I have a dataframe with 10 columns and have created a plot which allows user to input four different values. I have used select() function for extracting columns for plot purpose. It works well using shiny, however, when the user selects same input value twice, the columns not getting selected twice. An example would be helpful which is given below.
names <- c("kamal", "vimal", "shamal")
age <- c(45,23,35)
weight <- c(50,34,42)
data <- data.frame(names, age, weight)
data_select <- data %>% select(names,age,names)
print(data_select)
Please guide me to extract same column twice, if selected by the user.
You could use [ from base R:
data[c("names", "age", "names")]
names age names.1
1 kamal 45 kamal
2 vimal 23 vimal
3 shamal 35 shamal
Or data.table:
data.table(data)[, .(names, age, names)]
names age names
1: kamal 45 kamal
2: vimal 23 vimal
3: shamal 35 shamal
Using dplyr you could do:
data_select <- data %>%
{sapply(c("names", "age", "names"), function(x) pull(., x))} %>%
as.data.frame()
# names age names
# 1 kamal 45 kamal
# 2 vimal 23 vimal
# 3 shamal 35 shamal
dplyr::select does not allow multiple selection of same column. You could use base here for subsetting.
cols_to_select <- c('names','age','names')
data[cols_to_select]
# names age names.1
#1 kamal 45 kamal
#2 vimal 23 vimal
#3 shamal 35 shamal
Although this renames the column name with suffixes.
Related
I know this is a classic question and there are also similar ones in the archive, but I feel like the answers did not really apply to this case. Basically I want to take one dataframe (covid cases in Berlin per district), calculate the sum of the columns and create a new dataframe with a column representing the name of the district and another one representing the total number. So I wrote
covid_bln <- read.csv('https://www.berlin.de/lageso/gesundheit/infektionsepidemiologie-infektionsschutz/corona/tabelle-bezirke-gesamtuebersicht/index.php/index/all.csv?q=', sep=';')
c_tot<-data.frame('district'=c(), 'number'=c())
for (n in colnames(covid_bln[3:14])){
x<-data.frame('district'=c(n), 'number'=c(sum(covid_bln$n)))
c_tot<-rbind(c_tot, x)
next
}
print(c_tot)
Which works properly with the names but returns only the number of cases for the 8th district, but for all the districts. If you have any suggestion, even involving the use of other functions, it would be great. Thank you
Here's a base R solution:
number <- colSums(covid_bln[3:14])
district <- names(covid_bln[3:14])
c_tot <- cbind.data.frame(district, number)
rownames(c_tot) <- NULL
# If you don't want rownames:
rownames(c_tot) <- NULL
This gives us:
district number
1 mitte 16030
2 friedrichshain_kreuzberg 10679
3 pankow 10849
4 charlottenburg_wilmersdorf 10664
5 spandau 9450
6 steglitz_zehlendorf 9218
7 tempelhof_schoeneberg 12624
8 neukoelln 14922
9 treptow_koepenick 6760
10 marzahn_hellersdorf 6960
11 lichtenberg 7601
12 reinickendorf 9752
I want to provide a solution using tidyverse.
The final result is ordered alphabetically by districts
c_tot <- covid_bln %>%
select( mitte:reinickendorf) %>%
gather(district, number, mitte:reinickendorf) %>%
group_by(district) %>%
summarise(number = sum(number))
The rusult is
# A tibble: 12 x 2
district number
* <chr> <int>
1 charlottenburg_wilmersdorf 10736
2 friedrichshain_kreuzberg 10698
3 lichtenberg 7644
4 marzahn_hellersdorf 7000
5 mitte 16064
6 neukoelln 14982
7 pankow 10885
8 reinickendorf 9784
9 spandau 9486
10 steglitz_zehlendorf 9236
11 tempelhof_schoeneberg 12656
12 treptow_koepenick 6788
When you use a dplyr join function like full_join, columns with identical names are duplicated and given suffixes like "col.x", "col.y", "col.x.x", etc. when they are not used to join the tables.
library(dplyr)
data1<-data.frame(
Code=c(2,1,18,5),
Country=c("Canada", "USA", "Brazil", "Iran"),
x=c(50,29,40,29))
data2<-data.frame(
Code=c(2,40,18),
Country=c("Canada","Japan","Brazil"),
y=c(22,30,94))
data3<-data.frame(
Code=c(25,14,52),
Country=c("China","Japan","Australia"),
z=c(22,30,94))
data4<-Reduce(function(...) full_join(..., by="Code"), list(data1,data2,data3))
This results in "Country", "Country.x", and "Country.y" columns.
