I am trying to get the first value of the next group in r to estimate a ratio. I have created a group based on the type column in my the df. Then estimated some influence factors using the sample position within the group. Finally, I am trying to estimate a ratio like this: RRF=response/(F1*first(response)+(F2*??????)) where the F1*first(response) is the cal in the group but I don't know how to call the first value of the next group to finish the ratio. Can someone help with this? This is my code and my data:
library(dplyr)
conc_zero_test <- zero_test %>%
gather(gas, response, -time,-type)%>%
group_by(group = cumsum(type == "current_std"),gas)%>%
mutate(X1= row_number()-1, #estimates the position of the sample within the group -1 removes std
F1=1-(X1/n()), #relative factor influence of the cal in the current group
F2=1-F1, #relative factor influence of the cal in the next group
RRF=response/(F1*first(response)+(F2*????))
structure(list(time = structure(c(1564468200, 1564475400, 1564484400,
1564486200, 1564493400, 1564497000, 1564498800, 1564506000, 1564509600,
1564511400, 1564518600, 1564522200, 1564524000, 1564527600, 1564531200
), class = c("POSIXct", "POSIXt"), tzone = "UTC"), type = c("current_std",
"n2", "n2", "current_std", "n2", "-", "current_std", "-", "n2",
"current_std", "n2", "-", "current_std", "-", "-"), ben = c(2293951.5,
12703.1, 6392.7, 1762512.6, 10748.4, 25468.3, 1597679, 24400.4,
6019.4, 1510760.2, 10329.1, 29292.6, 1495942.8, 61227.5, 25379.5
), xyl = c(210975.6, 4482, 2910.8, 127612.4, 3792.6, 10295.7,
113439.1, 10628.8, 2064.3, 107134.3, 3764.1, 10380.6, 107353.6,
23639.1, 10317.4), cym = c(546894.5, 12202.6, 8400.8, 302091.6,
11072.2, 16349.2, 291637.5, 18891.8, 6500.7, 276997.5, 10821.2,
18672, 274149.4, 61379.2, 19254.7), isop = c(397288.2, 0, 0,
239779.9, 0, 1364.8, 199081.5, 1511.2, 0, 179364, 0, 1318.4,
174450.7, 7137.5, 9567), macr = c(221195.8, 0, 0, 138806.3, 0,
0, 116644, 0, 0, 108893.3, 0, 0, 105689, 4325.4, 0), pin = c(50795.3,
0, 0, 28436, 0, 1020.1, 26482.9, 925.2, 0, 27394.1, 0, 989.7,
24344.6, 1414.7, 736.3), tmb = c(9314.5, 0, 0, 5798, 0, 0, 5136.4,
2252.5, 0, 4542.9, 0, 0, 4398.4, 3794.4, 2186.3), tol = c(880567.1,
7430.6, 4225.5, 569616.2, 6091.8, 65642.6, 495780.5, 52129.9,
3226, 456079.6, 5874, 34725.9, 453944.8, 56594.4, 66148.1), mvk = c(169036.8,
0, 0, 108738, 0, 0, 56712.5, 0, 0, 79148.9, 0, 0, 64065, 0, 0
), euc = c(12815.2, 0, 0, 8012.6, 0, 0, 5411.8, 0, 0, 5839.9,
0, 491.7, 5450.7, 1990.8, 500.7)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -15L), spec = structure(list(
cols = list(time = structure(list(format = ""), class = c("collector_datetime",
"collector")), type = structure(list(), class = c("collector_character",
"collector")), ben = structure(list(), class = c("collector_double",
"collector")), xyl = structure(list(), class = c("collector_double",
"collector")), cym = structure(list(), class = c("collector_double",
"collector")), isop = structure(list(), class = c("collector_double",
"collector")), macr = structure(list(), class = c("collector_double",
"collector")), pin = structure(list(), class = c("collector_double",
"collector")), tmb = structure(list(), class = c("collector_double",
"collector")), tol = structure(list(), class = c("collector_double",
"collector")), mvk = structure(list(), class = c("collector_double",
"collector")), euc = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 2), class = "col_spec"))
Example of expected output
time type gas response group X1 F1 F2 RRF
<dttm> <chr> <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
1 2019-07-30 06:30:00 current_std ben 2293952. 1 0 1 0 1
2 2019-07-30 08:30:00 n2 ben 12703. 1 1 0.667 0.333 0.006005
3 2019-07-30 11:00:00 n2 ben 6393. 1 2 0.333 0.667 0.003962
I would use a self-join to get the first response of the next group:
library(tidyverse)
