I want to remove all rows after a certain string occurrence in a data frame column. I want to only return the 3 rows that appear above "total" appearing in column A. The 2 rows appearing below "total" would be excluded.
A B
Bob Smith 01005
Carl Jones 01008
Syndey Lewis 01185
total
Adam Price 01555
Megan Watson 02548
We can subset with row_numberand which
library(dplyr)
df %>% filter(row_number() < which(A=='total'))
A B
1 Bob Smith 01005
2 Carl Jones 01008
3 Syndey Lewis 01185
You could use
library(dplyr)
df %>%
filter(cumsum(A == "total") == 0)
This returns
# A tibble: 3 x 2
A B
<chr> <chr>
1 Bob Smith 01005
2 Carl Jones 01008
3 Syndey Lewis 01185
Data
structure(list(A = c("Bob Smith", "Carl Jones", "Syndey Lewis",
"total", "Adam Price", "Megan Watson"), B = c("01005", "01008",
"01185", NA, "01555", "02548")), problems = structure(list(row = 4L,
col = NA_character_, expected = "2 columns", actual = "1 columns",
file = "literal data"), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame")), class = c("spec_tbl_df", "tbl_df", "tbl",
"data.frame"), row.names = c(NA, -6L), spec = structure(list(
cols = list(A = structure(list(), class = c("collector_character",
"collector")), B = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
A <- c('Bob Smith','Carl Jones','Syndey Lewis','total','Adam Price','Megan Watson')
B <- c('01005','01008','01185','','01555','02548')
df <- data.frame(A, B)
val = which(df$A=="total") #get index of total
C = df[1:val-1,]
It's a little clunky but this should solve what you're wanting it to do:
library(dplyr)
df <- data.frame(A = c("Bob Smith", "Carl Jones", "Sydney Lewis", "total", "Adam Price", "Megan Watson"),
B = c("01005", "01008", "01185", NA, "01555", "02548"))
index <- df[df$A=="total",] %>% rownames()
df %>% slice(1:index)
Related
I have a table with prefixes (here in csv format):
PREFIX,LABEL
A,Infectious diseases
B,Infectious diseases
C,Tumor
D1,Tumor
D2,Tumor
D31,Tumor
D32,Tumor
D33,Blood disorder
D4,Blood disorder
D5,Blood disorder
And I want to join it with this one:
AGE,DEATH_CODE
67,A02
85,D318
75,C007+X
62,D338
To get obviously:
AGE,LABEL
67,Infectious diseases
85,Tumor
75,Tumor
62,Blood disorder
I know how to do that with SQL and LIKE but not with tidyverse left_join or base R.
Dput of data
Table 1: CIM_CODES
structure(list(PREFIX = c("A", "B", "C", "D1", "D2", "D31", "D32",
"D33", "D4", "D5"), LABEL = c("Infectious diseases", "Infectious diseases",
"Tumor", "Tumor", "Tumor", "Tumor", "Tumor", "Blood disorder",
"Blood disorder", "Blood disorder")), row.names = c(NA, -10L), spec = structure(list(
cols = list(PREFIX = structure(list(), class = c("collector_character",
"collector")), LABEL = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), problems = <pointer: 0x000002527d306190>, class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"))
Table 2: DEATH_CAUSES
structure(list(AGE = c(67, 85, 75, 62), DEATH_CODE = c("A02",
"D318", "C007+X", "D338")), row.names = c(NA, -4L), spec = structure(list(
cols = list(AGE = structure(list(), class = c("collector_double",
"collector")), DEATH_CODE = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), problems = <pointer: 0x0000025273898c60>, class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"))
You could do a partial string match that has the lowest difference between the two columns:
library(tidyverse)
DEATH_CAUSES |>
mutate(LABEL = map_chr(DEATH_CODE,
~CIM_CODES$LABEL[
which.min(stringdist::stringdist(.x, CIM_CODES$PREFIX))
]))
#> # A tibble: 4 x 3
#> AGE DEATH_CODE LABEL
#> <dbl> <chr> <chr>
#> 1 67 A02 Infectious diseases
#> 2 85 D318 Tumor
#> 3 75 C007+X Tumor
#> 4 62 D338 Blood disorder
UPDATE
not using the stringdist package as requested.