Is there a way to combine the three columns into one, such that if a row has NA for a "Country", it takes the value from "Country.x" or "Country.y"?
I attempted a solution based on this similar question, but it gives me a warning and returns only values from the top three rows.
data4<-Reduce(function(...) full_join(..., by="Code"), list(data1,data2,data3)) %>%
mutate(Country=coalesce(Country.x,Country.y,Country)) %>%
select(-Country.x, -Country.y)
This returns the warning invalid factor level, NA generated.
Any ideas?
You could use my package safejoin, make a full join and deal with the conflicts using dplyr::coalesce.
First we'll have to rename the tables to have value columns named the same.
library(dplyr)
data1 <- rename_at(data1,3, ~"value")
data2 <- rename_at(data2,3, ~"value")
data3 <- rename_at(data3,3, ~"value")
Then we can join
# devtools::install_github("moodymudskipper/safejoin")
library(safejoin)
data1 %>%
safe_full_join(data2, by = c("Code","Country"), conflict = coalesce) %>%
safe_full_join(data3, by = c("Code","Country"), conflict = coalesce)
# Code Country value
# 1 2 Canada 50
# 2 1 USA 29
# 3 18 Brazil 40
# 4 5 Iran 29
# 5 40 Japan 30
# 6 25 China 22
# 7 14 Japan 30
# 8 52 Australia 94
You get some warnings because you're joining factor columns with different levels, add parameter check="" to remove them.
In R (or other language), I want to transform an upper data frame to lower one.
How can I do that?
Thank you beforehand.
year month income expense
2016 07 50 15
2016 08 30 75
month income_expense
1 2016-07 50
2 2016-07 -15
3 2016-08 30
4 2016-08 -75
Well, it seems that you are trying to do multiple operations in the same question: combine dates columns, melt your data, some colnames transformations and sorting
This will give your expected output:
library(tidyr); library(reshape2); library(dplyr)
df %>% unite("date", c(year, month)) %>%
mutate(expense=-expense) %>% melt(value.name="income_expense") %>%
select(-variable) %>% arrange(date)
#### date income_expense
#### 1 2016_07 50
#### 2 2016_07 -15
#### 3 2016_08 30
#### 4 2016_08 -75
I'm using three different libraries here, for better readability of the code. It might be possible to do it with base R, though.
Here's a solution using only two packages, dplyr and tidyr
First, your dataset:
df <- dplyr::data_frame(
year =2016,
month = c("07", "08"),
income = c(50,30),
expense = c(15, 75)
)
The mutate() function in dplyr creates/edits individual variables. The gather() function in tidyr will bring multiple variables/columns together in the way that you specify.
df <- df %>%
dplyr::mutate(
month = paste0(year, "-", month)
) %>%
tidyr::gather(
key = direction, #your name for the new column containing classification 'key'
value = income_expense, #your name for the new column containing values
income:expense #which columns you're acting on
) %>%
dplyr::mutate(income_expense =
ifelse(direction=='expense', -income_expense, income_expense)
)
The output has all the information you'd need (but we will clean it up in the last step)
> df
# A tibble: 4 × 4
year month direction income_expense
<dbl> <chr> <chr> <dbl>
1 2016 2016-07 income 50
2 2016 2016-08 income 30
3 2016 2016-07 expense -15
4 2016 2016-08 expense -75
Finally, we select() to drop columns we don't want, and then arrange it so that df shows the rows in the same order as you described in the question.
df <- df %>%
dplyr::select(-year, -direction) %>%
dplyr::arrange(month)
> df
# A tibble: 4 × 2
month income_expense
<chr> <dbl>
1 2016-07 50
2 2016-07 -15
3 2016-08 30
4 2016-08 -75
NB: I guess that I'm using three libraries, including magrittr for the pipe operator %>%. But, since the pipe operator is the best thing ever, I often forget to count magrittr.
This question already has answers here:
Combine Multiple Columns Into Tidy Data [duplicate]
(3 answers)
Closed 5 years ago.
In R, I have data where each person has multiple session dates, and the scores on some tests, but this is all in one row. I would like to change it so I have multiple rows with the persons info, but only one of the session dates and corresponding test scores, and do this for every person. Also, each person may have completed different number of sessions.