# the OPs example data (long!)
zero_test <-
structure(
list(
time = structure(
c(
1564468200,
1564475400,
1564484400,
1564486200,
1564493400,
1564497000,
1564498800,
1564506000,
1564509600,
1564511400,
1564518600,
1564522200,
1564524000,
1564527600,
1564531200
),
class = c("POSIXct", "POSIXt"),
tzone = "UTC"
),
type = c(
"current_std",
"n2",
"n2",
"current_std",
"n2",
"-",
"current_std",
"-",
"n2",
"current_std",
"n2",
"-",
"current_std",
"-",
"-"
),
ben = c(
2293951.5,
12703.1,
6392.7,
1762512.6,
10748.4,
25468.3,
1597679,
24400.4,
6019.4,
1510760.2,
10329.1,
29292.6,
1495942.8,
61227.5,
25379.5
),
xyl = c(
210975.6,
4482,
2910.8,
127612.4,
3792.6,
10295.7,
113439.1,
10628.8,
2064.3,
107134.3,
3764.1,
10380.6,
107353.6,
23639.1,
10317.4
),
cym = c(
546894.5,
12202.6,
8400.8,
302091.6,
11072.2,
16349.2,
291637.5,
18891.8,
6500.7,
276997.5,
10821.2,
18672,
274149.4,
61379.2,
19254.7
),
isop = c(
397288.2,
0,
0,
239779.9,
0,
1364.8,
199081.5,
1511.2,
0,
179364,
0,
1318.4,
174450.7,
7137.5,
9567
),
macr = c(
221195.8,
0,
0,
138806.3,
0,
0,
116644,
0,
0,
108893.3,
0,
0,
105689,
4325.4,
0
),
pin = c(
50795.3,
0,
0,
28436,
0,
1020.1,
26482.9,
925.2,
0,
27394.1,
0,
989.7,
24344.6,
1414.7,
736.3
),
tmb = c(
9314.5,
0,
0,
5798,
0,
0,
5136.4,
2252.5,
0,
4542.9,
0,
0,
4398.4,
3794.4,
2186.3
),
tol = c(
880567.1,
7430.6,
4225.5,
569616.2,
6091.8,
65642.6,
495780.5,
52129.9,
3226,
456079.6,
5874,
34725.9,
453944.8,
56594.4,
66148.1
),
mvk = c(169036.8,
0, 0, 108738, 0, 0, 56712.5, 0, 0, 79148.9, 0, 0, 64065, 0, 0),
euc = c(
12815.2,
0,
0,
8012.6,
0,
0,
5411.8,
0,
0,
5839.9,
0,
491.7,
5450.7,
1990.8,
500.7
)
),
class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"),
row.names = c(NA,-15L),
spec = structure(list(
cols = list(
time = structure(list(format = ""), class = c("collector_datetime",
"collector")),
type = structure(list(), class = c("collector_character",
"collector")),
ben = structure(list(), class = c("collector_double",
"collector")),
xyl = structure(list(), class = c("collector_double",
"collector")),
cym = structure(list(), class = c("collector_double",
"collector")),
isop = structure(list(), class = c("collector_double",
"collector")),
macr = structure(list(), class = c("collector_double",
"collector")),
pin = structure(list(), class = c("collector_double",
"collector")),
tmb = structure(list(), class = c("collector_double",
"collector")),
tol = structure(list(), class = c("collector_double",
"collector")),
mvk = structure(list(), class = c("collector_double",
"collector")),
euc = structure(list(), class = c("collector_double",
"collector"))
),
default = structure(list(), class = c("collector_guess",
"collector")),
skip = 2
), class = "col_spec")
)
temp1 <- zero_test %>%
gather(gas, response, -time,-type) %>%
group_by(group = cumsum(type == "current_std"), gas) %>%
mutate(X1= row_number()-1, #estimates the position of the sample within the group -1 removes std
F1=1-(X1/n()), #relative factor influence of the cal in the current group
F2=1-F1,
first_response = first(response)) %>%
ungroup
conc_zero_test <- temp1 %>%
left_join(y = {temp1 %>%
mutate(group = group - 1) %>%
select(gas, group, first_response_next = first_response) %>%
distinct},
by = c("gas", "group")) %>%
mutate(RRF = response / ((F1 * first_response) + (F2 * first_response_next)))
conc_zero_test
#> # A tibble: 150 x 11
#> time type gas response group X1 F1 F2
