Hi All,
today I have a more general question concerning the approach of storing
different values from the analysis of multiple variables.
My task is to compare distributions in a universe with distributions from
the respondents using a whole bunch of variables. Comparison shall be done
on relative frequencies (proportions).
I was thinking about the structure I should store the results in and came
up with the following:
-- cut --
library(stringi)
# Result data frame
# Some sort of tidytidy data set where
# each value is stored as an identity.
# This way all values for all variables could be stored in
# one unique data structure.
# If an additional variable added for the name of the
# research one could also build result data set across
# surveys.
# Values for measure could be "number" for 'raw' values or
# "freq" for frequencies/counts.
# Values for unit could be "n" for 'numbers' and
# "%" for percentages.
d_test <- data.frame(
group = rep(c("Universe", "Respondents"), each = 16),
variable = rep("State", 32),
value = rep(c(11.3,
12.7,
3.3,
5,
0.6,
8.1,
6.2,
5.8,
6.4,
14.5,
8.3,
0.3,
3.8,
2.5,
8.1,
3), 2),
label = rep(c("Baden-Wuerttemberg",
"Bayern",
"Berlin",
"Brandenburg",
"Bremen",
"Hamburg",
"Hessen",
"Mecklenburg-Vorpommern",
"Niedersachsen",
"Nordrhein-Westfalen",
"Rheinland-Pfalz",
"Saarland",
"Sachsen",
"Sachsen-Anhalt",
"Schleswig-Holstein",
"Thueringen"),2),
measure = rep("freq", 32),
unit = rep("%", 32),
stringsAsFactors = FALSE
)
# This way the variables can be selected using simple
# value selection from Base R functionality.
data <- d_test[d_test$variable == "State" ,]
# And plot results for every variable.
ggplot(
data = data,
aes(
x = label,
y = value,
fill = group)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_discrete(name = stringi::stri_trans_totitle(names(data)[1]))
+
scale_x_discrete(name = data$variable[1]) +
scale_y_discrete(name = data$unit[1])
-- cut --
The reporting / presentation is done in R Markdown. I would load the
result data set once at the beginning and running the comparisons as plots
on each variable named in the results data set under "variable".
If I follow this approach for my customer relationship survey, do think I
would face drawbacks or run into serious trouble?
I am interested in your opinion and open for other approaches and
suggestions.
Kind regards
Georg
This does not appear to be a legitimate topic for r-help: it is are not a consulting service. Please see the posting guide. Of course, others may disagree and reply. Wouldn't be the first time I'm wrong. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Mar 8, 2017 at 7:27 AM, <G.Maubach at weinwolf.de> wrote:> Hi All, > > today I have a more general question concerning the approach of storing > different values from the analysis of multiple variables. > > My task is to compare distributions in a universe with distributions from > the respondents using a whole bunch of variables. Comparison shall be done > on relative frequencies (proportions). > > I was thinking about the structure I should store the results in and came > up with the following: > > -- cut -- > > library(stringi) > > # Result data frame > # Some sort of tidytidy data set where > # each value is stored as an identity. > # This way all values for all variables could be stored in > # one unique data structure. > # If an additional variable added for the name of the > # research one could also build result data set across > # surveys. > # Values for measure could be "number" for 'raw' values or > # "freq" for frequencies/counts. > # Values for unit could be "n" for 'numbers' and > # "%" for percentages. > d_test <- data.frame( > group = rep(c("Universe", "Respondents"), each = 16), > variable = rep("State", 32), > value = rep(c(11.3, > 12.7, > 3.3, > 5, > 0.6, > 8.1, > 6.2, > 5.8, > 6.4, > 14.5, > 8.3, > 0.3, > 3.8, > 2.5, > 8.1, > 3), 2), > label = rep(c("Baden-Wuerttemberg", > "Bayern", > "Berlin", > "Brandenburg", > "Bremen", > "Hamburg", > "Hessen", > "Mecklenburg-Vorpommern", > "Niedersachsen", > "Nordrhein-Westfalen", > "Rheinland-Pfalz", > "Saarland", > "Sachsen", > "Sachsen-Anhalt", > "Schleswig-Holstein", > "Thueringen"),2), > measure = rep("freq", 32), > unit = rep("%", 32), > stringsAsFactors = FALSE > ) > > # This way the variables can be selected using simple > # value selection from Base R functionality. > data <- d_test[d_test$variable == "State" ,] > > # And plot results for every variable. > ggplot( > data = data, > aes( > x = label, > y = value, > fill = group)) + > geom_bar(stat = "identity", position = "dodge") + > theme(axis.text.x = element_text(angle = 45, hjust = 1)) + > scale_fill_discrete(name = stringi::stri_trans_totitle(names(data)[1])) > + > scale_x_discrete(name = data$variable[1]) + > scale_y_discrete(name = data$unit[1]) > > -- cut -- > > The reporting / presentation is done in R Markdown. I would load the > result data set once at the beginning and running the comparisons as plots > on each variable named in the results data set under "variable". > > If I follow this approach for my customer relationship survey, do think I > would face drawbacks or run into serious trouble? > > I am interested in your opinion and open for other approaches and > suggestions. > > Kind regards > > Georg > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Seems pretty normal except that your one-by-one lookup process usually gets old eventually, and comparing results is much easier if you merge the study data with the lookup data all at once and then use aggregate() (or any of numerous equivalents from contributed packages) to collect results or color/linetype/panel/etc plotted graphical presentations with lattice or ggplot2. -- Sent from my phone. Please excuse my brevity. On March 8, 2017 7:27:08 AM PST, G.Maubach at weinwolf.de wrote:>Hi All, > >today I have a more general question concerning the approach of storing > >different values from the analysis of multiple variables. > >My task is to compare distributions in a universe with distributions >from >the respondents using a whole bunch of variables. Comparison shall be >done >on relative frequencies (proportions). > >I was thinking about the structure I should store the results in and >came >up with the following: > >-- cut -- > >library(stringi) > ># Result data frame ># Some sort of tidytidy data set where ># each value is stored as an identity. ># This way all values for all variables could be stored in ># one unique data structure. ># If an additional variable added for the name of the ># research one could also build result data set across ># surveys. ># Values for measure could be "number" for 'raw' values or ># "freq" for frequencies/counts. ># Values for unit could be "n" for 'numbers' and ># "%" for percentages. >d_test <- data.frame( > group = rep(c("Universe", "Respondents"), each = 16), > variable = rep("State", 32), > value = rep(c(11.3, > 12.7, > 3.3, > 5, > 0.6, > 8.1, > 6.2, > 5.8, > 6.4, > 14.5, > 8.3, > 0.3, > 3.8, > 2.5, > 8.1, > 3), 2), > label = rep(c("Baden-Wuerttemberg", > "Bayern", > "Berlin", > "Brandenburg", > "Bremen", > "Hamburg", > "Hessen", > "Mecklenburg-Vorpommern", > "Niedersachsen", > "Nordrhein-Westfalen", > "Rheinland-Pfalz", > "Saarland", > "Sachsen", > "Sachsen-Anhalt", > "Schleswig-Holstein", > "Thueringen"),2), > measure = rep("freq", 32), > unit = rep("%", 32), > stringsAsFactors = FALSE >) > ># This way the variables can be selected using simple ># value selection from Base R functionality. >data <- d_test[d_test$variable == "State" ,] > ># And plot results for every variable. >ggplot( > data = data, > aes( > x = label, > y = value, > fill = group)) + > geom_bar(stat = "identity", position = "dodge") + > theme(axis.text.x = element_text(angle = 45, hjust = 1)) + >scale_fill_discrete(name = stringi::stri_trans_totitle(names(data)[1])) > >+ > scale_x_discrete(name = data$variable[1]) + > scale_y_discrete(name = data$unit[1]) > >-- cut -- > >The reporting / presentation is done in R Markdown. I would load the >result data set once at the beginning and running the comparisons as >plots >on each variable named in the results data set under "variable". > >If I follow this approach for my customer relationship survey, do think >I >would face drawbacks or run into serious trouble? > >I am interested in your opinion and open for other approaches and >suggestions. > >Kind regards > >Georg > >______________________________________________ >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code.
