Here is my problem. I've got data on city codes (GeoCode) and zip codes (PostCode). Often several zip codes correspond to a single city code. If that's the case, I want to make a column with a string of zip codes corresponding to the same city:
ID<-1:10
GeoCode<-c("AA","BB","BB","CC","CC","CC","DD","DD","DD","DD")
PostCode<-c("01","10","11","20","21","22","30","31","32","33")
data<-data.frame(ID,GeoCode,PostCode)
I want to make such table. For example "20_21_22" belong to City code CC
ID GeoCode PostCode strPostcode
1 1 AA 01 01
2 2 BB 10 10_11
3 3 BB 11 10_11
4 4 CC 20 20_21_22
5 5 CC 21 20_21_22
6 6 CC 22 20_21_22
7 7 DD 30 30_31_32_33
8 8 DD 31 30_31_32_33
9 9 DD 32 30_31_32_33
10 10 DD 33 30_31_32_33
We could group by 'GeoCode' and paste all the unique 'PostCode' in mutate
library(dplyr)
library(stringr)
data %>%
group_by(GeoCode) %>%
mutate(strPostcode = str_c(unique(PostCode), collapse="_"))
# A tibble: 10 x 4
# Groups: GeoCode [4]
# ID GeoCode PostCode strPostcode
# <int> <chr> <chr> <chr>
# 1 1 AA 01 01
# 2 2 BB 10 10_11
# 3 3 BB 11 10_11
# 4 4 CC 20 20_21_22
# 5 5 CC 21 20_21_22
# 6 6 CC 22 20_21_22
# 7 7 DD 30 30_31_32_33
# 8 8 DD 31 30_31_32_33
# 9 9 DD 32 30_31_32_33
#10 10 DD 33 30_31_32_33
Or an option with base R
data$strPostcode <- with(data, ave(PostCode, GeoCode, FUN =
function(x) paste(unique(x), collapse="_")))
The base R option with ave by #akrun is efficient. Here is another workaround
merge(data,
aggregate(PostCode ~ ., data[-1], paste0, collapse = "_"),
by = "GeoCode",
all = TRUE
)
which gives
GeoCode ID PostCode.x PostCode.y
1 AA 1 01 01
2 BB 2 10 10_11
3 BB 3 11 10_11
4 CC 4 20 20_21_22
5 CC 5 21 20_21_22
6 CC 6 22 20_21_22
7 DD 7 30 30_31_32_33
8 DD 8 31 30_31_32_33
9 DD 9 32 30_31_32_33
10 DD 10 33 30_31_32_33
Or you can try this one
data2 <- data %>%
group_by(GeoCode) %>%
mutate(strPostCode = paste0(unique(PostCode), collapse = "_"))
# ID GeoCode PostCode strPostCode
# <int> <chr> <chr> <chr>
# 1 1 AA 01 01
# 2 2 BB 10 10_11
# 3 3 BB 11 10_11
# 4 4 CC 20 20_21_22
# 5 5 CC 21 20_21_22
# 6 6 CC 22 20_21_22
# 7 7 DD 30 30_31_32_33
# 8 8 DD 31 30_31_32_33
# 9 9 DD 32 30_31_32_33
# 10 10 DD 33 30_31_32_33
Related
Consider a dataframe in R where I want to drop row 6 because it has missing observations for the variables var1:var3. But the dataframe has valid observations for id and year. See code below.
In python, this can be done in two ways:
use df.dropna(subset = ['var1', 'var2', 'var3'], inplace=True)
use df.set_index(['id', 'year']).dropna()
How to do this in R with tidyverse?
library(tidyverse)
df <- tibble(id = c(seq(1,10)), year=c(seq(2001,2010)),
var1 = c(sample(1:100, 10, replace=TRUE)),
var2 = c(sample(1:100, 10, replace=TRUE)),
var3 = c(sample(1:100, 10, replace=TRUE)))
df[3,4] = NA
df[6,3:5] = NA
df[8,3:4] = NA
df[10,4:5] = NA
We may use complete.cases
library(dplyr)
df %>%
filter(if_any(var1:var3, complete.cases))
-output
# A tibble: 9 x 5
id year var1 var2 var3
<int> <int> <int> <int> <int>
1 1 2001 48 55 82
2 2 2002 22 83 67
3 3 2003 89 NA 19
4 4 2004 56 1 38
5 5 2005 17 58 35
6 7 2007 4 30 94
7 8 2008 NA NA 36
8 9 2009 97 100 80
9 10 2010 37 NA NA
We can use pmap for this case also:
library(dplyr)
library(purrr)
df %>%
filter(!pmap_lgl(., ~ {x <- c(...)[-c(1, 2)];
all(is.na(x))}))
# A tibble: 9 x 5
id year var1 var2 var3
<int> <int> <int> <int> <int>
1 1 2001 90 55 77
2 2 2002 77 5 18
3 3 2003 17 NA 70
4 4 2004 72 33 33
5 5 2005 10 55 77
6 7 2007 22 81 17
7 8 2008 NA NA 46
8 9 2009 93 28 100
9 10 2010 50 NA NA
Or we could also use complete.cases function in pmap as suggested by dear #akrun:
df %>%
filter(pmap_lgl(select(., 3:5), ~ any(complete.cases(c(...)))))
