Related
I have a dataset like this
Time1 Time2 Time3
A
Median 0.046 0.12 0
Q1, Q3 -0.12, 0.22 -1.67, -4.59 -0.245, 0.289
Range -2.75 -4.65 -2.20 - 1.425 -3.12, -1.928
B
Median 0.016 0.42 0.067
Q1, Q3 -0.21, 0.63 -1.17, -2.98 -0.478, 0.187
Range -2.15 -2.15 -1.12 - 1.125 -1.45, -1.478
What I want is to make this look like this
Time1 Time2 Time3
Median Q1,Q3 Range Median Q1,Q3 Range Median Q1,Q3 Range
A 0.046 -0.12, 0.22 2.75 -4.65 0.12 -1.67, -4.59 -2.20 - 1.425 0 -0.245, 0.289 -3.12, -1.928
B 0.016 -0.21, 0.63 -2.15 -2.15 0.42 -1.17, -2.98 -1.12 - 1.125 0.067 -0.478, 0.187 -1.45, -1.478
I have used spread function before to change long to wide, not sure how to turn this into a nested wide. Any suggestions is much appreciated.
df <- structure(list(Col1 = c("A", "Median", "Q1, Q3", "Range", "B",
"Median", "Q1, Q3", "Range"), Time1 = c("", "0.046", "-0.12, 0.22",
"-2.75 -4.65", "", "0.016", "-0.21, 0.63", "-2.15 -2.15"), Time2 = c("",
"0.12", "-1.67, -4.59", "-2.20 - 1.425", "", "0.42", "-1.17, -2.98",
"-1.12 - 1.125"), Time3 = c("", "0 ", "-0.245, 0.289 ",
"-3.12, -1.928", "", "0.067 ", "-0.478, 0.187 ", "-1.45, -1.478"
)), class = "data.frame", row.names = c(NA, -8L))
Here is a potential solution, see comments for the step by step.
library(tidyr)
#find rows containing the ids
namerows <- which(df$Time1=="")
#create and fill in the id column
df$id <- ifelse(df$Time1=="", df$Col1, NA)
df <- fill(df, id, .direction="down")
#clean up the dataframe
df <- df[-namerows, ]
#pivot
pivot_wider(df, id_cols = "id", names_from = "Col1", values_from = starts_with("Time"))
The result:
# A tibble: 2 × 10
id Time1_Median `Time1_Q1, Q3` Time1_Range Time2_Median `Time2_Q1, Q3` Time2_Range Time3_Median `Time3_Q1, Q3` Time3_Range
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 A 0.046 -0.12, 0.22 -2.75 -4.65 0.12 -1.67, -4.59 -2.20 - 1.425 "0 " "-0.245, 0.289 " -3.12, -1.928
2 B 0.016 -0.21, 0.63 -2.15 -2.15 0.42 -1.17, -2.98 -1.12 - 1.125 "0.067 " "-0.478, 0.187 " -1.45, -1.478
I am trying to calculate cumulated acetone and acetaldehyde emission from different soil incubations across three time points. Emission of the compounds was measured from six soils (of different soil_types) on three days. I wish to calculate the cumulated emission for each soil for each time point.
The end goal is to calculate the average emission from all soils and present a graph similar to this one (except there should be error bars on my graph):
Can anyone spot where I'm going wrong?
Here's the code:
library(tidyverse)
library(plotrix)
df%>%
group_by(soil, compound, days)%>%
mutate(cum_emission=cumsum(emission))%>%
summarise(mean=mean(cum_emission, na.rm = TRUE),
sd = sd(cum_emission, na.rm = TRUE),
se = std.error(cum_emission, na.rm = TRUE))
Here's the data:
df <- structure(list(days = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,
4, 4, 4, 4, 4, 4, 4, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 4, 4, 4, 4), soil = c(12, 12, 2, 2, 1, 1, 9, 9, 13, 13,
3, 3, 12, 12, 2, 2, 1, 1, 9, 9, 12, 12, 2, 2, 1, 1, 9, 9, 13,
13, 3, 3, 13, 13, 3, 3), soil_type = c("organic", "organic",
"mineral", "mineral", "mineral", "mineral", "organic", "organic",
"organic", "organic", "mineral", "mineral", "organic", "organic",
"mineral", "mineral", "mineral", "mineral", "organic", "organic",
"organic", "organic", "mineral", "mineral", "mineral", "mineral",
"organic", "organic", "organic", "organic", "mineral", "mineral",
"organic", "organic", "mineral", "mineral"), compound = c("Acetone",
"Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde",
"Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone",
"Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde",
"Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone",
"Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde",
"Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone",
"Acetaldehyde", "Acetone", "Acetaldehyde", "Acetone", "Acetaldehyde"
), emission = c(0.01, 0, 0.03, 0.03, 0.07, 0.06, 0.33, 0.1, 0.02,
0.01, 0.01, 0, 0.02, 0.01, 0.07, 0.08, 0.09, 0.07, 0.32, 0.22,
0.01, 0, 0.06, 0.06, 0.08, 0.06, 0.23, 0.14, 0.4, 0.04, 0.14,
0, 0.05, 0.05, 0.14, 0)), row.names = c(NA, -36L), class = c("tbl_df",
"tbl", "data.frame"))
This only addresses the setup of the data, not the plotting. (sorry for the partial answer!)