G.Maubach at weinwolf.de
2017-Mar-09 09:02 UTC
[R] Antwort: Re: Approach for Storing Result Data
Hi Gunter, Hi Jeff, Hi Readers, many thanks for your reply. My questions seems to be a little off topic cause it is not about using the programming language itself but how to use it in a analytics context. It is about processes and approaches of how to do things in R from a conception point of view. That is a subject I don't see in the community but would help me a lot to enhance my work. Do you know I place where these things are discussed? Kind regards Georg Von: Jeff Newmiller <jdnewmil at dcn.davis.ca.us> An: r-help at r-project.org, G.Maubach at weinwolf.de, Datum: 08.03.2017 17:54 Betreff: Re: [R] Approach for Storing Result Data Seems pretty normal except that your one-by-one lookup process usually gets old eventually, and comparing results is much easier if you merge the study data with the lookup data all at once and then use aggregate() (or any of numerous equivalents from contributed packages) to collect results or color/linetype/panel/etc plotted graphical presentations with lattice or ggplot2. Von: Bert Gunter <bgunter.4567 at gmail.com> An: G.Maubach at weinwolf.de, Kopie: R-help <r-help at r-project.org> Datum: 08.03.2017 17:43 Betreff: Re: [R] Approach for Storing Result Data This does not appear to be a legitimate topic for r-help: it is are not a consulting service. Please see the posting guide. Of course, others may disagree and reply. Wouldn't be the first time I'm wrong. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Mar 8, 2017 at 7:27 AM, <G.Maubach at weinwolf.de> wrote:> Hi All, > > today I have a more general question concerning the approach of storing > different values from the analysis of multiple variables. > > My task is to compare distributions in a universe with distributionsfrom> the respondents using a whole bunch of variables. Comparison shall bedone> on relative frequencies (proportions). > > I was thinking about the structure I should store the results in andcame> up with the following: > > -- cut -- > > library(stringi) > > # Result data frame > # Some sort of tidytidy data set where > # each value is stored as an identity. > # This way all values for all variables could be stored in > # one unique data structure. > # If an additional variable added for the name of the > # research one could also build result data set across > # surveys. > # Values for measure could be "number" for 'raw' values or > # "freq" for frequencies/counts. > # Values for unit could be "n" for 'numbers' and > # "%" for percentages. > d_test <- data.frame( > group = rep(c("Universe", "Respondents"), each = 16), > variable = rep("State", 32), > value = rep(c(11.3, > 12.7, > 3.3, > 5, > 0.6, > 8.1, > 6.2, > 5.8, > 6.4, > 14.5, > 8.3, > 0.3, > 3.8, > 2.5, > 8.1, > 3), 2), > label = rep(c("Baden-Wuerttemberg", > "Bayern", > "Berlin", > "Brandenburg", > "Bremen", > "Hamburg", > "Hessen", > "Mecklenburg-Vorpommern", > "Niedersachsen", > "Nordrhein-Westfalen", > "Rheinland-Pfalz", > "Saarland", > "Sachsen", > "Sachsen-Anhalt", > "Schleswig-Holstein", > "Thueringen"),2), > measure = rep("freq", 32), > unit = rep("%", 32), > stringsAsFactors = FALSE > ) > > # This way the variables can be selected using simple > # value selection from Base R functionality. > data <- d_test[d_test$variable == "State" ,] > > # And plot results for every variable. > ggplot( > data = data, > aes( > x = label, > y = value, > fill = group)) + > geom_bar(stat = "identity", position = "dodge") + > theme(axis.text.x = element_text(angle = 45, hjust = 1)) + > scale_fill_discrete(name =stringi::stri_trans_totitle(names(data)[1]))> + > scale_x_discrete(name = data$variable[1]) + > scale_y_discrete(name = data$unit[1]) > > -- cut -- > > The reporting / presentation is done in R Markdown. I would load the > result data set once at the beginning and running the comparisons asplots> on each variable named in the results data set under "variable". > > If I follow this approach for my customer relationship survey, do thinkI> would face drawbacks or run into serious trouble? > > I am interested in your opinion and open for other approaches and > suggestions. > > Kind regards > > Georg > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.