You can use if_any in filter -
library(dplyr)
df %>% filter(if_any(var1:var3, Negate(is.na)))
# id year var1 var2 var3
# <int> <int> <int> <int> <int>
#1 1 2001 14 99 43
#2 2 2002 25 72 76
#3 3 2003 90 NA 15
#4 4 2004 91 7 32
#5 5 2005 69 42 7
#6 7 2007 57 83 41
#7 8 2008 NA NA 74
#8 9 2009 9 78 23
#9 10 2010 93 NA NA
In base R, we can use rowSums to select rows which has atleast 1 non-NA value.
cols <- grep('var', names(df))
df[rowSums(!is.na(df[cols])) > 0, ]
If looking for complete cases, use the following (kernel of this is based on other answers):
library(tidyverse)
df <- tibble(id = c(seq(1,10)), year=c(seq(2001,2010)),
var1 = c(sample(1:100, 10, replace=TRUE)),
var2 = c(sample(1:100, 10, replace=TRUE)),
var3 = c(sample(1:100, 10, replace=TRUE)))
df[3,4] = NA
df[6,3:5] = NA
df[8,3:4] = NA
df[10,4:5] = NA
df %>% filter(!if_any(var1:var3, is.na))
#> # A tibble: 6 x 5
#> id year var1 var2 var3
#> <int> <int> <int> <int> <int>
#> 1 1 2001 13 28 26
#> 2 2 2002 61 77 58
#> 3 4 2004 95 38 58
#> 4 5 2005 38 34 91
#> 5 7 2007 85 46 14
#> 6 9 2009 45 60 40
Created on 2021-06-24 by the reprex package (v2.0.0)
First time posting something here, forgive any missteps in my question.
In my example below I've got a data.frame where the unique identifier is the tripID with the name of the vessel, the species code, and a catch metric.
> testFrame1 <- data.frame('tripID' = c(1,1,2,2,3,4,5),
'name' = c('SS Anne','SS Anne', 'HMS Endurance', 'HMS Endurance','Salty Hippo', 'Seagallop', 'Borealis'),
'SPP' = c(101,201,101,201,102,102,103),
'kept' = c(12, 22, 14, 24, 16, 18, 10))
> testFrame1
tripID name SPP kept
1 1 SS Anne 101 12
2 1 SS Anne 201 22
3 2 HMS Endurance 101 14
4 2 HMS Endurance 201 24
5 3 Salty Hippo 102 16
6 4 Seagallop 102 18
7 5 Borealis 103 10
I need a way to basically condense the data.frame so that all there is only one row per tripID as shown below.
> testFrame1
tripID name SPP kept SPP.1 kept.1
1 1 SS Anne 101 12 201 22
2 2 HMS Endurance 101 14 201 24
3 3 Salty Hippo 102 16 NA NA
4 4 Seagallop 102 18 NA NA
5 5 Borealis 103 10 NA NA
I've looked into tidyr and reshape but neither of those are can deliver quite what I'm asking for. Is there anything out there that does this quasi-reshaping?
Here are two alternatives using base::reshape and data.table::dcast:
1) base R
reshape(transform(testFrame1,
timevar = ave(tripID, tripID, FUN = seq_along)),
idvar = cbind("tripID", "name"),
timevar = "timevar",
direction = "wide")
# tripID name SPP.1 kept.1 SPP.2 kept.2
#1 1 SS Anne 101 12 201 22
#3 2 HMS Endurance 101 14 201 24
#5 3 Salty Hippo 102 16 NA NA
#6 4 Seagallop 102 18 NA NA
#7 5 Borealis 103 10 NA NA
2) data.table
library(data.table)
setDT(testFrame1)
dcast(testFrame1, tripID + name ~ rowid(tripID), value.var = c("SPP", "kept"))
# tripID name SPP_1 SPP_2 kept_1 kept_2
#1: 1 SS Anne 101 201 12 22
#2: 2 HMS Endurance 101 201 14 24
#3: 3 Salty Hippo 102 NA 16 NA
#4: 4 Seagallop 102 NA 18 NA
#5: 5 Borealis 103 NA 10 NA
Great reproducible post considering it's your first. Here's a way to do it with dplyr and tidyr -
testFrame1 %>%
group_by(tripID, name) %>%
summarise(
SPP = toString(SPP),
kept = toString(kept)
) %>%
ungroup() %>%
separate("SPP", into = c("SPP", "SPP.1"), sep = ", ", extra = "drop", fill = "right") %>%
separate("kept", into = c("kept", "kept.1"), sep = ", ", extra = "drop", fill = "right")
# A tibble: 5 x 6
tripID name SPP SPP.1 kept kept.1
<dbl> <chr> <chr> <chr> <chr> <chr>
1 1.00 SS Anne 101 201 12 22
2 2.00 HMS Endurance 101 201 14 24
3 3.00 Salty Hippo 102 <NA> 16 <NA>
4 4.00 Seagallop 102 <NA> 18 <NA>
5 5.00 Borealis 103 <NA> 10 <NA>
I have a list of dataframes that I need to be combined into a single one.
