R: Unlist pairwise comparisons p-values into dataframe - r

I can apply the following code to apply a pairwiset.t.test function on each column of a certain dataframe, A, against a dependent parameter, DEP.
apply(A, 2, function(x) pairwise.t.test(DEP, x, p.adj = 'bonferroni')$p.value)
The output for this function results in a list type data. Something like the following shows:
$A
1 2 3 4
1 0.7236751 NA NA NA
2 0.7236751 0.7236751 NA NA
3 0.7236751 0.7236751 0.7236751 NA
4 0.7236751 0.7236751 0.7236751 0.7236751
$B
0
1 0.7236751
I would like to ask if there's a way to combine the two list mode data such that:
1 2 3 4 5
1 0.7236751 NA NA NA 0.7236751
2 0.7236751 0.7236751 NA NA NA
3 0.7236751 0.7236751 0.7236751 NA NA
4 0.7236751 0.7236751 0.7236751 0.7236751 NA
The output of dput(apply(A, 2, function(x) pairwise.t.test(DEP, x, p.adj = 'bonferroni')$p.value))
is as follows:
Dimnames = list(c("10", "11", "14", "15", "16", "17",
"18", "19", "20", "21", "23", "31", "33", "34", "36", "39", "40",
"42", "43", "45", "46", "47", "49", "50", "51", "52", "54", "59",
"60", "63", "64", "66", "70", "73"), c("6", "10", "11", "14",
"15", "16", "17", "18", "19", "20", "21", "23", "31", "33", "34",
"36", "39", "40", "42", "43", "45", "46", "47", "49", "50", "51",
"52", "54", "59", "60", "63", "64", "66", "70"))), Gender = structure(0.723675133647025, .Dim = c(1L,
1L), .Dimnames = list("1", "0"))), .Names = c("A", "B"
))

Related

Adding rows with changing variable values in R

I have the following extract of my dataset about the occupancy of a football match:
example <- data.frame(Date <- c("2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30", "2019-04-07",
"2019-03-21", "2019-03-30",
"2019-03-21", "2019-03-30",
"2019-03-21", "2019-03-30",
"2019-03-21"),
Block <- c("43L","43L", "43L", "15B", "15B", "15B", "43L", "43L",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B",
"15B", "15B",
"15B", "15B",
"15B"),
Preis <- as.numeric(c("24", "35", "30", "35", "45",
"40", "26", "30",
"35", "45", "40",
"34", "43", "42",
"35", "42", "45",
"36", "45", "43",
"36", "43", "40",
"35", "41",
"32", "42",
"30", "42",
"35")),
Max <- c("3", "3", "3", "10", "10","10","3", "3",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10",
"10", "10",
"10", "10",
"10"),
Actual <- c("2", "1", "2", "10", "9", "6","2", "2",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9",
"10", "9",
"10", "9",
"10"),
Temperatur <- c("15", "20", "18","15", "20", "18", "15", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20",
"15", "20",
"15", "20",
"15"),
Placesold <- c("1", "1", "1", "1", "1","1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1",
"1", "1",
"1", "1",
"1") )
colnames(example) <- c("Date", "Block", "Price", "Max", "Actual", "Temprature", "Placesold")
In reality, the dataset contains over 100 blocks and 46 different dates.
If you take a closer look at the data, you can see that different numbers of seats are sold out in block 15B and 43L on different days.
table(example$Date, example$Block)
table(example$Placesold)
15B 43L
2019-03-21 10 2
2019-03-30 9 1
2019-4-07 6 2
> table(example$Placesold)
1
30
My goal is to add the seats that were not sold to the data set. The variable Placesold should be 0 instead of 1. In addition, the average price of the sold tickets should be used instead of the price (without 0).
To clarify my goal, I have added the missing rows for the reduced data set.
result <- data.frame(Date <- c("2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07",
"2019-03-21", "2019-03-30", "2019-4-07"),
Block <- c("43L","43L", "43L",
"15B", "15B", "15B",
"43L", "43L","43L",
"15B", "15B", "15B",
"43L", "43L","43L",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B",
"15B", "15B", "15B"),
Preis <- c("24", "35", "30",
"35", "45", "40",
"26", "35","30",
"35", "45", "40",
"25", "35","30",
"34", "43", "42",
"35", "42", "45",
"36", "45", "43",
"36", "43", "40",
"35", "41", "41.67",
"32", "42", "41.67",
"30", "42", "41.67",
"35","43.11","41.67"),
Max <- c("3", "3", "3", "10", "10","10",
"3", "3", "3",
"10", "10","10",
"3", "3", "3",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10",
"10", "10","10"),
Actual <- c("2", "1", "2",
"10", "9", "6",
"2", "1","2",
"10", "9", "6",
"2", "1","2",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6",
"10", "9", "6"),
Temperatur <- c("15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18",
"15", "20", "18"),
Placesold <- c("1", "1", "1", "1", "1","1",
"1", "0", "1",
"1", "1", "1",
"0", "0", "0",
"1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1", "1",
"1", "1", "0",
"1", "1", "0",
"1", "1", "0",
"1", "0", "0") )
colnames(result) <- c("Date", "Block", "Price", "Max", "Actual", "Temprature", "Placesold")
The results of the blocks and the data as well as the occurrence of the variable "Placesold" look like this:
table(result$Date, result$Block)
table(result$Placesold)
15B 43L
2019-03-21 10 3
2019-03-30 10 3
2019-4-07 10 3
> table(result$Placesold)
0 1
9 30
My first thought was to create a matrix with more rows, but to be honest I don't really know how. I hope you can help me.
Thank you very much.