library(tidyverse)
get_match <- function(code, prefix, target){
map(code, \(x){
map(prefix, \(y){
grepl(paste0("^", y), x)
})
}) |>
map_chr(\(z) target[unlist(z) |> which()] )
}
DEATH_CAUSES |>
mutate(LABEL = get_match(DEATH_CAUSES$DEATH_CODE,
CIM_CODES$PREFIX,
CIM_CODES$LABEL))
#> # A tibble: 4 x 3
#> AGE DEATH_CODE LABEL
#> <dbl> <chr> <chr>
#> 1 67 A02 Infectious diseases
#> 2 85 D318 Tumor
#> 3 75 C007+X Tumor
#> 4 62 D338 Blood disorder
EDIT
how to do this with a join:
library(tidyverse)
library(fuzzyjoin)
fuzzy_left_join(DEATH_CAUSES,
CIM_CODES,
by = c("DEATH_CODE" = "PREFIX"),
str_detect)
#> # A tibble: 4 x 4
#> AGE DEATH_CODE PREFIX LABEL
#> <dbl> <chr> <chr> <chr>
#> 1 67 A02 A Infectious diseases
#> 2 85 D318 D31 Tumor
#> 3 75 C007+X C Tumor
#> 4 62 D338 D33 Blood disorder
My code below, I used mysql:
select a.age, p.label
from prefix p
left join age a on a.death_code like CONCAT("%",p.prefix,"%");
You can refer here: how to use a like with a join in sql?
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 have a dataframe that looks like this (but for every US county)
county
state
n_state_1
n_state_2
n_state_3
n_state_4
Autauga County
AL
NA
FL
NA
NA
Baldwin County
AL
GA
NA
TN
NA
Catron County
AL
FL
GA
NA
CA
I want to move the non-missing values (FL,GA,TN etc.) to the first columns starting from n_state_1 and then delete the columns containing only missing values to get:
county
state
n_state_1
n_state_2
n_state_3
Autauga County
AL
FL
NA
NA
Baldwin County
AL
GA
TN
NA
Catron County
AL
FL
GA
CA
I am struggling with the first step. I thought about using the function distinct but it doesn't work because there are non-empty elements in each column.
You could use dplyr and tidyr:
library(dplyr)
library(tidyr)
df %>%
pivot_longer(starts_with("n_state")) %>%
drop_na() %>%
group_by(county, state) %>%
mutate(name=row_number()) %>%
pivot_wider(names_prefix="n_state_")
which returns
county state n_state_1 n_state_2 n_state_3
<chr> <chr> <chr> <chr> <chr>
1 Autauga_County AL FL NA NA
2 Baldwin_County AL GA TN NA
3 Catron_County AL FL GA CA
What happened here?
pivot_longer takes the n_state_{n}-columns and collapses them into two columns: the name-column contains the original column name (n_state_1, n_state_2 etc), the value-column contains the states (FL, GA or <NA> in many cases).
Next we remove every <NA> entry. (Note: I use <NA> to make clear it's an NA-value).)
After a grouping by county and state we add a rownumber. These numbers will be later used to create the new column names.
pivot_wider now takes these row numbers and prefixes them with n_state_ to get the new columns. The values are taken from the value-column created in the second line of code. pivot_wider fills the missing values with <NA>-values (default behaviour).
Data
structure(list(county = c("Autauga_County", "Baldwin_County",
"Catron_County"), state = c("AL", "AL", "AL"), n_state_1 = c(NA,
"GA", "FL"), n_state_2 = c("FL", NA, "GA"), n_state_3 = c(NA,
"TN", NA), n_state_4 = c(NA, NA, "CA")), problems = structure(list(
row = 3L, col = "n_state_4", expected = "", actual = "embedded null",
file = "literal data"), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame")), class = c("spec_tbl_df", "tbl_df", "tbl",
"data.frame"), row.names = c(NA, -3L), spec = structure(list(
cols = list(county = structure(list(), class = c("collector_character",
"collector")), state = structure(list(), class = c("collector_character",
"collector")), n_state_1 = structure(list(), class = c("collector_character",
"collector")), n_state_2 = structure(list(), class = c("collector_character",
"collector")), n_state_3 = structure(list(), class = c("collector_character",
"collector")), n_state_4 = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
Or another option with dapply from collapse and select only columns with any non-NA elements
library(collapse)
library(dplyr)
dapply(df1, MARGIN = 1, FUN = function(x) c(x[!is.na(x)], x[is.na(x)])) %>%
select(where(~ any(complete.cases(.))))
