocGenerics':
## 
##     combine

library(tweenr)

## Warning: package 'tweenr' was built under R version 3.5.2

library(plotly)

## Warning: package 'plotly' was built under R version 3.5.2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:SafeQuant':
## 
##     export

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(ggdendro)

## Warning: package 'ggdendro' was built under R version 3.5.2

library(ape)

## Warning: package 'ape' was built under R version 3.5.2

library(RColorBrewer)
library(gplots)

## Warning: package 'gplots' was built under R version 3.5.2

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

library(graphics)
library(lattice)
library(Rmpfr)

## Warning: package 'Rmpfr' was built under R version 3.5.2

## Loading required package: gmp

## Warning: package 'gmp' was built under R version 3.5.2

## 
## Attaching package: 'gmp'

## The following objects are masked from 'package:Matrix':
## 
##     crossprod, tcrossprod

## The following objects are masked from 'package:base':
## 
##     %*%, apply, crossprod, matrix, tcrossprod

## C code of R package 'Rmpfr': GMP using 64 bits per limb

## 
## Attaching package: 'Rmpfr'

## The following objects are masked from 'package:BiocGenerics':
## 
##     cbind, pmax, pmin, rbind

## The following objects are masked from 'package:stats':
## 
##     dbinom, dnorm, dpois, pnorm

## The following objects are masked from 'package:base':
## 
##     cbind, pmax, pmin, rbind

library(HH)

## Warning: package 'HH' was built under R version 3.5.2

## Loading required package: latticeExtra

## Warning: package 'latticeExtra' was built under R version 3.5.2

## 
## Attaching package: 'latticeExtra'

## The following object is masked from 'package:ggplot2':
## 
##     layer

## Loading required package: multcomp

## Warning: package 'multcomp' was built under R version 3.5.2

## Loading required package: mvtnorm

## Warning: package 'mvtnorm' was built under R version 3.5.2

## Loading required package: survival

## Loading required package: TH.data

## Warning: package 'TH.data' was built under R version 3.5.2

## Loading required package: MASS

## Warning: package 'MASS' was built under R version 3.5.2

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:plotly':
## 
##     select

## The following object is masked from 'package:dplyr':
## 
##     select

## 
## Attaching package: 'TH.data'

## The following object is masked from 'package:MASS':
## 
##     geyser

## 
## Attaching package: 'HH'

## The following object is masked from 'package:gplots':
## 
##     residplot

library(lawstat)

## Warning: package 'lawstat' was built under R version 3.5.2

## Loading required package: Kendall

## Warning: package 'Kendall' was built under R version 3.5.2

## Loading required package: VGAM

## Warning: package 'VGAM' was built under R version 3.5.2

## Loading required package: stats4

## Loading required package: splines

## 
## Attaching package: 'VGAM'

## The following object is masked from 'package:HH':
## 
##     logit

## The following objects are masked from 'package:Rmpfr':
## 
##     erf, erfc, zeta

library(agricolae)

## Warning: package 'agricolae' was built under R version 3.5.2

## 
## Attaching package: 'agricolae'

## The following object is masked from 'package:ape':
## 
##     consensus

library(MASS)
library(gapminder)

## Warning: package 'gapminder' was built under R version 3.5.2

library(highcharter)

## Warning: package 'highcharter' was built under R version 3.5.2

## Highcharts (www.highcharts.com) is a Highsoft software product which is

## not free for commercial and Governmental use

library(readxl)
PS <- read_excel("N:/TSever/20181218_vsaka_frakcija_scx_posebaj_kidney_ko+wt/combined/txt/Phospho (STY)Sites.xlsx")
#View(PS)

library(readr)

## Warning: package 'readr' was built under R version 3.5.2

Phospho_STY_Sites <- read_delim("N:/TSever/20181218_vsaka_frakcija_scx_posebaj_kidney_ko+wt/combined/txt/Phospho (STY)Sites.txt", 
   "\t", escape_double = FALSE, trim_ws = TRUE)

