So I have two dataframes:
DF1
X Y ID
banana 14 1
orange 20 2
pineapple 1 3
guava 300 4
grapes 1 5
DF2
Store State ID
Walmart NY 1
Sears AL 1;2
Target DC 3
Old Navy PA 3
Popeye's HA 5
Footlocker NJ 4;5
I join with the following and get:
df1 %>%
inner_join(df2, by = "ID")
X Y ID Store State
banana 14 1 Walmart NY
pineapple 1 3 Target DC
pineapple 1 3 Old Navy PA
grapes 1 5 Popeye's HA
But due to the semi-colons I'm not capturing those data points on the join, the end result should look like this:
X Y ID Store State
banana 14 1 Walmart NY
banana 14 1 Sears AL
orange 20 2 Sears AL
pineapple 1 3 Target DC
pineapple 1 3 Old Navy PA
guava 300 4 Foot Locker NJ
grapes 1 5 Popeye's HA
grapes 1 5 Popeye's HA
Using separate_rows from tidyr in combination with dplyr will get you there.
First table I called fruit, the other stores.
library(dplyr)
library(tidyr)
fruit %>%
inner_join(separate_rows(stores, ID) %>% mutate(ID = as.integer(ID)))
Joining, by = "ID"
X Y ID Store State
1 banana 14 1 Walmart NY
2 banana 14 1 Sears AL
3 orange 20 2 Sears AL
4 pineapple 1 3 Target DC
5 pineapple 1 3 Old Navy PA
6 guava 300 4 Footlocker NJ
7 grapes 1 5 Popeye's HA
8 grapes 1 5 Footlocker NJ
With base R, we can use strsplit with merge
lst1 <- strsplit(DF2$ID, ";")
merge(DF1, transform(DF2[rep(seq_len(nrow(DF2)),
lengths(lst1)), 1:2], ID = unlist(lst1)))
# ID X Y Store State
#1 1 banana 14 Walmart NY
#2 1 banana 14 Sears AL
#3 2 orange 20 Sears AL
#4 3 pineapple 1 Target DC
#5 3 pineapple 1 Old Navy PA
#6 4 guava 300 Footlocker NJ
#7 5 grapes 1 Popeye's HA
#8 5 grapes 1 Footlocker NJ
Related
I have a dataframe that contains several fields related to an identifier but some are disjointed:
id store manager fruit vegetable
1 Grocery1 Joe apple NA
1 Grocery1 Joe lemon NA
1 Grocery1 Joe NA zucchini
2 Grocery2 Amy orange NA
2 Grocery2 Amy NA asparagus
2 Grocery2 Amy NA spinach
3 Grocery3 Bill NA NA
I want the dataframe to look like:
id store manager fruit vegetable
1 Grocery1 Joe apple zucchini
1 Grocery1 Joe lemon zucchini
2 Grocery2 Amy orange asparagus
2 Grocery2 Amy orange spinach
3 Grocery3 Bill NA NA
Is there a way to easily do this?
You can use tidyr::fill to fill the NA, and only keep the non-duplicated rows using distinct.
library(dplyr)
library(tidyr)
df %>%
group_by(store, manager) %>%
fill(fruit, vegetable, .direction = "updown") %>%
distinct()
# A tibble: 5 × 5
# Groups: store, manager [3]
id store manager fruit vegetable
<int> <chr> <chr> <chr> <chr>
1 1 Grocery1 Joe apple zucchini
2 1 Grocery1 Joe lemon zucchini
3 2 Grocery2 Amy orange asparagus
4 2 Grocery2 Amy orange spinach
5 3 Grocery3 Bill NA NA
I have a question about creating new columns in my dataset by checking whether a value is present in one of the columns of my dataframe, and assigning the columns of a different dataframe based on that presence. As this description is quite vague, see the example dataset below:
newDf <- data.frame(c("Juice 1", "Juice 2", "Juice 3", "Juice 4","Juice 5"),
c("Banana", "Banana", "Orange", "Pear", "Apple"),
c("Blueberry", "Mango", "Rasberry", "Spinach", "Pear"),
c("Kale", NA, "Cherry", NA, "Peach"))
colnames(newDf) <- c("Juice", "Fruit 1", "Fruit 2", "Fruit 3")
dfChecklist <- data.frame(c("Banana", "Cherry"),
c("100", "80"),
c("5", "3"),
c("4", "5"))
colnames(dfChecklist) <- c("FruitCheck", "NutritionalValue", "Deliciousness", "Difficulty")
This gives the following dataframes:
Juice Fruit 1 Fruit 2 Fruit 3
1 Juice 1 Banana Blueberry Kale
2 Juice 2 Banana Mango <NA>
3 Juice 3 Orange Rasberry Cherry
4 Juice 4 Pear Spinach <NA>
5 Juice 5 Apple Pear Peach
FruitCheck NutritionalValue Deliciousness Difficulty
1 Banana 100 5 4
2 Cherry 80 3 5
I want to combine the two and make the result to be like this:
Juice Fruit 1 Fruit 2 Fruit 3 FruitCheck NutritionalValue Deliciousness Difficulty
1 Juice 1 Banana Blueberry Kale Banana 100 5 4
2 Juice 2 Banana Mango <NA> Banana 100 5 4
3 Juice 3 Orange Rasberry Cherry Cherry 80 3 5
4 Juice 4 Pear Spinach <NA> <NA> <NA> <NA> <NA>
5 Juice 5 Apple Pear Peach <NA> <NA> <NA> <NA>
The dataset above is an example, my own dataset is much larger and complexer.
