I have a data frame with yes/no values for different days and hours. For each day, I want to get a total number of hours where I have data, as well as the total number of hours where there is a value of Y.
df <- data.frame(day = c(1,1,1,2,2,3,3,3,3,4),
hour = c(1,2,3,1,2,1,2,3,4,1),
YN = c("Y","Y","Y","Y","Y","Y","N","N","N","N"))
df %>%
group_by(day) %>%
summarise(tot.hour = n(),
totY = WHAT DO I PUT HERE?)
Using boolean then add it up
df %>%
group_by(day) %>%
dplyr::summarise(tot.hour = n(),
totY = sum(YN=='Y'))
# A tibble: 4 x 3
day tot.hour totY
<dbl> <int> <int>
1 1 3 3
2 2 2 2
3 3 4 1
4 4 1 0
Related
https://www.kaggle.com/nowke9/ipldata ----- Contains the IPL Data.
This is exploratory study performed for the IPL data set. (link for the data attached above) After merging both the files with "id" and "match_id", I have created four more variables namely total_extras, total_runs_scored, total_fours_hit and total_sixes_hit. Now I wish to combine these newly created variables into one single data frame. When I assign these variables into one single variable namely batsman_aggregate and selecting only the required columns, I am getting an error message.
library(tidyverse)
deliveries_tbl <- read.csv("deliveries_edit.csv")
matches_tbl <- read.csv("matches.csv")
combined_matches_deliveries_tbl <- deliveries_tbl %>%
left_join(matches_tbl, by = c("match_id" = "id"))
# Add team score and team extra columns for each match, each inning.
total_score_extras_combined <- combined_matches_deliveries_tbl%>%
group_by(id, inning, date, batting_team, bowling_team, winner)%>%
mutate(total_score = sum(total_runs, na.rm = TRUE))%>%
mutate(total_extras = sum(extra_runs, na.rm = TRUE))%>%
group_by(total_score, total_extras, id, inning, date, batting_team, bowling_team, winner)%>%
select(id, inning, total_score, total_extras, date, batting_team, bowling_team, winner)%>%
distinct(total_score, total_extras)%>%
glimpse()%>%
ungroup()
# Batsman Aggregate (Runs Balls, fours, six , Sr)
# Batsman score in each match
batsman_score_in_a_match <- combined_matches_deliveries_tbl %>%
group_by(id, inning, batting_team, batsman)%>%
mutate(total_batsman_runs = sum(batsman_runs, na.rm = TRUE))%>%
distinct(total_batsman_runs)%>%
glimpse()%>%
ungroup()
# Number of deliveries played .
balls_faced <- combined_matches_deliveries_tbl %>%
filter(wide_runs == 0)%>%
group_by(id, inning, batsman)%>%
summarise(deliveries_played = n())%>%
ungroup()
# Number of 4 and 6s by a batsman in each match.
fours_hit <- combined_matches_deliveries_tbl %>%
filter(batsman_runs == 4)%>%
group_by(id, inning, batsman)%>%
summarise(fours_hit = n())%>%
glimpse()%>%
ungroup()
sixes_hit <- combined_matches_deliveries_tbl %>%
filter(batsman_runs == 6)%>%
group_by(id, inning, batsman)%>%
summarise(sixes_hit = n())%>%
glimpse()%>%
ungroup()
batsman_aggregate <- c(batsman_score_in_a_match, balls_faced, fours_hit, sixes_hit)%>%
select(id, inning, batsman, total_batsman_runs, deliveries_played, fours_hit, sixes_hit)
The error message is displayed as:-
Error: `select()` doesn't handle lists.
The required output is the data set created newly constructed variables.
You'll have to join those four tables, not combine using c.
And the join type is left_join so that all batsman are included in the output. Those who didn't face any balls or hit any boundaries will have NA, but you can easily replace these with 0.
I've ignored the by since dplyr will assume you want c("id", "inning", "batsman"), the only 3 common columns in all four data sets.
batsman_aggregate <- left_join(batsman_score_in_a_match, balls_faced) %>%
left_join(fours_hit) %>%
left_join(sixes_hit) %>%
select(id, inning, batsman, total_batsman_runs, deliveries_played, fours_hit, sixes_hit) %>%
replace(is.na(.), 0)
# A tibble: 11,335 x 7
id inning batsman total_batsman_runs deliveries_played fours_hit sixes_hit
<int> <int> <fct> <int> <dbl> <dbl> <dbl>
1 1 1 DA Warner 14 8 2 1
2 1 1 S Dhawan 40 31 5 0
3 1 1 MC Henriques 52 37 3 2
4 1 1 Yuvraj Singh 62 27 7 3
5 1 1 DJ Hooda 16 12 0 1
6 1 1 BCJ Cutting 16 6 0 2
7 1 2 CH Gayle 32 21 2 3
8 1 2 Mandeep Singh 24 16 5 0
9 1 2 TM Head 30 22 3 0
10 1 2 KM Jadhav 31 16 4 1
# ... with 11,325 more rows
There are also 2 batsmen who didn't face any delivery:
batsman_aggregate %>% filter(deliveries_played==0)
# A tibble: 2 x 7
id inning batsman total_batsman_runs deliveries_played fours_hit sixes_hit
<int> <int> <fct> <int> <dbl> <dbl> <dbl>
1 482 2 MK Pandey 0 0 0 0
2 7907 1 MJ McClenaghan 2 0 0 0
One of which apparently scored 2 runs! So I think the batsman_runs column has some errors. The game is here and clearly says that on the second last delivery of the first innings, 2 wides were scored, not runs to the batsman.
