So I am trying to find the number of occurrences of each name in another dataset. The code I am trying to run is:
Data$Count <- grep(Data$Name,OtherDataSet$LeadName) %>% length()
The issue is when I run this, the number for the first name gets mapped to each spot in that column. Why is this happening?
library(tidyverse)
Data <- data_frame(Name=c("Dog","Cat","Bird"))
OtherDataSet <- data_frame(LeadName=c("Frog","Cat","Catfish","BirdOfPrey","Bird","Bird"))
Data <- Data %>% mutate(Count=map(.x = Name,~str_detect(.,pattern = OtherDataSet$LeadName)) %>% map_int(~sum(.)))
Related
I've got a huge df that include the following:
subsetdf <- data_frame(Id=c(1:6),TicketNo=c(15,16,15,17,17,17))
I want to add a column, GroupSize, that tells for each Id how many other Ids share the same TicketNo value. In other words, I want output like this:
TheDream <- data_frame(Id=c(1:6),TicketNo=c(15,16,15,17,17,17),GroupSize=c(2,1,2,3,3,3)
I've unsuccessfully tried:
subsetdf <- subsetdf %>%
group_by(TicketNo) %>%
add_count(name = "GroupSize")
I'd like to use mutate() but I can't seem to get it right.
Edit
With the GroupSize column now added, I want to add a final column that looks at the values in two other columns and returns the value of whichever is higher. So I've got:
df <- data_frame(Id=c(1:6),TicketNo=c(15,16,15,17,17,17),GroupSize=c(2,1,2,3,3,3),FamilySize=c(2,2,1,1,4,4)
And I want:
df <- data_frame(Id=c(1:6),TicketNo=c(15,16,15,17,17,17),GroupSize=c(2,1,2,3,3,3),FamilySize=c(2,2,1,1,4,4),FinalSize=c(2,2,2,3,4,4)
I've unsuccessfully tried:
df <- df %>% pmax(df$GroupSize, df$FamilySize) %>% dplyr::mutate(FinalSize = n())
That attempt earns me the error: Error: ! Subscript iis a matrix, the datavalue` must have size 1.
Backtrace:
... %>% dplyr::mutate(Groupsize = n())
base::pmax(., train_data$Family_size, train_data$PartySize)
tibble:::[<-.tbl_df(*tmp*, change, value = <int>)
tibble:::tbl_subassign_matrix(x, j, value, j_arg, substitute(value))`
If we need to use mutate use n() to get the group size. Also, make sure that the mutate is from dplyr (as there is also a plyr::mutate which could mask the function if it is loaded later)
library(dplyr)
subsetdf %>%
group_by(TicketNo) %>%
dplyr::mutate(GroupSize = n())
I am trying to streamline the process of auditing chemistry laboratory data. When we encounter data where an analyte is not detected I need to change the recorded result to a value equal to 1/2 of the level of detection (LOD) for the analytical method. I have LOD's contained within another dataframe to be used as a lookup table.
I have multiple columns representing data from different analytical tests, each with it's own unique LOD. Here's an example of the type of data I am working with:
library(tidyverse)
dat <- tibble("Lab_ID" = as.character(seq(1,10,1)),
"Tributary" = c('sawmill','paint', 'herring', 'water',
'paint', 'sawmill', 'bolt', 'water',
'herring', 'sawmill'),
"date" = rep(as.POSIXct("2021-10-01 12:00:00"), 10),
"TP" = c(1.5,15.7,-2.3,7.6,0.1,45.6,12.2,-0.1,22.2,0.6),
"TN" = c(100.3,56.2,-10.5,0.4,-0.3,11.0,45.8,256.0,12.2,144.0),
"DOC" = c(56.0,120.3,-10.5,0.2,14.6,489.3,0.3,14.4,54.6,88.8))
dat
detect_level <- tibble("Parameter" = c('TP', 'TN', 'DOC'),
'LOD' = c(0.6, 11, 0.3)) %>%
mutate(halfLOD=LOD/2)
detect_level
I have poured over multiple other questions with a similar theme:
Change values in multiple columns of a dataframe using a lookup table
R - Match values from multiple columns in a data.frame to a lookup table.
Replace values in multiple columns using different thresholds
and gotten to a point where I have pivoted the data and split it out into a list of dataframes that are specific analytes:
dat %>%
pivot_longer(cols = c('TP','TN','DOC')) %>%
arrange(name) %>%
split(.$name)
I have tried to apply a function using map(), however I cannot figure out how to integrate the values from the lookup table (detect_level) into my code. If someone could help me continue this pipe, or finish the process to achieve a final product dat2 that should look like this I would appreciate it:
dat2 <- tibble("Lab_ID" = as.character(seq(1,10,1)),
"Tributary" = c('sawmill','paint', 'herring', 'water',
'paint', 'sawmill', 'bolt', 'water',
'herring', 'sawmill'),
"date" = rep(as.POSIXct("2021-10-01 12:00:00"), 10),
"TP" = c(1.5,15.7,0.3,7.6,0.3,45.6,12.2,0.3,22.2,0.6),
"TN" = c(100.3,56.2,5.5,5.5,5.5,11.0,45.8,256.0,12.2,144.0),
"DOC" = c(56.0,120.3,0.15,0.15,14.6,489.3,0.3,14.4,54.6,88.8))
dat2
Another possibility would be from the closest similar question I have found is:
Lookup multiple column from a single table
Here's a snippet of code that I have adapted from this question, however, if you run it you will see that where values exist that are not found in detect_level an NA is returned. Additionally, it does not appear to have worked for $TN or $DOC, even in cases when the $LOD value from detect_level was present.
dat %>%
mutate(across(all_of(unique(detect_level$Parameter)),
~ {i1 <- detect_level$Parameter == cur_column()
detect_level$LOD[i1][match(., detect_level$LOD)]}))
I am not comfortable at all with the purrr language here and have only adapted this code from the question linked, so I would appreciate if this is the direction an answerer chooses, that they might comment code to explain briefly what is happening "under the hood".