year<-1990:2000
v1<-1:11
v2<-20:30
df1<-data.frame(year,v1)
df2<-data.frame(year,v2)
ldf<-list(df1,df2)
I now want to unlist this dataframe and get
> head(df)
year v1 v2
1 1990 1 20
2 1991 2 21
3 1992 3 22
4 1993 4 23
Note that my question is different from the solution provided in a similar question, where the solution to that question was: `df <- ldply(ldf, data.frame)
Because what I am essentially looking for, is a more automatic way of doing this: df<-merge(df1,df2, by="year")
With more number of list elements, a convenient option is reduce with one of the join functions
library(tidyverse)
ldf %>%
reduce(inner_join, by = "year")
# year v1 v2
#1 1990 1 20
#2 1991 2 21
#3 1992 3 22
#4 1993 4 23
#5 1994 5 24
#6 1995 6 25
#7 1996 7 26
#8 1997 8 27
#9 1998 9 28
#10 1999 10 29
#11 2000 11 30
Is there anything wrong with:
df <- merge(ldf[[1]], ldf[[2]], by="year")
Or for a long list:
df1 <- ldf[[1]]
for (x in 2:length(ldf)) {
df1 <- merge(df1, ldf[[x]])
}
# year v1 v2
# 1 1990 1 20
# 2 1991 2 21
# 3 1992 3 22
# 4 1993 4 23
# 5 1994 5 24
# 6 1995 6 25
# 7 1996 7 26
# 8 1997 8 27
# 9 1998 9 28
# 10 1999 10 29
# 11 2000 11 30
I have a data frame as below
p1_bin and f1_bin are calculated by cut function by me with
Bins <- function(x) cut(x, breaks = c(0, seq(1, 1000, by = 5)), labels = 1:200)
binned <- as.data.frame (sapply(df[,-1], Bins))
colnames(binned) <- paste("Bin", colnames(binned), sep = "_")
df<- cbind(df, binned)
Now how to calculate mean/avg for previous two years and replace in NA values with in that bin
for example : at row-5 value is NA for p1 and f1 is 30 with corresponding bin 7.. now replace NA with previous 2 years mean for same bin (7) ,i.e
df
ID year p1 f1 Bin_p1 Bin_f1
1 2013 20 30 5 7
2 2013 24 29 5 7
3 2014 10 16 2 3
4 2014 11 17 2 3
5 2015 NA 30 NA 7
6 2016 10 NA 2 NA
df1
ID year p1 f1 Bin_p1 Bin_f1
1 2013 20 30 5 7
2 2013 24 29 5 7
3 2014 10 16 2 3
4 2014 11 17 2 3
5 2015 **22** 30 NA 7
6 2016 10 **16.5** 2 NA
Thanks in advance
I believe the following code produces the desired output. There's probably a much more elegant way than using mean(rev(lag(f1))[1:2]) to get the average of the last two values of f1 but this should do the trick anyway.
library(dplyr)
df %>%
arrange(year) %>%
mutate_at(c("p1", "f1"), "as.double") %>%
group_by(Bin_p1) %>%
mutate(f1 = ifelse(is.na(f1), mean(rev(lag(f1))[1:2]), f1)) %>%
group_by(Bin_f1) %>%
mutate(p1 = ifelse(is.na(p1), mean(rev(lag(p1))[1:2]), p1)) %>%
ungroup
and the output is:
# A tibble: 6 x 6
ID year p1 f1 Bin_p1 Bin_f1
<int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2013 20 30.0 5 7
2 2 2013 24 29.0 5 7
3 3 2014 10 16.0 2 3
4 4 2014 11 17.0 2 3
5 5 2015 22 30.0 NA 7
6 6 2016 10 16.5 2 NA
I have a dataset with the following format:
name1 year name2 profits2010 profits2009 count
AA 2009 AA 10 15 20
AA 2010 AA 10 15 3
BB 2009 BB 4 NA 34
BB 2010 BB 4 NA 4
I need to reshape the data to this format.Any ideas on how this can be done?
name1 year name2 profits count
AA 2009 AA 15 20
AA 2010 AA 10 3
BB 2009 BB NA 34
BB 2010 BB 4 4
Try
indx <- grep('profits', names(df1))
indx2 <- cbind(1:nrow(df1), match(df1$year,
as.numeric(sub('\\D+', '', names(df1)[indx]))))
df1$profits <- df1[indx][indx2]
df1[-indx]
# name1 year name2 count profits
#1 AA 2009 AA 20 15
#2 AA 2010 AA 3 10
#3 BB 2009 BB 34 NA
#4 BB 2010 BB 4 4
This isn't really reshaping, just defining a new variable. Try this:
df$profits <- ifelse(df$year==2009,df$profits2009,df$profits2010)