Thanks so much in advance for your help!
First find the first match for each row
tmp=unlist(
apply(
newDf[,grepl("Fruit",colnames(newDf))],
1,
function(x){
y=as.vector(x)
y=y[which.min(match(y,dfChecklist$FruitCheck))]
ifelse(length(y)==0,NA,y)
}
)
)
add this to your original df and then a simple merge
newDf$FruitCheck=tmp
merge(
newDf,
dfChecklist,
by="FruitCheck",
all.x=T
)
resulting in
FruitCheck Juice Fruit 1 Fruit 2 Fruit 3 NutritionalValue Deliciousness
1 Banana Juice 1 Banana Blueberry Kale 100 5
2 Banana Juice 2 Banana Mango <NA> 100 5
3 Cherry Juice 3 Orange Rasberry Cherry 80 3
4 <NA> Juice 4 Pear Spinach <NA> <NA> <NA>
5 <NA> Juice 5 Apple Pear Peach <NA> <NA>
Difficulty
1 4
2 4
3 5
4 <NA>
5 <NA>
I have a dataframe with a column "Food."
dataframe <- data.frame(Color = c("red","red","red","red","red","blue","blue","blue","blue","blue","green","green","green","green","green","orange","orange","orange","orange","orange"),
Food = c("banana","apple","potato","orange","egg","strawberry","cheese","yogurt","kiwi","butter","kale","sugar","carrot","celery","radish","cereal","milk","blueberry","squash","lemon"), Count = c(2,5,4,8,10,7,5,6,9,11,1,8,5,3,7,9,2,3,6,4))
Every time a fruit appears I want to replace the name of the fruit with "fruit."
I've tried making a vector of the fruit names. Then I go through each row in the dataframe and where the string matches the fruit, I want to replace the fruit name with "fruit."
fruit_list <- c("banana","apple","orange","strawberry","kiwi","blueberry","lemon")
for (r in 1:nrow(dataframe)) {
for (i in 1:length(fruit_list)){
if (length(grep(fruit_list[i], dataframe$Food[r])) != 0) {
dataframe$Food[r] <- paste("fruit")
}
}
}
How do I use this general format so that dataframe$Food doesn't just end up filled with NA?
With dplyr:
library(dplyr)
ataframe %>%
mutate(Food=as.character(Food),
Food=ifelse(Food%in%fruit_list,"Fruit",Food))#can change to fruit
Result:
Color Food Count
1 red Fruit 2
2 red Fruit 5
3 red potato 4
4 red Fruit 8
5 red egg 10
6 blue Fruit 7
7 blue cheese 5
8 blue yogurt 6
9 blue Fruit 9
10 blue butter 11
11 green kale 1
12 green sugar 8
13 green carrot 5
14 green celery 3
15 green radish 7
16 orange cereal 9
17 orange milk 2
18 orange Fruit 3
19 orange squash 6
20 orange Fruit 4
Only R base:
dataframe$Food <-
sapply(dataframe$Food,
function(x,fruit_list) ifelse(x %in% fruit_list, "fruit", as.character(x) ),
fruit_list = fruit_list )
You don't necessarily need dplyr for this.
Just use:
dataframe$Food <- ifelse(dataframe$Food %in% fruit_list, "Fruit", as.character(dataframe$Food))
You can do this in one line by using data.table package-
> setDT(dataframe)[,Food:=ifelse(Food %in% fruit_list,"fruit",as.character(Food))]
Color Food Count
1: red fruit 2
2: red fruit 5
3: red potato 4
4: red fruit 8
5: red egg 10
6: blue fruit 7
7: blue cheese 5
8: blue yogurt 6
9: blue fruit 9
10: blue butter 11
11: green kale 1
12: green sugar 8
13: green carrot 5
14: green celery 3
15: green radish 7
16: orange cereal 9
17: orange milk 2
18: orange fruit 3
19: orange squash 6
20: orange fruit 4
I have a dataframe called table like this
a m g c1 c2 c3 c4
1 2015 5 13 bread wine <NA> <NA>
2 2015 8 30 wine eggs rice cake
3 2015 1 21 wine rice eggs <NA>
...
I want to count the elements in column c1 to c4 and order them
I tried to use:
library(plyr)
c<-count(table,"c1")
But i don't know how to count more than one column.
