I'm trying to transfer some work previously done in Excel into R. All I need to do is transform two basic count_if formulae into readable R script. In Excel, I would use three tables and calculate across those using 'point-and-click' methods, but now I'm lost in how I should address it in R.
My original dataframes are large, so for this question I've posted sample dataframes:
OperatorData <- data.frame(
Operator = c("A","B","C"),
Locations = c(850, 575, 2175)
)
AreaData <- data.frame(
Area = c("Torbay","Torquay","Tooting","Torrington","Taunton","Torpley"),
SumLocations = c(1000,500,500,250,600,750)
)
OperatorAreaData <- data.frame(
Operator = c("A","A","A","B","B","B","C","C","C","C","C"),
Area = c("Torbay","Tooting","Taunton",
"Torbay","Taunton","Torrington",
"Tooting","Torpley","Torquay","Torbay","Torrington"),
Locations = c(250,400,200,
100,400,75,
100,750,500,650,175)
)
What I'm trying to do is add two new columns to the OperatorData dataframe: one indicating the count of Areas that operator operates in and another count indicating how many areas in which that operator operates in and owns more than 50% of locations.
So the new resulting dataframe would look like this
Operator Locations AreaCount Own_GE_50percent
A 850 3 1
B 575 3 1
C 2715 5 4
So far, I've managed to calculate the first column using the table function and then appending:
OpAreaCount <- data.frame(table(OperatorAreaData$Operator))
names(OpAreaCount)[2] <- "AreaCount"
OperatorData$"AreaCount" <- cbind(OpAreaCount$AreaCount)
This is fairly straightforward, but I'm stuck in how to calculate the second column calculation with the condition of 50%.
library(dplyr)
OperatorAreaData %>%
inner_join(AreaData, by="Area") %>%
group_by(Operator) %>%
summarise(AreaCount = n_distinct(Area),
Own_GE_50percent = sum(Locations > (SumLocations/2)))
# # A tibble: 3 x 3
# Operator AreaCount Own_GE_50percent
# <fct> <int> <int>
# 1 A 3 1
# 2 B 3 1
# 3 C 5 4
You can use AreaCount = n() if you're sure you have unique Area values for each Operator.
Related
I have seen some similar questions, but none of them was exactly the same as the thing I want to do - which is why I am asking.
I have a dataframe (dummy_data) which contains indices of some observations (obs) regarding given subjects (ID). The dataframe consists only the meaningful data (in other words: the desired conditions are met). The last column in this example data contains the total number of observations (total_obs).
ID <-c(rep("item_001",5),rep("item_452",8),rep("item_0001",7),rep("item_31",9),rep("item_007",5))
obs <- c(1,2,3,5,6,3,4,5,7,8,9,12,16,1,2,4,5,6,7,8,2,4,6,7,8,10,13,14,15,3,4,6,7,11)
total_obs <- c(rep(6,5),rep(16,8),rep(9,7),rep(18,9),rep(11,5))
dummy_data <- data.frame(ID, obs, total_obs)
I would like to create a new column (interval) with 3 possible values: "start", "center", "end" based on following condition(s):
it should split total number of observations (total_obs) into 3 groups (based on indices - from 1st to the last - which is the value stored in the total_obs column) and assign the interval value according to the indices stored in obs column.
Here is the expected output:
ID <- c(rep("item_001",5),rep("item_452",8),rep("item_0001",7),rep("item_31",9),rep("item_007",5))
segment <- c(1,2,3,5,6, 3,4,5,7,8,9,12,16, 1,2,4,5,6,7,8, 2,4,6,7,8,10,13,14,15, 3,4,6,7,11)
total_segments <- c(rep(6,5),rep(16,8),rep(9,7),rep(18,9),rep(11,5))
interval <- c("start","start","center","end","end","start","start","start","center","center","center","end","end","start","start","center","center","center","end","end","start","start","start","center","center","center","end","end","end", "start","start","center","center","end")
wanted_data <- data.frame(ID, segment, total_segments, interval)
I would like to use use dplyr::ntile() with dplyr::mutate() and dplyr::case_when() but I could not make my code function properly. Any solutions?
You just need dplyr::mutate() and dplyr::case_when().