You wrote that you wanted to group by soil, compound, days, did you mean soil_type, compound, days? As #maarvd pointed out, with soil, every row is unique.
When I modified the content to
df %>%
group_by(soil_type, compound, days)%>%
mutate(cum_emission=cumsum(emission))%>%
summarise(mean=mean(cum_emission, na.rm = TRUE),
sd = sd(cum_emission, na.rm = TRUE),
se = std.error(cum_emission, na.rm = TRUE))
I was able to render the following results
# A tibble: 12 x 6
# Groups: soil_type, compound [4]
soil_type compound days mean sd se
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 mineral Acetaldehyde 0 0.0700 0.0346 0.02
2 mineral Acetaldehyde 4 0.127 0.0404 0.0233
3 mineral Acetaldehyde 10 0.10 0.0346 0.02
4 mineral Acetone 0 0.08 0.0436 0.0252
5 mineral Acetone 4 0.177 0.116 0.0669
6 mineral Acetone 10 0.16 0.111 0.0643
7 organic Acetaldehyde 0 0.07 0.0608 0.0351
8 organic Acetaldehyde 4 0.173 0.144 0.0829
9 organic Acetaldehyde 10 0.107 0.0945 0.0546
10 organic Acetone 0 0.237 0.197 0.113
11 organic Acetone 4 0.25 0.201 0.116
12 organic Acetone 10 0.297 0.319 0.184
** changes based on #Tiptop's comment
If you're looking for the cumulative, moving averages, how about this?
I'm sure some of this I didn't originally write, but wherever it originated, I've repurposed it many times.
You won't need plotrix, but you will need the library tidyquant.
library(tidyverse)
library(tidyquant)
UDF_roll <- function(x, na.rm = TRUE) {
m <- mean(x, na.rm = na.rm) # calculate the average (for the rolling average)
s <- sd(x, na.rm = na.rm) # calculate the sd to find the confidence interval
hi <- m + 2*s # CI HI
lo <- m - 2*s # CI Low
vals <- c(Mean = m, SD = s, HI.95 = hi, LO.95 = lo)
return(vals)
}
# loop for each type of compound (I'm assuming that the data you provided is a sample and you have more.)
trends <- vector("list") # empty list to store the results
cp = unique(df$compound) # create a list of unique compound names
for(i in 1:length(unique(df$compound))){ # loop through each compound
trends[[i]] <- df %>% as.data.frame() %>% # add results to the list
filter(compound == cp[i]) %>% # for one compound
arrange(days) %>%
# the rolling functions requires time series with a date; so random dates added as controller
mutate(time = seq(as.Date("2010/1/1"),
by = "month",
length.out = nrow(.)),
cum_emission = cumsum(emission)) %>%
arrange(compound,-days) %>% # most recent on top for TS
tq_mutate(select = cum_emission, # collect mean, sd, error
mutate_fun = rollapply,
width = 2, # 2: current & previous reading
align = "right",
by.column = FALSE,
FUN = UDF_roll, # calls the function UDF
na.rm = TRUE) %>%
ggplot(aes(x = seq_along(time))) +
geom_point(aes(y = cum_emission),
color = "black", alpha = 0.2) + # cumulative
geom_ribbon(aes(ymin = LO.95, ymax = HI.95),
fill = "azure3", alpha = 0.4) + # confidence interval
geom_jitter(aes(y = Mean, color= Mean),
size = 1, alpha = 0.9) + # rolling average
labs(title = paste0(cp[[i]], ": Trends and Volatility\nIncremental Moving Average with 95% CI Bands (+/-2 SD)"),
x = "", y = "Soil Emissions") +
scale_color_viridis_c(end = .8) + theme_bw() +
theme(legend.position="none")
}
trends[[1]]
trends[[2]]
trends[[1]]$data # you can NULL the time column if you use the data another way
This makes the data time series. The plots:
The data is shown below. If you wanted to group it differently, you'll have to add the argument .groups = "drop" to the summarise() call, or you won't be able to get it through tq_mutate.
