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Restructuring data in R


Restructuring data in R

By : user2957010
Date : November 22 2020, 03:03 PM
will help you You could do a transpose of the dataset ie. t(df) which swaps the columns for rows and the output will be a matrix (ie. a vector with dimension attributes). To strip off the dimensions and create the real vector, you could use as.vector or simply c (concatenate). This can be used for creating a single column data.frame:
code :
data.frame(C3 = c(t(df)))
data.frame(C3 = c(mapply(c, df$C1, df$C2)))
Map(`c`, df$C1, df$C2)
df <- structure(list(C1 = c(1L, 1L, 1L), C2 = c(2L, 2L, 2L)), .Names = c("C1", 
"C2"), class = "data.frame", row.names = c(NA, -3L))
n <- 1e4
df <- data.frame(C1 = rep(1, n), C2 = rep(2, n))

library(microbenchmark)
microbenchmark(akrun = c(t(df)), David = c(mapply(c, df$C1, df$C2)))
# Unit: microseconds
#  expr       min         lq       mean     median         uq       max neval
# akrun   204.608   215.7795   259.9504   265.4155   275.9485   374.741   100
# David 11933.612 12245.7890 13190.8289 12399.0050 13463.8565 30267.502   100


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Restructuring csv data with read.csv in R

Restructuring csv data with read.csv in R


By : Thinh Ha
Date : March 29 2020, 07:55 AM
Does that help As Ricardo pointed out in a comment, this is not directly doable with read.csv. Instead, you can read the data in, and then use reshape to get your output. I've added a few extra steps to drop rows with NA values and so on, but this is not entirely necessary.
The data, as you have presented it. You mention it is a CSV, so you'll probably be using read.csv instead of read.table.
code :
out <- read.table(text = "code  x   y   x   y  x  y  x  y
1     1   0   2   2  3  3  4  5   // 1rst graphic with 4 points
2     1   1   2   3               // 2nd graphic with only 2 points
3     0   2   3   5  5  12 10 23  // 3rd graphic with 4 points", 
                  fill = TRUE, comment.char = "/", header = TRUE)
names(out)[2:3] <- c("x.0", "y.0")
out
#   code x.0 y.0 x.1 y.1 x.2 y.2 x.3 y.3
# 1    1   1   0   2   2   3   3   4   5
# 2    2   1   1   2   3  NA  NA  NA  NA
# 3    3   0   2   3   5   5  12  10  23
outL <- reshape(out, direction = "long", idvar="code", varying = 2:ncol(out))
outL <- outL[order(outL$code), ]
outL[complete.cases(outL), -2]
#     code  x  y
# 1.0    1  1  0
# 1.1    1  2  2
# 1.2    1  3  3
# 1.3    1  4  5
# 2.0    2  1  1
# 2.1    2  2  3
# 3.0    3  0  2
# 3.1    3  3  5
# 3.2    3  5 12
# 3.3    3 10 23
Data restructuring using R

Data restructuring using R


By : Jens Vogel
Date : March 29 2020, 07:55 AM
help you fix your problem I have a dataset (dat) that looks like this: , Here is one approach.
code :
library(dplyr)

mydf %>%
    group_by(Person, IPaddress) %>% # For each combination of person and IPaddress
    summarize(freq = n()) %>% # Get total number of log-in
    arrange(Person, desc(freq)) %>% # The most frequent IP address is in the 1st row for each user
    group_by(Person) %>% # For each user
    mutate(total = sum(freq)) %>% # Get total number of log-in
    select(-freq) %>% # Remove count
    do(head(.,1)) # Take the first row for each user

#    Person     IPaddress total
#1 36598035 222.999.22.99     6
#2 37811171 111.88.111.88     5
mydf %>%
    count(Person, IPaddress) %>%
    arrange(Person, desc(n)) %>%
    group_by(Person) %>%
    mutate(total = sum(n)) %>%
    select(-n) %>%
    slice(1)
Restructuring data for sorting C#

Restructuring data for sorting C#


By : Kristen Watson
Date : March 29 2020, 07:55 AM
To fix this issue You are missing the OOP Concept of Composition.
If you composite that key and Value to one object type you don't have to transpose it at all. You can use just a List instead of a Dictionary. The fact that you have to transpose gives a bad design smell.
Restructuring JSON Data

Restructuring JSON Data


By : Zxp
Date : March 29 2020, 07:55 AM
wish of those help You could iterate the keys and test for name, then build an object with the name and assign a temp object. Otherwise assing the values.
code :
var array = [{ "name": ["Adelphi University"], "supp": ["Yes: E, WS"], "ed": ["\u00a0"], "online": ["$40"], "ea": ["12/1"], "mid": ["No"], "rd": ["Rolling"], "recs": ["Yes: CR"], "mail": ["$40"], "schoolr": ["Yes"] }, { "name": ["Dartmouth College"], "supp": ["Yes: E, WS"], "ed": ["\u00a0"], "online": ["$40"], "ea": ["12/1"], "mid": ["No"], "rd": ["Rolling"], "recs": ["Yes: CR"], "mail": ["$40"], "schoolr": ["Yes"] }],
    schools = {},
    result = { schools: schools };

array.forEach(function (a) {
    var temp = {};
    Object.keys(a).forEach(function (k) {
        if (k === 'name') {
            schools[a[k][0]] = temp;
            return;
        }
        temp[k] = a[k][0];
    });
});

console.log(JSON.stringify(result));
console.log(result);
.as-console-wrapper { max-height: 100% !important; top: 0; }
How to restructuring the data by the data's property in Django-Rest-Framework?

How to restructuring the data by the data's property in Django-Rest-Framework?


By : priyadarshini
Date : March 29 2020, 07:55 AM
may help you . I have a Disk serializer, my Disk serializer is bellow : , views.py
code :
class Postlar(ListAPIView):
    permission_classes = []
    queryset = Disk.objects.all()
    serializer_class = DiskSerializer

    def list(self, request, *args, **kwargs):
        result = {}
        SerializerClass = self.get_serializer_class()
        for u in ["系统盘", "数据盘"]:
            serializer = SerializerClass(Disk.objects.filter(diskOsType__name=u), many=True)
            result[u] = serializer.data

        return Response(result)
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