The following should give you something to work off of.
dummy_data %>%
mutate(interval = case_when(obs < (total_obs/3) ~ "start",
obs < 2*(total_obs/3) ~ "center",
TRUE ~ "end"))
# TRUE ~ "end" is the 'else' case when everything else is false
Which gives slightly different results.
I think more careful deliberation should be made regarding where the endpoints are for each interval, but if you know what you are doing, using a combination of <=, %/%, and ceil() should give you the result you desire.
First, because dummy_data$obs is identical withwanted_data$segment, and dummy_data$total_obs is identical with wanted_data$total_segments, you just need to rename these columns.
For the interval column, here is one approach of creating it:
group the data based on segment column
create a column, say tile, and fill it with ntile(segment) results.
create interval column, and use case_when to fill it with the category labels created from tile. It means, fill interval with "start" when tile = 1, "center" when 2, and "end" when 3.
drop the tile column.
wanted_data <- dummy_data %>%
rename(segment = obs, total_segments = total_obs) %>%
group_by(total_segments) %>%
mutate(tile = ntile(segment, 3)) %>%
mutate(interval = case_when(tile == 1~"start",
tile == 2~"center",
tile == 3~"end")) %>%
select(-tile)
wanted_data
# A tibble: 34 × 4
# Groups: total_segments [5]
ID segment total_segments interval
<chr> <dbl> <dbl> <chr>
1 item_001 1 6 start
2 item_001 2 6 start
3 item_001 3 6 center
4 item_001 5 6 center
5 item_001 6 6 end
6 item_452 3 16 start
7 item_452 4 16 start
8 item_452 5 16 start
9 item_452 7 16 center
10 item_452 8 16 center
# … with 24 more rows
It's slightly different from wanted_data$interval that you showed because based on your comment, you said that the division into categories is just as dplyr::ntile() does.
I am trying to merge two datasets using two separate column names, but that share same unique values. For instance, column A in dataset 1== xyzw, while in dataset 2, the column's name is B but the value == xyzw.
However, the problem is that in dataset 2, column's B value == xyzw refers to firm names and appears several times, depending on how many employees are in that firm that exist in the dataset.
Essentially, I want to create a new column, let's call it C in dataset 1 telling me how many employees are in each firm.
I have tried the following:
## Counting how many teachers are in each matched school, using the "Matched" column from matching_file_V4, along with the school_name column from the sample11 dataset:
merged_dataset <- left_join(sample11,matched_datasets,by="school_name")
While this code works, it is not really providing me with the number of employees per firm.
If you could provide a sample data and expected output, It'd makes it easier for others to help. But that notwithstanding, I hope this gives you what you want:
Assuming we have these two data frames:
df_1 <- data.frame(
A = letters[1:5],
B = c('empl_1','empl_2','empl_3','empl_4','empl_5')
)
df_2 <- data.frame(
C = sample(rep(c('empl_1','empl_2','empl_3','empl_4','empl_5'), 15), 50),
D = sample(letters[1:5], 50, replace=T)
)
# I suggest you find the number of employees for each firm in the second data frame
df_2%>%group_by(C)%>%
summarise(
num_empl = n()
)%>% ### Then do the left join
left_join(
df_1,., by=c('B' = 'C') ## this is how you can join on two different column names
)
# A B num_empl
# 1 a empl_1 8
# 2 b empl_2 11
# 3 c empl_3 10
# 4 d empl_4 10
# 5 e empl_5 11
I try to create a simple function how to sum some variables in a nested data set.
Here is a much simpler example
df <- data.frame(ID=c(1,1,1,1,2,3,3,4,4,4,5,6,7,7,7,7,7,7,7,7),
var=c("A","B","C","D","B","A","D","A","C","D","D","D","A","D","A","A","A","B","B","B"),
N=c(50,50,50,50,298,156,156,85,85,85,278,301,98,98,98,98,98,98,98,98))
Think of this as a dataframe containing results of 7 different studies. Each study has investigated one or more Variables (A, B, C, D). The variables mean
ID = The ID of a respective study.
var = The respective variable measured in each study. Some studies have measured only one variable (e.g., ID=2, which only contained b), some several
N = The sample size of each study. That is, each ID has a sample size
I would like to create a function that summarizes three things:
k = how many studies measured each variable (e.g., "A")
m = how often each variable was measured (regardless whether some studies measured a variable more than once)--a simple frequency.