## Parsed with column specification:
## cols(
##   .default = col_logical(),
##   Proteins = col_character(),
##   `Positions within proteins` = col_character(),
##   `Leading proteins` = col_character(),
##   Protein = col_character(),
##   `Fasta headers` = col_character(),
##   `Localization prob` = col_double(),
##   `Score diff` = col_double(),
##   PEP = col_double(),
##   Score = col_double(),
##   `Delta score` = col_double(),
##   `Score for localization` = col_double(),
##   `Localization prob KO_015` = col_double(),
##   `Score diff KO_015` = col_double(),
##   `PEP KO_015` = col_double(),
##   `Score KO_015` = col_double(),
##   `Localization prob WT_000` = col_double(),
##   `Score diff WT_000` = col_double(),
##   `PEP WT_000` = col_double(),
##   `Score WT_000` = col_double(),
##   `Localization prob WT_035` = col_double()
##   # ... with 193 more columns
## )

## See spec(...) for full column specifications.

#View(Phospho_STY_Sites)

KO_005 <-rowMeans(Phospho_STY_Sites[c('Intensity KO_005' )])

ggplot(Phospho_STY_Sites, aes(Phospho_STY_Sites$`Intensity WT_000`)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

a <- data.frame(intensity = c(0,    0,  192040, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  348255787,  0,  0,  0,  0,  0,  715160, 0,  0,  5337080,    0,  0,  0,  0,  0))

a[,2]<- c('Intensity KO_005',   'Intensity KO_010', 'Intensity KO_015', 'Intensity KO_020', 'Intensity KO_025', 'Intensity KO_030', 'Intensity KO_035', 'Intensity KO_040', 'Intensity KO_045', 'Intensity KO_050','Intensity KO_060',  'Intensity KO_070', 'Intensity KO_080', 'Intensity KO_090', 'Intensity KO_100', 'Intensity WT_000', 'Intensity Wt_010', 'Intensity WT_015', 'Intensity WT_020', 'Intensity WT_025', 'Intensity WT_030', 'Intensity WT_035', 'Intensity WT_040', 'Intensity WT_045', 'Intensity WT_050', 'Intensity WT_060', 'Intensity WT_070', 'Intensity WT_080', 'Intensity WT_090', 'Intensity WT_100')

Phospho_STY_Sites2<- filter(Phospho_STY_Sites,
       Score > 40)
Phospho_STY_Sites2<- filter(Phospho_STY_Sites2,
                            `Localization prob` > 0.75)

Phospho_STY_Sites2 <-Phospho_STY_Sites2 [ ! (Phospho_STY_Sites2$Reverse %in% '+'), ]
Phospho_STY_Sites2 <-Phospho_STY_Sites2 [ ! (Phospho_STY_Sites2$`Potential contaminant` %in% '+'), ]

wt000<-
  ggplot(Phospho_STY_Sites2, aes(`Intensity WT_000`, 1:length(Score))) +
  geom_point(color='#77b800', size=2)+
  labs(y='Leading protein')+
  scale_y_continuous(breaks=c(1:88),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  coord_flip()+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=80, hjust=1))
  
wt000

  ggplot(Phospho_STY_Sites2, aes( 1:length(Score),`Intensity WT_000`)) +
  geom_line(color='#77b800', size=0.7)+
  labs(y='Leading protein')+
  scale_x_continuous(breaks=c(1:88),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  #coord_flip()+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=80, hjust=1))

plot(Phospho_STY_Sites2$`Intensity WT_000`, 1:88)

wt050<-
  ggplot(Phospho_STY_Sites2, aes(`Intensity WT_050`, 1:length(Score))) +
  geom_point(color='#77b800')+
  labs(y='Leading protein')+
  scale_y_continuous(breaks=c(1:length(Phospho_STY_Sites2$`Leading proteins`)),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  coord_flip()+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=80, hjust=1))

wt010<-
  ggplot(Phospho_STY_Sites2, aes(`Intensity WT_010`, 1:length(Score))) +
  geom_point(color='#77b800', size=2)+
  labs(y='Leading protein')+
  scale_y_continuous(breaks=c(1:length(Phospho_STY_Sites2$`Leading proteins`)),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  coord_flip()+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=80, hjust=1))