I use dplyr functions and base::merge. merge can perform cross join between data frames, vectors and other types.
Construction of each date and block pair - it includes unsold blocks of a date:
# ordered, unique vector of dates
dates <- example$Date %>% unique() %>% sort()
# ordered, unique vector of blocks
blocks <- example$Block %>% unique() %>% sort()
# insert dummy block to demonstrate effects of missing blocks
blocks <- c("11B", blocks)
# cross join of dates and blocks: each date paired with each block
# (it results a data.frame)
eachDateBlock <- merge(dates, blocks, by = NULL)
# merge generate x and y as names for the resulted data.frame
# I rename them as a preparation for left_join
eachDateBlock <- eachDateBlock %>% rename(Date = x, Block = y)
# rows from 'eachDateBlock' with matchig row in 'example' get values of variables,
# otherwise they filled by NAs
extendedData <- eachDateBlock %>%
left_join(example, by = c("Date" = "Date", "Block" = "Block"))
# NOTE: before avgPrice you need something similar conversion - I ignore
# other numeric columns here
#example$Price <- as.double(example$Price)
#example$Placesold <- as.double(example$Placesold)
# Overwrite NAs in rows of supplied unsold blocks
avgPrice <- mean(example$Price)
result <- extendedData %>% mutate(
Price = if_else(is.na(Price), avgPrice, Price),
Placesold = if_else(is.na(Placesold), 0, Placesold)
) %>% arrange(Date)
> table(result$Date, result$Block)
11B 15B 43L
2019-03-21 1 10 2
2019-03-30 1 9 1
2019-04-07 1 6 2
> table(result$Placesold)
0 1
3 30
> result
Date Block Price Max Actual Temprature Placesold
1 2019-03-21 11B 37.53333 <NA> <NA> <NA> 0
.
.
.
12 2019-03-21 43L 24.00000 3 2 15 1
13 2019-03-21 43L 26.00000 3 2 15 1
14 2019-03-30 11B 37.53333 <NA> <NA> <NA> 0
15 2019-03-30 15B 45.00000 10 9 20 1
.
.
.
24 2019-03-30 43L 35.00000 3 1 20 1
25 2019-04-07 11B 37.53333 <NA> <NA> <NA> 0
.
.
.
31 2019-04-07 15B 40.00000 10 6 18 1
32 2019-04-07 43L 30.00000 3 2 18 1
33 2019-04-07 43L 30.00000 3 2 18 1

Mark vector of dates using calendR (or other package?)

I have a vector of dates with a certain amount of visits per date and the specific type of visit.
Data (the 'ficol' column can be ignored):
structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
What I would love is to have a calendar with the dates (Var1 in data) to be marked with a color per visit type. For example, visit T0s dates are green, T1s dates purple, T3s dates blue, etc..
I found the package calendR but I can't figure it out for a whole year, only per month using an example found here: . https://r-coder.com/calendar-plot-r/
Can anyone help me out? Greatly appreciated!