N = the sample size per variable--but only once per study. That is, no duplications per study ID are allowed.
My current version (I am a real noob, so please forgive the form), results in exactly what I want:
model km N
1 A 4 (7) 389
2 B 3 (5) 446
3 C 2 (2) 135
4 D 6 (6) 968
For instance, variable A was measured 7times, but only by 4 studies (i.e., study #7 measured it several times. The (non-redundant) sample size was N=389 (not counting the several measures of study #7 more than one time).
(Note: The parentheses in the table are helpful as I intend to copy the results into a document)
Here is the current version of the code. The problems begin with the part containing the pipes
kmn <- function(data, x, ID, N) {
m <-table(data[[x]])
k <-apply(table(data[[x]],data[[ID]]), 1, function(x) length(x[x>0]) )
model <- levels(data[[x]])
km <- cbind(k,m)
colnames(km)<-c("k","m")
km <- paste0(k," (",m,")")
smpsize <- data %>%
group_by(data[[x]]) %>%
summarise(N = sum(N[!duplicated(ID)])) %>%
select(N)
cbind(model,km,smpsize)
}
kmn(data=df, x="var", ID = "ID", N="N")
The above code works but only if the df-dataframe really contains the N-variable (but not with a different variable name). I guess the "data %>%" prompts R to look into the dataframe and not to use the "sum(N..." part as reference to the call.
I can guess that this looks horrible for someone with some idea :)
Thank you for any ideas
Holger
First, remove duplicates by using the unique function and sum by var.
Secondly take df and group by var, n() gives the count and n_distinct(ID) the number of unique IDs, then you join the dataframe stats_N
library(dplyr)
stats_N <- df %>%
select(ID,var,N) %>%
unique() %>%
group_by(var) %>%
summarise(N=sum(N))
df %>%
group_by(var) %>%
summarise(n=n(),km=n_distinct(ID)) %>%
left_join(stats_N)
# A tibble: 4 x 4
# var n km N
# <fct> <int> <int> <dbl>
#1 A 7 4 389
#2 B 5 3 446
#3 C 2 2 135
#4 D 6 6 968
in addition to the #fmarm's answer, it can be also done without a join, where do the group by 'var', get the number of distinct elements in 'D' (n_distinct), number of rows (n()) and the sum of non-duplicated 'N's
library(dplyr)
df %>%
group_by(model = var) %>%
summarise(km = sprintf("%d (%d)", n_distinct(ID), n()),
N = sum(N[!duplicated(N)]))
# A tibble: 4 x 3
# model km N
# <fct> <chr> <dbl>
#1 A 4 (7) 389
#2 B 3 (5) 446
#3 C 2 (2) 135
#4 D 6 (6) 968
This question already has answers here:
Numbering rows within groups in a data frame
(10 answers)
Closed 3 years ago.
I am looking to add a column to my data that will list the individual count of the observation in the dataset. I have data on NBA teams and each of their games. They are listed by date, and I want to create a column that lists what # in each season each game is for each team.
My data looks like this:
# gmDate teamAbbr opptAbbr id
# 2012-10-30 WAS CLE 2012-10-30WAS
# 2012-10-30 CLE WAS 2012-10-30CLE
# 2012-10-30 BOS MIA 2012-10-30BOS
Commas separate each column
I've tried to use "add_count" but this has provided me with the total # of games each team has played in the dataset.
Prior attempts:
nba_box %>% add_count()
I expect the added column to display the # game for each team (1-82), but instead it now shows the total number of games in the dataset (82).
Here is a base R example that approaches the problem from a for loop standpoint. Given that a team can be either column, we keep track of the teams position by unlisting the data and using the table function to sum the previous rows.