Ex:
ID Name Session1Date Score Score Session2Date Score Score
23 sjfd 20150904 2 3 20150908 5 7
28 addf 20150905 3 4 20150910 6 8
To:
ID Name SessionDate Score Score
23 sjfd 20150904 2 3
23 sjfd 20150908 5 7
28 addf 20150905 3 4
28 addf 20150910 6 8
You can use melt from the devel version of data.table ie. v1.9.5. It can take multiple 'measure' columns as a list. Instructions to install are here
library(data.table)#v1.9.5+
melt(setDT(df1), measure = patterns("Date$", "Score(\\.2)*$", "Score\\.[13]"))
# ID Name variable value1 value2 value3
#1: 23 sjfd 1 20150904 2 3
#2: 28 addf 1 20150905 3 4
#3: 23 sjfd 2 20150908 5 7
#4: 28 addf 2 20150910 6 8
Or using reshape from base R, we can specify the direction as 'long' and varying as a list of column index
res <- reshape(df1, idvar=c('ID', 'Name'), varying=list(c(3,6), c(4,7),
c(5,8)), direction='long')
res
# ID Name time Session1Date Score Score.1
#23.sjfd.1 23 sjfd 1 20150904 2 3
#28.addf.1 28 addf 1 20150905 3 4
#23.sjfd.2 23 sjfd 2 20150908 5 7
#28.addf.2 28 addf 2 20150910 6 8
If needed, the rownames can be changed
row.names(res) <- NULL
Update
If the columns follow a specific order i.e. 3rd grouped with 6th, 4th with 7th, 5th with 8th, we can create a matrix of column index and then split to get the list for the varying argument in reshape.
m1 <- matrix(3:8,ncol=2)
lst <- split(m1, row(m1))
reshape(df1, idvar=c('ID', 'Name'), varying=lst, direction='long')
If your data frame name is data
Use this
data1 <- data[1:5]
data2 <- data[c(1,2,6,7,8)]
newdata <- rbind(data1,data2)
This works for the example you've given. You might have to change column names appropriately in data1 and data2 for a proper rbind
Hello I have a dataset with multiple patients, each with multiple observations.
I want to select the earliest observation for each patient.
Example:
Patient ID Tender Swollen pt_visit
101 1 10 6
101 6 12 12
101 4 3 18
102 9 5 18
102 3 6 24
103 5 2 12
103 2 1 18
103 8 0 24
The pt_visit variable is the number of months the patient was in the study at the time of the observation. What I need is the first observation from each patient based on the lowest number of months in the pt_visit column. However I need the earliest observation for each patient ID.
My desired results:
Patient ID Tender Swollen pt_visit
101 1 10 6
102 9 5 18
103 5 2 12
Assuming your data frame is called df, use the ddply function in the plyr package:
require(plyr)
firstObs <- ddply(df, "PatientID", function(x) x[x$pt_visit == min(x$pt_visit), ])
I would use the data.table package:
Data <- data.table(Data)
setkey(Data, Patient_ID, pt_visit)
Data[,.SD[1], by=Patient_ID]
Assuming that the Patient ID column is actually named Patient_ID, here are a few approaches. DF is assumed to be the name of the input data frame:
sqldf
library(sqldf)
sqldf("select Patient_ID, Tender, Swollen, min(pt_visit) pt_visit
from DF
group by Patient_ID")
or
sqldf("select *, min(pt_visit) pt_visit from DF group by Patient_ID")[-ncol(DF)]
Note: The above two alternatives use an extension to SQL only found in SQLite so be sure you are using the SQLite backend. (SQLite is the default backend for sqldf unless RH2, RProgreSQL or RMYSQL is loaded.)
subset/ave
subset(DF, ave(pt_visit, Patient_ID, FUN = rank) == 1)
Note: This makes use of the fact that there are no duplicate pt_visit values within the same Patient_ID. If there were we would need to specify the ties= argument to rank.
I almost think they should be a subset parameter named "by" that would do the same as it does in data.table. This is a base-solution:
do.call(rbind, lapply( split(dfr, dfr$PatientID),
function(x) x[which.min(x$pt_visit),] ) )
PatientID Tender Swollen pt_visit
101 101 1 10 6
102 102 9 5 18
103 103 5 2 12
I guess you can see why #hadley built 'plyr'.