library(tidyverse)
library(lubridate)
library(assertr)
library(calendR)
df <- structure(c("2021-06-15", "15", "60", "T0s", "2021-06-16", "15",
"60", "T0s", "2021-06-17", " 8", "32", "T0s", "2021-06-21", "15",
"60", "T0s", "2021-06-22", "15", "60", "T0s", "2021-06-23", "15",
"60", "T0s", "2021-06-24", "15", "60", "T0s", "2021-06-28", "15",
"60", "T0s", "2021-06-29", "15", "60", "T0s", "2021-06-30", "15",
"60", "T0s", "2021-07-01", "15", "60", "T0s", "2021-07-05", "15",
"60", "T0s", "2021-07-06", "15", "60", "T0s", "2021-07-07", "15",
"60", "T0s", "2021-07-08", "15", "60", "T0s", "2021-07-12", "15",
"60", "T0s", "2021-07-13", "15", "60", "T0s", "2021-07-14", "15",
"60", "T0s", "2021-07-15", "15", "60", "T0s", "2021-07-19", "15",
"60", "T0s", "2021-07-20", "15", "60", "T0s", "2021-07-21", "15",
"60", "T0s", "2021-07-22", "15", "60", "T0s", "2021-07-26", "15",
"60", "T0s", "2021-07-27", "15", "60", "T0s", "2021-07-28", "15",
"60", "T0s", "2021-07-29", "15", "60", "T0s", "2021-06-30", "30",
"60", "T1s", "2021-07-01", " 8", "16", "T1s", "2021-07-05", "26",
"52", "T1s", "2021-07-06", "30", "60", "T1s", "2021-07-07", "30",
"60", "T1s", "2021-07-08", " 4", " 8", "T1s", "2021-07-12", " 4",
" 8", "T1s", "2021-07-13", "29", "58", "T1s", "2021-07-14", "27",
"54", "T1s", "2021-07-20", "30", "60", "T1s", "2021-07-21", "30",
"60", "T1s", "2021-07-26", "30", "60", "T1s", "2021-07-27", "30",
"60", "T1s", "2021-07-28", "30", "60", "T1s", "2021-08-02", "30",
"60", "T1s", "2021-08-03", " 8", "16", "T1s", "2021-08-23", "12",
"60", "T3s", "2021-08-24", "12", "60", "T3s", "2021-08-25", "12",
"60", "T3s", "2021-08-26", " 2", "10", "T3s", "2021-08-30", "12",
"60", "T3s", "2021-08-31", "12", "60", "T3s", "2021-09-01", "12",
"60", "T3s", "2021-09-06", "12", "60", "T3s", "2021-09-07", "12",
"60", "T3s", "2021-09-08", "12", "60", "T3s", "2021-09-13", "12",
"60", "T3s", "2021-09-14", "12", "60", "T3s", "2021-09-15", "12",
"60", "T3s", "2021-09-16", "12", "60", "T3s", "2021-09-20", "12",
"60", "T3s", "2021-09-21", "12", "60", "T3s", "2021-09-22", "12",
"60", "T3s", "2021-09-23", "12", "60", "T3s", "2021-09-27", "12",
"60", "T3s", "2022-01-10", "15", "60", "T5s", "2022-01-11", "15",
"60", "T5s", "2022-01-12", " 8", "32", "T5s", "2022-01-17", "15",
"60", "T5s", "2022-01-18", "15", "60", "T5s", "2022-01-19", " 6",
"24", "T5s", "2022-01-24", "15", "60", "T5s", "2022-01-25", "15",
"60", "T5s", "2022-01-26", " 6", "24", "T5s", "2022-01-31", "15",
"60", "T5s", "2022-02-01", "15", "60", "T5s", "2022-02-02", " 6",
"24", "T5s", "2022-02-03", "12", "48", "T5s", "2022-02-07", "15",
"60", "T5s", "2022-02-08", "15", "60", "T5s", "2022-02-09", " 6",
"24", "T5s", "2022-02-10", "15", "60", "T5s", "2022-02-14", " 9",
"36", "T5s"), .Dim = c(4L, 80L), .Dimnames = list(c("Var1", "Freq",
"ficol", "visit"), NULL))
df1 <- df %>%
t() %>%
as_tibble() %>%
mutate(
Var1 = ymd(Var1),
Freq = as.integer(Freq),
ficol = as.integer(ficol)) %>%
rename(date = Var1) %>%
arrange(date)
df2 <- df1 %>%
filter(year(date) == 2021) # choose only 1 year
df2
#> # A tibble: 62 x 4
#> date Freq ficol visit
#> <date> <int> <int> <chr>
#> 1 2021-06-15 15 60 T0s
#> 2 2021-06-16 15 60 T0s
#> 3 2021-06-17 8 32 T0s
#> 4 2021-06-21 15 60 T0s
#> 5 2021-06-22 15 60 T0s
#> 6 2021-06-23 15 60 T0s
#> 7 2021-06-24 15 60 T0s
#> 8 2021-06-28 15 60 T0s
#> 9 2021-06-29 15 60 T0s
#> 10 2021-06-30 15 60 T0s
#> # ... with 52 more rows
# some days have many types of visits, so it's necessary to group them
df3 <- df2 %>%
group_by(date) %>%
summarise(visits = str_c(sort(visit), collapse = ", "),
.groups = "drop")
df3
#> # A tibble: 48 x 2
#> date visits
#> <date> <chr>
#> 1 2021-06-15 T0s
#> 2 2021-06-16 T0s
#> 3 2021-06-17 T0s
#> 4 2021-06-21 T0s
#> 5 2021-06-22 T0s
#> 6 2021-06-23 T0s
#> 7 2021-06-24 T0s
#> 8 2021-06-28 T0s
#> 9 2021-06-29 T0s
#> 10 2021-06-30 T0s, T1s
#> # ... with 38 more rows
df3 %>%
count(visits)
#> # A tibble: 4 x 2
#> visits n
#> <chr> <int>
#> 1 T0s 13
#> 2 T0s, T1s 14
#> 3 T1s 2
#> 4 T3s 19
df4 <- df3 %>%
mutate(color = case_when(
visits == "T0s" ~ "red",
visits == "T0s, T1s" ~ "orange",
visits == "T1s" ~ "yellow",
visits == "T3s" ~ "green"
)) %>%
assertr::verify(!is.na(color)) %>%
full_join(
tibble(date = seq(as.Date("2021-01-01"), as.Date("2022-01-01") - 1, by = "days")),
by = "date"
) %>%
mutate(yday = lubridate::yday(date)) %>%
arrange(date)
df4 %>%
filter(!is.na(visits))
#> # A tibble: 48 x 4
#> date visits color yday
#> <date> <chr> <chr> <dbl>
#> 1 2021-06-15 T0s red 166
#> 2 2021-06-16 T0s red 167
#> 3 2021-06-17 T0s red 168
#> 4 2021-06-21 T0s red 172
#> 5 2021-06-22 T0s red 173
#> 6 2021-06-23 T0s red 174
#> 7 2021-06-24 T0s red 175
#> 8 2021-06-28 T0s red 179
#> 9 2021-06-29 T0s red 180
#> 10 2021-06-30 T0s, T1s orange 181
#> # ... with 38 more rows
calendR::calendR(year = 2021,
start = "M",
special.days = df4$visits,
special.col = unique(na.omit(df4$color)),
legend.pos = "right")