# A tibble: 18 x 11
days soil soil_type compound emission time cum_emission Mean SD HI.95 LO.95
<dbl> <dbl> <chr> <chr> <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 12 organic Acetone 0.01 2010-01-01 0.01 NA NA NA NA
2 0 2 mineral Acetone 0.03 2010-02-01 0.04 0.025 0.0212 0.0674 -0.0174
3 0 1 mineral Acetone 0.07 2010-03-01 0.11 0.075 0.0495 0.174 -0.0240
4 0 9 organic Acetone 0.33 2010-04-01 0.44 0.275 0.233 0.742 -0.192
5 0 13 organic Acetone 0.02 2010-05-01 0.46 0.45 0.0141 0.478 0.422
6 0 3 mineral Acetone 0.01 2010-06-01 0.47 0.465 0.00707 0.479 0.451
7 4 12 organic Acetone 0.02 2010-07-01 0.49 0.48 0.0141 0.508 0.452
8 4 2 mineral Acetone 0.07 2010-08-01 0.56 0.525 0.0495 0.624 0.426
9 4 1 mineral Acetone 0.09 2010-09-01 0.65 0.605 0.0636 0.732 0.478
10 4 9 organic Acetone 0.32 2010-10-01 0.97 0.81 0.226 1.26 0.357
11 4 13 organic Acetone 0.05 2010-11-01 1.02 0.995 0.0354 1.07 0.924
12 4 3 mineral Acetone 0.14 2010-12-01 1.16 1.09 0.0990 1.29 0.892
13 10 12 organic Acetone 0.01 2011-01-01 1.17 1.16 0.00707 1.18 1.15
14 10 2 mineral Acetone 0.06 2011-02-01 1.23 1.2 0.0424 1.28 1.12
15 10 1 mineral Acetone 0.08 2011-03-01 1.31 1.27 0.0566 1.38 1.16
16 10 9 organic Acetone 0.23 2011-04-01 1.54 1.42 0.163 1.75 1.10
17 10 13 organic Acetone 0.4 2011-05-01 1.94 1.74 0.283 2.31 1.17
18 10 3 mineral Acetone 0.14 2011-06-01 2.08 2.01 0.0990 2.21 1.81
I have the following data.
name x1 x2 x3 x4
1 V1_3 1 0 999 999
2 V2_3 1.12 0.044 25.4 0
3 V3_3 0.917 0.045 20.4 0
4 V1_15 1 0 999 999
5 V2_15 1.07 0.036 29.8 0
6 V3_15 0.867 0.039 22.5 0
7 V1_25 1 0 999 999
8 V2_25 1.07 0.034 31.1 0
9 V3_25 0.917 0.037 24.6 0
10 V1_35 1 0 999 999
11 V2_35 1.05 0.034 31.2 0
12 V3_35 0.994 0.037 26.6 0
13 V1_47 1 0 999 999
14 V2_47 1.03 0.031 33.6 0
15 V3_47 0.937 0.034 27.4 0
16 V1_57 1 0 999 999
17 V2_57 1.13 0.036 31.9 0
18 V3_57 1.03 0.037 28.1 0
I want to convert this data to the following data. Can someone give me some suggestion, please?
name est_3 est_15 est_25 est_35 est_47 est_57
1 V2 1.12 1.07 1.07 1.05 1.03 1.13
2 V3 0.917 0.867 0.917 0.994 0.937 1.03
Here is one approach for you. Your data is called mydf here. First, you want to choose necessary columns (i.e., name and x1) using select(). Then, you want to subset rows using filter(). You want to grab rows that begin with V2 or V3 in strings. grepl() checks if each string has the pattern. Then, you want to split the column, name and create two columns (i.e., name and est). Finally, you want to convert the data to a long-format data using pivot_wider().
library(dplyr)
library(tidyr)
select(mydf, name:x1) %>%
filter(grepl(x = name, pattern = "^V[2|3]")) %>%
separate(col = name, into = c("name", "est"), sep = "_") %>%
pivot_wider(names_from = "est",values_from = "x1", names_prefix = "est_")
# name est_3 est_15 est_25 est_35 est_47 est_57
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 V2 1.12 1.07 1.07 1.05 1.03 1.13
#2 V3 0.917 0.867 0.917 0.994 0.937 1.03
For your reference, when you ask questions, you want to provide a minimal sample data and code. If you can do that, SO users can easily help you out. Please read this question.