wt015<-
  ggplot(Phospho_STY_Sites2, aes(`Intensity WT_015`, 1:length(Score))) +
  geom_point(color='#77b800', size=2)+
  labs(y='Leading protein')+
  scale_y_continuous(breaks=c(1:length(Phospho_STY_Sites2$`Leading proteins`)),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  coord_flip()+
  theme_minimal()+
  theme(axis.text.x=element_text(angle=80, hjust=1))

wt_unique<-c(
length(unique(c(Phospho_STY_Sites2$`Intensity WT_000`))),
1,
length(unique(c(Phospho_STY_Sites2$`Intensity Wt_010`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_015`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_020`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_025`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_030`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_035`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_040`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_045`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_050`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_060`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_070`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_080`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_090`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_100`)))
)-1

ko_unique<-c(
1,  
length(unique(c(Phospho_STY_Sites2$`Intensity KO_005`))),
length(unique(c(Phospho_STY_Sites2$`Intensity KO_010`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_015`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_020`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_025`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_030`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_035`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_040`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_045`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_050`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_060`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_070`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_080`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_090`))),
length(unique(c(Phospho_STY_Sites2$`Intensity WT_100`)))
)-1

ggplot()+
  geom_bar(stat='identity',
           aes(1:length(wt_unique),wt_unique), fill='#00cfcf', alpha=0.5, size=1.5)+
  
  geom_bar(stat='identity',
             aes(1:length(wt_unique),ko_unique), fill='#cf0000', alpha=0.5, size=1.5)+
  theme_light()+
  scale_x_continuous(breaks = c(1:length(ko_unique)),
    labels=c('0 M','0.05 M','0.10 M','0.15 M', '0.20 M', '0.25 M', '0.30 M', '0.35 M', '0.40 M', '0.45 M', '0.50 M', '0.60 M', '0.70 M',        '0.80 M', '0.90 M',' 1 M' ))+
  labs(x='NaCl fraction', y='number of proteins')+
  theme(legend.position = 'right')+
  ggtitle('number of phophoprylated protein in each scx fraction')+
  ylim(0,90)+
  theme(plot.title = element_text(hjust=0.5))+
  geom_rect(aes(xmin=13, xmax=16, ymin=60,ymax=83),color='#000000', fill='#ffffff', alpha=1)+
  geom_rect(aes(xmin=13.5, xmax=14, ymin=63,ymax=66),color='#000000', fill='#00cfcf', alpha=0.5)+
  geom_rect(aes(xmin=13.5, xmax=14, ymin=70,ymax=73),color='#000000', fill='#cf0000', alpha=0.5)+
  geom_rect(aes(xmin=13.5, xmax=14, ymin=77,ymax=80),color='#000000', fill='#844B4B', alpha=0.5)+
  annotate("text", x = 15, y = 65, label = "WT",color='#000000')+
  annotate("text", x = 15, y = 72, label = "KO",color='#000000')+
  annotate("text", x = 15, y = 79, label = "overlap",color='#000000')+
  theme(axis.text.x=element_text(angle=45, hjust=1))

#ggsave("haha.pdf")

multiplot <- function(..., plotlist = NULL, file, cols = 1, layout = NULL) {
  require(grid)

  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  if (is.null(layout)) {
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                 ncol = cols, nrow = ceiling(numPlots/cols))
}

if (numPlots == 1) {
print(plots[[1]])

} else {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

for (i in 1:numPlots) {
  matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

  print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                  layout.pos.col = matchidx$col))
 }
}
 }

wk1<-
  ggplot(Phospho_STY_Sites2, aes( 1:length(Score),`Intensity WT_000`)) +
  geom_line(color='#77b800', size=0.7)+
  labs(y='Leading protein')+
  scale_x_continuous (breaks=c(1:length(Phospho_STY_Sites2$`Leading proteins`)),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  theme_classic()+
  theme(axis.text.x=element_text(angle=80, hjust=1))+

  geom_line(aes( 1:length(Score),`Intensity WT_035`), color='#EC0030')+
  geom_line(aes( 1:length(Score),`Intensity WT_050`), color='#07B1BC')+
  geom_line(aes( 1:length(Score),`Intensity KO_035`), color='#AB00FF')+
  geom_line(aes( 1:length(Score),`Intensity KO_050`), color='#FB6277')

