I have a large dataset with the two first columns that serve as ID (one is an ID and the other one is a year variable). I would like to compute a count by group and to loop over each variable that is not an ID one. This code below shows what I want to achieve for one variable:
library(tidyverse)
df <- tibble(
ID1 = c(rep("a", 10), rep("b", 10)),
year = c(2001:2020),
var1 = rnorm(20),
var2 = rnorm(20))
df %>%
select(ID1, year, var1) %>%
filter(if_any(starts_with("var"), ~!is.na(.))) %>%
group_by(year) %>%
count() %>%
print(n = Inf)
I cannot use a loop that starts with for(i in names(df)) since I want to keep the variables "ID1" and "year". How can I run this piece of code for all the columns that start with "var"? I tried using quosures but it did not work as I receive the error select() doesn't handle lists. I also tried to work with select(starts_with("var") but with no success.
Many thanks!
Another possible solution:
library(tidyverse)
df %>%
group_by(ID1) %>%
summarise(across(starts_with("var"), ~ length(na.omit(.x))))
#> # A tibble: 2 × 3
#> ID1 var1 var2
#> <chr> <int> <int>
#> 1 a 10 10
#> 2 b 10 10
for(i in names(df)[grepl('var',names(df))])
Related
I have data that were collected from a year but are broken up by months. For my code, I labeled them df1-df12 for each corresponding month. I am trying to group these data using the group_by function to group all the dataframes similarly. When I do the following code- it works fine alone:
df <- df %>%
group_by(date,id) %>%
slice(n()) %>%
ungroup()
However, I would like to streamline this code so that I can use this function for all 12 dataframes without having to copy/paste 12 times, since there is a lot of data to go through. Here is what I have tried to do to that end:
func1<-function(df)
{
df <- df %>%
group_by(date,id) %>%
slice(n()) %>%
ungroup()
}
yr19<-c(df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12)
map(yr19, func1)
However, i get the following error message: Error in UseMethod("group_by") :
no applicable method for 'group_by' applied to an object of class "character". As stated above- i don't get this error message if I go through and do it individually, but there are many months and many years to be analyzed and from a time perspective I don't think doing this code manually is feasible. Thanks for your help
Two ways you can approach this, first using the approach suggested by #ktiu:
## Create example data
library(dplyr) # for pipe and group_by()
set.seed(914)
df1 <- tibble(
date = sample(1:30, 50, replace = T),
id = sample(1:10, 50, replace = T),
var1 = rnorm(50, mean = 10, sd = 3)
)
df2 <- tibble(
date = sample(1:30, 50, replace = T),
id = sample(1:10, 50, replace = T),
var1 = rnorm(50, mean = 10, sd = 3)
)
Modifying your function to address error
func1<-function(df)
{
df <- df %>%
group_by(date,id) %>%
slice(n()) %>%
ungroup()
df
}
## And using list rather than c to combine data frames.
yr19 <- list(df1, df2)
yr19_data <- lapply(yr19, func1)
# This will return a list of data frames you can access with `yr19_data[[1]]`
Alternative approach is to add variable for your source data frames, then collapse it all into a single data frame and manipulate from there. Which approach makes more sense will depend on what else you want to do later.
func2 <- function(df.name){
mutate(get(df.name), source = df.name)
}
# This is set up to get objects given their names, so we'll use a character vector
# of names to iterate off of.
yr19 = c("df1", "df2")
df.list <- lapply(yr19, func2)
df.long <- do.call(bind_rows, df.list)
df.long
# # A tibble: 100 x 4
# date id var1 source
# <int> <int> <dbl> <chr>
# 1 27 9 9.31 df1
# 2 5 3 16.5 df1
# 3 28 3 2.67 df1
# 4 24 4 8.94 df1
# 5 13 3 1.68 df1
At this point you can manipulate one data frame in your original pipe:
df <- df.long %>%
group_by(source, date,id) %>%
slice(n()) %>%
ungroup()
df
# # A tibble: 93 x 4
# date id var1 source
# <int> <int> <dbl> <chr>
# 1 1 8 9.89 df1
# 2 2 4 10.9 df1
# 3 4 3 8.45 df1
# 4 5 3 16.5 df1
# 5 5 7 10.6 df1
I have the following data:
set.seed(1)
data <- data.frame(
id = 1:500, ht_1 = rnorm(500,10:20), ht_2 = rnorm(500,15:25),
ht_3 = rnorm(500,20:30), ht_4 = rnorm(500,25:35),
ht_5 = rnorm(500,20:40)
)
I would like to identify the values in columns ht_1:ht_4 that are greater than the values in column ht_5 (number of observations and means).
For each of these columns, I would then like to replace any values that are greater than ht_5 with ht_5.
Hi you can use the mutate_at function like this:
library(tidyverse)
data %>% as_tibble %>%
mutate_at(vars(paste0("ht_", 1:4)), ~if_else(.x > ht_5, ht_5, .x))
In this case you can also use pmin instead of if_else which should be faster.
data %>% as_tibble %>%
mutate_at(vars(paste0("ht_", 1:4)), ~pmin(.x, ht_5))
To see how many values are greater than ht_5 you can use the summarise_atfunction:
data %>% as_tibble %>%
summarize_at(vars(paste0("ht_", 1:4)), ~ length(.x[.x > ht_5]))
# A tibble: 1 x 4
ht_1 ht_2 ht_3 ht_4
<int> <int> <int> <int>
1 6 39 131 258
Im trying to perform a sum function to count the number of interactions for Unique Id's
So I have something like this:
Client ID
JOE12_EMI
ABC12_CANC
ABC12_EMI
ABC12_RENE
and so on...