#> <dttm> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-07-30 06:30:00 curr… ben 2293952. 1 0 1 0
#> 2 2019-07-30 08:30:00 n2 ben 12703. 1 1 0.667 0.333
#> 3 2019-07-30 11:00:00 n2 ben 6393. 1 2 0.333 0.667
#> 4 2019-07-30 11:30:00 curr… ben 1762513. 2 0 1 0
#> 5 2019-07-30 13:30:00 n2 ben 10748. 2 1 0.667 0.333
#> 6 2019-07-30 14:30:00 - ben 25468. 2 2 0.333 0.667
#> 7 2019-07-30 15:00:00 curr… ben 1597679 3 0 1 0
#> 8 2019-07-30 17:00:00 - ben 24400. 3 1 0.667 0.333
#> 9 2019-07-30 18:00:00 n2 ben 6019. 3 2 0.333 0.667
#> 10 2019-07-30 18:30:00 curr… ben 1510760. 4 0 1 0
#> # … with 140 more rows, and 3 more variables: first_response <dbl>,
#> # first_response_next <dbl>, RRF <dbl>
Created on 2020-08-16 by the reprex package (v0.3.0)
Related
This is a related question from my original post found here: How to create a new variable based on condition from different dataframe in R
I have 2 data frames from an experiment. The 1st df reads a (roughly) continuous signal over 40 mins. There are 5 columns, 1:3 are binary - saying whether a button was pushed. The 4th column is a binary of if either from column 2 or 3 was pushed. The 5th column is an approximate time in seconds. Example from df below:
initiate
left
right
l or r
time
0
0
1
1
2.8225
0
0
1
1
2.82375
0
0
1
1
2.82500
0
0
1
1
2.82625
1
0
0
0
16.8200
1
0
0
0
16.8212
etc.
The 2nd data frame is session info where each row is a trial, usually 100-150 rows depending on the day. I have a column that marks trial start time and another column that marks trial end time in seconds. I have another column that states whether or not the trial had an intervention. Example from df below (I omitted several irrelevant columns):
trial
control
t start
t end
1
0
16.64709
35.49431
2
0
41.81843
57.74304
3
0
65.54510
71.16612
4
0
82.65743
87.30914
11
3
187.0787
193.5898
12
0
200.0486
203.1883
30
3
415.1710
418.0405
etc.
For the 1st data frame, I want to create a column that indicates whether or not the button was pushed within a trial. If the button was indeed pushed within a trial, I need to label it based on intervention. This is based on those start and end times in the 2nd df, along with the control info. In this table, 0 = intervention and 3 = control.
I would like it to look something like this (iti = inter-trial, wt_int = within trial & intervention, wt_control = within trial & control):
initiate
left
right
l or r
time
trial_type
0
0
1
1
2.8225
iti
0
0
1
1
2.82375
iti
0
0
1
1
2.82500
iti
0
0
1
1
2.82625
iti
1
0
0
0
16.82000
wt_int
1
0
0
0
16.82125
wt_int
1
0
0
0
187.0800
wt_control
etc.
Going off previous recommendations, I've tried nested ifelse statements with no success. I can get it to label all of the trials as either "iti" or "wt_int" with different failed attempts, or an error at row 1037 (when it changes from iti to wt). From my original question I have a "trial" column now in my 1st df which I'm using for the following code. Perhaps there is a more straightforward approach that combines the original code?
Errors out part way through:
df %>%
rowwise() %>%
mutate(trial_type = ifelse(any(trial == "wt" & df2$control == 0,
ifelse(trial == "wt" & df2$control == 3,
"wt_omission", "iti"), "wt_odor")))
Also tried this, which labels all as wt_int:
df$trial_type <- ifelse(df$trial == 'wt' && df2$control == 0,
ifelse(df$trial == 'wt' && df2$control == 3,
"wt_control", "iti"), "wt_int")
Thank you!