time-series formating: Cannot replicate a time-series format

I am trying to get my data into a specific time-series format. The format I am trying to make my data is similar to the following time series format;
library(fpp)
data(ausbeer)
> str(ausbeer)
Time-Series [1:211] from 1956 to 2008: 284 213 227 308 262 228 236 320 272 233 ...
However my data is currently the following;
> str(wide_DF)
Time-Series [1:5, 1:53] from 1 to 5: 2008 2009 2010 2011 2012 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:53] "year" "1" "2" "3" ...
No matter what I am trying I cannot seem to get it into the same format as the ausbeer data.
Any push in the right direction would be appreciated.
Data:
wide_DF <- structure(c(2008, 2009, 2010, 2011, 2012, 0.149697356812732,
0.506609550726262, 0.483701772054131, 0.340553948856928, 0.333626779091713,
0.0244998111324886, 0.112120844942705, 0.0900944558373256, 0,
0.0415224265012151, 0.0550537737139359, 0.17295508953821, 0.163929433720004,
0.0576641678554561, 0.0906564289945531, 0.0274508134712055, 0.16192922040378,
0.142555512496348, 0.0625454708565096, 0.0713307780915137, 0.213984247872558,
0.388163795230051, 0.164139722545731, 0.0770529539010844, 0.0938540183343052,
0.0783422018092716, 0.227206496783438, 0.35237013136599, 0.258504104665886,
0.321155525044278, 0.0310253280164216, 0.145802804370329, 0.235904612124217,
0.099505662356445, 0.184479613065924, 0.0541816334844162, 0.171606159981382,
0.115107342701831, 0.0741165588765761, 0.108561719279517, 0.0806492605345567,
0.154086848847159, 0.128553880389704, 0.0619227096142703, 0.102441840801919,
0.0877855302949306, 0.210374588670803, 0.168914894757668, 0.0892981276862553,
0.107796585571731, 0.152766825529036, 0.197064573460434, 0.157147609673816,
0.0794331221751312, 0.130178451495829, NA, 0.288013610146669,
0.218033903861127, 0.144165085504355, 0.265694549788369, 0.168423392180753,
0.220217969236187, 0.192778260148724, 0.0616202640553713, 0.208895233807108,
0.172908899350928, 0.273558409751774, NA, 0.131826476698887,
0.214943212753592, 0.185482743591095, NA, 0.264010141661686,
0.137209722798776, NA, 0.213353668598008, 0.288506341574192,
0.265934476984103, 0.166437178815794, 0.213012834405297, 0.229097493059307,
0.326273737259306, 0.209431740094857, NA, 0.240648159088921,
0.261158363124192, 0.317036580243605, 0.244681209115455, 0.166687664239444,
0.240465787771525, 0.282936314890266, 0.376241375996475, 0.288711990429523,
0.218930682309907, 0.294307615813644, 0.340039521860067, 0.381665974567176,
0.289509990005749, 0.222712288785976, 0.302326040229749, 0.592897079173477,
0.707453475415865, 0.315092875222347, 0.238746934161925, 0.360467454111782,
0.437728811188524, 0.485169961326965, 0.686681695697921, 0.513927986995597,
0.657805801598166, 0.413066850628898, 0.420451063363391, 0.452317417206126,
0.392680063685442, 0.467494633248041, 0.490885152462683, 0.449702773878119,
0.374537214449401, 0.314372316775567, 0.352543088557757, 0.456852949424961,
0.502821656395841, 0.473850571102317, 0.37271347773425, 0.468025614416299,
0.492964518353547, 0.491841956261615, 0.451832204837682, 0.330054166675406,
0.452103599554613, 0.972882256833953, 1, 0.836981605987354, 0.735454399633936,
0.625060089794185, 0.420276672512582, 0.44763479957363, 0.51920428542675,
0.484249008420553, 0.828415542650317, 0.439876590158875, 0.458798662510525,
0.446969106246101, 0.329267937698866, 0.402265340895058, 0.443357095278529,
0.48161107578401, 0.421502554574427, 0.35492302612805, 0.389391661815002,
0.480802216652516, 0.496614239968388, 0.41709701215027, 0.355395255525041,
0.427983230181801, 0.426624787626307, 0.47619764751241, 0.390323036410375,
0.346946500338582, 0.444962482661289, 0.398178487457366, 0.460418831412368,
0.365705653465875, 0.314414354295281, 0.404995279601097, 0.395484743345358,
0.447895106385658, 0.333904920716383, 0.315905256117267, 0.38580728350725,
0.61293865090702, 0.392285202440178, 0.300121453991199, 0.318457847197856,
0.382196506098525, 0.42777529076777, 0.655937896884758, 0.579486246422688,
0.512463359506227, 0.601431192394729, 0.283409977946298, 0.430264772601089,
0.321055545570556, 0.311027552565597, 0.419878449584049, 0.295947790026711,
0.323869738229137, 0.215519275318642, 0.192393768801782, 0.326484958316528,
0.317550712975473, 0.303764772399812, 0.215565915142833, 0.177813119709567,
0.288920671391334, 0.299640010568774, 0.258602815268962, 0.208650826721134,
0.192887375961921, 0.273866371013686, 0.300719638221296, 0.260930408982457,
0.214130384575884, 0.20094859121612, 0.262324215127644, 0.291610161608615,
0.240764266638331, 0.232400949526744, 0.190638711181672, 0.194923630854379,
0.247095733415861, 0.250696875411684, 0.17072512824086, 0.142654512656176,
0.221234530015598, 0.336036187889497, 0.337172813493932, 0.241964382857466,
0.188030459289294, 0.247565234387846, 0.228521023231508, 0.227452403443811,
0.156667771761189, 0.131392002677444, 0.229392396017928, 0.213955172137217,
0.229145352317625, 0.145988572682793, 0.1354966579701, 0.164468590746803,
0.273338090020996, 0.28521986301974, 0.199009246024986, 0.178427989941778,
0.218632123403024, 0.485758317106326, 0.478231444703654, 0.371723057102618,
0.358665186970456, 0.437144925882923, 0.143782632825279, 0.132760650342865,
0.0511910889931185, 0.0421970278185858, 0.0830346125807046, 0.169335703112876,
0.127452787871597, 0.0947298145120868, 0.0660661513870076, 0.136073219608577,
0.328666970899003, 0.296331593970631, 0.154669507656273, 0.12819972894051,
0.134629124753297, 0.491065758190125, 0.47654036029283, 0.357442986752192,
NA, 0.388556693139287), .Dim = c(5L, 53L), .Dimnames = list(NULL,
c("year", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
"11", "12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "23", "24", "25", "26", "27", "28", "29", "30",
"31", "32", "33", "34", "35", "36", "37", "38", "39", "40",
"41", "42", "43", "44", "45", "46", "47", "48", "49", "50",
"51", "52")), .Tsp = c(1, 5, 1), class = c("mts", "ts", "matrix"
))
EDIT:
audbeer shape:
> head(ausbeer, 32)
Qtr1 Qtr2 Qtr3 Qtr4
1956 284 213 227 308
1957 262 228 236 320
1958 272 233 237 313
1959 261 227 250 314
1960 286 227 260 311
1961 295 233 257 339
1962 279 250 270 346
1963 294 255 278 363

I think I see what you're trying to do now. Let's just transpose, and fix the column names.
wide_df2 <- as.data.frame(t(as.data.frame(wide_DF)))
# we need as.data.frame() twice here to strip the ts class, and then add the data.frame class back after t()
Set the first row as column names:
names(wide_df2) <- wide_df2[1,]
Remove the column names from the data:
wide_df2 <- wide_df2[-1, ]
And convert back to ts:
wide_df2 <- ts(wide_df2)
2008 2009 2010 2011 2012
1 0.14969736 0.5066096 0.48370177 0.34055395 0.33362678
2 0.02449981 0.1121208 0.09009446 0.00000000 0.04152243
3 0.05505377 0.1729551 0.16392943 0.05766417 0.09065643
4 0.02745081 0.1619292 0.14255551 0.06254547 0.07133078
5 0.21398425 0.3881638 0.16413972 0.07705295 0.09385402
6 0.07834220 0.2272065 0.35237013 0.25850410 0.32115553
7 0.03102533 0.1458028 0.23590461 0.09950566 0.18447961
8 0.05418163 0.1716062 0.11510734 0.07411656 0.10856172