DATA
mydf <- structure(list(name = c("V1_3", "V2_3", "V3_3", "V1_15", "V2_15",
"V3_15", "V1_25", "V2_25", "V3_25", "V1_35", "V2_35", "V3_35",
"V1_47", "V2_47", "V3_47", "V1_57", "V2_57", "V3_57"), x1 = c(1,
1.122, 0.917, 1, 1.069, 0.867, 1, 1.066, 0.917, 1, 1.048, 0.994,
1, 1.03, 0.937, 1, 1.133, 1.032), x2 = c(0, 0.044, 0.045, 0,
0.036, 0.039, 0, 0.034, 0.037, 0, 0.034, 0.037, 0, 0.031, 0.034,
0, 0.036, 0.037), x3 = c(999, 25.446, 20.385, 999, 29.751, 22.478,
999, 31.134, 24.565, 999, 31.18, 26.587, 999, 33.637, 27.405,
999, 31.883, 28.081), x4 = c(999, 0, 0, 999, 0, 0, 999, 0, 0,
999, 0, 0, 999, 0, 0, 999, 0, 0)), row.names = c(NA, -18L), class = c("tbl_df",
"tbl", "data.frame"))
I have a dataset of 240 columns and 146 rows. I am providing only first to chunks from the dataset with 5 rows
DF <- data.frame(
D1 = c(-0.253, 0.253, -0.951, 0.951, 0.501, -0.501),
D2 = c(-0.52, -0.52, 0.52, 0.52, -0.172, -0.172),
D3 = c(0.014, 0.014, 0.014, 0.014, -0.014, -0.014),
S3 = c(0.095, 0.095, 0.095, 0.095, 0.095, 0.095),
D1 = c(-0.966, 0.966, -0.647, 0.647, 0.905, -0.905),
D2 = c(-0.078, -0.078, 0.078, 0.078, -0.943, -0.943),
D3 = c(-0.046, -0.046, -0.046, -0.046, 0.046, 0.046),
S3 = c(0.07, 0.07, 0.07, 0.07, 0.07, 0.07)
)
I want to add every 4th column (i.e. S3) with the preceding 3 columns as the following combinations
D1+S3
D2+S3
D3+S3
D1+D2+S3
D1+D3+S3
Now in the new dataframe the columns should be
D1 D2 D3 S3 D1+S3 D2+S3 D3+S3 D1+D2+S3 D1+D3+S3 D1 D2 D3 S3 D1+S3 D2+S3 D3+S3 D1+D2+S3 D1+D3+S3
How to do it in R? Any help in this regard is highly appreciated.
In the following code I reshape your data frame so that it brings all the values into 4 columns. To distinguish between the original columns, I have added an ID column. After that the operation you want to do becomes easy.
library(tidyverse)
df <- read_table(
"D1 D2 D3 S3 D1 D2 D3 S3
-0.253 -0.520 0.014 0.095 -0.966 -0.078 -0.046 0.070
0.253 -0.520 0.014 0.095 0.966 -0.078 -0.046 0.070
-0.951 0.520 0.014 0.095 -0.647 0.078 -0.046 0.070
0.951 0.520 0.014 0.095 0.647 0.078 -0.046 0.070
0.501 -0.172 -0.014 0.095 0.905 -0.943 0.046 0.070
-0.501 -0.172 -0.014 0.095 -0.905 -0.943 0.046 0.070
")
i <- seq(1, ncol(df)-3, 4)
df_out <- map_dfr(i, ~select(df, seq(., .+3)) %>% set_names(c("D1", "D2", "D3", "S3")))
df_out %>%
mutate(d1d2s3 = D1 + D2 + D3,
d1d3s3 = D1 + D3 + D3,
id = rep(1:length(i), each = nrow(df))) %>%
mutate_at(1:3, ~.+S3) %>%
bind_cols(df_out, .)