thank you in advance for your response! I am working in Rstudio, trying to create a specific table format that my customer is looking for. Specifically, I would like to show each metric as a row and the group_by variable, in this case application type, as a column. I'm using group_by to consolidate all my data by application type, and I'm using the summarise function to create the new variables.
subs <- data.frame(
App_type = c('A','A','A','B','B','B','C','C','C','C'),
Has_error = c(1,1,1,0,0,1,1,0,1,1),
Has_critical_error = c(1,0,1,0,0,1,0,0,1,1)
)
I'm able to group the submissions together by application type to see total submissions with errors and total with critical errors. Here's what I've done -
subs %>%
group_by(App_type) %>%
summarise(
total_sub = n(),
total_error = sum(Has_error),
total_critical_error = sum(Has_critical_error)
)
# A tibble: 3 x 4
App_type total_sub total_error total_critical_error
<fct> <int> <dbl> <dbl>
1 A 3 3 2
2 B 3 1 1
3 C 4 3 2
However, my customer would like to see it this way with application totals.
A B C TOTAL
1 total_sub 3 3 4 10
2 total_error 3 1 3 7
3 total_critical_error 2 1 2 5
We can pivot to 'wide' format after reshaping to 'long' and then change the column name 'name' to rowname
library(dplyr)
library(tidyr)
library(tibble)
subs %>%
group_by(App_type) %>%
summarise(
total_sub = n(),
total_error = sum(Has_error),
total_critical_error = sum(Has_critical_error)) %>%
pivot_longer(cols = -App_type) %>%
pivot_wider(names_from = App_type, values_from = value) %>%
mutate(TOTAL = A + B + C) %>%
column_to_rownames("name")
# A B C TOTAL
#total_sub 3 3 4 10
#total_error 3 1 3 7
#total_critical_error 2 1 2 5
Or another option is transpose from data.table
library(data.table)
data.table::transpose(setDT(out), make.names = 'App_type',
keep.names = 'name')[, TOTAL := A + B + C][]
where out is the OP's summarised output
out <- subs %>%
group_by(App_type) %>%
summarise(
total_sub = n(),
total_error = sum(Has_error),
total_critical_error = sum(Has_critical_error)
)
Or with base R
addmargins(t(cbind(total_sub = as.integer(table(subs$App_type)),
rowsum(subs[-1], subs$App_type))), 2)
# A B C Sum
#total_sub 3 3 4 10
#Has_error 3 1 3 7
#Has_critical_error 2 1 2 5
I have a dataset with ID, date, days of life, and medication variables. Each ID has multiple observations indicating different administrations of a certain drug. I want to find UNIQUE meds that were administered within 365 days of each other. A sample of the data frame is as follows:
ID date dayoflife meds
1 2003-11-24 16361 lasiks
1 2003-11-24 16361 vigab
1 2004-01-09 16407 lacos
1 2013-11-25 20015 pheno
1 2013-11-26 20016 vigab
1 2013-11-26 20016 lasiks
2 2008-06-05 24133 pheno
2 2008-04-07 24074 vigab
3 2014-11-25 8458 pheno
3 2014-12-22 8485 pheno
I expect the outcome to be:
ID N
1 3
2 2
3 1
indicating that individual 1 had a max of 3 different types of medications administered within 365 days of each other. I am not sure if it is best to use days of life or the date to get to this expected outcome.Any help is appreciated
An option would be to convert the 'date' to Date class, grouped by 'ID', get the absolute difference of 'date' and the lag of the column, check whether it is greater than 365, create a grouping index with cumsum, get the number of distinct elements of 'meds' in summarise
library(dplyr)
df1 %>%
mutate(date = as.Date(date)) %>%
group_by(ID) %>%
mutate(diffd = abs(as.numeric(difftime(date, lag(date, default = first(date)),
units = 'days')))) %>%
group_by(grp = cumsum(diffd > 365), add = TRUE) %>%
summarise(N = n_distinct(meds)) %>%
group_by(ID) %>%
summarise(N = max(N))
# A tibble: 3 x 2
# ID N
# <int> <int>
#1 1 2
#2 2 2
#3 3 1
You can try:
library(dplyr)
df %>%
group_by(ID) %>%
mutate(date = as.Date(date),
lag_date = abs(date - lag(date)) <= 365,
lead_date = abs(date - lead(date)) <= 365) %>%
mutate_at(vars(lag_date, lead_date), ~ ifelse(., ., NA)) %>%
filter(coalesce(lag_date, lead_date)) %>%
summarise(N = n_distinct(meds))
Output:
# A tibble: 3 x 2
ID N
<int> <int>
1 1 2
2 2 2
3 3 1
My data frame looks something like the first two columns of the following
I want to add a third column, equal to the sum of the ID-group's last three observations for VAL.
