Exchange NA with mean of previous and next value - r

I searched for an answer to how to exchange NAs with the mean of the previous and next values in a DataFrame for specifically one column. But I didn't find an answer which shows how to do this on base R with the addition that NAs can be next to each other.
the DataFrame:
name number
1 John 56
2 Garry NA
3 Carl 70
4 Doris 96
5 Wendy NA
6 Louis NA
7 Becky 40
whished output:
name number
1 John 56
2 Garry 63
3 Carl 70
4 Doris 96
5 Wendy 68
6 Louis 68
7 Becky 40


within(df1, number.fill <-
rowMeans(cbind(ave(number, cumsum(!is.na(number)),
FUN=function(x) x[1]),
rev(ave(rev(number), cumsum(!is.na(rev(number))),
FUN=function(x) x[1])))))
#> name number number.fill
#> 1 John 56 56
#> 2 Garry NA 63
#> 3 Carl 70 70
#> 4 Doris 96 96
#> 5 Wendy NA 68
#> 6 Louis NA 68
#> 7 Becky 40 40
Data:
read.table(text = "name number
John 56
Garry NA
Carl 70
Doris 96
Wendy NA
Louis NA
Becky 40",
header = T, stringsAsFactors = F) -> df1


In Base R you could do:
idx <- is.na(df$number)
df$number[idx] <- 0
b <- with(rle(df$number), rep(stats::filter(values, c(1,0,1)/2), lengths))
df$number[idx] <- b[idx]
df
name number
1 John 56
2 Garry 63
3 Carl 70
4 Doris 96
5 Wendy 68
6 Louis 68
7 Becky 40

Related

Conditional calculations in dataframe based on values in other column R

I have a data.frame where most, but not all, data are recorded over a 12-month period. This is specified in the months column.
I need to transform the revenue and cost variables only (since they are flow data, compared to total_assets which is stock data) so I get the 12-month values.
In this example, for Michael and Ravi I need to replace the values in revenue and cost by (12/months)*revenue and (12/months)*cost, respectively.
What would be a possible way to do this?
df1 = data.frame(name = c('George','Andrea', 'Micheal','Maggie','Ravi'),
months=c(12,12,4,12,9),
revenue=c(45,78,13,89,48),
cost=c(56,52,15,88,24),
total_asset=c(100,121,145,103,119))
df1
name months revenue cost total_asset
1 George 12 45 56 100
2 Andrea 12 78 52 121
3 Micheal 4 13 15 145
4 Maggie 12 89 88 103
5 Ravi 9 48 24 119

Using dplyr:
library(dplyr)
df1 %>%
mutate(cost = (12/months)*cost,
revenue = (12/months)*revenue)

An alternative if for any reason you have to use base R is:
df1$revenue <- 12/df1$months * df1$revenue
df1$cost <- 12/df1$months * df1$cost
df1
#> name months revenue cost total_asset
#> 1 George 12 45 56 100
#> 2 Andrea 12 78 52 121
#> 3 Micheal 4 39 45 145
#> 4 Maggie 12 89 88 103
#> 5 Ravi 9 64 32 119
Created on 2022-06-01 by the reprex package (v2.0.1)

Slightly different base R approach with with():
df1 = data.frame(name = c('George','Andrea', 'Micheal','Maggie','Ravi'),
months=c(12,12,4,12,9),
revenue=c(45,78,13,89,48),
cost=c(56,52,15,88,24),
total_asset=c(100,121,145,103,119))
df1$revenue <- with(df1, 12/months * revenue)
df1$cost <- with(df1, 12/months * cost)
head(df1)
#> name months revenue cost total_asset
#> 1 George 12 45 56 100
#> 2 Andrea 12 78 52 121
#> 3 Micheal 4 39 45 145
#> 4 Maggie 12 89 88 103
#> 5 Ravi 9 64 32 119
Created on 2022-06-01 by the reprex package (v2.0.1)