If you want to return it to the original shape after that you can do the following.
df_out %>%
group_split(id) %>%
bind_cols()
Edit:
I have rewritten the code so as to work for a for a variable number of decompositions. You should just have to change n_decomp <- 3 to the appropriate number. It creates variables for all possible combinations of the decomposition variables with S3. So it'll escalate quickly with an increasing number of decompositions.
n_decomp <- 3
n_var <- n_decomp + 1
i <- seq(1, ncol(df), n_var)
df_names <- names(df[1:n_var])
df_out <-
map_dfr(i,
~select(df, seq(., .+n_decomp)) %>%
set_names(df_names)) %>%
mutate(id = rep(1:length(i), each = nrow(df)))
decomp_combn <- map(1:n_decomp,
~combn(df_names[1:n_decomp], .) %>%
as_tibble %>%
as.list) %>%
flatten() %>%
map(c, "S3")
decomp_combn %>%
map(~select(df_out, .)) %>%
set_names(map(., ~str_c(names(.), collapse = "_"))) %>%
map(~apply(., 1, sum)) %>%
as_tibble %>%
bind_cols(df_out, .)
Quite long but should work:
data<-read.csv("Decompositions_1.csv")
nc_input=ncol(data)
nc_output = (ncol(data)/4)*5
output <- data.frame(as.data.frame(matrix(0,ncol=nc_output,nrow=nrow(data))))
firsts=data[,seq(1,nc_input,4)]
seconds=data[,seq(2,nc_input,4)]
thirds=data[,seq(3,nc_input,4)]
fourths=data[,seq(4,nc_input,4)]
starts_ou=seq(1,nc_output,5)
subsets=1:length(starts_ou)
for(i in subsets) {
ou_index=starts_ou[i]
output[,ou_index]=firsts[i]+fourths[i]
output[,ou_index+1]=seconds[i]+fourths[i]
output[,ou_index+2]=thirds[i]+fourths[i]
output[,ou_index+3]=firsts[i]+thirds[i]+fourths[i]
output[,ou_index+4]=seconds[i]+thirds[i]+fourths[i]
}
A little late - but here is a data.table approach:
library(data.table)
DT <- data.table(
D1 = c(-0.253, 0.253, -0.951, 0.951, 0.501, -0.501),
D2 = c(-0.52, -0.52, 0.52, 0.52, -0.172, -0.172),
D3 = c(0.014, 0.014, 0.014, 0.014, -0.014, -0.014),
S3 = c(0.095, 0.095, 0.095, 0.095, 0.095, 0.095),
D1 = c(-0.966, 0.966, -0.647, 0.647, 0.905, -0.905),
D2 = c(-0.078, -0.078, 0.078, 0.078, -0.943, -0.943),
D3 = c(-0.046, -0.046, -0.046, -0.046, 0.046, 0.046),
S3 = c(0.07, 0.07, 0.07, 0.07, 0.07, 0.07)
)
DT[, c("D1+S3", "D2+S3", "D3+S3", "D1+D2+S3", "D1+D3+S3") := list(D1+S3, D2+S3, D3+S3, D1+D2+S3, D1+D3+S3)]
DT
D1 D2 D3 S3 D1 D2 D3 S3 D1+S3 D2+S3 D3+S3 D1+D2+S3 D1+D3+S3
1: -0.253 -0.520 0.014 0.095 -0.966 -0.078 -0.046 0.07 -0.158 -0.425 0.109 -0.678 -0.144
2: 0.253 -0.520 0.014 0.095 0.966 -0.078 -0.046 0.07 0.348 -0.425 0.109 -0.172 0.362
3: -0.951 0.520 0.014 0.095 -0.647 0.078 -0.046 0.07 -0.856 0.615 0.109 -0.336 -0.842
4: 0.951 0.520 0.014 0.095 0.647 0.078 -0.046 0.07 1.046 0.615 0.109 1.566 1.060
5: 0.501 -0.172 -0.014 0.095 0.905 -0.943 0.046 0.07 0.596 -0.077 0.081 0.424 0.582
6: -0.501 -0.172 -0.014 0.095 -0.905 -0.943 0.046 0.07 -0.406 -0.077 0.081 -0.578 -0.420
When using aggregate with compound function, the resulting data.frame has matrices inside columns.