Avoid reordering and grouping duplicate x-values ggplot (geom_point)

I'm trying to plot the following data frame with ggplot. This data frame has an ordered summary of top10 scores from 13 separate clusters (10x13 = 130 total observations)
Cell.Type Score
1 GN_Thio_PC 2677.80617009519
2 GN_UrAc_PC 2637.41032364779
3 Mo_6C+II-_Bl 2556.92913594902
4 GN_Arth_SynF 2391.45433001888
5 Mo_6C+II+_Bl 2315.52547519278
6 GN_Bl 2304.98201202492
7 MF_Thio5_II-480int_PC 2285.71825571867
8 MF_Thio5_II+480int_PC 2248.11270401521
9 MF_RP_Sp 2224.65822294734
10 GN_BM 2069.57828218951
11 T_8Mem_Sp 3650.92933141558
12 NK_b2m-_Sp 3623.07526981183
13 NK_DAP10-_Sp 3568.82957776803
14 T_8Nve_Sp_OT1 3535.57114525684
15 T_8Mem_Sp_OT1_d100_LisOva 3532.02217747173
16 NK_49H+_Sp 3519.49779859704
17 NK_DAP12-_Sp 3500.5532642101
18 T_8Eff_Sp_OT1_d10_LisOva 3448.56816636704
19 NKT_4+_Lv 3445.33162798595
20 T_8Nve_LN 3442.41228249856
21 DC_8+_Th 1384.56532273906
22 DC_8+_SLN 1333.48389922898
23 DC_8-_Th 1329.54597466529
24 DC_103+11b-_Lu 1293.2048614532
25 B_GC_Sp 1291.13567921318
26 DC_8+_MLN 1266.18747131352
27 DC_8+_Sp_ST 1251.44702553637
28 SC_MEP_BM 1229.9373063931
29 DC_8-4-11b-_Sp 1204.49733058435
30 DC_103+11b-_Lv 1196.79647629317
31 T_8Mem_Sp 4888.53608836612
32 T_8Nve_Sp_OT1 4844.62387372193
33 T_8Nve_LN 4803.08591833927
34 T_8Nve_MLN 4801.60498804064
35 T_4Nve_LN 4704.34332374882
36 T_8Nve_PP 4680.54098917638
37 T_8Nve_Sp 4668.51868809073
38 T_4Nve_PP 4654.6363768553
39 T_4Nve_MLN 4644.53632493487
40 T_4FP3-_Sp 4643.81716614074
41 MF_Thio5_II-480int_PC 2104.44279848771
42 MF_Thio5_II-480hi_PC 2051.91548567208
43 MF_PPAR-_Lu 1971.01987723135
44 MF_Thio5_II+480int_PC 1968.1818651747
45 MF_Lu 1941.36173402858
46 MF_II-480hi_PC 1821.13874693704
47 MF_Thio5_II+480lo_PC 1787.05712946341
48 GN_Thio_PC 1728.7523034795
49 MF_Microglia_CNS 1711.6599582643
50 MF_II+480lo_PC 1711.26332938833
51 Mo_6C+II-_Bl 2962.12603781126
52 Mo_6C-II-_Bl 2758.18375042302
53 Mo_6C-IIint_Bl 2638.16680094079
54 GN_Bl 2620.07541962536
55 GN_UrAc_PC 2537.2896087047
56 Mo_6C+II+_Bl 2435.36956544536
57 GN_BM 2387.13935841906
58 GN_Thio_PC 2337.29884997719
59 GN_Arth_BM 2311.2451915426
60 GN_Arth_SynF 2128.725307006
61 T_8Eff_Sp_OT1_48hr_LisOva 3285.38489328741
62 T_8Eff_Sp_OT1_24hr_LisOva 3069.01874851731
63 T_8Mem_Sp 3022.56013472619
64 T_ISP_Th 2983.45678085374
65 T_8Eff_Sp_OT1_12hr_LisOva 2964.79056150505
66 T_8Eff_Sp_OT1_d5_VSVOva 2950.29516615634
67 T_8Nve_Sp_OT1 2893.7887214778
68 T_8Eff_Sp_OT1_d6_LisOva 2891.41381948125
69 Tgd_vg3+24alo_e17_Th 2875.40895460188
70 T_8SP24-_Th 2858.05344865649
71 MF_Thio5_II-480int_PC 2783.70950776927
72 MF_Thio5_II-480hi_PC 2737.88858084566
73 MF_Thio5_II+480int_PC 2708.73493567958
74 MF_II-480hi_PC 2377.99196863673
75 MF_RP_Sp 2281.78751440853
76 MF_Microglia_CNS 2145.29897799
77 MF_PPAR-_Lu 2089.46703313723
78 Fi_Sk 2077.00426240616
79 MF_II+480lo_PC 2070.33177217184
80 MF_Thio5_II+480lo_PC 2049.00134439134
81 GN_Thio_PC 3158.65427762739
82 GN_UrAc_PC 2993.45396316058
83 Mo_6C+II-_Bl 2807.36027869234
84 GN_Arth_SynF 2783.56931762011
85 GN_Bl 2666.31559591767
86 Mo_6C+II+_Bl 2472.0977029947
87 GN_BM 2422.62741443588
88 Mo_6C-II-_Bl 2309.43925461481
89 Mo_6C-IIint_Bl 2238.0777055497
90 GN_Arth_BM 2215.70702972594
91 GN_Arth_SynF 3027.3451404511
92 GN_Thio_PC 2939.74912223882
93 GN_UrAc_PC 2694.04395500259
94 MF_Thio5_II-480hi_PC 2507.39045396954
95 GN_Bl 2407.18406139123
96 MF_Thio5_II-480int_PC 2380.65584862485
97 Fi_Sk 2211.83581518875
98 Fi_MTS15+_Th 2209.41411415371
99 MF_Microglia_CNS 2149.92996548155
100 MF_RP_Sp 2111.76895702472
101 T_8Eff_Sp_OT1_48hr_LisOva 4316.4227070348
102 T_ISP_Th 4280.25335061696
103 T_DPbl_Th 4102.01504953757
104 preT_DN3B_Th 3910.32665898991
105 preT_DN3-4_Th 3907.5798288054
106 T_DN4_Th 3840.18882533614
107 SC_MEP_BM 3780.57141700037
108 T_8Eff_Sp_OT1_d5_VSVOva 3757.01659494412
109 Tgd_vg3+24alo_e17_Th 3685.84648926922
110 Tgd_vg5+24ahi_Th 3616.99224871103
111 DC_IIhilang+103+11blo_SLN 4519.50952669406
112 DC_IIhilang+103-11b+_SLN 4415.97080261725
113 DC_IIhilang-103-11blo_SLN 4170.40873917108
114 DC_IIhilang-103-11b+_SLN 3963.46358118485
115 DC_8-4-11b-_MLN 3631.2974118135
116 DC_8-4-11b+_MLN 3386.67029828899
117 DC_8-4-11b+_SLN 3026.47679955977
118 Ep_MEChi_Th 2844.36034968535
119 DC_8-4-11b-_SLN 2835.94178377956
120 DC_8+_MLN 2183.62863550565
121 DC_pDC_8+_SLN 3785.05815189249
122 DC_pDC_8+_Sp 3767.7193092587
123 DC_pDC_8+_MLN 3758.58340817543
124 DC_pDC_8-_Sp 3747.60526193027
125 B_T1_Sp 2325.20093650829
126 B_T2_Sp 2316.21996448563
127 B_Fo_Sp 2253.78988549461
128 B_T3_Sp 2225.2075463753
129 B_Fo_MLN 2159.05742315915
130 B_Fo_PC 2142.51258891406
to obtain a graph like this (which was generated by using the base graphing functions by using a rather ugly code):
The problem I'm running into is that ggplot groups and reorders the data instead of keeping the original order. How can I stop this behavior?
On another note, is there a better way of structuring this data frame for it to better work with ggplot? I'd like to visualize the top10 scores of all clusters visually separated but side-by-side (like in the image I attached above). My ultimate goal is making my code scalable so it can work with different numbers of starting clusters (i.e. 18 clusters as opposed to 13 clusters here) and top scores (ie. top5 as opposed to top10 here) amount just as well with minimal code re-writing.