# A tibble: 3 x 5
county state n_state_1 n_state_2 n_state_3
<chr> <chr> <chr> <chr> <chr>
1 Autauga_County AL FL <NA> <NA>
2 Baldwin_County AL GA TN <NA>
3 Catron_County AL FL GA CA
I have a list of dfs:
lst<-list(`101-01-101` = structure(list(SubjectID = "101-01-101",
BRTHDTC = "1953-07-07", SEX = "Female"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")), `101-02-102` = structure(list(
SubjectID = "101-02-102", BRTHDTC = "1963-07-02", SEX = "Female"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")), `101-03-103` = structure(list(
SubjectID = "101-03-103", BRTHDTC = "1940-09-11", SEX = "Male"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")), `101-04-104` = structure(list(
SubjectID = "101-04-104", BRTHDTC = "1955-12-31", SEX = "Male"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")), `104-05-201` = structure(list(
SubjectID = "104-05-201", BRTHDTC = "1950-12-04", SEX = "Female"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")))
I would like to build a new df which contain info:
How can i produce such file automatically, where I will fill FileName with df name in lst, and Gender with first input for SEX in that df?
This is easy to do with the purrr library:
library(purrr)
imap_dfr(lst, ~ data.frame(FileName = .y, Gender = .x[1, "SEX"]))
Output
FileName SEX
1 101-01-101 Female
2 101-02-102 Female
3 101-03-103 Male
4 101-04-104 Male
5 104-05-201 Female
We can specify .id in map_dfr
library(dplyr)
library(purrr)
map_dfr(lst, ~ .x %>%
select(Gender = SEX) %>%
slice(1), .id = 'FileName')
-output
# A tibble: 5 x 2
# FileName Gender
# <chr> <chr>
#1 101-01-101 Female
#2 101-02-102 Female
#3 101-03-103 Male
#4 101-04-104 Male
#5 104-05-201 Female
Or use bind_rows and then do a group by summarise to get the first observation
bind_rows(lst, .id ='FileName') %>%
group_by(FileName) %>%
summarise(Gender = first(SEX), .groups = 'drop')
For a sample dataframe:
df <- structure(list(code = c("a1", "a1", "b2", "v4", "f5", "f5", "h7",
"a1"), name = c("katie", "katie", "sally", "tom", "amy", "amy",
"ash", "james"), number = c(3.5, 3.5, 2, 6, 4, 4, 7, 3)), .Names = c("code",
"name", "number"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-8L), spec = structure(list(cols = structure(list(code = structure(list(), class = c("collector_character",
"collector")), name = structure(list(), class = c("collector_character",
"collector")), number = structure(list(), class = c("collector_double",
"collector"))), .Names = c("code", "name", "number")), default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
I want to highlight all the records which are have two or more values of 'code' which are the same. I know I could use:
df[duplicated(df$name), ]
But this only highlights the duplicated records, but I want all of the code values which are duplicated (i.e. 3 a1s and 2 f5s).
Any ideas?
df[duplicated(df$code) | duplicated(df$code, fromLast=TRUE), ]
code name number
1 a1 katie 3.5
2 a1 katie 3.5
5 f5 amy 4.0
6 f5 amy 4.0
8 a1 james 3.0
Another solution inspired by Alok VS:
ta <- table(df$code)
df[df$code %in% names(ta)[ta > 1], ]
Edit: If you are ok with leaving base R then gdata::duplicated2() allows for more concision.
library(gdata)
df[duplicated2(df$code), ]
turn the indexes to values - and then check if 'code' fits this values:
df[df$code %in% df$code[duplicated(df$code)], ]
code name number
1 a1 katie 3.5
2 a1 katie 3.5
5 f5 amy 4.0
6 f5 amy 4.0
8 a1 james 3.0
I've come up with a crude solution,
temp<-aggregate(df$code, by=list(df$code), FUN=length)
temp<-temp[temp$x>1,]
df[df$code %in% temp$Group.1,]