wk2<-
ggplot(Phospho_STY_Sites2, aes( 1:length(Score),`Intensity WT_000`)) +
  geom_point(color='#77b800', size=1)+
  labs(y='Leading protein')+
  scale_x_continuous (breaks=c(1:length(Phospho_STY_Sites2$`Leading proteins`)),
  labels=c(Phospho_STY_Sites2$`Leading proteins`))+
  theme_classic()+
  theme(axis.text.x=element_text(angle=80, hjust=1))+

  geom_point(aes( 1:length(Score),`Intensity WT_035`), color='#EC0030', size=1)+
  geom_point(aes( 1:length(Score),`Intensity WT_050`), color='#07B1BC', size=1)+
  geom_point(aes( 1:length(Score),`Intensity KO_035`), color='#AB00FF', size=1)+
  geom_point(aes( 1:length(Score),`Intensity KO_050`), color='#FB6277', size=1)

multiplot(wk1, wk2, cols=2)

grid.arrange(wk1, wk2, nrow=1)

wk2

library(readxl)
proteinGroups <- read_excel("N:/TSever/20181218_vsaka_frakcija_scx_posebaj_kidney_ko+wt/combined/txt/proteinGroups.xlsx")
#View(proteinGroups)

proteinGroups2<- filter(proteinGroups,
       Score > 40)
#proteinGroups2<- filter(proteinGroups, `Localization prob` > 0.75)

proteinGroups2 <- proteinGroups2 [ ! (proteinGroups2$Reverse %in% '+'), ]
proteinGroups2 <- proteinGroups2 [ ! (proteinGroups2$`Potential contaminant` %in% '+'), ]
proteinGroups2 <- proteinGroups2 [ ! (proteinGroups2$`Only identified by site` %in% '+'), ]

pg<-
ggplot(proteinGroups2, aes(`Majority protein IDs`, Intensity))+
  geom_point()

kop<-c(
0,
sum(proteinGroups2$`Peptides KO_005`),
sum(proteinGroups2$`Peptides KO_010`),
sum(proteinGroups2$`Peptides KO_015`),
sum(proteinGroups2$`Peptides KO_020`),
sum(proteinGroups2$`Peptides KO_025`),
sum(proteinGroups2$`Peptides KO_030`),
sum(proteinGroups2$`Peptides KO_035`),
sum(proteinGroups2$`Peptides KO_040`),
sum(proteinGroups2$`Peptides KO_045`),
sum(proteinGroups2$`Peptides KO_050`),
sum(proteinGroups2$`Peptides KO_060`),
sum(proteinGroups2$`Peptides KO_070`),
sum(proteinGroups2$`Peptides KO_080`),
sum(proteinGroups2$`Peptides KO_090`),
sum(proteinGroups2$`Peptides KO_100`)
)

wtp<-c(
sum(proteinGroups2$`Peptides WT_000`),
sum(proteinGroups2$`Peptides WT_050`),
sum(proteinGroups2$`Peptides Wt_010`), 
sum(proteinGroups2$`Peptides WT_015`), 
sum(proteinGroups2$`Peptides WT_020`), 
sum(proteinGroups2$`Peptides WT_025`), 
sum(proteinGroups2$`Peptides WT_030`), 
sum(proteinGroups2$`Peptides WT_035`), 
sum(proteinGroups2$`Peptides WT_040`), 
sum(proteinGroups2$`Peptides WT_045`), 
0, 
sum(proteinGroups2$`Peptides WT_060`), 
sum(proteinGroups2$`Peptides WT_070`), 
sum(proteinGroups2$`Peptides WT_080`), 
sum(proteinGroups2$`Peptides WT_090`),
sum(proteinGroups2$`Peptides WT_100`)
)

ggplot()+
 
  geom_bar(stat='identity',
           aes(1:length(kop),kop), fill='#00cfcf', alpha=0.5, size=1.5)+
  geom_rect(xmin=0, xmax= 0, ymin= 00, ymax=00, aes(fill='ko'), alpha=0.4)+
  
  geom_bar(stat='identity',
             aes(1:length(kop),wtp), fill='#cf0000', alpha=0.4, size=1.5)+
  theme_classic()+
  scale_x_continuous(breaks = c(1:length(kop)),
    labels=c('0 M','0.05 M','0.10 M','0.15 M', '0.20 M', '0.25 M', '0.30 M', '0.35 M', '0.40 M', '0.45 M', '0.50 M', '0.60 M', '0.70 M',        '0.80 M', '0.90 M',' 1 M' ))+
  labs(x='NaCl fraction', y='number of proteins')+
  