Using the following command, I managed to get the output below:
df %>%
group_by(ID) %>%
mutate(SUM=rollsumr(VAL, k=3)) %>%
ungroup()
ID VAL SUM
1 2 NA
1 1 NA
1 3 6
1 4 8
...
I am now hoping to be able to fill the NAs that result for the group's cells in the first two rows.
ID VAL SUM
1 2 2
1 1 3
1 3 6
1 4 8
...
How do I do that?
I have tried doing the following
df %>%
group_by(ID) %>%
mutate(SUM=rollsumr(VAL, k=min(3, row_number())) %>%
ungroup()
and
df %>%
group_by(ID) %>%
mutate(SUM=rollsumr(VAL, k=3), fill = "extend") %>%
ungroup()
But both give me the same error, because I have groups of sizes <= 2.
Evaluation error: need at least two non-NA values to interpolate.
What do I do?
Alternatively, you can use rollapply() from the same package:
df %>%
group_by(ID) %>%
mutate(SUM = rollapply(VAL, width = 3, FUN = sum, partial = TRUE, align = "right"))
ID VAL SUM
<int> <int> <int>
1 1 2 2
2 1 1 3
3 1 3 6
4 1 4 8
Due to argument partial = TRUE, also the rows that are below the desired window of length three are summed.
Not a direct answer but one way would be to replace the values which are NAs with cumsum of VAL
library(dplyr)
library(zoo)
df %>%
group_by(ID) %>%
mutate(SUM = rollsumr(VAL, k=3, fill = NA),
SUM = ifelse(is.na(SUM), cumsum(VAL), SUM))
# ID VAL SUM
# <int> <int> <int>
#1 1 2 2
#2 1 1 3
#3 1 3 6
#4 1 4 8
Or since you know the window size before hand, you could check with row_number() as well
df %>%
group_by(ID) %>%
mutate(SUM = rollsumr(VAL, k=3, fill = NA),
SUM = ifelse(row_number() < 3, cumsum(VAL), SUM))
I am trying to calculate the percent difference in ht between all possible pairs of data, per group of individuals, as well as the time difference between the ht measures. This is my data:
hc1<- data.frame(id= c(1,1,1,2,2,2,3,3),
testoccasion= c(1,2,3,1,2,3,1,2),
ht= c(0.2,0.1,0.8,0.9,1.0,0.5,0.4,0.8),
time= c(5,4,8,5,6,5,2,1))
This is my code.
library(dplyr)
a<-hc1 %>%
group_by(id) %>%
arrange(id,testoccasion) %>%
mutate(fd = (ht-lag(ht))/lag(ht)*100) %>%
mutate(t = time-lag(time))
b<-hc1 %>%
group_by(id) %>%
arrange(id,testoccasion) %>%
mutate(fd = (ht-lag(ht,2))/lag(ht,2)*100) %>%
mutate(t = time-lag(time,2))
c<-hc1 %>%
group_by(id) %>%
arrange(id,testoccasion) %>%
mutate(fd = (ht-lag(ht,3))/lag(ht,3)*100) %>%
mutate(t = time-lag(time,3))
diff<-rbind(a,b,c)
diff<-na.omit(diff)
I am curious how I can make this code shorter. I want to be able to find the difference across all possible pairs of ht, for all test occasions, where the number of test occasions differs between individual id's.It would be great if I didn't have to do it iteratively like this, because it's a huge dataset I have. Thanks!
We can use map to loop the n used in lag
library(tidyverse)
map_df(1:3, ~
hc1 %>%
group_by(id) %>%
arrange(id, testoccasion) %>%
mutate(fd = (ht -lag(ht, .x))/lag(ht, .x) * 100,
t = time -lag(time, .x))) %>%
na.omit
# A tibble: 7 x 6
# Groups: id [3]
# id testoccasion ht time fd t
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 2 0.1 4 -50 -1
#2 1 3 0.8 8 700 4
#3 2 2 1 6 11.1 1
#4 2 3 0.5 5 -50 -1
#5 3 2 0.8 1 100 -1
#6 1 3 0.8 8 300. 3
#7 2 3 0.5 5 -44.4 0