find max column value in r conditional on another column

I have a data frame of baseball player information:
playerID nameFirst nameLast bats throws yearID stint teamID lgID G AB R H X2B X3B HR RBI SB CS BB SO IBB
81955 rolliji01 Jimmy Rollins B R 2007 1 PHI NL 162 716 139 212 38 20 30 94 41 6 49 85 5
103358 wilsowi02 Willie Wilson B R 1980 1 KCA AL 161 705 133 230 28 15 3 49 79 10 28 81 3
93082 suzukic01 Ichiro Suzuki L R 2004 1 SEA AL 161 704 101 262 24 5 8 60 36 11 49 63 19
83973 samueju01 Juan Samuel R R 1984 1 PHI NL 160 701 105 191 36 19 15 69 72 15 28 168 2
15201 cashda01 Dave Cash R R 1975 1 PHI NL 162 699 111 213 40 3 4 57 13 6 56 34 5
75531 pierrju01 Juan Pierre L L 2006 1 CHN NL 162 699 87 204 32 13 3 40 58 20 32 38 0
HBP SH SF GIDP average
81955 7 0 6 11 0.2960894
103358 6 5 1 4 0.3262411
93082 4 2 3 6 0.3721591
83973 7 0 1 6 0.2724679
15201 4 0 7 8 0.3047210
75531 8 10 1 6 0.2918455
I want to return a maximum value of the batting average ('average') column where the at-bats ('AB') are greater than 100. There are also 'NaN' in the average column.

If you want to return the entire row for which the two conditions are TRUE, you can do something like this.
library(tidyverse)
data <- tibble(
AB = sample(seq(50, 150, 10), 10),
avg = c(runif(9), NaN)
)
data %>%
filter(AB >= 100) %>%
filter(avg == max(avg, na.rm = TRUE))
Where the first filter is to only keep rows where AB is greater than or equal to 100 and the second filter is to select the entire row where it is max. If you want to to only get the maximum value, you can do something like this:
data %>%
filter(AB >= 100) %>%
summarise(max = max(avg, na.rm = TRUE))

Adding a column according to norm data in R

I have a longitudinal dataset in the long form with the length of around 2800, with around 400 participants in total. Here's a sample of my data.
# ID wave score sex age edu
#1 1001 1 28 1 69 12
#2 1001 2 27 1 70 12
#3 1001 3 28 1 71 12
#4 1001 4 26 1 72 12
#5 1002 1 30 2 78 9
#6 1002 3 30 2 80 9
#7 1003 1 30 2 65 16
#8 1003 2 30 2 66 16
#9 1003 3 29 2 67 16
#10 1003 4 28 2 68 16
#11 1004 1 22 2 85 4
#12 1005 1 20 2 60 9
#13 1005 2 18 1 61 9
#14 1006 1 22 1 74 9
#15 1006 2 23 1 75 9
#16 1006 3 25 1 76 9
#17 1006 4 19 1 77 9
I want to create a new column "cutoff" with values "Normal" or "Impaired" because my outcome data, "score" has a cutoff score indicating impairment according to norm. The norm consists of different -1SD measures(the cutoff point) according to Sex, Edu(year of education), and Age.
Below is what I'm currently doing, checking an excel file myself and putting in the corresponding cutoff score according to the three conditions. First of all, I am not sure if I am creating the right column.
data$cutoff <- ifelse(data$sex==1 & data$age<70
& data$edu<3
& data$score<19.91, "Impaired", "Normal")
data$cutoff <- ifelse(data$sex==2 & data$age<70
& data$edu<3
& data$score<18.39, "Impaired", "Normal")
Additionally, I am wondering if I can import an excel file stating the norm, and create a column according to the values in it.
The excel file has a structure as shown below.
# Sex Male Female
#60-69 Edu(yr) 0-3 4-6 7-12 13>= 0-3 4-6 7-12 13>=
#Age Number 22 51 119 72 130 138 106 51
# Mean 24.45 26.6 27.06 27.83 23.31 25.86 27.26 28.09
# SD 3.03 1.89 1.8 1.53 3.28 2.55 1.85 1.44
# -1.5SD' 19.92 23.27 23.76 24.8 18.53 21.81 23.91 25.15
#70-79 Edu(yr) 0-3 4-6 7-12 13>= 0-3 4-6 7-12 13>=
....
I have created new columns "agecat" and "educat," allocating each ID into a group of age and education used in the norm. Now I want to make use of these columns, matching it with rows and columns of the excel file above. One of the motivations is to create a code that can be used for further research using the test scores of my data.

I think your ifelse statements should work fine, but I would definitely import the Excel file rather than hardcoding it, though you may need to structure it a bit differently. I would structure it just like a dataset, with columns for Sex, Edu, Age, Mean, SD, -1.5SD, etc., import it into R, then do a left outer join on Sex+Edu+Age:
merge(x = long_df, y = norm_df, by = c("Sex", "Edu(yr)", "Age"), all.x = TRUE)
Then you can compare the columns directly.