Then i want to use arrange(c,desc(freq)) to order them but when i try with one column the value NA is always on top, and i want only top 3 elements. Like this
c freq
1 wine 3
2 eggs 2
3 rice 2
Can someone please get me some solution for this. Thanks
Use melt and table:
df1 <- read.table(text="a m g c1 c2 c3 c4
2015 5 13 bread wine NA NA
2015 8 30 wine eggs rice cake
2015 1 21 wine rice eggs NA", header=TRUE, stringsAsFactors=FALSE)
c_col <- melt(as.matrix(df1[,4:7]))
sort(table(c_col$value),decreasing=TRUE)
wine eggs rice bread cake
3 2 2 1 1
With qdaptools, with the example dataframe (having name table) provided:
library(qdapTools)
counts <- data.frame(count=sort(colSums(mtabulate(table[,4:7])), decreasing=TRUE))
subset(counts,rownames(counts)!='<NA>')[1:3,1,drop=FALSE] #remove <NA>, select top 3 elements
# count
# wine 3
# eggs 2
# rice 2
I inherited a data set coded in an unusual way. I would like to learn a less verbose way of reshaping it. The data frame looks like this:
# Input.
participant = c(rep("John",6), rep("Mary",6))
day = c(rep(1,3), rep(2,3), rep(1,3), rep(2,3))
likes = c("apples", "apples", "18", "apples", "apples", "7", "bananas", "bananas", "24", "bananas", "bananas", "3")
question = rep(c(1,1,0),4)
number = c(rep(18,3), rep(7,3), rep(24,3), rep(3,3))
df = data.frame(participant, day, question, likes)
participant day question likes
1 John 1 1 apples
2 John 1 1 apples
3 John 1 0 18
4 John 2 1 apples
5 John 2 1 apples
6 John 2 0 7
7 Mary 1 1 bananas
8 Mary 1 1 bananas
9 Mary 1 0 24
10 Mary 2 1 bananas
11 Mary 2 1 bananas
12 Mary 2 0 3
As you can see, the column likes is heterogeneous. When question equals 0, likes conveys a number chosen by the participants, not their preferred fruit. So I would like to re-code it in a new column as follows:
participant day question likes number
1 John 1 1 apples 18
2 John 1 1 apples 18
3 John 1 0 18 18
4 John 2 1 apples 7
5 John 2 1 apples 7
6 John 2 0 7 7
7 Mary 1 1 bananas 24
8 Mary 1 1 bananas 24
9 Mary 1 0 24 24
10 Mary 2 1 bananas 3
11 Mary 2 1 bananas 3
12 Mary 2 0 3 3
My current solution with base R involves subsetting the initial data frame, creating a lookup table, changing the column names and then merging the lookup table with the original data frame. But this involves several steps and I worry that there should be a simpler solution. I think that tidyr might be the answer, but I don't know how to use it to spread values in one column (likes) conditional other columns (day and question).
Do you have any suggestions? Thanks a lot!
Using the data set above, you can try the following. You group your data by participant and day and look for a row with question == 0 for each group.
library(dplyr)
group_by(df, participant, day) %>%
mutate(age = as.numeric(as.character(likes[which(question == 0)])))
Or as alistaire suggested, you can use grep() too.
group_by(df, participant, day) %>%
mutate(age = as.numeric(grep('\\d+', likes, value = TRUE)))
# participant day question likes age
# (fctr) (dbl) (dbl) (fctr) (dbl)
#1 John 1 1 apples 18
#2 John 1 1 apples 18
#3 John 1 0 18 18
#4 John 2 1 apples 7
#5 John 2 1 apples 7
#6 John 2 0 7 7
#7 Mary 1 1 bananas 24
#8 Mary 1 1 bananas 24
#9 Mary 1 0 24 24
#10 Mary 2 1 bananas 3
#11 Mary 2 1 bananas 3
#12 Mary 2 0 3 3
If you want to use data.table, you can do:
library(data.table)
setDT(df)[, age := as.numeric(as.character(likes[which(question == 0)])),
by = list(participant, day)]
NOTE
The present data set is a new one. Jota's answer works for the deleted data set.
Addressing the new example data:
# create a key column, overwrite it later
df$number <- paste0(df$participant, df$day) # use as a key
# create lookup table
lookup <- df[!is.na(as.numeric(as.character(df$likes))), c("number", "likes")]
# use lookup to overwrite df$number with the appropriate number
df$number <- lookup$likes[match(df$number, lookup$number)]
# participant day question likes number
#1 John 1 1 apples 18
#2 John 1 1 apples 18
#3 John 1 0 18 18
#4 John 2 1 apples 7
#5 John 2 1 apples 7
#6 John 2 0 7 7
#7 Mary 1 1 bananas 24
#8 Mary 1 1 bananas 24
#9 Mary 1 0 24 24
#10 Mary 2 1 bananas 3
#11 Mary 2 1 bananas 3
#12 Mary 2 0 3 3
The warning about NAs be introduced by coercion is expected due to converting characters to numeric (as.numeric(as.character(df$likes))),.
If you're data are ordered like in the example, you can use na.locf from the zoo package:
library(zoo)
df$age <- na.locf(as.numeric(as.character(df$likes)), fromLast = TRUE)