# intialize some fake data
test <- as.data.frame(t(replicate(6, sample( LETTERS[1:3],2))),
stringsAsFactors = F)
colnames(test) <- c("team1","team2")
# initialize two new columns
test$team2_gamenum <- test$team1_gamenum <- NA
count <- NULL
for(i in 1:nrow(test)){
out <- c(count, table(unlist(test[i,c("team1","team2")])))
count <- table(rep(names(out), out)) # prob not optimum way of combining two table results
test$team1_gamenum[i] <- count[which(names(count) == test[i,1])]
test$team2_gamenum[i] <- count[which(names(count) == test[i,2])]
}
test
# team1 team2 team1_gamenum team2_gamenum
#1 B A 1 1
#2 A C 2 1
#3 C B 2 2
#4 C B 3 3
#5 A C 3 4
#6 A C 4 5
I just started working with R for my master thesis and up to now all my calculations worked out as I read a lot of questions and answers here (and it's a lot of trial and error, but thats ok).
Now i need to process a more sophisticated code and i can't find a way to do this.
Thats the situation: I have multiple sub-data-sets with a lot of entries, but they are all structured in the same way. In one of them (50000 entries) I want to change only one value every row. The new value should be the amount of the existing entry plus a few values from another sub-data-set (140000 entries) where the 'ID'-variable is the same.
As this is the third day I'm trying to solve this, I already found and tested for and apply but both are running for hours (canceled after three hours).
Here is an example of one of my attempts (with for):
for (i in 1:50000) {
Entry_ID <- Sub02[i,4]
SUM_Entries <- sum(Sub03$Source==Entry_ID)
Entries_w_ID <- subset(Sub03, grepl(Entry_ID, Sub03$Source)) # The Entry_ID/Source is a character
Value1 <- as.numeric(Entries_w_ID$VAL1)
SUM_Value1 <- sum(Value1)
Value2 <- as.numeric(Entries_w_ID$VAL2)
SUM_Value2 <- sum(Value2)
OLD_Val1 <- Sub02[i,13]
OLD_Val <- as.numeric(OLD_Val1)
NEW_Val <- SUM_Entries + SUM_Value1 + SUM_Value2 + OLD_Val
Sub02[i,13] <- NEW_Val
}
I know this might be a silly code, but thats the way I tried it as a beginner. I would be very grateful if someone could help me out with this so I can get along with my thesis.
Thank you!
Here's an example of my data-structure:
Text VAL0 Source ID VAL1 VAL2 VAL3 VAL4 VAL5 VAL6 VAL7 VAL8 VAL9
XXX 12 456335667806925_1075080942599058 10153901516433434_10153902087098434 4 1 0 0 4 9 4 6 8
ABC 8 456335667806925_1057045047735981 10153677787178434_10153677793613434 6 7 1 1 5 3 6 8 11
DEF 8 456747267806925_2357045047735981 45653677787178434_94153677793613434 5 8 2 1 5 4 1 1 9
The output I expect is an updated value 'VAL9' in every row.
From what I understood so far, you need 2 things:
sum up some values in one dataset
add them to another dataset, using an ID variable
Besides what #yoland already contributed, I would suggest to break it down in two separate tasks. Consider these two datasets:
a = data.frame(x = 1:2, id = letters[1:2], stringsAsFactors = FALSE)
a
# x id
# 1 1 a
# 2 2 b
b = data.frame(values = as.character(1:4), otherid = letters[1:2],
stringsAsFactors = FALSE)
sapply(b, class)
# values otherid
# "character" "character"
Values is character now, we need to convert it to numeric:
b$values = as.numeric(b$values)
sapply(b, class)
# values otherid
# "numeric" "character"
Then sum up the values in b (grouped by otherid):
library(dplyr)
b = group_by(b, otherid)
b = summarise(b, sum_values = sum(values))
b
# otherid sum_values
# <chr> <dbl>
# 1 a 4
# 2 b 6
Then join it with a - note that identifiers are specified in c():
ab = left_join(a, b, by = c("id" = "otherid"))
ab
# x id sum_values
# 1 1 a 4
# 2 2 b 6
We can then add the result of the sum from b to the variable x in a:
ab$total = ab$x + ab$sum_values
ab
# x id sum_values total
# 1 1 a 4 5
# 2 2 b 6 8
(Updated.)
From what I understand you want to create a new variable that uses information from two different data sets indexed by the same ID. The easiest way to do this is probably to join the data sets together (if you need to safe memory, just join the columns you need). I found dplyr's join functions very handy for these cases (explained neatly here) Once you joined the data sets into one, it should be easy to create the new columns you need. e.g.: df$new <- df$old1 + df$old2