If I understand correctly, the OP wants to mark a certain type of outliers in his dataset. So, there are two tasks here:
Compute the statistics mean(score), sd(score), and cutoff value mean(score) - 1.5 * sd(score) for each group of sex, age category agecat, and edu category edcat.
Find all rows where score is lower than the cutoff value for the particular group.
As already mentioned by hannes101, the second step can be implemented by a non-equi join.
library(data.table)
# categorize age and edu (left closed intervals)
mydata[, c("agecat", "educat") := .(cut(age, c(seq(0, 90, 10), Inf), right = FALSE),
cut(edu, c(0, 4, 7, 13, Inf), right = FALSE))][]
# compute statistics
cutoffs <- mydata[, .(.N, Mean = mean(score), SD = sd(score),
m1.5SD = mean(score) - 1.5 * sd(score)),
by = .(sex, agecat, educat)]
# non-equi update join
mydata[, cutoff := "Normal"]
mydata[cutoffs, on = .(sex, agecat, educat, score < m1.5SD), cutoff := "Impaired"][]
mydata
ID wave score sex age edu agecat educat cutoff
1: 1001 1 28 1 69 12 [60,70) [7,13) Normal
2: 1001 2 27 1 70 12 [70,80) [7,13) Normal
3: 1001 3 28 1 71 12 [70,80) [7,13) Normal
4: 1001 4 26 1 72 12 [70,80) [7,13) Normal
5: 1002 1 30 2 78 9 [70,80) [7,13) Normal
6: 1002 3 30 2 80 9 [80,90) [7,13) Normal
7: 1003 1 33 2 65 16 [60,70) [13,Inf) Normal
8: 1003 2 32 2 66 16 [60,70) [13,Inf) Normal
9: 1003 3 31 2 67 16 [60,70) [13,Inf) Normal
10: 1003 4 24 2 68 16 [60,70) [13,Inf) Impaired
11: 1004 1 22 2 85 4 [80,90) [4,7) Normal
12: 1005 1 20 2 60 9 [60,70) [7,13) Normal
13: 1005 2 18 1 61 9 [60,70) [7,13) Normal
14: 1006 1 22 1 74 9 [70,80) [7,13) Normal
15: 1006 2 23 1 75 9 [70,80) [7,13) Normal
16: 1006 3 25 1 76 9 [70,80) [7,13) Normal
17: 1006 4 19 1 77 9 [70,80) [7,13) Normal
18: 1007 1 33 2 65 16 [60,70) [13,Inf) Normal
19: 1007 2 32 2 66 16 [60,70) [13,Inf) Normal
20: 1007 3 31 2 67 16 [60,70) [13,Inf) Normal
21: 1007 4 31 2 68 16 [60,70) [13,Inf) Normal
ID wave score sex age edu agecat educat cutoff
In this made-up example there is only one row which meets the "Impaired" conditions.
Likewise, the statistics is rather sparsely populated:
cutoffs
sex agecat educat N Mean SD m1.5SD
1: 1 [60,70) [7,13) 2 23.00000 7.071068 12.39340
2: 1 [70,80) [7,13) 7 24.28571 3.147183 19.56494
3: 2 [70,80) [7,13) 1 30.00000 NA NA
4: 2 [80,90) [7,13) 1 30.00000 NA NA
5: 2 [60,70) [13,Inf) 8 30.87500 2.900123 26.52482
6: 2 [80,90) [4,7) 1 22.00000 NA NA
7: 2 [60,70) [7,13) 1 20.00000 NA NA
Data
OP's sample dataset has been modified in one group for demonstration.
library(data.table)
mydata <- fread("
# ID wave score sex age edu
#1 1001 1 28 1 69 12
#2 1001 2 27 1 70 12
#3 1001 3 28 1 71 12
#4 1001 4 26 1 72 12
#5 1002 1 30 2 78 9
#6 1002 3 30 2 80 9
#7 1003 1 33 2 65 16
#8 1003 2 32 2 66 16
#9 1003 3 31 2 67 16
#10 1003 4 24 2 68 16
#11 1004 1 22 2 85 4
#12 1005 1 20 2 60 9
#13 1005 2 18 1 61 9
#14 1006 1 22 1 74 9
#15 1006 2 23 1 75 9
#16 1006 3 25 1 76 9
#17 1006 4 19 1 77 9
#18 1007 1 33 2 65 16
#19 1007 2 32 2 66 16
#20 1007 3 31 2 67 16
#21 1007 4 31 2 68 16
", drop = 1L)