Thank you in advance!
Perhaps this helps
library(dplyr)
dat %>%
mutate(across(all_of(detect_level$Parameter),
~ pmax(., detect_level$LOD[match(cur_column(), detect_level$Parameter)])))
For the updated case
dat %>%
mutate(across(all_of(detect_level$Parameter),
~ replace(., . < detect_level$LOD[match(cur_column(),
detect_level$Parameter)],detect_level$halfLOD[match(cur_column(),
detect_level$Parameter)])))
I am having this weird issue.
The following code works:
Jakarta_Covid <- left_join(DKI_Jakarta, Covid_DF,
by = c("Sub_District" = "Sub_District"))
However the code chunk below is giving me 'Join columns must be present in data.
x Problem with Sub_District.
Jakarta_Death <- Covid_DF %>%
inner_join(DKI_Jakarta, by=c("Sub_District"="Sub_District")) %>%
group_by(Sub_District, Month) %>%
summarise(`Covid Death Per 10,000 Population` = (((sum(Death))/(Total_Population))*10000))
Jakarta_Death <- Jakarta_Death %>% left_join(DKI_Jakarta,
by=c("Sub_District"="Sub_District"))
How can I calculate the 'Covid Death Per 10,000 Population' from two DF and I need the Geometry column in DKI_Jakarta to plot into a map later on.
left_join(DKI_Jakarta, Covid_DF, by = c("Sub_District")
If you have the same column name in both tables just left one in the by=()
and thanks to all in advance.
I have the following data:
set.seed(123)
data <- data.frame (name=LETTERS[sample(1:26, 500, replace=T)],present=sample(0:1,500,replace = T))
And I want to quickly calculate the percentage of present observations (1's) for each letter. I can do it manually, but I believe there is an easier way to do this:
library(dplyr)
A <- filter(data, name=="A" & present==1)
A2 <- filter(data, name=="A")
data$Percentage[data$name=="A"] <- nrow(A)/nrow(A2)
And so on until I arrive to "Z".
Can I make this task automatically without having to change the values of the "name" colum manually?
Best regards,
We can use prop.table with table to get the proportion
prop.table(table(data), 1)[,2]
To add it as a column, we can expand it by matching with the 'names'
data$Percentage <- prop.table(table(data), 1)[,2][as.character(data$name)]
Or as #Lars Lau Raket suggested, we don't need to convert to character
prop.table(table(data), 1)[,2][data$name]
If we need to create a column
library(dplyr)
data %>%
group_by(name) %>%
mutate(Percentage = mean(present==1))
This is my first stackoverflow question.
I'm trying to use dplyr to process and output a summary of data grouped by a categorical variable (inj_length_cat3) in my dataset. Actually, I generate this variable (from inj_length) on the fly using mutate(). I also want to output the same summary of the data without grouping. The only way I figured out how to do that is to do the analysis twice over, once with, once without grouping, and then combine the outputs. Ugh.
I'm sure there is a more elegant solution than this and it bugs me. I wonder if anyone would be able to help.
Thanks!
library(dplyr)
df<-data.frame(year=sample(c(2005,2006),20,replace=T),inj_length=sample(1:10,20,replace=T),hiv_status=sample(0:1,20,replace=T))
tmp <- df %>%
mutate(inj_length_cat3 = cut(inj_length, breaks=c(0,3,100), labels = c('<3 years','>3 years')))%>%
group_by(year,inj_length_cat3)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
) %>%
filter(inj_length_cat3%in%c('<3 years','>3 years'))
tmp_all <- df %>%
group_by(year)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
)
tmp_all$inj_length_cat3=as.factor('All')
tmp<-merge(tmp_all,tmp,all=T)
I'm not sure you consider this more elegant, but you can get a solution to work if you first create a dataframe that has all your data twice: once so that you can get the subgroups and once to get the overall summary:
df1 <- rbind(df,df)
df1$inj_length_cat3 <- cut(df$inj_length, breaks=c(0,3,100,Inf),
labels = c('<3 years','>3 years','All'))
df1$inj_length_cat3[-(1:nrow(df))] <- "All"
Now you just need to run your first analysis without mutate():
tmp <- df1 %>%
group_by(year,inj_length_cat3)%>%
summarise(
r=sum(hiv_status,na.rm=T),
n=length(hiv_status),
p=prop.test(r,n)$estimate,
cilow=prop.test(r,n)$conf.int[1],
cihigh=prop.test(r,n)$conf.int[2]
) %>%
filter(inj_length_cat3%in%c('<3 years','>3 years','All'))