You could use cut to create intervals and check, if a values falls into them:
library(dplyr)
df1 %>%
mutate(
check_1 = cut(time, breaks = df2$t_start, labels = FALSE),
check_2 = coalesce(cut(time, breaks = df2$t_end, labels = FALSE), 0),
check_3 = df2$control[check_1],
trial_type = case_when(
check_1 - check_2 == 1 & check_3 == 0 ~ "wt_int",
check_1 - check_2 == 1 & check_3 == 3 ~ "wt_control",
TRUE ~ "iti"
)
) %>%
select(-starts_with("check_"))
This returns
# A tibble: 7 x 6
initiate left right l_or_r time trial_type
<dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 0 0 1 1 2.82 iti
2 0 0 1 1 2.82 iti
3 0 0 1 1 2.82 iti
4 0 0 1 1 2.83 iti
5 1 0 0 0 16.8 wt_int
6 1 0 0 0 16.8 wt_int
7 1 0 0 0 187. wt_control
Data
df1 <- structure(list(initiate = c(0, 0, 0, 0, 1, 1, 1), left = c(0,
0, 0, 0, 0, 0, 0), right = c(1, 1, 1, 1, 0, 0, 0), l_or_r = c(1,
1, 1, 1, 0, 0, 0), time = c(2.8225, 2.82375, 2.825, 2.82625,
16.82, 16.8212, 187.08)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -7L), spec = structure(list(
cols = list(initiate = structure(list(), class = c("collector_double",
"collector")), left = structure(list(), class = c("collector_double",
"collector")), right = structure(list(), class = c("collector_double",
"collector")), l_or_r = structure(list(), class = c("collector_double",
"collector")), time = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
df2 <- structure(list(trial = c(1, 2, 3, 4, 11, 12, 30), control = c(0,
0, 0, 0, 3, 0, 3), t_start = c(16.64709, 41.81843, 65.5451, 82.65743,
187.0787, 200.0486, 415.171), t_end = c(35.49431, 57.74304, 71.16612,
87.30914, 193.5898, 203.1883, 418.0405)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -7L), spec = structure(list(
cols = list(trial = structure(list(), class = c("collector_double",
"collector")), control = structure(list(), class = c("collector_double",
"collector")), t_start = structure(list(), class = c("collector_double",
"collector")), t_end = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
I am learning how to create a function in R, but I am struggling to understand how to write for loop. My understanding is that
for (item I list_items) {
do_something(itemn)
}
I would like to write a for loop to replace with 333 the cells that are equal with 123. So the item is 123 and the list of items is the df from sec1 till sec4.
Could somebody explain this to me, please? And how this can be included in a function?
Sample code:
structure(list(sec1 = c(1, 123, 1), sec2 = c(123, 1, 1), sec3 = c(123,
0, 0), sec4 = c(1, 123, 1)), spec = structure(list(cols = list(
sec1 = structure(list(), class = c("collector_double", "collector"
)), sec2 = structure(list(), class = c("collector_double",
"collector")), sec3 = structure(list(), class = c("collector_double",
"collector")), sec4 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), row.names = c(NA,
-3L), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"))
We do not need a for loop here:
df[df==123]<-333
If we really need for loops:
for(i in 1:ncol(df)){
df[i][df[i]==123] <-333
}
output
df
# A tibble: 3 x 4
sec1 sec2 sec3 sec4
<dbl> <dbl> <dbl> <dbl>
1 1 333 333 1
2 333 1 0 333
3 1 1 0 1
Here's how it would work for one column of your data:
dat <- structure(list(sec1 = c(1, 123, 1),
sec2 = c(123, 1, 1),
sec3 = c(123, 0, 0),
sec4 = c(1, 123, 1)),
spec = structure(list(cols = list(
sec1 = structure(list(),
class = c("collector_double", "collector")),
sec2 = structure(list(),
class = c("collector_double","collector")),
sec3 = structure(list(),
class = c("collector_double", "collector")),
sec4 = structure(list(),
class = c("collector_double","collector"))),
default = structure(list(),
class = c("collector_guess","collector")),
delim = ","), class = "col_spec"),
row.names = c(NA,-3L), class =
c("spec_tbl_df", "tbl_df", "tbl", "data.frame"))
for(i in 1:nrow(dat)){
dat$sec1[i] <- ifelse(dat$sec1[i] == 123, 333, dat$sec1[i])
}
dat
#> sec1 sec2 sec3 sec4
#> 1 1 123 123 1
#> 2 333 1 0 123
#> 3 1 1 0 1
Created on 2022-01-31 by the reprex package (v2.0.1)
To replace all of them, using for loops, you could do a double loop over columns and rows.