How to update and replace part of old data

I want to merge the df OldData and NewData.
In this case, Nov-2015 and Dec 2015 are present in both df.
Since NewData is the most accurate update available, I want to update the value of Nov-2015 and Dec 2015 using the value in df NewData and of course adding the records of Jan-2016 and Feb-2016 as well.
Can anyone help?
OldData
Month Value
1 Jan-2015 3
2 Feb-2015 76
3 Mar-2015 31
4 Apr-2015 45
5 May-2015 99
6 Jun-2015 95
7 Jul-2015 18
8 Aug-2015 97
9 Sep-2015 61
10 Oct-2015 7
11 Nov-2015 42
12 Dec-2015 32
NewData
Month Value
1 Nov-2015 88
2 Dec-2015 45
3 Jan-2016 32
4 Feb-2016 11
Here is the output I want:
JoinData
Month Value
1 Jan-2015 3
2 Feb-2015 76
3 Mar-2015 31
4 Apr-2015 45
5 May-2015 99
6 Jun-2015 95
7 Jul-2015 18
8 Aug-2015 97
9 Sep-2015 61
10 Oct-2015 7
11 Nov-2015 88
12 Dec-2015 45
13 Jan-2016 32
14 Feb-2016 11
Thanks for #akrun, the problem is solved, and the following code works smoothly!!
rbindlist(list(OldData, NewData))[!duplicated(Month, fromLast=TRUE)]
Update: Now, let's upgrade our problem little bit.
suppose our OldData and NewData have another column called "Type".
How do we merge/update it this time?
> OldData
Month Type Value
1 2015-01 A 3
2 2015-02 A 76
3 2015-03 A 31
4 2015-04 A 45
5 2015-05 A 99
6 2015-06 A 95
7 2015-07 A 18
8 2015-08 A 97
9 2015-09 A 61
10 2015-10 A 7
11 2015-11 B 42
12 2015-12 C 32
13 2015-12 D 77
> NewData
Month Type Value
1 2015-11 A 88
2 2015-12 C 45
3 2015-12 D 22
4 2016-01 A 32
5 2016-02 A 11
The JoinData will suppose to update all value from NewData ass following:
> JoinData
Month Type Value
1 2015-01 A 3
2 2015-02 A 76
3 2015-03 A 31
4 2015-04 A 45
5 2015-05 A 99
6 2015-06 A 95
7 2015-07 A 18
8 2015-08 A 97
9 2015-09 A 61
10 2015-10 A 7
11 2015-11 B 42
12 2015-11 A 88 (originally not included, added from the NewData)
12 2015-12 C 45 (Updated the value by NewData)
13 2015-12 D 22 (Updated the value by NewData)
14 2016-01 A 32 (newly added from NewData)
15 2016-02 A 11 (newly added from NewData)
Thanks for #akrun: I have got the solution here for the second question as well.
Thanks for the help for everyone here!
Here is the answer:
d1 <- merge(OldData, NewData, by = c("Month","Type"), all = TRUE);d2 <- transform(d1, Value.x= ifelse(!is.na(Value.y), Value.y, Value.x))[-4];d2[!duplicated(d2[1:2], fromLast=TRUE),]

Here is an option using data.table (similar approach as #thelatemail mentioned in the comments)
library(data.table)
rbindlist(list(OldData, NewData))[!duplicated(Month, fromLast=TRUE)]
Or
rbindlist(list(OldData, NewData))[,if(.N >1) .SD[.N] else .SD, Month]

Loading the table to R very quickly

Let say that I found such table on the internet and I want to load it to R. How can I do it quickly ?
Name No1 No2 No3 No4 No5 No6 No7
Gregg 4 11 53 11 0 23 52
Monica 2 1 31 6 2 1 4
Finn 54 3 13 2 165 53 3
Elijah 1 43 31 16 5 2 1

Like this :
df <- read.table(text="Name No1 No2 No3 No4 No5 No6 No7
Gregg 4 11 53 11 0 23 52
Monica 2 1 31 6 2 1 4
Finn 54 3 13 2 165 53 3
Elijah 1 43 31 16 5 2 1 ", header=TRUE)

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