It'll also have a column next to it that counts the how many times each unique ID repeats.
Frequency
1
2
2
1
Is there a way that i can have all the activity types (EMI, TELI, PFL) summed for each ID and then placed into new columns?
I've tried to transpose the data by separating the actual ID from the activity type but this doesn't return the sums, thank you for any help. I'm not sure if that's the best way or if transposing the data to wide format and then doing another sum function but I am unsure how to go about it.
separate(frequency, id, c("id", "act_code") )
nd <- melt(frequency, id=(c("id")))
Try this:
library(dplyr)
data=data.frame(Client_ID= c("JOE12_EMI",
"ABC12_CANC",
"ABC12_EMI",
"ABC12_RENE"),
frequency= c(1,2,2,1))
client_and_id <- as.data.frame(do.call(rbind, strsplit(as.character(data$Client_ID), "_")))
names(client_and_id) <- c("client", "id")
data <- cbind(data, client_and_id)
data_sum <- data %>% group_by(id) %>% mutate(sum_freq = sum(frequency))
The output
> data_sum
# A tibble: 4 x 5
# Groups: id [3]
Client_ID frequency client id sum_freq
<fct> <dbl> <fct> <fct> <dbl>
1 JOE12_EMI 1 JOE12 EMI 3
2 ABC12_CANC 2 ABC12 CANC 2
3 ABC12_EMI 2 ABC12 EMI 3
4 ABC12_RENE 1 ABC12 RENE 1
You can also display the output by ID:
distinct(data_sum %>% dplyr::select(id, sum_freq))
# A tibble: 3 x 2
# Groups: id [3]
id sum_freq
<fct> <dbl>
1 EMI 3
2 CANC 2
3 RENE 1
You're on the right track; I think the only thing you need is a group_by. Something like this:
library(dplyr)
library(tidyr)
df = data.frame(ClientID = c("JOE12_EMI",
"ABC12_CANC",
"ABC12_EMI",
"ABC12_RENE"))
df %>%
separate(ClientID, into = c("id", "act_code"), sep = "_") %>%
group_by(id) %>%
mutate(frequency = n()) %>%
ungroup() %>%
group_by(id, act_code) %>%
mutate(act_frequency = n()) %>%
ungroup() %>%
spread(act_code, act_frequency)
(This does the sum by user and the pivot by activity type separately; it's possible to calculate the sum by user after pivoting, but this way is easier for me to read.)
Hi All,
Example :- The above is the data I have. I want to group age 1-2 and count the values. In this data value is 4 for age group 1-2. Similarly I want to group age 3-4 and count the values. Here the value for age group 3-4 is 6.
How can I group age and aggregate the values correspond to it?
I know this way: code-
data.frame(df %>% group_by(df$Age) %>% tally())
But the values are aggregating on individual Age.
I want the values aggregating on multiple age to be a group as mentioned above example.
Any help on this will be greatly helpful.
Thanks a lot to All.
Here are two solutions, with base R and with package dplyr.
I will use the data posted by Shree.
First, base R.
I create a grouping variable grp and then aggregate on it.
grp <- with(df, c((age %in% 1:2) + 2*(age %in% 3:4)))
aggregate(age ~ grp, df, length)
# grp age
#1 1 4
#2 2 6
Second a dplyr way.
Function case_when is used to create a grouping variable. This allows for meaningful names to be given to the groups in an easy way.
library(dplyr)
df %>%
mutate(grp = case_when(
age %in% 1:2 ~ "2:3",
age %in% 3:4 ~ "3:4",
TRUE ~ NA_character_
)) %>%
group_by(grp) %>%
tally()
## A tibble: 2 x 2
# grp n
# <chr> <int>
#1 1:2 4
#2 3:4 6
Here's one way using dplyr and ?cut from base R -
df <- data.frame(age = c(1,1,2,2,3,3,3,4,4,4),
Name = letters[1:10],
stringsAsFactors = F)
df %>%
count(grp = cut(age, breaks = c(0,2,4)))
# A tibble: 2 x 2
grp n
<fct> <int>
1 (0,2] 4
2 (2,4] 6
I would like to find the minimum value of a variable (time) that several other variables are equal to 1 (or any other value). Basically my application is finding the first year that x ==1, for several x. I know how to find this for one x but would like to avoid generating multiple reduced data frames of minima, then merging these together. Is there an efficient way to do this? Here is my example data and solution for one variable.
d <- data.frame(cat = c(rep("A",10), rep("B",10)),
time = c(1:10),
var1 = c(0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1),
var2 = c(0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1))
ddply(d[d$var1==1,], .(cat), summarise,
start= min(time))
How about this using dplyr
d %>%
group_by(cat) %>%
summarise_at(vars(contains("var")), funs(time[which(. == 1)[1]]))
Which gives
# A tibble: 2 x 3
# cat var1 var2
# <fct> <int> <int>
# 1 A 4 5
# 2 B 7 8
We can use base R to get the minimum 'time' among all the columns of 'var' grouped by 'cat'
sapply(split(d[-1], d$cat), function(x)
x$time[min(which(x[-1] ==1, arr.ind = TRUE)[, 1])])
#A B
#4 7
Is this something you are expecting?
library(dplyr)
df <- d %>%
group_by(cat, var1, var2) %>%
summarise(start = min(time)) %>%
filter()
I have left a blank filter argument that you can use to specify any filter condition you want (say var1 == 1 or cat == "A")