ta=aggregate(cbind(precision,result,prPo)~rstx+qx+laplace,t0
,function(x) c(x=mean(x),m=min(x),M=max(x)))
ta=head(ta)
dput(ta)
structure(list(rstx = c(3, 3, 2, 3, 2, 3), qx = c(0.2, 0.25,
0.3, 0.3, 0.33, 0.33), laplace = c(0, 0, 0, 0, 0, 0), precision = structure(c(0.174583333333333,
0.186833333333333, 0.3035, 0.19175, 0.30675, 0.193666666666667,
0.106, 0.117, 0.213, 0.101, 0.22, 0.109, 0.212, 0.235, 0.339,
0.232, 0.344, 0.232), .Dim = c(6L, 3L), .Dimnames = list(NULL,
c("x", "m", "M"))), result = structure(c(-142.333333333333,
-108.316666666667, -69.1, -85.7, -59.1666666666667, -68.5666666666667,
-268.8, -198.2, -164, -151.6, -138.2, -144.8, -30.8, -12.2, -14.2,
-3.8, -12.6, -3.4), .Dim = c(6L, 3L), .Dimnames = list(NULL,
c("x", "m", "M"))), prPo = structure(c(3.68416666666667,
3.045, 2.235, 2.53916666666667, 2.0775, 2.23666666666667, 1.6,
1, 1.02, 0.54, 0.87, 0.31, 5.04, 4.02, 2.77, 3.53, 2.63, 3.25
), .Dim = c(6L, 3L), .Dimnames = list(NULL, c("x", "m", "M")))), .Names = c("rstx",
"qx", "laplace", "precision", "result", "prPo"), row.names = c(NA,
6L), class = "data.frame")
Is there a function that transform data.frame matrix-colum into columns?
Manually, for each matrix-column, column bind plus column delete works:
colnames(ta)
[1] "rstx" "qx" "laplace" "precision" "result" "prPo"
ta[,"precision"] # ta[,4]
x m M
[1,] 0.1745833 0.106 0.212
[2,] 0.1868333 0.117 0.235
[3,] 0.3035000 0.213 0.339
[4,] 0.1917500 0.101 0.232
[5,] 0.3067500 0.220 0.344
[6,] 0.1936667 0.109 0.232
#column bind + column delete
ta=cbind(ta,precision=ta[,4])
ta=ta[,-4]
colnames(ta)
[1] "rstx" "qx" "laplace" "result" "prPo" "precision.x" "precision.m"
[8] "precision.M"
ta
rstx qx laplace result.x result.m result.M prPo.x prPo.m prPo.M precision.x precision.m
1 3 0.20 0 -142.33333 -268.80000 -30.80000 3.684167 1.600000 5.040000 0.1745833 0.106
2 3 0.25 0 -108.31667 -198.20000 -12.20000 3.045000 1.000000 4.020000 0.1868333 0.117
3 2 0.30 0 -69.10000 -164.00000 -14.20000 2.235000 1.020000 2.770000 0.3035000 0.213
4 3 0.30 0 -85.70000 -151.60000 -3.80000 2.539167 0.540000 3.530000 0.1917500 0.101
5 2 0.33 0 -59.16667 -138.20000 -12.60000 2.077500 0.870000 2.630000 0.3067500 0.220
6 3 0.33 0 -68.56667 -144.80000 -3.40000 2.236667 0.310000 3.250000 0.1936667 0.109
precision.M
1 0.212
2 0.235
3 0.339
4 0.232
5 0.344
6 0.232
matrix doesn't support matrix-column. So as.matrix() transform data.frame into matrix, breaking up matrix-column.
Here is my idea:
library(tidyverse)
ta2 <- ta %>%
as.matrix() %>%
as.data.frame()
Somewhere in Stackoverflow I found a very simple solution:
cbind(ta[-ncol(ta)],ta[[ncol(ta)]])
rstx qx laplace precision.x precision.m precision.M result.x result.m result.M x m
1 3 0.20 0 0.1745833 0.1060000 0.2120000 -142.33333 -268.80000 -30.80000 3.684167 1.60
2 3 0.25 0 0.1868333 0.1170000 0.2350000 -108.31667 -198.20000 -12.20000 3.045000 1.00
3 2 0.30 0 0.3035000 0.2130000 0.3390000 -69.10000 -164.00000 -14.20000 2.235000 1.02
4 3 0.30 0 0.1917500 0.1010000 0.2320000 -85.70000 -151.60000 -3.80000 2.539167 0.54
5 2 0.33 0 0.3067500 0.2200000 0.3440000 -59.16667 -138.20000 -12.60000 2.077500 0.87
6 3 0.33 0 0.1936667 0.1090000 0.2320000 -68.56667 -144.80000 -3.40000 2.236667 0.31
M
1 5.04
2 4.02
3 2.77
4 3.53
5 2.63
6 3.25
Just that!