It is a little tricky because you have duplicate labels in the different groups, so the standard advice to convert the x-axis labels to a factor is not quite enough. Here I made a copy of cell.type before merging cell.type and group id, the graph is plotted with with this and then the labels switched. I used facets to show the labels
The graph needs to be quite large to work
library("tidyverse")
df2 <- df %>% mutate(id = rep(1:13, each = 10),
Cell.Type.label = Cell.Type,
Cell.Type = paste(Cell.Type, id, sep = "_"),
Cell.Type = factor(Cell.Type, levels = Cell.Type))
df2 %>%
ggplot(aes(x = Cell.Type, y = Score, colour = as.factor(id))) +
geom_point(show.legend = FALSE) +
facet_wrap(~id, nrow = 1, scales = "free_x") +
scale_x_discrete(labels = df2$Cell.Type.label) +
theme(panel.spacing = unit(x = 0, units = "pt"),
axis.text.x = element_text(angle = 90, hjust = 1, size = 4))
data
df <- structure(list(Cell.Type = c("GN_Thio_PC", "GN_UrAc_PC", "Mo_6C+II-_Bl",
"GN_Arth_SynF", "Mo_6C+II+_Bl", "GN_Bl", "MF_Thio5_II-480int_PC",
"MF_Thio5_II+480int_PC", "MF_RP_Sp", "GN_BM", "T_8Mem_Sp", "NK_b2m-_Sp",
"NK_DAP10-_Sp", "T_8Nve_Sp_OT1", "T_8Mem_Sp_OT1_d100_LisOva",
"NK_49H+_Sp", "NK_DAP12-_Sp", "T_8Eff_Sp_OT1_d10_LisOva", "NKT_4+_Lv",
"T_8Nve_LN", "DC_8+_Th", "DC_8+_SLN", "DC_8-_Th", "DC_103+11b-_Lu",
"B_GC_Sp", "DC_8+_MLN", "DC_8+_Sp_ST", "SC_MEP_BM", "DC_8-4-11b-_Sp",
"DC_103+11b-_Lv", "T_8Mem_Sp", "T_8Nve_Sp_OT1", "T_8Nve_LN",
"T_8Nve_MLN", "T_4Nve_LN", "T_8Nve_PP", "T_8Nve_Sp", "T_4Nve_PP",
"T_4Nve_MLN", "T_4FP3-_Sp", "MF_Thio5_II-480int_PC", "MF_Thio5_II-480hi_PC",
"MF_PPAR-_Lu", "MF_Thio5_II+480int_PC", "MF_Lu", "MF_II-480hi_PC",
"MF_Thio5_II+480lo_PC", "GN_Thio_PC", "MF_Microglia_CNS", "MF_II+480lo_PC",
"Mo_6C+II-_Bl", "Mo_6C-II-_Bl", "Mo_6C-IIint_Bl", "GN_Bl", "GN_UrAc_PC",
"Mo_6C+II+_Bl", "GN_BM", "GN_Thio_PC", "GN_Arth_BM", "GN_Arth_SynF",
"T_8Eff_Sp_OT1_48hr_LisOva", "T_8Eff_Sp_OT1_24hr_LisOva", "T_8Mem_Sp",
"T_ISP_Th", "T_8Eff_Sp_OT1_12hr_LisOva", "T_8Eff_Sp_OT1_d5_VSVOva",
"T_8Nve_Sp_OT1", "T_8Eff_Sp_OT1_d6_LisOva", "Tgd_vg3+24alo_e17_Th",
"T_8SP24-_Th", "MF_Thio5_II-480int_PC", "MF_Thio5_II-480hi_PC",
"MF_Thio5_II+480int_PC", "MF_II-480hi_PC", "MF_RP_Sp", "MF_Microglia_CNS",
"MF_PPAR-_Lu", "Fi_Sk", "MF_II+480lo_PC", "MF_Thio5_II+480lo_PC",
"GN_Thio_PC", "GN_UrAc_PC", "Mo_6C+II-_Bl", "GN_Arth_SynF", "GN_Bl",
"Mo_6C+II+_Bl", "GN_BM", "Mo_6C-II-_Bl", "Mo_6C-IIint_Bl", "GN_Arth_BM",
"GN_Arth_SynF", "GN_Thio_PC", "GN_UrAc_PC", "MF_Thio5_II-480hi_PC",
"GN_Bl", "MF_Thio5_II-480int_PC", "Fi_Sk", "Fi_MTS15+_Th", "MF_Microglia_CNS",
"MF_RP_Sp", "T_8Eff_Sp_OT1_48hr_LisOva", "T_ISP_Th", "T_DPbl_Th",
"preT_DN3B_Th", "preT_DN3-4_Th", "T_DN4_Th", "SC_MEP_BM", "T_8Eff_Sp_OT1_d5_VSVOva",
"Tgd_vg3+24alo_e17_Th", "Tgd_vg5+24ahi_Th", "DC_IIhilang+103+11blo_SLN",
"DC_IIhilang+103-11b+_SLN", "DC_IIhilang-103-11blo_SLN", "DC_IIhilang-103-11b+_SLN",
"DC_8-4-11b-_MLN", "DC_8-4-11b+_MLN", "DC_8-4-11b+_SLN", "Ep_MEChi_Th",
"DC_8-4-11b-_SLN", "DC_8+_MLN", "DC_pDC_8+_SLN", "DC_pDC_8+_Sp",
"DC_pDC_8+_MLN", "DC_pDC_8-_Sp", "B_T1_Sp", "B_T2_Sp", "B_Fo_Sp",
"B_T3_Sp", "B_Fo_MLN", "B_Fo_PC"), Score = c(2677.80617009519,
2637.41032364779, 2556.92913594902, 2391.45433001888, 2315.52547519278,
2304.98201202492, 2285.71825571867, 2248.11270401521, 2224.65822294734,
2069.57828218951, 3650.92933141558, 3623.07526981183, 3568.82957776803,
3535.57114525684, 3532.02217747173, 3519.49779859704, 3500.5532642101,
3448.56816636704, 3445.33162798595, 3442.41228249856, 1384.56532273906,
1333.48389922898, 1329.54597466529, 1293.2048614532, 1291.13567921318,
1266.18747131352, 1251.44702553637, 1229.9373063931, 1204.49733058435,
1196.79647629317, 4888.53608836612, 4844.62387372193, 4803.08591833927,
4801.60498804064, 4704.34332374882, 4680.54098917638, 4668.51868809073,
4654.6363768553, 4644.53632493487, 4643.81716614074, 2104.44279848771,
2051.91548567208, 1971.01987723135, 1968.1818651747, 1941.36173402858,
1821.13874693704, 1787.05712946341, 1728.7523034795, 1711.6599582643,
1711.26332938833, 2962.12603781126, 2758.18375042302, 2638.16680094079,
2620.07541962536, 2537.2896087047, 2435.36956544536, 2387.13935841906,
2337.29884997719, 2311.2451915426, 2128.725307006, 3285.38489328741,
3069.01874851731, 3022.56013472619, 2983.45678085374, 2964.79056150505,
2950.29516615634, 2893.7887214778, 2891.41381948125, 2875.40895460188,
2858.05344865649, 2783.70950776927, 2737.88858084566, 2708.73493567958,
2377.99196863673, 2281.78751440853, 2145.29897799, 2089.46703313723,
2077.00426240616, 2070.33177217184, 2049.00134439134, 3158.65427762739,
2993.45396316058, 2807.36027869234, 2783.56931762011, 2666.31559591767,
2472.0977029947, 2422.62741443588, 2309.43925461481, 2238.0777055497,
2215.70702972594, 3027.3451404511, 2939.74912223882, 2694.04395500259,
2507.39045396954, 2407.18406139123, 2380.65584862485, 2211.83581518875,
2209.41411415371, 2149.92996548155, 2111.76895702472, 4316.4227070348,
4280.25335061696, 4102.01504953757, 3910.32665898991, 3907.5798288054,
3840.18882533614, 3780.57141700037, 3757.01659494412, 3685.84648926922,
3616.99224871103, 4519.50952669406, 4415.97080261725, 4170.40873917108,
3963.46358118485, 3631.2974118135, 3386.67029828899, 3026.47679955977,
2844.36034968535, 2835.94178377956, 2183.62863550565, 3785.05815189249,
3767.7193092587, 3758.58340817543, 3747.60526193027, 2325.20093650829,
2316.21996448563, 2253.78988549461, 2225.2075463753, 2159.05742315915,
2142.51258891406)), .Names = c("Cell.Type", "Score"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79",
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90",
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100",
"101", "102", "103", "104", "105", "106", "107", "108", "109",
"110", "111", "112", "113", "114", "115", "116", "117", "118",
"119", "120", "121", "122", "123", "124", "125", "126", "127",
"128", "129", "130"))