for(j in names(dat)){
for(i in 1:nrow(dat)){
dat[[j]][i] <- ifelse(dat[[j]][i] == 123, 333, dat[[j]][i])
}
}
Of course, as others have identified, you certainly don't need a for loop to accomplish this.
in addition to DaveArmstrong Answer this would work for all rows and columns:
dat <- structure(list(sec1 = c(1, 123, 1), sec2 = c(123, 1, 1), sec3 = c(123,
0, 0), sec4 = c(1, 123, 1)), spec = structure(list(cols = list(
sec1 = structure(list(), class = c("collector_double", "collector"
)), sec2 = structure(list(), class = c("collector_double",
"collector")), sec3 = structure(list(), class = c("collector_double",
"collector")), sec4 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), row.names = c(NA,
-3L), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"))
for(i in 1:nrow(dat)){
for(j in 1:ncol(dat)){
dat[i,j] <- ifelse(dat[i,j] == 123, 333, dat[i,j])
}
}
I have my data in the wide-format
what is the easiest way to change it to long without having a X in front of the time variables
Sample data:
structure(list(X1 = c("01/12/2019", "02/12/2019"), `00:30` = c(41.95,
39.689), `01:00` = c(44.96, 40.47), `01:30` = c(42.939, 38.95
), `02:00` = c(43.221, 40.46), `02:30` = c(44.439, 41.97)), class = "data.frame", row.names = c(NA,
-2L), spec = structure(list(cols = list(X1 = structure(list(), class = c("collector_character",
"collector")), `00:30` = structure(list(), class = c("collector_double",
"collector")), `01:00` = structure(list(), class = c("collector_double",
"collector")), `01:30` = structure(list(), class = c("collector_double",
"collector")), `02:00` = structure(list(), class = c("collector_double",
"collector")), `02:30` = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
with pivot_longer and pivot_wider from tidyr:
dat |>
pivot_longer(names_to="time",values_to="val",`00:30`:`02:30`) |>
pivot_wider(names_from="X1", values_from="val")
Output:
# A tibble: 5 x 3
time `01/12/2019` `02/12/2019`
<chr> <dbl> <dbl>
1 00:30 42.0 39.7
2 01:00 45.0 40.5
3 01:30 42.9 39.0
4 02:00 43.2 40.5
5 02:30 44.4 42.0
I this special case, you could transpose the part of your data.frame containing numbers and assign the column names:
df_new <- data.frame(t(df[,-1]))
colnames(df_new) <- df[, 1]
This returns a data.frame df_new:
01/12/2019 02/12/2019
00:30 41.950 39.689
01:00 44.960 40.470
01:30 42.939 38.950
02:00 43.221 40.460
02:30 44.439 41.970
Edit (Thanks to jay.sf)
For versions of R >= 4.1, you could use the natural pipe:
t(df[, -1]) |>
data.frame() |>
`colnames<-`(df[, 1])
I need a chart of accounts to stay in order when new accounts are added or dropped in future years. This is because in Accounting the accounts are sorted by type (for example Asset, Liability Equity) but it is not explicit in the dataset. This is an example of the code that is putting new "Accounts" from Year2 and Year3 at the bottom.
XYZCompany_Consolidated <- XYZCompany_Year1 %>%
full_join(XYZCompany_Year2 by = "Account") %>%
full_join(XYZCompany_Year3, by = "Account")
Example: This picture is just to give a simplified example. The highlight in orange is where the new accounts are going and to the right is the code i'm using, and the green is what I'm trying to achieve
Perhaps I'm overthinking this problem but I find it hard to solve. Let's define some data first:
df_year1 <- structure(list(Account = c("Cash", "Accounts", "Loan1", "Auto",
"JaneDoe"), Year_1 = c(100, 1000, 20, 300, 500)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -5L), spec = structure(list(
cols = list(Account = structure(list(), class = c("collector_character",
"collector")), Year_1 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
df_year2 <- structure(list(Account = c("Cash", "Accounts", "Loan1", "Auto",
"Laptop", "JaneDoe"), Year_2 = c(80, 1200, 50, 300, 500, 0)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), spec = structure(list(
cols = list(Account = structure(list(), class = c("collector_character",
"collector")), Year_2 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
df_year3 <- structure(list(Account = c("Cash", "Accounts", "Loan1", "Auto",
"Rent", "JaneDoe"), Year_3 = c(80, 1200, 50, 300, 1000, 0)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), spec = structure(list(
cols = list(Account = structure(list(), class = c("collector_character",
"collector")), Year_3 = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
Those are similar to the data shown in the OP's picture, e.g. df_year1 looks like
# A tibble: 5 x 2
Account Year_1
<chr> <dbl>
1 Cash 100
2 Accounts 1000
3 Loan1 20
4 Auto 300
5 JaneDoe 500
Next we transform those data a little bit, namely
library(dplyr)
library(tidyr)
df_y1 <- df_year1 %>%
mutate(Year = 1,
no = row_number()) %>%
rename(value = Year_1)
which returns
# A tibble: 5 x 4
Account value Year no
<chr> <dbl> <dbl> <int>
1 Cash 100 1 1
2 Accounts 1000 1 2
3 Loan1 20 1 3
4 Auto 300 1 4
5 JaneDoe 500 1 5
The new column no stores the account's original position, column Year stores the chart's year. All three data.frames are processed like this, so we get df_y1, df_y2, df_y3.