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  
  geom_rect(xmin=0, xmax= 0, ymin= 00, ymax=00, aes(fill='wt', name = NULL), alpha=0.4)+
  theme(legend.title =element_blank())

ggplotly(pg)

ggplotly(wk2)

df <- data.frame(
  x= 1:length(kop),
  y= kop,
  f=c('0 M','0.05 M','0.10 M','0.15 M', '0.20 M', '0.25 M', '0.30 M', '0.35 M', '0.40 M', '0.45 M', '0.50 M', '0.60 M', '0.70 M', '0.80 M', '0.90 M',' 1 M' )
  )
p <- ggplot(df, aes(x, y)) +
    geom_point(aes(frame = f))

## Warning: Ignoring unknown aesthetics: frame

p <- ggplotly(p)

p

p <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
  geom_point(aes(size = pop, frame = year, ids = country)) +
  scale_x_log10()

## Warning: Ignoring unknown aesthetics: frame, ids

p <- ggplotly(p)

p

df <- data.frame(
  x = c(1,2,3,4), 
  y = c(1,2,3,4), 
  f = c(1,2,3,4)
)

p <- ggplot(df, aes(x, y)) +
    geom_point(aes(frame = f))

## Warning: Ignoring unknown aesthetics: frame

p <- ggplotly(p)

df1<- data.frame(
  ak = c( seq( 0 ,length.out=wt_unique[1])),
  yak = c (rep(0, each=85)),
  fak = c(seq(length.out=wt_unique[1]))
    )


df2<- data.frame(
  kk = c(seq(0,length.out=wt_unique[11])),
  ykk = c (rep(0.50, each=wt_unique[11])),
  fkk = c(seq(length.out=wt_unique[11]))
    )

df3<- data.frame(
  hk = c(0,1),
  yhk = c (0.35,0.35),
  fhk = c(1,2)
    )



p<-ggplot() +
    geom_point( aes(x= df1$yak, y= df1$ak, frame= df1$fak))+
    geom_point( aes(x= df2$ykk, y= df2$kk, frame= df2$fkk))+
    geom_point( aes(x= df3$yhk, y= df3$hk, frame= df3$fhk))+
  geom_point( aes(x= 0.05, y= 0, frame= 1))+
  geom_point( aes(x= 0.1, y= 0, frame= 1))+
  geom_point( aes(x= 0.15, y= 0, frame= 1))+
  geom_point( aes(x= 0.20, y= 0, frame= 1))+
  geom_point( aes(x= 0.25, y= 0, frame= 1))+
  geom_point( aes(x= 0.30, y= 0, frame= 1))+
  geom_point( aes(x= 0.40, y= 0, frame= 1))+
  geom_point( aes(x= 0.45, y= 0, frame= 1))+
  geom_point( aes(x= 0.60, y= 0, frame= 1))+
  geom_point( aes(x= 0.70, y= 0, frame= 1))+
  geom_point( aes(x= 0.80, y= 0, frame= 1))+
  geom_point( aes(x= 0.90, y= 0, frame= 1))+
  geom_point( aes(x= 0.10, y= 0, frame= 1))

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

## Warning: Ignoring unknown aesthetics: frame

ggplotly(p)

## Warning: Only one `frame` variable is allowed

accumulate_by <- function(dat, var) {
  var <- lazyeval::f_eval(var, dat)
  lvls <- plotly:::getLevels(var)
  dats <- lapply(seq_along(lvls), function(x) {
    cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
  })
  dplyr::bind_rows(dats)
}

df <- df %>%
  accumulate_by(~df1$ak)

p <- ggplot(df1,aes(x=yak,y= ak, frame = fak)) +
  geom_line()

ggplotly(p)