Sorting rows of data frame based on time stamp

I have a data frame, the rows of which I would like to sort based on time stamp.
V1 V2 V3 V4 V5 V6
1 {"2014-08-01T01:00:00": "64", "2014-08-01T13:00:00": "53", "2014-08-01T01:20:00": "73",
2 {"2014-08-02T18:00:00": "37", "2014-08-02T22:00:00": "56", "2014-08-02T17:00:00": "24",
3 {"2014-08-03T17:50:00": "78", "2014-08-03T04:20:00": "83", "2014-08-03T00:20:00": "73",
4 {"2014-08-04T15:00:00": "37", "2014-08-04T21:00:00": "39", "2014-08-04T15:20:00": "43",
5 {"2014-08-05T19:20:00": "78", "2014-08-05T13:20:00": "46", "2014-08-05T00:00:00": "62",
6 {"2014-08-06T11:00:00": "45", "2014-08-06T09:00:00": "56", "2014-08-06T21:50:00": "68",
V7 V8 V9 V10 V11 V12
1 "2014-08-01T13:20:00": "57", "2014-08-01T13:50:00": "47", "2014-08-01T20:50:00": "44",
2 "2014-08-02T01:00:00": "56", "2014-08-02T17:20:00": "42", "2014-08-02T01:20:00": "68",
3 "2014-08-03T23:00:00": "81", "2014-08-03T00:00:00": "63", "2014-08-03T00:50:00": "73",
4 "2014-08-04T02:00:00": "81", "2014-08-04T18:00:00": "29", "2014-08-04T02:20:00": "88",
5 "2014-08-05T00:20:00": "72", "2014-08-05T00:50:00": "77", "2014-08-05T19:00:00": "75",
6 "2014-08-06T14:20:00": "53", "2014-08-06T14:00:00": "40", "2014-08-06T23:20:00": "77",
Desired out
The output of only one row is shown below.
{"2014-08-01T01:00:00": "64", "2014-08-01T01:20:00": "73", "2014-08-01T13:00:00": "53", "2014-08-01T13:20:00": "57", "2014-08-01T13:50:00": "47", "2014-08-01T20:50:00": "44",