Finally we bind them together
bind_rows(df_y1, df_y2, df_y3) %>%
mutate(num_years = max(Year)) %>%
group_by(Account) %>%
mutate(rank = sum((num_years - n() + 1) * no), .keep = "unused") %>%
pivot_wider(names_from = Year) %>%
arrange(rank) %>%
select(-rank) %>%
ungroup()
and calculate a rank for each account. The accounts are ordered by this rank. As a result, we get
# A tibble: 7 x 4
Account Year_1 Year_2 Year_3
<chr> <dbl> <dbl> <dbl>
1 Cash 100 80 80
2 Accounts 1000 1200 1200
3 Loan1 20 50 50
4 Auto 300 300 300
5 Laptop NA 500 NA
6 Rent NA NA 1000
7 JaneDoe 500 0 0
Note
I believe, there are better approaches, but at least this works for the example data.
I'm not sure about the calculated rank's stability. Take care.
I'm trying to create a stacked bar chart that will have mean abundance on the y-axis, main trophic group on the x-axis and each bar will be filled by the specific trophic group (the main trophic groups are subdivided further)
I've created an example of my data that you should be able to just put straight into R:
Example<-structure(list(Species = c("Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7", "Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7", "Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7"), Trophic = c("Herbivore", "Omnivore",
"Herbivore", "Predator", "Predator", "Omnivore", "Omnivore",
"Herbivore", "Omnivore", "Herbivore", "Predator", "Predator",
"Omnivore", "Omnivore", "Herbivore", "Omnivore", "Herbivore",
"Predator", "Predator", "Omnivore", "Omnivore"), Trophic_Specific = c("Grazer",
"Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore",
"Grazer", "Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore",
"Grazer", "Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore"
), Transect = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3,
3, 3, 3, 3, 3, 3), Count = c(1, 2, 34, 0, 4, 2, 1, 0, 2, 25,
1, 4, 2, 1, 1, 4, 50, 3, 6, 7, 3)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -21L), spec = structure(list(
cols = list(Species = structure(list(), class = c("collector_character",
"collector")), Trophic = structure(list(), class = c("collector_character",
"collector")), Trophic_Specific = structure(list(), class = c("collector_character",
"collector")), Transect = structure(list(), class = c("collector_double",
"collector")), Count = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
I know how to plot this in a bar chart using ggplots if I work out the mean abundances (i.e. the mean number of each species/trophic group across the 3 transects) manually in Excel (but then I don't know how to get my error bars).
How can I summarise this raw data in R so that I can get the mean abundance for each specific trophic group using transects 1-3 as my repeats, which then I can plot in a bar chart as described above?
I'm not 100% confident this is what you are looking for, but thought I would take a shot.
library(tidyverse)
Example %>%
group_by(Trophic, Trophic_Specific) %>%
summarise(Mean = mean(Count),
SD = sd(Count),
n = n(),
SE = SD/n)
# A tibble: 5 x 6
# Groups: Trophic [3]
Trophic Trophic_Specific Mean SD n SE
<chr> <chr> <dbl> <dbl> <int> <dbl>
1 Herbivore Browser 36.3 12.7 3 4.22
2 Herbivore Grazer 0.667 0.577 3 0.192
3 Omnivore Benthic_Omnivore 1.67 1.15 3 0.385
4 Omnivore Generalist_Omnivore 3.17 2.04 6 0.340
5 Predator Micro-invertebrate_Predator 3 2.19 6 0.365