We convert the datetime columns (df2[c(TRUE, FALSE)]- we are subsetting by recycling the logical vector) to POSIXct class by looping through the columns with lapply, then order by row using apply with MARGIN=1 ('m1'). We split the time columns and the value columns by row to create two lists 'l1', 'l2', then use Map with paste to concatenate the string together after we order the elements based on 'm1'. This can be converted to data.frame with one column.
df2[c(TRUE, FALSE)] <- lapply(df1[c(TRUE, FALSE)], function(x) as.POSIXct(sub('[{]', '', x), format = '%Y-%m-%dT%H:%M:%S:'))
m1 <- apply(df2[c(TRUE, FALSE)], 1, order)
l1 <- split(as.matrix(df1[c(TRUE, FALSE)]), row(df1[c(TRUE, FALSE)]))
l2 <- split(as.matrix(df2[c(FALSE, TRUE)]), row(df2[c( FALSE, TRUE)]))
data.frame(col1=unlist(Map(function(x,y,z) paste0('{',
paste(gsub('^\\{*(\\d+.*)(\\:)', '"\\1"\\2', x[z]),
gsub('(\\d+)', '"\\1"', y[z]), sep=' ', collapse=' ')),
l1, l2, split(m1, col(m1)))), stringsAsFactors=FALSE)
col1
#1 {"2014-08-01T01:00:00": "64", "2014-08-01T01:20:00": "73", "2014-08-01T13:00:00": "53", "2014-08-01T13:20:00": "57", "2014-08-01T13:50:00": "47", "2014-08-01T20:50:00": "44",
#2 {"2014-08-02T01:00:00": "56", "2014-08-02T01:20:00": "68", "2014-08-02T17:00:00": "24", "2014-08-02T17:20:00": "42", "2014-08-02T18:00:00": "37", "2014-08-02T22:00:00": "56",
#3 {"2014-08-03T00:00:00": "63", "2014-08-03T00:20:00": "73", "2014-08-03T00:50:00": "73", "2014-08-03T04:20:00": "83", "2014-08-03T17:50:00": "78", "2014-08-03T23:00:00": "81",
#4 {"2014-08-04T02:00:00": "81", "2014-08-04T02:20:00": "88", "2014-08-04T15:00:00": "37", "2014-08-04T15:20:00": "43", "2014-08-04T18:00:00": "29", "2014-08-04T21:00:00": "39",
#5 {"2014-08-05T00:00:00": "62", "2014-08-05T00:20:00": "72", "2014-08-05T00:50:00": "77", "2014-08-05T13:20:00": "46", "2014-08-05T19:00:00": "75", "2014-08-05T19:20:00": "78",
#6 {"2014-08-06T09:00:00": "56", "2014-08-06T11:00:00": "45", "2014-08-06T14:00:00": "40", "2014-08-06T14:20:00": "53", "2014-08-06T21:50:00": "68", "2014-08-06T23:20:00": "77",
data
lines <- readLines(textConnection('V1 V2 V3 V4 V5 V6
1 {"2014-08-01T01:00:00": "64", "2014-08-01T13:00:00": "53", "2014-08-01T01:20:00": "73",
2 {"2014-08-02T18:00:00": "37", "2014-08-02T22:00:00": "56", "2014-08-02T17:00:00": "24",
3 {"2014-08-03T17:50:00": "78", "2014-08-03T04:20:00": "83", "2014-08-03T00:20:00": "73",
4 {"2014-08-04T15:00:00": "37", "2014-08-04T21:00:00": "39", "2014-08-04T15:20:00": "43",
5 {"2014-08-05T19:20:00": "78", "2014-08-05T13:20:00": "46", "2014-08-05T00:00:00": "62",
6 {"2014-08-06T11:00:00": "45", "2014-08-06T09:00:00": "56", "2014-08-06T21:50:00": "68",'))
lines2 <- readLines(textConnection('V7 V8 V9 V10 V11 V12
1 "2014-08-01T13:20:00": "57", "2014-08-01T13:50:00": "47", "2014-08-01T20:50:00": "44",
2 "2014-08-02T01:00:00": "56", "2014-08-02T17:20:00": "42", "2014-08-02T01:20:00": "68",
3 "2014-08-03T23:00:00": "81", "2014-08-03T00:00:00": "63", "2014-08-03T00:50:00": "73",
4 "2014-08-04T02:00:00": "81", "2014-08-04T18:00:00": "29", "2014-08-04T02:20:00": "88",
5 "2014-08-05T00:20:00": "72", "2014-08-05T00:50:00": "77", "2014-08-05T19:00:00": "75",
6 "2014-08-06T14:20:00": "53", "2014-08-06T14:00:00": "40", "2014-08-06T23:20:00": "77",'))
d1 <- read.table(text=gsub('^\\d+\\s+|"', '', lines), header=TRUE, stringsAsFactors=FALSE)
d2 <- read.table(text=gsub('^\\d+\\s+|"', '', lines2), header=TRUE, stringsAsFactors=FALSE)
df1 <- cbind(d1, d2)
df2 <- df1

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